From fc837c4daa27a18ff0e86128f4d52089b88fa5fb Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 3 May 2022 10:14:17 +0200 Subject: [PATCH 001/518] add freqao backend machinery, user interface, documentation --- .gitignore | 3 + config_examples/config_freqai.example.json | 100 ++++ docs/freqai.md | 265 +++++++++++ freqtrade/commands/__init__.py | 1 + freqtrade/commands/arguments.py | 5 +- freqtrade/commands/cli_options.py | 12 + freqtrade/commands/freqai_commands.py | 24 + freqtrade/configuration/configuration.py | 12 + freqtrade/constants.py | 1 + freqtrade/enums/runmode.py | 3 +- freqtrade/freqai/data_handler.py | 434 ++++++++++++++++++ freqtrade/freqai/freqai_interface.py | 158 +++++++ freqtrade/freqai/strategy_bridge.py | 12 + freqtrade/optimize/backtesting.py | 6 + freqtrade/resolvers/freqaimodel_resolver.py | 45 ++ freqtrade/templates/ExamplePredictionModel.py | 139 ++++++ freqtrade/templates/FreqaiExampleStrategy.py | 179 ++++++++ mkdocs.yml | 1 + requirements-freqai.txt | 8 + 19 files changed, 1405 insertions(+), 3 deletions(-) create mode 100644 config_examples/config_freqai.example.json create mode 100644 docs/freqai.md create mode 100644 freqtrade/commands/freqai_commands.py create mode 100644 freqtrade/freqai/data_handler.py create mode 100644 freqtrade/freqai/freqai_interface.py create mode 100644 freqtrade/freqai/strategy_bridge.py create mode 100644 freqtrade/resolvers/freqaimodel_resolver.py create mode 100644 freqtrade/templates/ExamplePredictionModel.py create mode 100644 freqtrade/templates/FreqaiExampleStrategy.py create mode 100644 requirements-freqai.txt diff --git a/.gitignore b/.gitignore index 97f77f779..17823f642 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,8 @@ logfile.txt user_data/* !user_data/strategy/sample_strategy.py !user_data/notebooks +!user_data/models +user_data/models/* user_data/notebooks/* freqtrade-plot.html freqtrade-profit-plot.html @@ -105,3 +107,4 @@ target/ !config_examples/config_ftx.example.json !config_examples/config_full.example.json !config_examples/config_kraken.example.json +!config_examples/config_freqai.example.json diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json new file mode 100644 index 000000000..0092a8c51 --- /dev/null +++ b/config_examples/config_freqai.example.json @@ -0,0 +1,100 @@ +{ + "max_open_trades": 1, + "stake_currency": "USDT", + "stake_amount": 800, + "tradable_balance_ratio": 1, + "fiat_display_currency": "USD", + "dry_run": true, + "timeframe": "5m", + "dry_run_wallet":1000, + "cancel_open_orders_on_exit": true, + "unfilledtimeout": { + "entry": 10, + "exit": 30 + }, + "exchange": { + "name": "ftx", + "key": "", + "secret": "", + "ccxt_config": {"enableRateLimit": true}, + "ccxt_async_config": { + "enableRateLimit": true, + "rateLimit": 200 + }, + "pair_whitelist": [ + "BTC/USDT" + ], + "pair_blacklist": [ + ] + }, + "entry_pricing": { + "price_side": "same", + "use_order_book": true, + "order_book_top": 1, + "price_last_balance": 0.0, + "check_depth_of_market": { + "enabled": false, + "bids_to_ask_delta": 1 + } + }, + "exit_pricing": { + "price_side": "same", + "use_order_book": true, + "order_book_top": 1 + }, + "pairlists": [ + {"method": "StaticPairList"} + ], + + "freqai": { + "btc_pair" : "BTC/USDT", + "timeframes" : ["5m","15m","1h"], + "full_timerange" : "20210601-20220101", + "train_period" : 30, + "backtest_period" : 7, + "identifier" : "example", + "base_features": [ + "rsi", + "close_over_20sma", + "relative_volume", + "bb_width", + "mfi", + "roc", + "pct-change", + "adx", + "macd" + ], + "corr_pairlist": [ + "ETH/USDT", + "LINK/USDT", + "DOT/USDT" + ], + "training_timerange" : "20211220-20220117", + + "feature_parameters" : { + "period": 12, + "shift": 2, + "drop_features": false, + "DI_threshold": 1, + "weight_factor": 0, + "principal_component_analysis": false, + "remove_outliers": false + }, + "data_split_parameters" : { + "test_size": 0.25, + "random_state": 1 + }, + "model_training_parameters" : { + "n_estimators": 2000, + "random_state": 1, + "learning_rate": 0.02, + "task_type": "CPU" + } + }, + "bot_name": "", + "initial_state": "running", + "forcebuy_enable": false, + "internals": { + "process_throttle_secs": 5 + } +} diff --git a/docs/freqai.md b/docs/freqai.md new file mode 100644 index 000000000..6bc1e9365 --- /dev/null +++ b/docs/freqai.md @@ -0,0 +1,265 @@ +# Freqai + +!!! Note + Freqai is still experimental, and should be used at the user's own discretion. + +Freqai is a module designed to automate a variety of tasks associated with +training a regressor to predict signals based on input features. Among the +the features includes: + +* Easy large feature set construction based on simple user input +* Sweep model training and backtesting to simulate consistent model retraining through time +* Smart outlier removal of data points from prediction sets using a Dissimilarity Index. +* Data dimensionality reduction with Principal Component Analysis +* Automatic file management for storage of models to be reused during live +* Smart and safe data standardization +* Cleaning of NaNs from the data set before training and prediction. + +TODO: +* live is not automated, still some architectural work to be done + +## Background and vocabulary + +**Features** are the quantities with which a model is trained. $X_i$ represents the +vector of all features for a single candle. In Freqai, the user +builds the features from anything they can construct in the strategy. + +**Labels** are the target values with which the weights inside a model are trained +toward. Each set of features is associated with a single label, which is also +defined within the strategy by the user. These labels look forward into the +future, and are not available to the model during dryrun/live/backtesting. + +**Training** refers to the process of feeding individual feature sets into the +model with associated labels with the goal of matching input feature sets to +associated labels. + +**Train data** is a subset of the historic data which is fed to the model during +training to adjust weights. This data directly influences weight connections +in the model. + +**Test data** is a subset of the historic data which is used to evaluate the +intermediate performance of the model during training. This data does not +directly influence nodal weights within the model. + +## Configuring the bot +### Example config file +The user interface is isolated to the typical config file. A typical Freqai +config setup includes: + +```json + "freqai": { + "timeframes" : ["5m","15m","4h"], + "full_timerange" : "20211220-20220220", + "train_period" : "month", + "backtest_period" : "week", + "identifier" : "unique-id", + "base_features": [ + "rsi", + "mfi", + "roc", + ], + "corr_pairlist": [ + "ETH/USD", + "LINK/USD", + "BNB/USD" + ], + "train_params" : { + "period": 24, + "shift": 2, + "drop_features": false, + "DI_threshold": 1, + "weight_factor": 0, + }, + "SPLIT_PARAMS" : { + "test_size": 0.25, + "random_state": 42 + }, + "CLASSIFIER_PARAMS" : { + "n_estimators": 100, + "random_state": 42, + "learning_rate": 0.02, + "task_type": "CPU", + }, + }, + +``` + +### Building the feature set + +Most of these parameters are controlling the feature data set. The `base_features` +indicates the basic indicators the user wishes to include in the feature set. +The `timeframes` are the timeframes of each base_feature that the user wishes to +include in the feature set. In the present case, the user is asking for the +`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, etc. to be included +in the feature set. + +In addition, the user can ask for each of these features to be included from +informative pairs using the `corr_pairlist`. This means that the present feature +set will include all the `base_features` on all the `timeframes` for each of +`ETH/USD`, `LINK/USD`, and `BNB/USD`. + +`shift` is another user controlled parameter which indicates the number of previous +candles to include in the present feature set. In other words, `shift: 2`, tells +Freqai to include the the past 2 candles for each of the features included +in the dataset. + +In total, the number of features the present user has created is:_ + +no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_ +3 * 3 * 3 * 2 = 54._ + +### Deciding the sliding training window and backtesting duration + +`full_timerange` lets the user set the full backtesting range to train and +backtest through. Meanwhile `train_period` is the sliding training window and +`backtest_period` is the sliding backtesting window. In the present example, +the user is asking Freqai to train and backtest the range of `20211220-20220220` (`month`). +The user wishes to backtest each `week` with a newly trained model. This means that +Freqai will train 8 separate models (because the full range comprises 8 weeks), +and then backtest the subsequent week associated with each of the 8 training +data set timerange months. Users can think of this as a "sliding window" which +emulates Freqai retraining itself once per week in live using the previous +month of data. + + +## Running Freqai +### Training and backtesting + +The freqai training/backtesting module can be executed with the following command: + +```bash +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel +``` + +where the user needs to have a FreqaiExampleStrategy that fits to the requirements outlined +below. The ExamplePredictionModel is a user built class which lets users design their +own training procedures and data analysis. + +### Building a freqai strategy + +The Freqai strategy requires the user to include the following lines of code in `populate_ any _indicators()` + +```python + from freqtrade.freqai.strategy_bridge import CustomModel + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + # the configuration file parameters are stored here + self.freqai_info = self.config['freqai'] + + # the model is instantiated here + self.model = CustomModel(self.config) + + print('Populating indicators...') + + # the following loops are necessary for building the features + # indicated by the user in the configuration file. + for tf in self.freqai_info['timeframes']: + dataframe = self.populate_any_indicators(metadata['pair'], + dataframe.copy(), tf) + for i in self.freqai_info['corr_pairlist']: + dataframe = self.populate_any_indicators(i, + dataframe.copy(), tf, coin=i.split("/")[0]+'-') + + # the model will return 4 values, its prediction, an indication of whether or not the prediction + # should be accepted, the target mean/std values from the labels used during each training period. + (dataframe['prediction'], dataframe['do_predict'], + dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata) + + return dataframe +``` +The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds +the feature set with a proper naming convention for the IFreqaiModel to use later. + +### Building an IFreqaiModel + +Freqai has a base example model in `templates/ExamplePredictionModel.py`, but users can customize and create +their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()`, `predict()`, +and `make_labels()` to let them customize various aspects of their training procedures. + +### Running the model live + +After the user has designed a desirable featureset, Freqai can be run in dry/live +using the typical trade command: + +```bash +freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --training_timerange '20211220-20220120' +``` + +Where the user has now specified exactly which of the models from the sliding window +that they wish to run live using `--training_timerange` (typically this would be the most +recent model trained). As of right now, freqai will +not automatically retain itself, so the user needs to manually retrain and then +reload the config file with a new `--training_timerange` in order to update the +model. + + +## Data anylsis techniques +### Controlling the model learning process + +The user can define model settings for the data split `data_split_parameters` and learning parameters +`model_training_parameters`. Users are encouraged to visit the Catboost documentation +for more information on how to select these values. `n_estimators` increases the +computational effort and the fit to the training data. If a user has a GPU +installed in their system, they may benefit from changing `task_type` to `GPU`. +The `weight_factor` allows the user to weight more recent data more strongly +than past data via an exponential function: + +$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ + +where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._ + +`drop_features` tells Freqai to train the model on the user defined features, +followed by a feature importance evaluation where it drops the top and bottom +performing features (there is evidence to suggest the top features may not be +helpful in equity/crypto trading since the ultimate objective is to predict low +frequency patterns, source: numerai)._ + +Finally, `period` defines the offset used for the `labels`. In the present example, +the user is asking for `labels` that are 24 candles in the future. + +### Removing outliers with the Dissimilarity Index + +The Dissimilarity Index (DI) aims to quantiy the uncertainty associated with each +prediction by the model. To do so, Freqai measures the distance between each training +data point and all other training data points: + +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ + +where $d_{ab}$ is the distance between the standardized points $a$ and $b$. $p$ +is the number of features i.e. the length of the vector $X$. The +characteristic distance, $\overline{d}$ for a set of training data points is simply the mean +of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to +the distance between the new prediction feature vectors, $X_k$ and all the training +data: + +$$ d_k = \argmin_i d_{k,i} $$ + +which enables the estimation of a Dissimilarity Index: + +$$ DI_k = d_k/\overline{d} $$ + +Equity and crypto markets suffer from a high level of non-patterned noise in the +form of outlier data points. The dissimilarity index allows predictions which +are outliers and not existent in the model feature space, to be thrown out due +to low levels of certainty. The user can tweak the DI with `DI_threshold` to increase +or decrease the extrapolation of the trained model. + +### Reducing data dimensionality with Principal Component Analysis + +TO BE WRITTEN + +## Additional information +### Feature standardization + +The feature set created by the user is automatically standardized to the training +data only. This includes all test data and unseen prediction data (dry/live/backtest). + +### File structure + +`user_data_dir/models/` contains all the data associated with the trainings and +backtestings. This file structure is heavily controlled and read by the `DataHandler()` +and should thus not be modified. diff --git a/freqtrade/commands/__init__.py b/freqtrade/commands/__init__.py index 0e637c487..d5aea62be 100644 --- a/freqtrade/commands/__init__.py +++ b/freqtrade/commands/__init__.py @@ -19,6 +19,7 @@ from freqtrade.commands.list_commands import (start_list_exchanges, start_list_m start_show_trades) from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show, start_edge, start_hyperopt) +from freqtrade.commands.freqai_commands import (start_training) from freqtrade.commands.pairlist_commands import start_test_pairlist from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit from freqtrade.commands.trade_commands import start_trading diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 815e28175..4388e84e4 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -12,7 +12,7 @@ from freqtrade.constants import DEFAULT_CONFIG ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"] -ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search"] +ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel", "freqaimodel_path"] ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"] @@ -190,7 +190,8 @@ class Arguments: start_list_markets, start_list_strategies, start_list_timeframes, start_new_config, start_new_strategy, start_plot_dataframe, start_plot_profit, start_show_trades, - start_test_pairlist, start_trading, start_webserver) + start_test_pairlist, start_trading, start_webserver, + start_training) subparsers = self.parser.add_subparsers(dest='command', # Use custom message when no subhandler is added diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index aac9f5713..4061418f7 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -614,4 +614,16 @@ AVAILABLE_CLI_OPTIONS = { "that do not contain any parameters."), action="store_true", ), + + "freqaimodel": Arg( + '--freqaimodel', + help='Specify a custom freqaimodels.', + metavar='NAME', + ), + + "freqaimodel_path": Arg( + '--freqaimodel-path', + help='Specify additional lookup path for freqaimodels.', + metavar='PATH', + ), } diff --git a/freqtrade/commands/freqai_commands.py b/freqtrade/commands/freqai_commands.py new file mode 100644 index 000000000..2733c851a --- /dev/null +++ b/freqtrade/commands/freqai_commands.py @@ -0,0 +1,24 @@ +import logging +from typing import Any, Dict + +from freqtrade import constants +from freqtrade.configuration import setup_utils_configuration +from freqtrade.enums import RunMode +from freqtrade.exceptions import OperationalException +from freqtrade.misc import round_coin_value + + +logger = logging.getLogger(__name__) + +def start_training(args: Dict[str, Any]) -> None: + """ + Train a model for predicting signals + :param args: Cli args from Arguments() + :return: None + """ + from freqtrade.freqai.training import Training + + config = setup_utils_configuration(args, RunMode.FREQAI) + + training = Training(config) + training.start() diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py index 96b585cd1..e13985270 100644 --- a/freqtrade/configuration/configuration.py +++ b/freqtrade/configuration/configuration.py @@ -95,6 +95,8 @@ class Configuration: self._process_data_options(config) + self._process_freqai_options(config) + # Check if the exchange set by the user is supported check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True)) @@ -446,6 +448,16 @@ class Configuration: config.update({'runmode': self.runmode}) + def _process_freqai_options(self, config: Dict[str, Any]) -> None: + + self._args_to_config(config, argname='freqaimodel', + logstring='Using freqaimodel class name: {}') + + self._args_to_config(config, argname='freqaimodel_path', + logstring='Using freqaimodel path: {}') + + return + def _args_to_config(self, config: Dict[str, Any], argname: str, logstring: str, logfun: Optional[Callable] = None, deprecated_msg: Optional[str] = None) -> None: diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 372472db8..f8a9dc06d 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -55,6 +55,7 @@ FTHYPT_FILEVERSION = 'fthypt_fileversion' USERPATH_HYPEROPTS = 'hyperopts' USERPATH_STRATEGIES = 'strategies' USERPATH_NOTEBOOKS = 'notebooks' +USERPATH_FREQAIMODELS = 'freqaimodels' TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent'] WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw'] diff --git a/freqtrade/enums/runmode.py b/freqtrade/enums/runmode.py index 6545aaec7..c280edf7c 100644 --- a/freqtrade/enums/runmode.py +++ b/freqtrade/enums/runmode.py @@ -15,9 +15,10 @@ class RunMode(Enum): UTIL_NO_EXCHANGE = "util_no_exchange" PLOT = "plot" WEBSERVER = "webserver" + FREQAI = "freqai" OTHER = "other" TRADING_MODES = [RunMode.LIVE, RunMode.DRY_RUN] -OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT] +OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT, RunMode.FREQAI] NON_UTIL_MODES = TRADING_MODES + OPTIMIZE_MODES diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py new file mode 100644 index 000000000..d399cd12b --- /dev/null +++ b/freqtrade/freqai/data_handler.py @@ -0,0 +1,434 @@ +import json +import os +import copy +import numpy as np +import pandas as pd +from pandas import DataFrame +from joblib import dump +from joblib import load +from sklearn.model_selection import train_test_split +from sklearn.metrics.pairwise import pairwise_distances +import datetime +from typing import Any, Dict, List, Tuple +import pickle as pk +from freqtrade.configuration import TimeRange + +SECONDS_IN_DAY = 86400 + +class DataHandler: + """ + Class designed to handle all the data for the IFreqaiModel class model. + Functionalities include holding, saving, loading, and analyzing the data. + """ + + def __init__(self, config: Dict[str, Any], dataframe: DataFrame, data: List): + self.full_dataframe = dataframe + (self.training_timeranges, + self.backtesting_timeranges) = self.split_timerange( + config['freqai']['full_timerange'], + config['freqai']['train_period'], + config['freqai']['backtest_period']) + self.data = data + self.data_dictionary = {} + self.config = config + self.freq_config = config['freqai'] + + def save_data(self, model: Any) -> None: + """ + Saves all data associated with a model for a single sub-train time range + :params: + :model: User trained model which can be reused for inferencing to generate + predictions + """ + + if not os.path.exists(self.model_path): os.mkdir(self.model_path) + save_path = self.model_path + self.model_filename + # Save the trained model + dump(model, save_path+"_model.joblib") + self.data['model_path'] = self.model_path + self.data['model_filename'] = self.model_filename + self.data['training_features_list'] = list(self.data_dictionary['train_features'].columns) + # store the metadata + with open(save_path+"_metadata.json", 'w') as fp: + json.dump(self.data, fp, default=self.np_encoder) + + # save the train data to file so we can check preds for area of applicability later + self.data_dictionary['train_features'].to_pickle(save_path+"_trained_df.pkl") + + return + + def load_data(self) -> Any: + """ + loads all data required to make a prediction on a sub-train time range + :returns: + :model: User trained model which can be inferenced for new predictions + """ + model = load(self.model_path+self.model_filename+"_model.joblib") + + with open(self.model_path+self.model_filename+"_metadata.json", 'r') as fp: + self.data = json.load(fp) + if self.data.get('training_features_list'): + self.training_features_list = [*self.data.get('training_features_list')] + + self.data_dictionary['train_features'] = pd.read_pickle(self.model_path+ + self.model_filename+"_trained_df.pkl") + + self.model_path = self.data['model_path'] + self.model_filename = self.data['model_filename'] + if self.config['freqai']['feature_parameters']['principal_component_analysis']: + self.pca = pk.load(open(self.model_path+self.model_filename+"_pca_object.pkl","rb")) + + return model + + def make_train_test_datasets(self, filtered_dataframe: DataFrame, labels: DataFrame) -> None: + ''' + Given the dataframe for the full history for training, split the data into + training and test data according to user specified parameters in configuration + file. + :filtered_dataframe: cleaned dataframe ready to be split. + :labels: cleaned labels ready to be split. + ''' + + if self.config['freqai']['feature_parameters']['weight_factor'] > 0: + weights = self.set_weights_higher_recent(len(filtered_dataframe)) + else: weights = np.ones(len(filtered_dataframe)) + + (train_features, test_features, train_labels, + test_labels, train_weights, test_weights) = train_test_split( + filtered_dataframe[:filtered_dataframe.shape[0]], + labels, + weights, + **self.config['freqai']['data_split_parameters'] + ) + + return self.build_data_dictionary( + train_features,test_features, + train_labels,test_labels, + train_weights,test_weights) + + + + def filter_features(self, unfiltered_dataframe: DataFrame, training_feature_list: List, + labels: DataFrame = None, training_filter: bool=True) -> Tuple[DataFrame, DataFrame]: + ''' + Filter the unfiltered dataframe to extract the user requested features and properly + remove all NaNs. Any row with a NaN is removed from training dataset or replaced with + 0s in the prediction dataset. However, prediction dataset do_predict will reflect any + row that had a NaN and will shield user from that prediction. + :params: + :unfiltered_dataframe: the full dataframe for the present training period + :training_feature_list: list, the training feature list constructed by self.build_feature_list() + according to user specified parameters in the configuration file. + :labels: the labels for the dataset + :training_filter: boolean which lets the function know if it is training data or + prediction data to be filtered. + :returns: + :filtered_dataframe: dataframe cleaned of NaNs and only containing the user + requested feature set. + :labels: labels cleaned of NaNs. + ''' + filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1) + drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs, + + if training_filter: # we don't care about total row number (total no. datapoints) in training, we only care about removing any row with NaNs + drop_index_labels = pd.isnull(labels) + filtered_dataframe = filtered_dataframe[(drop_index==False) & (drop_index_labels==False)] # dropping values + labels = labels[(drop_index==False) & (drop_index_labels==False)] # assuming the labels depend entirely on the dataframe here. + print('dropped',len(unfiltered_dataframe)-len(filtered_dataframe), + 'training data points due to NaNs, ensure you have downloaded all historical training data') + self.data['filter_drop_index_training'] = drop_index + + else: # we are backtesting so we need to preserve row number to send back to strategy, so now we use do_predict to avoid any prediction based on a NaN + drop_index = pd.isnull(filtered_dataframe).any(1) + self.data['filter_drop_index_prediction'] = drop_index + filtered_dataframe.fillna(0, inplace=True) # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction that was based on a single NaN is ultimately protected from buys with do_predict + drop_index = ~drop_index + self.do_predict = np.array(drop_index.replace(True,1).replace(False,0)) + print('dropped',len(self.do_predict) - self.do_predict.sum(),'of',len(filtered_dataframe), + 'prediction data points due to NaNs. These are protected from prediction with do_predict vector returned to strategy.') + + + return filtered_dataframe, labels + + def build_data_dictionary(self, train_df: DataFrame, test_df: DataFrame, + train_labels: DataFrame, test_labels: DataFrame, + train_weights: Any, test_weights: Any) -> Dict: + + self.data_dictionary = {'train_features': train_df, + 'test_features': test_df, + 'train_labels': train_labels, + 'test_labels': test_labels, + 'train_weights': train_weights, + 'test_weights': test_weights} + + return self.data_dictionary + + def standardize_data(self, data_dictionary: Dict) -> None: + ''' + Standardize all data in the data_dictionary according to the training dataset + :params: + :data_dictionary: dictionary containing the cleaned and split training/test data/labels + :returns: + :data_dictionary: updated dictionary with standardized values. + ''' + # standardize the data by training stats + train_mean = data_dictionary['train_features'].mean() + train_std = data_dictionary['train_features'].std() + data_dictionary['train_features'] = (data_dictionary['train_features'] - train_mean) / train_std + data_dictionary['test_features'] = (data_dictionary['test_features'] - train_mean) / train_std + + train_labels_std = data_dictionary['train_labels'].std() + train_labels_mean = data_dictionary['train_labels'].mean() + data_dictionary['train_labels'] = (data_dictionary['train_labels'] - train_labels_mean) / train_labels_std + data_dictionary['test_labels'] = (data_dictionary['test_labels'] - train_labels_mean) / train_labels_std + + for item in train_std.keys(): + self.data[item+'_std'] = train_std[item] + self.data[item+'_mean'] = train_mean[item] + + self.data['labels_std'] = train_labels_std + self.data['labels_mean'] = train_labels_mean + + return data_dictionary + + def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: + ''' + Standardizes a set of data using the mean and standard deviation from + the associated training data. + :params: + :df: Dataframe to be standardized + ''' + + for item in df.keys(): + df[item] = (df[item] - self.data[item+'_mean']) / self.data[item+'_std'] + + return df + + def split_timerange(self, tr: Dict, train_split: int=28, bt_split: int=7) -> list: + ''' + Function which takes a single time range (tr) and splits it + into sub timeranges to train and backtest on based on user input + tr: str, full timerange to train on + train_split: the period length for the each training (days). Specified in user + configuration file + bt_split: the backtesting length (dats). Specified in user configuration file + ''' + + train_period = train_split * SECONDS_IN_DAY + bt_period = bt_split * SECONDS_IN_DAY + + full_timerange = TimeRange.parse_timerange(tr) + timerange_train = copy.deepcopy(full_timerange) + timerange_backtest = copy.deepcopy(full_timerange) + + tr_training_list = [] + tr_backtesting_list = [] + first = True + while True: + if not first: timerange_train.startts = timerange_train.startts + bt_period + timerange_train.stopts = timerange_train.startts + train_period + + # if a full training period doesnt fit, we stop + if timerange_train.stopts > full_timerange.stopts: break + first = False + start = datetime.datetime.utcfromtimestamp(timerange_train.startts) + stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts) + tr_training_list.append(start.strftime("%Y%m%d")+'-'+stop.strftime("%Y%m%d")) + + ## associated backtest period + timerange_backtest.startts = timerange_train.stopts + timerange_backtest.stopts = timerange_backtest.startts + bt_period + start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) + stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) + tr_backtesting_list.append(start.strftime("%Y%m%d")+'-'+stop.strftime("%Y%m%d")) + + return tr_training_list, tr_backtesting_list + + def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: + """ + Given a full dataframe, extract the user desired window + :params: + :tr: timerange string that we wish to extract from df + :df: Dataframe containing all candles to run the entire backtest. Here + it is sliced down to just the present training period. + """ + timerange = TimeRange.parse_timerange(tr) + start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc) + stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc) + df = df.loc[df['date'] >= start, :] + df = df.loc[df['date'] <= stop, :] + + return df + + def principal_component_analysis(self) -> None: + """ + Performs Principal Component Analysis on the data for dimensionality reduction + and outlier detection (see self.remove_outliers()) + No parameters or returns, it acts on the data_dictionary held by the DataHandler. + """ + + from sklearn.decomposition import PCA # avoid importing if we dont need it + + n_components = self.data_dictionary['train_features'].shape[1] + pca = PCA(n_components=n_components) + pca = pca.fit(self.data_dictionary['train_features']) + n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999) + pca2 = PCA(n_components=n_keep_components) + self.data['n_kept_components'] = n_keep_components + pca2 = pca2.fit(self.data_dictionary['train_features']) + print('reduced feature dimension by',n_components-n_keep_components) + print("explained variance",np.sum(pca2.explained_variance_ratio_)) + train_components = pca2.transform(self.data_dictionary['train_features']) + test_components = pca2.transform(self.data_dictionary['test_features']) + + self.data_dictionary['train_features'] = pd.DataFrame(data=train_components, + columns = ['PC'+str(i) for i in range(0,n_keep_components)], + index = self.data_dictionary['train_features'].index) + + self.data_dictionary['test_features'] = pd.DataFrame(data=test_components, + columns = ['PC'+str(i) for i in range(0,n_keep_components)], + index = self.data_dictionary['test_features'].index) + + self.data['n_kept_components'] = n_keep_components + self.pca = pca2 + if not os.path.exists(self.model_path): os.mkdir(self.model_path) + pk.dump(pca2, open(self.model_path + self.model_filename+"_pca_object.pkl","wb")) + + return None + + def compute_distances(self) -> float: + print('computing average mean distance for all training points') + pairwise = pairwise_distances(self.data_dictionary['train_features'],n_jobs=-1) + avg_mean_dist = pairwise.mean(axis=1).mean() + print('avg_mean_dist',avg_mean_dist) + + return avg_mean_dist + + def remove_outliers(self,predict: bool) -> None: + """ + Remove data that looks like an outlier based on the distribution of each + variable. + :params: + :predict: boolean which tells the function if this is prediction data or + training data coming in. + """ + + lower_quantile = self.data_dictionary['train_features'].quantile(0.001) + upper_quantile = self.data_dictionary['train_features'].quantile(0.999) + + if predict: + + df = self.data_dictionary['prediction_features'][(self.data_dictionary['prediction_features']lower_quantile)] + drop_index = pd.isnull(df).any(1) + self.data_dictionary['prediction_features'].fillna(0,inplace=True) + drop_index = ~drop_index + do_predict = np.array(drop_index.replace(True,1).replace(False,0)) + + print('remove_outliers() tossed',len(do_predict)-do_predict.sum(),'predictions because they were beyond 3 std deviations from training data.') + self.do_predict += do_predict + self.do_predict -= 1 + + else: + + filter_train_df = self.data_dictionary['train_features'][(self.data_dictionary['train_features']lower_quantile)] + drop_index = pd.isnull(filter_train_df).any(1) + self.data_dictionary['train_features'] = self.data_dictionary['train_features'][(drop_index==False)] + self.data_dictionary['train_labels'] = self.data_dictionary['train_labels'][(drop_index==False)] + self.data_dictionary['train_weights'] = self.data_dictionary['train_weights'][(drop_index==False)] + + # do the same for the test data + filter_test_df = self.data_dictionary['test_features'][(self.data_dictionary['test_features']lower_quantile)] + drop_index = pd.isnull(filter_test_df).any(1) + #pdb.set_trace() + self.data_dictionary['test_labels'] = self.data_dictionary['test_labels'][(drop_index==False)] + self.data_dictionary['test_features'] = self.data_dictionary['test_features'][(drop_index==False)] + self.data_dictionary['test_weights'] = self.data_dictionary['test_weights'][(drop_index==False)] + + return + + def build_feature_list(self, config: dict) -> int: + """ + Build the list of features that will be used to filter + the full dataframe. Feature list is construced from the + user configuration file. + :params: + :config: Canonical freqtrade config file containing all + user defined input in config['freqai] dictionary. + """ + features = [] + for tf in config['freqai']['timeframes']: + for ft in config['freqai']['base_features']: + for n in range(config['freqai']['feature_parameters']['shift']+1): + shift='' + if n>0: shift = '_shift-'+str(n) + features.append(ft+shift+'_'+tf) + for p in config['freqai']['corr_pairlist']: + features.append(p.split("/")[0]+'-'+ft+shift+'_'+tf) + + print('number of features',len(features)) + return features + + def check_if_pred_in_training_spaces(self) -> None: + """ + Compares the distance from each prediction point to each training data + point. It uses this information to estimate a Dissimilarity Index (DI) + and avoid making predictions on any points that are too far away + from the training data set. + """ + + print('checking if prediction features are in AOA') + distance = pairwise_distances(self.data_dictionary['train_features'], + self.data_dictionary['prediction_features'],n_jobs=-1) + + do_predict = np.where(distance.min(axis=0) / + self.data['avg_mean_dist'] < self.config['freqai']['feature_parameters']['DI_threshold'],1,0) + + print('Distance checker tossed',len(do_predict)-do_predict.sum(), + 'predictions for being too far from training data') + + self.do_predict += do_predict + self.do_predict -= 1 + + def set_weights_higher_recent(self, num_weights: int) -> int: + """ + Set weights so that recent data is more heavily weighted during + training than older data. + """ + weights = np.zeros(num_weights) + for i in range(1, len(weights)): + weights[len(weights) - i] = np.exp(-i/ + (self.config['freqai']['feature_parameters']['weight_factor']*num_weights)) + return weights + + def append_predictions(self, predictions, do_predict, len_dataframe): + """ + Append backtest prediction from current backtest period to all previous periods + """ + + ones = np.ones(len_dataframe) + s_mean, s_std = ones*self.data['s_mean'], ones*self.data['s_std'] + + self.predictions = np.append(self.predictions,predictions) + self.do_predict = np.append(self.do_predict,do_predict) + self.target_mean = np.append(self.target_mean,s_mean) + self.target_std = np.append(self.target_std,s_std) + + return + + def fill_predictions(self, len_dataframe): + """ + Back fill values to before the backtesting range so that the dataframe matches size + when it goes back to the strategy. These rows are not included in the backtest. + """ + + filler = np.zeros(len_dataframe -len(self.predictions)) # startup_candle_count + self.predictions = np.append(filler,self.predictions) + self.do_predict = np.append(filler,self.do_predict) + self.target_mean = np.append(filler,self.target_mean) + self.target_std = np.append(filler,self.target_std) + + return + + def np_encoder(self, object): + if isinstance(object, np.generic): + return object.item() diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py new file mode 100644 index 000000000..eb15e7e49 --- /dev/null +++ b/freqtrade/freqai/freqai_interface.py @@ -0,0 +1,158 @@ + +import os +import numpy as np +import pandas as pd +from pandas import DataFrame +import shutil +import gc +from typing import Any, Dict, Optional, Tuple +from abc import ABC +from freqtrade.freqai.data_handler import DataHandler + +pd.options.mode.chained_assignment = None + +class IFreqaiModel(ABC): + """ + Class containing all tools for training and prediction in the strategy. + User models should inherit from this class as shown in + templates/ExamplePredictionModel.py where the user overrides + train(), predict(), fit(), and make_labels(). + """ + + def __init__(self, config: Dict[str, Any]) -> None: + + self.config = config + self.freqai_info = config['freqai'] + self.data_split_parameters = config['freqai']['data_split_parameters'] + self.model_training_parameters = config['freqai']['model_training_parameters'] + self.feature_parameters = config['freqai']['feature_parameters'] + self.full_path = (str(config['user_data_dir'])+ + "/models/"+self.freqai_info['full_timerange']+ + '-'+self.freqai_info['identifier']) + self.metadata = {} + self.data = {} + self.time_last_trained = None + self.current_time = None + self.model = None + self.predictions = None + + if not os.path.exists(self.full_path): + os.mkdir(self.full_path) + shutil.copy(self.config['config_files'][0],self.full_path+"/"+self.config['config_files'][0]) + + def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + """ + Entry point to the FreqaiModel, it will train a new model if + necesssary before making the prediction. + The backtesting and training paradigm is a sliding training window + with a following backtest window. Both windows slide according to the + length of the backtest window. This function is not intended to be + overridden by children of IFreqaiModel, but technically, it can be + if the user wishes to make deeper changes to the sliding window + logic. + :params: + :dataframe: Full dataframe coming from strategy - it contains entire + backtesting timerange + additional historical data necessary to train + the model. + :metadata: pair metadataa coming from strategy. + """ + self.pair = metadata['pair'] + self.dh = DataHandler(self.config, dataframe, self.data) + + print('going to train',len(self.dh.training_timeranges), + 'timeranges:',self.dh.training_timeranges) + predictions = np.array([]) + do_predict = np.array([]) + target_mean = np.array([]) + target_std = np.array([]) + + # Loop enforcing the sliding window training/backtesting paragigm + # tr_train is the training time range e.g. 1 historical month + # tr_backtest is the backtesting time range e.g. the week directly + # following tr_train. Both of these windows slide through the + # entire backtest + for tr_train, tr_backtest in zip(self.dh.training_timeranges, + self.dh.backtesting_timeranges): + gc.collect() + #self.config['timerange'] = tr_train + self.dh.data = {} # clean the pair specific data between models + self.freqai_info['training_timerange'] = tr_train + dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) + dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) + print("training",self.pair,"for",tr_train) + self.dh.model_path = self.full_path+"/"+ 'sub-train'+'-'+str(tr_train)+'/' + if not self.model_exists(self.pair, training_timerange=tr_train): + self.model = self.train(dataframe_train, metadata) + self.dh.save_data(self.model) + else: + self.model = self.dh.load_data(self.dh.model_path) + + preds, do_preds = self.predict(dataframe_backtest) + + self.dh.append_predictions(preds,do_preds,len(dataframe_backtest)) + + self.dh.fill_predictions(len(dataframe)) + + return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std + + def make_labels(self, dataframe: DataFrame) -> DataFrame: + """ + User defines the labels here (target values). + :params: + :dataframe: the full dataframe for the present training period + """ + + return dataframe + + def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: + """ + Filter the training data and train a model to it. Train makes heavy use of the datahandler + for storing, saving, loading, and managed. + :params: + :unfiltered_dataframe: Full dataframe for the current training period + :metadata: pair metadata from strategy. + :returns: + :model: Trained model which can be used to inference (self.predict) + """ + + return unfiltered_dataframe, unfiltered_dataframe + + def fit(self) -> Any: + """ + Most regressors use the same function names and arguments e.g. user + can drop in LGBMRegressor in place of CatBoostRegressor and all data + management will be properly handled by Freqai. + :params: + :data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + """ + + return None + + def predict(self) -> Optional[Tuple[DataFrame, DataFrame]]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :predictions: np.array of predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + return None + + def model_exists(self, pair: str, training_timerange: str = None) -> bool: + """ + Given a pair and path, check if a model already exists + :param pair: pair e.g. BTC/USD + :param path: path to model + """ + coin,_ = pair.split('/') + self.dh.model_filename = f"cb_"+coin.lower()+"_"+self.freqai_info['trained_stake']+"_"+training_timerange + file_exists = os.path.isfile(self.dh.model_path+ + self.dh.model_filename+"_model.joblib") + if file_exists: + print("Found model at", self.dh.model_path+self.dh.model_filename) + else: print("Could not find model at", + self.dh.model_path+self.dh.model_filename) + return file_exists diff --git a/freqtrade/freqai/strategy_bridge.py b/freqtrade/freqai/strategy_bridge.py new file mode 100644 index 000000000..c336e3c84 --- /dev/null +++ b/freqtrade/freqai/strategy_bridge.py @@ -0,0 +1,12 @@ +from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver + + +class CustomModel: + """ + A bridge between the user defined IFreqaiModel class + and the strategy. + """ + + def __init__(self,config): + + self.bridge = FreqaiModelResolver.load_freqaimodel(config) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 621812b0a..5051a8db0 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -204,6 +204,12 @@ class Backtesting: """ self.progress.init_step(BacktestState.DATALOAD, 1) + if self.config['freqaimodel']: + self.required_startup += int((self.config['freqai']['train_period']*86400) / + timeframe_to_seconds(self.config['timeframe'])) + self.config['startup_candle_count'] = self.required_startup + + data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, diff --git a/freqtrade/resolvers/freqaimodel_resolver.py b/freqtrade/resolvers/freqaimodel_resolver.py new file mode 100644 index 000000000..9545afd24 --- /dev/null +++ b/freqtrade/resolvers/freqaimodel_resolver.py @@ -0,0 +1,45 @@ +# pragma pylint: disable=attribute-defined-outside-init + +""" +This module load a custom model for freqai +""" +import logging +from pathlib import Path +from typing import Dict + +from freqtrade.constants import USERPATH_FREQAIMODELS +from freqtrade.exceptions import OperationalException +from freqtrade.freqai.freqai_interface import IFreqaiModel +from freqtrade.resolvers import IResolver + +logger = logging.getLogger(__name__) + + +class FreqaiModelResolver(IResolver): + """ + This class contains all the logic to load custom hyperopt loss class + """ + object_type = IFreqaiModel + object_type_str = "FreqaiModel" + user_subdir = USERPATH_FREQAIMODELS + initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve() + + @staticmethod + def load_freqaimodel(config: Dict) -> IFreqaiModel: + """ + Load the custom class from config parameter + :param config: configuration dictionary + """ + + freqaimodel_name = config.get('freqaimodel') + if not freqaimodel_name: + raise OperationalException( + "No freqaimodel set. Please use `--freqaimodel` to " + "specify the FreqaiModel class to use.\n" + ) + freqaimodel = FreqaiModelResolver.load_object(freqaimodel_name, + config, kwargs={'config': config}, + extra_dir=config.get('freqaimodel_path')) + + + return freqaimodel diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py new file mode 100644 index 000000000..a5370b5ac --- /dev/null +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -0,0 +1,139 @@ +import numpy as np +import pandas as pd +from catboost import CatBoostRegressor, Pool +from pandas import DataFrame +from typing import Any, Dict, Tuple +from freqtrade.freqai.freqai_interface import IFreqaiModel + +class ExamplePredictionModel(IFreqaiModel): + """ + User created prediction model. The class needs to override three necessary + functions, predict(), train(), fit(). The class inherits ModelHandler which + has its own DataHandler where data is held, saved, loaded, and managed. + """ + + def make_labels(self, dataframe: DataFrame) -> DataFrame: + """ + User defines the labels here (target values). + :params: + :dataframe: the full dataframe for the present training period + """ + + dataframe['s'] = (dataframe['close'].shift(-self.feature_parameters['period']).rolling( + self.feature_parameters['period']).max() / dataframe['close'] - 1) + self.dh.data['s_mean'] = dataframe['s'].mean() + self.dh.data['s_std'] = dataframe['s'].std() + + print('label mean',self.dh.data['s_mean'],'label std',self.dh.data['s_std']) + + return dataframe['s'] + + + def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: + """ + Filter the training data and train a model to it. Train makes heavy use of the datahandler + for storing, saving, loading, and managed. + :params: + :unfiltered_dataframe: Full dataframe for the current training period + :metadata: pair metadata from strategy. + :returns: + :model: Trained model which can be used to inference (self.predict) + """ + print("--------------------Starting training--------------------") + + # create the full feature list based on user config info + self.dh.training_features_list = self.dh.build_feature_list(self.config) + unfiltered_labels = self.make_labels(unfiltered_dataframe) + + # filter the features requested by user in the configuration file and elegantly handle NaNs + features_filtered, labels_filtered = self.dh.filter_features(unfiltered_dataframe, + self.dh.training_features_list, unfiltered_labels, training_filter=True) + + # split data into train/test data. + data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered) + # standardize all data based on train_dataset only + data_dictionary = self.dh.standardize_data(data_dictionary) + + # optional additional data cleaning + if self.feature_parameters['principal_component_analysis']: + self.dh.principal_component_analysis() + if self.feature_parameters["remove_outliers"]: + self.dh.remove_outliers(predict=False) + if self.feature_parameters['DI_threshold']: + self.dh.data['avg_mean_dist'] = self.dh.compute_distances() + + print("length of train data", len(data_dictionary['train_features'])) + + model = self.fit(data_dictionary) + + print('Finished training') + print(f'--------------------done training {metadata["pair"]}--------------------') + + return model + + def fit(self, data_dictionary: Dict) -> Any: + """ + Most regressors use the same function names and arguments e.g. user + can drop in LGBMRegressor in place of CatBoostRegressor and all data + management will be properly handled by Freqai. + :params: + :data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + """ + + train_data = Pool( + data=data_dictionary['train_features'], + label=data_dictionary['train_labels'], + weight=data_dictionary['train_weights'] + ) + + test_data = Pool( + data=data_dictionary['test_features'], + label=data_dictionary['test_labels'], + weight=data_dictionary['test_weights'] + ) + + model = CatBoostRegressor(verbose=100, early_stopping_rounds=400, + **self.model_training_parameters) + model.fit(X=train_data, eval_set=test_data) + + return model + + def predict(self, unfiltered_dataframe: DataFrame) -> Tuple[DataFrame, DataFrame]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :predictions: np.array of predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + print("--------------------Starting prediction--------------------") + + original_feature_list = self.dh.build_feature_list(self.config) + filtered_dataframe, _ = self.dh.filter_features(unfiltered_dataframe, original_feature_list, training_filter=False) + filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) + self.dh.data_dictionary['prediction_features'] = filtered_dataframe + + # optional additional data cleaning + if self.feature_parameters['principal_component_analysis']: + pca_components = self.dh.pca.transform(filtered_dataframe) + self.dh.data_dictionary['prediction_features'] = pd.DataFrame(data=pca_components, + columns = ['PC'+str(i) for i in range(0,self.dh.data['n_kept_components'])], + index = filtered_dataframe.index) + + if self.feature_parameters["remove_outliers"]: + self.dh.remove_outliers(predict=True) # creates dropped index + + if self.feature_parameters['DI_threshold']: + self.dh.check_if_pred_in_training_spaces() # sets do_predict + + predictions = self.model.predict(self.dh.data_dictionary['prediction_features']) + + # compute the non-standardized predictions + predictions = predictions * self.dh.data['labels_std'] + self.dh.data['labels_mean'] + + print("--------------------Finished prediction--------------------") + + return (predictions, self.dh.do_predict) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py new file mode 100644 index 000000000..d6b1295ec --- /dev/null +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -0,0 +1,179 @@ +import logging +import talib.abstract as ta +from pandas import DataFrame +import pandas as pd +from technical import qtpylib +import numpy as np +from freqtrade.strategy import (merge_informative_pair) +from freqtrade.strategy.interface import IStrategy +from freqtrade.freqai.strategy_bridge import CustomModel +from functools import reduce +logger = logging.getLogger(__name__) + +class FreqaiExampleStrategy(IStrategy): + """ + Example strategy showing how the user connects their own + IFreqaiModel to the strategy. Namely, the user uses: + self.model = CustomModel(self.config) + self.model.bridge.start(dataframe, metadata) + + to make predictions on their data. populate_any_indicators() automatically + generates the variety of features indicated by the user in the + canonical freqtrade configuration file under config['freqai']. + """ + + minimal_roi = { + "0": 0.01, + "240": -1 + } + + plot_config = { + 'main_plot': { + }, + 'subplots': { + "prediction":{ + 'prediction':{'color':'blue'} + }, + "target_roi":{ + 'target_roi':{'color':'brown'}, + }, + "do_predict":{ + 'do_predict':{'color':'brown'}, + }, + } + } + + stoploss = -0.05 + use_sell_signal = True + startup_candle_count: int = 1000 + + + def informative_pairs(self): + pairs = self.freqai_info['corr_pairlist'] + informative_pairs = [] + for tf in self.timeframes: + informative_pairs.append([(pair, tf) for pair in pairs]) + return informative_pairs + + def populate_any_indicators(self, pair, df, tf, informative=None,coin=''): + """ + Function designed to automatically generate, name and merge features + from user indicated timeframes in the configuration file. User can add + additional features here, but must follow the naming convention. + :params: + :pair: pair to be used as informative + :df: strategy dataframe which will receive merges from informatives + :tf: timeframe of the dataframe which will modify the feature names + :informative: the dataframe associated with the informative pair + :coin: the name of the coin which will modify the feature names. + """ + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + informative[coin+'rsi'] = ta.RSI(informative, timeperiod=14) + informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) + informative[coin+'adx'] = ta.ADX(informative, window=20) + + informative[coin+'20sma'] = ta.SMA(informative,timeperiod=20) + informative[coin+'21ema'] = ta.EMA(informative,timeperiod=21) + informative[coin+'bmsb'] = np.where(informative[coin+'20sma'].lt(informative[coin+'21ema']),1,0) + informative[coin+'close_over_20sma'] = informative['close']/informative[coin+'20sma'] + + informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) + + informative[coin+'ema21'] = ta.EMA(informative, timeperiod=21) + informative[coin+'sma20'] = ta.SMA(informative, timeperiod=20) + stoch = ta.STOCHRSI(informative, 15, 20, 2, 2) + informative[coin+'srsi-fk'] = stoch['fastk'] + informative[coin+'srsi-fd'] = stoch['fastd'] + + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) + informative[coin+'bb_lowerband'] = bollinger['lower'] + informative[coin+'bb_middleband'] = bollinger['mid'] + informative[coin+'bb_upperband'] = bollinger['upper'] + informative[coin+'bb_width'] = ((informative[coin+"bb_upperband"] - informative[coin+"bb_lowerband"]) / informative[coin+"bb_middleband"]) + informative[coin+'close-bb_lower'] = informative['close'] / informative[coin+'bb_lowerband'] + + informative[coin+'roc'] = ta.ROC(informative, timeperiod=3) + informative[coin+'adx'] = ta.ADX(informative, window=14) + + macd = ta.MACD(informative) + informative[coin+'macd'] = macd['macd'] + informative[coin+'pct-change'] = informative['close'].pct_change() + informative[coin+'relative_volume'] = informative['volume'] / informative['volume'].rolling(10).mean() + + informative[coin+'pct-change'] = informative['close'].pct_change() + + indicators = [col for col in informative if col.startswith(coin)] + + for n in range(self.freqai_info['feature_parameters']['shift']+1): + if n==0: continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix('_shift-'+str(n)) + informative = pd.concat((informative,informative_shift),axis=1) + + df = merge_informative_pair(df, informative, self.config['timeframe'], tf, ffill=True) + skip_columns = [(s + '_'+tf) for s in + ['date', 'open', 'high', 'low', 'close', 'volume']] + df = df.drop(columns=skip_columns) + + return df + + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + # the configuration file parameters are stored here + self.freqai_info = self.config['freqai'] + + # the model is instantiated here + self.model = CustomModel(self.config) + + print('Populating indicators...') + + # the following loops are necessary for building the features + # indicated by the user in the configuration file. + for tf in self.freqai_info['timeframes']: + dataframe = self.populate_any_indicators(metadata['pair'], + dataframe.copy(), tf) + for i in self.freqai_info['corr_pairlist']: + dataframe = self.populate_any_indicators(i, + dataframe.copy(), tf, coin=i.split("/")[0]+'-') + + # the model will return 4 values, its prediction, an indication of whether or not the prediction + # should be accepted, the target mean/std values from the labels used during each training period. + (dataframe['prediction'], dataframe['do_predict'], + dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata) + + dataframe['target_roi'] = dataframe['target_mean']+dataframe['target_std']*0.5 + dataframe['sell_roi'] = dataframe['target_mean']-dataframe['target_std']*1.5 + return dataframe + + + def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + buy_conditions = [ + (dataframe['prediction'] > dataframe['target_roi']) + & + (dataframe['do_predict'] == 1) + ] + + if buy_conditions: + dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'buy'] = 1 + + return dataframe + + + def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + # sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value') + sell_conditions = [ + (dataframe['prediction'] < dataframe['sell_roi']) + & + (dataframe['do_predict'] == 1) + ] + if sell_conditions: + dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), 'sell'] = 1 + + return dataframe + + def get_ticker_indicator(self): + return int(self.config['timeframe'][:-1]) diff --git a/mkdocs.yml b/mkdocs.yml index a43322f78..64d78363d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -36,6 +36,7 @@ nav: - Advanced Strategy: strategy-advanced.md - Advanced Hyperopt: advanced-hyperopt.md - Sandbox Testing: sandbox-testing.md + - Freqai: freqai.md - FAQ: faq.md - SQL Cheat-sheet: sql_cheatsheet.md - Strategy migration: strategy_migration.md diff --git a/requirements-freqai.txt b/requirements-freqai.txt new file mode 100644 index 000000000..f84d3df07 --- /dev/null +++ b/requirements-freqai.txt @@ -0,0 +1,8 @@ +# Include all requirements to run the bot. +-r requirements.txt + +# Required for freqai +scikit-learn==1.0.2 +scikit-optimize==0.9.0 +joblib==1.1.0 +catboost==1.0.4 From b40f8f88acb40591e338fc728aa5d17ea7374026 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 3 May 2022 10:28:13 +0200 Subject: [PATCH 002/518] cleaning and bug fixing --- freqtrade/freqai/data_handler.py | 5 +++++ freqtrade/freqai/freqai_interface.py | 7 ++++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py index d399cd12b..373063e42 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_handler.py @@ -19,6 +19,7 @@ class DataHandler: """ Class designed to handle all the data for the IFreqaiModel class model. Functionalities include holding, saving, loading, and analyzing the data. + author: Robert Caulk, rob.caulk@gmail.com """ def __init__(self, config: Dict[str, Any], dataframe: DataFrame, data: List): @@ -32,6 +33,10 @@ class DataHandler: self.data_dictionary = {} self.config = config self.freq_config = config['freqai'] + self.predictions = np.array([]) + self.do_predict = np.array([]) + self.target_mean = np.array([]) + self.target_std = np.array([]) def save_data(self, model: Any) -> None: """ diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index eb15e7e49..0f83793f1 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -17,6 +17,7 @@ class IFreqaiModel(ABC): User models should inherit from this class as shown in templates/ExamplePredictionModel.py where the user overrides train(), predict(), fit(), and make_labels(). + Author: Robert Caulk, rob.caulk@gmail.com """ def __init__(self, config: Dict[str, Any]) -> None: @@ -104,10 +105,10 @@ class IFreqaiModel(ABC): return dataframe - def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: + def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: """ Filter the training data and train a model to it. Train makes heavy use of the datahandler - for storing, saving, loading, and managed. + for storing, saving, loading, and analyzing the data. :params: :unfiltered_dataframe: Full dataframe for the current training period :metadata: pair metadata from strategy. @@ -115,7 +116,7 @@ class IFreqaiModel(ABC): :model: Trained model which can be used to inference (self.predict) """ - return unfiltered_dataframe, unfiltered_dataframe + return Any def fit(self) -> Any: """ From 630d201546892ad1565f871c594948770f8e44f0 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 3 May 2022 10:36:44 +0200 Subject: [PATCH 003/518] remove trained_stake --- freqtrade/freqai/freqai_interface.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 0f83793f1..2e840127c 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -62,10 +62,6 @@ class IFreqaiModel(ABC): print('going to train',len(self.dh.training_timeranges), 'timeranges:',self.dh.training_timeranges) - predictions = np.array([]) - do_predict = np.array([]) - target_mean = np.array([]) - target_std = np.array([]) # Loop enforcing the sliding window training/backtesting paragigm # tr_train is the training time range e.g. 1 historical month @@ -149,7 +145,7 @@ class IFreqaiModel(ABC): :param path: path to model """ coin,_ = pair.split('/') - self.dh.model_filename = f"cb_"+coin.lower()+"_"+self.freqai_info['trained_stake']+"_"+training_timerange + self.dh.model_filename = f"cb_"+coin.lower()+"_"+training_timerange file_exists = os.path.isfile(self.dh.model_path+ self.dh.model_filename+"_model.joblib") if file_exists: From 2600ba4e746a169f262c0c96447ea67df0dc72a7 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 3 May 2022 11:44:54 +0200 Subject: [PATCH 004/518] remove unused remnants --- freqtrade/commands/__init__.py | 1 - freqtrade/commands/arguments.py | 3 +-- freqtrade/commands/freqai_commands.py | 24 ------------------------ 3 files changed, 1 insertion(+), 27 deletions(-) delete mode 100644 freqtrade/commands/freqai_commands.py diff --git a/freqtrade/commands/__init__.py b/freqtrade/commands/__init__.py index d5aea62be..0e637c487 100644 --- a/freqtrade/commands/__init__.py +++ b/freqtrade/commands/__init__.py @@ -19,7 +19,6 @@ from freqtrade.commands.list_commands import (start_list_exchanges, start_list_m start_show_trades) from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show, start_edge, start_hyperopt) -from freqtrade.commands.freqai_commands import (start_training) from freqtrade.commands.pairlist_commands import start_test_pairlist from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit from freqtrade.commands.trade_commands import start_trading diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 4388e84e4..f47748502 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -190,8 +190,7 @@ class Arguments: start_list_markets, start_list_strategies, start_list_timeframes, start_new_config, start_new_strategy, start_plot_dataframe, start_plot_profit, start_show_trades, - start_test_pairlist, start_trading, start_webserver, - start_training) + start_test_pairlist, start_trading, start_webserver) subparsers = self.parser.add_subparsers(dest='command', # Use custom message when no subhandler is added diff --git a/freqtrade/commands/freqai_commands.py b/freqtrade/commands/freqai_commands.py deleted file mode 100644 index 2733c851a..000000000 --- a/freqtrade/commands/freqai_commands.py +++ /dev/null @@ -1,24 +0,0 @@ -import logging -from typing import Any, Dict - -from freqtrade import constants -from freqtrade.configuration import setup_utils_configuration -from freqtrade.enums import RunMode -from freqtrade.exceptions import OperationalException -from freqtrade.misc import round_coin_value - - -logger = logging.getLogger(__name__) - -def start_training(args: Dict[str, Any]) -> None: - """ - Train a model for predicting signals - :param args: Cli args from Arguments() - :return: None - """ - from freqtrade.freqai.training import Training - - config = setup_utils_configuration(args, RunMode.FREQAI) - - training = Training(config) - training.start() From 99f7e44c30441567615ac01b89bd4920f7b83c06 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 4 May 2022 17:42:34 +0200 Subject: [PATCH 005/518] flake8 passing, use pathlib in lieu of os.path to accommodate windows/mac OS --- config_examples/config_freqai.example.json | 104 ++-- freqtrade/freqai/data_handler.py | 513 +++++++++++------- freqtrade/freqai/freqai_interface.py | 121 +++-- freqtrade/freqai/strategy_bridge.py | 4 +- freqtrade/resolvers/freqaimodel_resolver.py | 16 +- freqtrade/templates/ExamplePredictionModel.py | 100 ++-- freqtrade/templates/FreqaiExampleStrategy.py | 174 +++--- 7 files changed, 593 insertions(+), 439 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 0092a8c51..47109ff31 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -6,17 +6,19 @@ "fiat_display_currency": "USD", "dry_run": true, "timeframe": "5m", - "dry_run_wallet":1000, + "dry_run_wallet": 1000, "cancel_open_orders_on_exit": true, "unfilledtimeout": { "entry": 10, "exit": 30 - }, + }, "exchange": { "name": "ftx", "key": "", "secret": "", - "ccxt_config": {"enableRateLimit": true}, + "ccxt_config": { + "enableRateLimit": true + }, "ccxt_async_config": { "enableRateLimit": true, "rateLimit": 200 @@ -24,8 +26,7 @@ "pair_whitelist": [ "BTC/USDT" ], - "pair_blacklist": [ - ] + "pair_blacklist": [] }, "entry_pricing": { "price_side": "same", @@ -43,54 +44,57 @@ "order_book_top": 1 }, "pairlists": [ - {"method": "StaticPairList"} + { + "method": "StaticPairList" + } ], - "freqai": { - "btc_pair" : "BTC/USDT", - "timeframes" : ["5m","15m","1h"], - "full_timerange" : "20210601-20220101", - "train_period" : 30, - "backtest_period" : 7, - "identifier" : "example", - "base_features": [ - "rsi", - "close_over_20sma", - "relative_volume", - "bb_width", - "mfi", - "roc", - "pct-change", - "adx", - "macd" - ], - "corr_pairlist": [ - "ETH/USDT", - "LINK/USDT", - "DOT/USDT" - ], - "training_timerange" : "20211220-20220117", - - "feature_parameters" : { - "period": 12, - "shift": 2, - "drop_features": false, - "DI_threshold": 1, - "weight_factor": 0, - "principal_component_analysis": false, - "remove_outliers": false - }, - "data_split_parameters" : { - "test_size": 0.25, - "random_state": 1 - }, - "model_training_parameters" : { - "n_estimators": 2000, - "random_state": 1, - "learning_rate": 0.02, - "task_type": "CPU" - } + "btc_pair": "BTC/USDT", + "timeframes": [ + "5m", + "15m" + ], + "full_timerange": "20210601-20210901", + "train_period": 30, + "backtest_period": 7, + "identifier": "example", + "base_features": [ + "rsi", + "close_over_20sma", + "relative_volume", + "bb_width", + "mfi", + "roc", + "pct-change", + "adx", + "macd" + ], + "corr_pairlist": [ + "ETH/USDT", + "LINK/USDT", + "DOT/USDT" + ], + "training_timerange": "20211220-20220117", + "feature_parameters": { + "period": 12, + "shift": 1, + "drop_features": false, + "DI_threshold": 1, + "weight_factor": 0, + "principal_component_analysis": false, + "remove_outliers": false }, + "data_split_parameters": { + "test_size": 0.25, + "random_state": 1 + }, + "model_training_parameters": { + "n_estimators": 2000, + "random_state": 1, + "learning_rate": 0.02, + "task_type": "CPU" + } + }, "bot_name": "", "initial_state": "running", "forcebuy_enable": false, diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py index 373063e42..7264c6fab 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_handler.py @@ -1,64 +1,77 @@ -import json -import os import copy +import datetime +import json +import pickle as pk +from pathlib import Path +from typing import Any, Dict, List, Tuple + import numpy as np import pandas as pd +from joblib import dump, load from pandas import DataFrame -from joblib import dump -from joblib import load -from sklearn.model_selection import train_test_split from sklearn.metrics.pairwise import pairwise_distances -import datetime -from typing import Any, Dict, List, Tuple -import pickle as pk +from sklearn.model_selection import train_test_split + from freqtrade.configuration import TimeRange + SECONDS_IN_DAY = 86400 + class DataHandler: """ - Class designed to handle all the data for the IFreqaiModel class model. + Class designed to handle all the data for the IFreqaiModel class model. Functionalities include holding, saving, loading, and analyzing the data. author: Robert Caulk, rob.caulk@gmail.com """ - def __init__(self, config: Dict[str, Any], dataframe: DataFrame, data: List): + def __init__(self, config: Dict[str, Any], dataframe: DataFrame): self.full_dataframe = dataframe - (self.training_timeranges, - self.backtesting_timeranges) = self.split_timerange( - config['freqai']['full_timerange'], - config['freqai']['train_period'], - config['freqai']['backtest_period']) - self.data = data - self.data_dictionary = {} + (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( + config["freqai"]["full_timerange"], + config["freqai"]["train_period"], + config["freqai"]["backtest_period"], + ) + self.data: Dict[Any, Any] = {} self.config = config - self.freq_config = config['freqai'] + self.freq_config = config["freqai"] self.predictions = np.array([]) self.do_predict = np.array([]) self.target_mean = np.array([]) self.target_std = np.array([]) + self.model_path = Path() + self.model_filename = "" def save_data(self, model: Any) -> None: """ Saves all data associated with a model for a single sub-train time range :params: - :model: User trained model which can be reused for inferencing to generate + :model: User trained model which can be reused for inferencing to generate predictions """ - if not os.path.exists(self.model_path): os.mkdir(self.model_path) - save_path = self.model_path + self.model_filename + if not self.model_path.is_dir(): + self.model_path.mkdir(parents=True, exist_ok=True) + + save_path = Path(self.model_path) + + # if not os.path.exists(self.model_path): + # os.mkdir(self.model_path) + # save_path = self.model_path + self.model_filename + # Save the trained model - dump(model, save_path+"_model.joblib") - self.data['model_path'] = self.model_path - self.data['model_filename'] = self.model_filename - self.data['training_features_list'] = list(self.data_dictionary['train_features'].columns) + dump(model, save_path / str(self.model_filename + "_model.joblib")) + self.data["model_path"] = self.model_path + self.data["model_filename"] = self.model_filename + self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns) # store the metadata - with open(save_path+"_metadata.json", 'w') as fp: - json.dump(self.data, fp, default=self.np_encoder) + with open(save_path / str(self.model_filename + "_metadata.json"), "w") as fp: + json.dump(self.data, fp, default=self.np_encoder) # save the train data to file so we can check preds for area of applicability later - self.data_dictionary['train_features'].to_pickle(save_path+"_trained_df.pkl") + self.data_dictionary["train_features"].to_pickle( + save_path / str(self.model_filename + "_trained_df.pkl") + ) return @@ -68,156 +81,210 @@ class DataHandler: :returns: :model: User trained model which can be inferenced for new predictions """ - model = load(self.model_path+self.model_filename+"_model.joblib") + model = load(self.model_path / str(self.model_filename + "_model.joblib")) - with open(self.model_path+self.model_filename+"_metadata.json", 'r') as fp: + with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) - if self.data.get('training_features_list'): - self.training_features_list = [*self.data.get('training_features_list')] + self.training_features_list = self.data["training_features_list"] + # if self.data.get("training_features_list"): + # self.training_features_list = [*self.data.get("training_features_list")] - self.data_dictionary['train_features'] = pd.read_pickle(self.model_path+ - self.model_filename+"_trained_df.pkl") + self.data_dictionary["train_features"] = pd.read_pickle( + self.model_path / str(self.model_filename + "_trained_df.pkl") + ) - self.model_path = self.data['model_path'] - self.model_filename = self.data['model_filename'] - if self.config['freqai']['feature_parameters']['principal_component_analysis']: - self.pca = pk.load(open(self.model_path+self.model_filename+"_pca_object.pkl","rb")) + self.model_path = self.data["model_path"] + self.model_filename = self.data["model_filename"] + if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: + self.pca = pk.load( + open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "rb") + ) return model - def make_train_test_datasets(self, filtered_dataframe: DataFrame, labels: DataFrame) -> None: - ''' - Given the dataframe for the full history for training, split the data into - training and test data according to user specified parameters in configuration - file. + def make_train_test_datasets( + self, filtered_dataframe: DataFrame, labels: DataFrame + ) -> Dict[Any, Any]: + """ + Given the dataframe for the full history for training, split the data into + training and test data according to user specified parameters in configuration + file. :filtered_dataframe: cleaned dataframe ready to be split. :labels: cleaned labels ready to be split. - ''' + """ - if self.config['freqai']['feature_parameters']['weight_factor'] > 0: + if self.config["freqai"]["feature_parameters"]["weight_factor"] > 0: weights = self.set_weights_higher_recent(len(filtered_dataframe)) - else: weights = np.ones(len(filtered_dataframe)) + else: + weights = np.ones(len(filtered_dataframe)) - (train_features, test_features, train_labels, - test_labels, train_weights, test_weights) = train_test_split( - filtered_dataframe[:filtered_dataframe.shape[0]], + ( + train_features, + test_features, + train_labels, + test_labels, + train_weights, + test_weights, + ) = train_test_split( + filtered_dataframe[: filtered_dataframe.shape[0]], labels, weights, - **self.config['freqai']['data_split_parameters'] + **self.config["freqai"]["data_split_parameters"] ) return self.build_data_dictionary( - train_features,test_features, - train_labels,test_labels, - train_weights,test_weights) + train_features, test_features, train_labels, test_labels, train_weights, test_weights + ) - - - def filter_features(self, unfiltered_dataframe: DataFrame, training_feature_list: List, - labels: DataFrame = None, training_filter: bool=True) -> Tuple[DataFrame, DataFrame]: - ''' - Filter the unfiltered dataframe to extract the user requested features and properly - remove all NaNs. Any row with a NaN is removed from training dataset or replaced with - 0s in the prediction dataset. However, prediction dataset do_predict will reflect any + def filter_features( + self, + unfiltered_dataframe: DataFrame, + training_feature_list: List, + labels: DataFrame = pd.DataFrame(), + training_filter: bool = True, + ) -> Tuple[DataFrame, DataFrame]: + """ + Filter the unfiltered dataframe to extract the user requested features and properly + remove all NaNs. Any row with a NaN is removed from training dataset or replaced with + 0s in the prediction dataset. However, prediction dataset do_predict will reflect any row that had a NaN and will shield user from that prediction. :params: :unfiltered_dataframe: the full dataframe for the present training period - :training_feature_list: list, the training feature list constructed by self.build_feature_list() - according to user specified parameters in the configuration file. + :training_feature_list: list, the training feature list constructed by + self.build_feature_list() according to user specified parameters in the configuration file. :labels: the labels for the dataset - :training_filter: boolean which lets the function know if it is training data or - prediction data to be filtered. + :training_filter: boolean which lets the function know if it is training data or + prediction data to be filtered. :returns: :filtered_dataframe: dataframe cleaned of NaNs and only containing the user requested feature set. :labels: labels cleaned of NaNs. - ''' + """ filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1) - drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs, - - if training_filter: # we don't care about total row number (total no. datapoints) in training, we only care about removing any row with NaNs + drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs, + drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement. + if ( + training_filter + ): # we don't care about total row number (total no. datapoints) in training, we only care + # about removing any row with NaNs drop_index_labels = pd.isnull(labels) - filtered_dataframe = filtered_dataframe[(drop_index==False) & (drop_index_labels==False)] # dropping values - labels = labels[(drop_index==False) & (drop_index_labels==False)] # assuming the labels depend entirely on the dataframe here. - print('dropped',len(unfiltered_dataframe)-len(filtered_dataframe), - 'training data points due to NaNs, ensure you have downloaded all historical training data') - self.data['filter_drop_index_training'] = drop_index + drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0) + filtered_dataframe = filtered_dataframe[ + (drop_index == 0) & (drop_index_labels == 0) + ] # dropping values + labels = labels[ + (drop_index == 0) & (drop_index_labels == 0) + ] # assuming the labels depend entirely on the dataframe here. + print( + "dropped", + len(unfiltered_dataframe) - len(filtered_dataframe), + "training data points due to NaNs, ensure you have downloaded", + "all historical training data", + ) + self.data["filter_drop_index_training"] = drop_index - else: # we are backtesting so we need to preserve row number to send back to strategy, so now we use do_predict to avoid any prediction based on a NaN + else: + # we are backtesting so we need to preserve row number to send back to strategy, + # so now we use do_predict to avoid any prediction based on a NaN drop_index = pd.isnull(filtered_dataframe).any(1) - self.data['filter_drop_index_prediction'] = drop_index - filtered_dataframe.fillna(0, inplace=True) # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction that was based on a single NaN is ultimately protected from buys with do_predict + self.data["filter_drop_index_prediction"] = drop_index + filtered_dataframe.fillna(0, inplace=True) + # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction + # that was based on a single NaN is ultimately protected from buys with do_predict drop_index = ~drop_index - self.do_predict = np.array(drop_index.replace(True,1).replace(False,0)) - print('dropped',len(self.do_predict) - self.do_predict.sum(),'of',len(filtered_dataframe), - 'prediction data points due to NaNs. These are protected from prediction with do_predict vector returned to strategy.') - + self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) + print( + "dropped", + len(self.do_predict) - self.do_predict.sum(), + "of", + len(filtered_dataframe), + "prediction data points due to NaNs. These are protected from prediction", + "with do_predict vector returned to strategy.", + ) return filtered_dataframe, labels - def build_data_dictionary(self, train_df: DataFrame, test_df: DataFrame, - train_labels: DataFrame, test_labels: DataFrame, - train_weights: Any, test_weights: Any) -> Dict: + def build_data_dictionary( + self, + train_df: DataFrame, + test_df: DataFrame, + train_labels: DataFrame, + test_labels: DataFrame, + train_weights: Any, + test_weights: Any, + ) -> Dict: - self.data_dictionary = {'train_features': train_df, - 'test_features': test_df, - 'train_labels': train_labels, - 'test_labels': test_labels, - 'train_weights': train_weights, - 'test_weights': test_weights} + self.data_dictionary = { + "train_features": train_df, + "test_features": test_df, + "train_labels": train_labels, + "test_labels": test_labels, + "train_weights": train_weights, + "test_weights": test_weights, + } return self.data_dictionary - def standardize_data(self, data_dictionary: Dict) -> None: - ''' + def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: + """ Standardize all data in the data_dictionary according to the training dataset :params: :data_dictionary: dictionary containing the cleaned and split training/test data/labels :returns: :data_dictionary: updated dictionary with standardized values. - ''' + """ # standardize the data by training stats - train_mean = data_dictionary['train_features'].mean() - train_std = data_dictionary['train_features'].std() - data_dictionary['train_features'] = (data_dictionary['train_features'] - train_mean) / train_std - data_dictionary['test_features'] = (data_dictionary['test_features'] - train_mean) / train_std + train_mean = data_dictionary["train_features"].mean() + train_std = data_dictionary["train_features"].std() + data_dictionary["train_features"] = ( + data_dictionary["train_features"] - train_mean + ) / train_std + data_dictionary["test_features"] = ( + data_dictionary["test_features"] - train_mean + ) / train_std - train_labels_std = data_dictionary['train_labels'].std() - train_labels_mean = data_dictionary['train_labels'].mean() - data_dictionary['train_labels'] = (data_dictionary['train_labels'] - train_labels_mean) / train_labels_std - data_dictionary['test_labels'] = (data_dictionary['test_labels'] - train_labels_mean) / train_labels_std + train_labels_std = data_dictionary["train_labels"].std() + train_labels_mean = data_dictionary["train_labels"].mean() + data_dictionary["train_labels"] = ( + data_dictionary["train_labels"] - train_labels_mean + ) / train_labels_std + data_dictionary["test_labels"] = ( + data_dictionary["test_labels"] - train_labels_mean + ) / train_labels_std for item in train_std.keys(): - self.data[item+'_std'] = train_std[item] - self.data[item+'_mean'] = train_mean[item] + self.data[item + "_std"] = train_std[item] + self.data[item + "_mean"] = train_mean[item] - self.data['labels_std'] = train_labels_std - self.data['labels_mean'] = train_labels_mean + self.data["labels_std"] = train_labels_std + self.data["labels_mean"] = train_labels_mean return data_dictionary def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: - ''' - Standardizes a set of data using the mean and standard deviation from + """ + Standardizes a set of data using the mean and standard deviation from the associated training data. :params: :df: Dataframe to be standardized - ''' + """ for item in df.keys(): - df[item] = (df[item] - self.data[item+'_mean']) / self.data[item+'_std'] + df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"] return df - def split_timerange(self, tr: Dict, train_split: int=28, bt_split: int=7) -> list: - ''' + def split_timerange( + self, tr: str, train_split: int = 28, bt_split: int = 7 + ) -> Tuple[list, list]: + """ Function which takes a single time range (tr) and splits it into sub timeranges to train and backtest on based on user input tr: str, full timerange to train on train_split: the period length for the each training (days). Specified in user configuration file bt_split: the backtesting length (dats). Specified in user configuration file - ''' + """ train_period = train_split * SECONDS_IN_DAY bt_period = bt_split * SECONDS_IN_DAY @@ -230,22 +297,24 @@ class DataHandler: tr_backtesting_list = [] first = True while True: - if not first: timerange_train.startts = timerange_train.startts + bt_period + if not first: + timerange_train.startts = timerange_train.startts + bt_period timerange_train.stopts = timerange_train.startts + train_period # if a full training period doesnt fit, we stop - if timerange_train.stopts > full_timerange.stopts: break + if timerange_train.stopts > full_timerange.stopts: + break first = False start = datetime.datetime.utcfromtimestamp(timerange_train.startts) stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts) - tr_training_list.append(start.strftime("%Y%m%d")+'-'+stop.strftime("%Y%m%d")) + tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) - ## associated backtest period - timerange_backtest.startts = timerange_train.stopts - timerange_backtest.stopts = timerange_backtest.startts + bt_period + # associated backtest period + timerange_backtest.startts = timerange_train.stopts + timerange_backtest.stopts = timerange_backtest.startts + bt_period start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) - tr_backtesting_list.append(start.strftime("%Y%m%d")+'-'+stop.strftime("%Y%m%d")) + tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) return tr_training_list, tr_backtesting_list @@ -260,8 +329,8 @@ class DataHandler: timerange = TimeRange.parse_timerange(tr) start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc) stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc) - df = df.loc[df['date'] >= start, :] - df = df.loc[df['date'] <= stop, :] + df = df.loc[df["date"] >= start, :] + df = df.loc[df["date"] <= stop, :] return df @@ -272,128 +341,171 @@ class DataHandler: No parameters or returns, it acts on the data_dictionary held by the DataHandler. """ - from sklearn.decomposition import PCA # avoid importing if we dont need it + from sklearn.decomposition import PCA # avoid importing if we dont need it - n_components = self.data_dictionary['train_features'].shape[1] + n_components = self.data_dictionary["train_features"].shape[1] pca = PCA(n_components=n_components) - pca = pca.fit(self.data_dictionary['train_features']) + pca = pca.fit(self.data_dictionary["train_features"]) n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999) pca2 = PCA(n_components=n_keep_components) - self.data['n_kept_components'] = n_keep_components - pca2 = pca2.fit(self.data_dictionary['train_features']) - print('reduced feature dimension by',n_components-n_keep_components) - print("explained variance",np.sum(pca2.explained_variance_ratio_)) - train_components = pca2.transform(self.data_dictionary['train_features']) - test_components = pca2.transform(self.data_dictionary['test_features']) + self.data["n_kept_components"] = n_keep_components + pca2 = pca2.fit(self.data_dictionary["train_features"]) + print("reduced feature dimension by", n_components - n_keep_components) + print("explained variance", np.sum(pca2.explained_variance_ratio_)) + train_components = pca2.transform(self.data_dictionary["train_features"]) + test_components = pca2.transform(self.data_dictionary["test_features"]) - self.data_dictionary['train_features'] = pd.DataFrame(data=train_components, - columns = ['PC'+str(i) for i in range(0,n_keep_components)], - index = self.data_dictionary['train_features'].index) + self.data_dictionary["train_features"] = pd.DataFrame( + data=train_components, + columns=["PC" + str(i) for i in range(0, n_keep_components)], + index=self.data_dictionary["train_features"].index, + ) - self.data_dictionary['test_features'] = pd.DataFrame(data=test_components, - columns = ['PC'+str(i) for i in range(0,n_keep_components)], - index = self.data_dictionary['test_features'].index) + self.data_dictionary["test_features"] = pd.DataFrame( + data=test_components, + columns=["PC" + str(i) for i in range(0, n_keep_components)], + index=self.data_dictionary["test_features"].index, + ) - self.data['n_kept_components'] = n_keep_components + self.data["n_kept_components"] = n_keep_components self.pca = pca2 - if not os.path.exists(self.model_path): os.mkdir(self.model_path) - pk.dump(pca2, open(self.model_path + self.model_filename+"_pca_object.pkl","wb")) + + if not self.model_path.is_dir(): + self.model_path.mkdir(parents=True, exist_ok=True) + pk.dump(pca2, open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "wb")) return None def compute_distances(self) -> float: - print('computing average mean distance for all training points') - pairwise = pairwise_distances(self.data_dictionary['train_features'],n_jobs=-1) + print("computing average mean distance for all training points") + pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1) avg_mean_dist = pairwise.mean(axis=1).mean() - print('avg_mean_dist',avg_mean_dist) + print("avg_mean_dist", avg_mean_dist) return avg_mean_dist - def remove_outliers(self,predict: bool) -> None: + def remove_outliers(self, predict: bool) -> None: """ - Remove data that looks like an outlier based on the distribution of each - variable. + Remove data that looks like an outlier based on the distribution of each + variable. :params: - :predict: boolean which tells the function if this is prediction data or - training data coming in. + :predict: boolean which tells the function if this is prediction data or + training data coming in. """ - lower_quantile = self.data_dictionary['train_features'].quantile(0.001) - upper_quantile = self.data_dictionary['train_features'].quantile(0.999) + lower_quantile = self.data_dictionary["train_features"].quantile(0.001) + upper_quantile = self.data_dictionary["train_features"].quantile(0.999) if predict: - df = self.data_dictionary['prediction_features'][(self.data_dictionary['prediction_features']lower_quantile)] + df = self.data_dictionary["prediction_features"][ + (self.data_dictionary["prediction_features"] < upper_quantile) + & (self.data_dictionary["prediction_features"] > lower_quantile) + ] drop_index = pd.isnull(df).any(1) - self.data_dictionary['prediction_features'].fillna(0,inplace=True) + self.data_dictionary["prediction_features"].fillna(0, inplace=True) drop_index = ~drop_index - do_predict = np.array(drop_index.replace(True,1).replace(False,0)) - - print('remove_outliers() tossed',len(do_predict)-do_predict.sum(),'predictions because they were beyond 3 std deviations from training data.') + do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) + + print( + "remove_outliers() tossed", + len(do_predict) - do_predict.sum(), + "predictions because they were beyond 3 std deviations from training data.", + ) self.do_predict += do_predict self.do_predict -= 1 else: - filter_train_df = self.data_dictionary['train_features'][(self.data_dictionary['train_features']lower_quantile)] + filter_train_df = self.data_dictionary["train_features"][ + (self.data_dictionary["train_features"] < upper_quantile) + & (self.data_dictionary["train_features"] > lower_quantile) + ] drop_index = pd.isnull(filter_train_df).any(1) - self.data_dictionary['train_features'] = self.data_dictionary['train_features'][(drop_index==False)] - self.data_dictionary['train_labels'] = self.data_dictionary['train_labels'][(drop_index==False)] - self.data_dictionary['train_weights'] = self.data_dictionary['train_weights'][(drop_index==False)] + drop_index = drop_index.replace(True, 1).replace(False, 0) + self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ + (drop_index == 0) + ] + self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][ + (drop_index == 0) + ] + self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][ + (drop_index == 0) + ] # do the same for the test data - filter_test_df = self.data_dictionary['test_features'][(self.data_dictionary['test_features']lower_quantile)] + filter_test_df = self.data_dictionary["test_features"][ + (self.data_dictionary["test_features"] < upper_quantile) + & (self.data_dictionary["test_features"] > lower_quantile) + ] drop_index = pd.isnull(filter_test_df).any(1) - #pdb.set_trace() - self.data_dictionary['test_labels'] = self.data_dictionary['test_labels'][(drop_index==False)] - self.data_dictionary['test_features'] = self.data_dictionary['test_features'][(drop_index==False)] - self.data_dictionary['test_weights'] = self.data_dictionary['test_weights'][(drop_index==False)] + drop_index = drop_index.replace(True, 1).replace(False, 0) + self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][ + (drop_index == 0) + ] + self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ + (drop_index == 0) + ] + self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ + (drop_index == 0) + ] return - def build_feature_list(self, config: dict) -> int: + def build_feature_list(self, config: dict) -> list: """ - Build the list of features that will be used to filter - the full dataframe. Feature list is construced from the + Build the list of features that will be used to filter + the full dataframe. Feature list is construced from the user configuration file. :params: :config: Canonical freqtrade config file containing all user defined input in config['freqai] dictionary. """ features = [] - for tf in config['freqai']['timeframes']: - for ft in config['freqai']['base_features']: - for n in range(config['freqai']['feature_parameters']['shift']+1): - shift='' - if n>0: shift = '_shift-'+str(n) - features.append(ft+shift+'_'+tf) - for p in config['freqai']['corr_pairlist']: - features.append(p.split("/")[0]+'-'+ft+shift+'_'+tf) + for tf in config["freqai"]["timeframes"]: + for ft in config["freqai"]["base_features"]: + for n in range(config["freqai"]["feature_parameters"]["shift"] + 1): + shift = "" + if n > 0: + shift = "_shift-" + str(n) + features.append(ft + shift + "_" + tf) + for p in config["freqai"]["corr_pairlist"]: + features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - print('number of features',len(features)) + print("number of features", len(features)) return features def check_if_pred_in_training_spaces(self) -> None: """ - Compares the distance from each prediction point to each training data + Compares the distance from each prediction point to each training data point. It uses this information to estimate a Dissimilarity Index (DI) - and avoid making predictions on any points that are too far away - from the training data set. + and avoid making predictions on any points that are too far away + from the training data set. """ - print('checking if prediction features are in AOA') - distance = pairwise_distances(self.data_dictionary['train_features'], - self.data_dictionary['prediction_features'],n_jobs=-1) + print("checking if prediction features are in AOA") + distance = pairwise_distances( + self.data_dictionary["train_features"], + self.data_dictionary["prediction_features"], + n_jobs=-1, + ) - do_predict = np.where(distance.min(axis=0) / - self.data['avg_mean_dist'] < self.config['freqai']['feature_parameters']['DI_threshold'],1,0) + do_predict = np.where( + distance.min(axis=0) / self.data["avg_mean_dist"] + < self.config["freqai"]["feature_parameters"]["DI_threshold"], + 1, + 0, + ) - print('Distance checker tossed',len(do_predict)-do_predict.sum(), - 'predictions for being too far from training data') + print( + "Distance checker tossed", + len(do_predict) - do_predict.sum(), + "predictions for being too far from training data", + ) - self.do_predict += do_predict + self.do_predict += do_predict self.do_predict -= 1 - + def set_weights_higher_recent(self, num_weights: int) -> int: """ Set weights so that recent data is more heavily weighted during @@ -401,8 +513,9 @@ class DataHandler: """ weights = np.zeros(num_weights) for i in range(1, len(weights)): - weights[len(weights) - i] = np.exp(-i/ - (self.config['freqai']['feature_parameters']['weight_factor']*num_weights)) + weights[len(weights) - i] = np.exp( + -i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights) + ) return weights def append_predictions(self, predictions, do_predict, len_dataframe): @@ -411,12 +524,12 @@ class DataHandler: """ ones = np.ones(len_dataframe) - s_mean, s_std = ones*self.data['s_mean'], ones*self.data['s_std'] + s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"] - self.predictions = np.append(self.predictions,predictions) - self.do_predict = np.append(self.do_predict,do_predict) - self.target_mean = np.append(self.target_mean,s_mean) - self.target_std = np.append(self.target_std,s_std) + self.predictions = np.append(self.predictions, predictions) + self.do_predict = np.append(self.do_predict, do_predict) + self.target_mean = np.append(self.target_mean, s_mean) + self.target_std = np.append(self.target_std, s_std) return @@ -426,14 +539,14 @@ class DataHandler: when it goes back to the strategy. These rows are not included in the backtest. """ - filler = np.zeros(len_dataframe -len(self.predictions)) # startup_candle_count - self.predictions = np.append(filler,self.predictions) - self.do_predict = np.append(filler,self.do_predict) - self.target_mean = np.append(filler,self.target_mean) - self.target_std = np.append(filler,self.target_std) + filler = np.zeros(len_dataframe - len(self.predictions)) # startup_candle_count + self.predictions = np.append(filler, self.predictions) + self.do_predict = np.append(filler, self.do_predict) + self.target_mean = np.append(filler, self.target_mean) + self.target_std = np.append(filler, self.target_std) return - + def np_encoder(self, object): if isinstance(object, np.generic): return object.item() diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 2e840127c..9f04b09cd 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -1,20 +1,23 @@ +import gc +import shutil +from abc import ABC +from pathlib import Path +from typing import Any, Dict, Tuple -import os import numpy as np import pandas as pd from pandas import DataFrame -import shutil -import gc -from typing import Any, Dict, Optional, Tuple -from abc import ABC + from freqtrade.freqai.data_handler import DataHandler + pd.options.mode.chained_assignment = None + class IFreqaiModel(ABC): """ Class containing all tools for training and prediction in the strategy. - User models should inherit from this class as shown in + User models should inherit from this class as shown in templates/ExamplePredictionModel.py where the user overrides train(), predict(), fit(), and make_labels(). Author: Robert Caulk, rob.caulk@gmail.com @@ -23,61 +26,71 @@ class IFreqaiModel(ABC): def __init__(self, config: Dict[str, Any]) -> None: self.config = config - self.freqai_info = config['freqai'] - self.data_split_parameters = config['freqai']['data_split_parameters'] - self.model_training_parameters = config['freqai']['model_training_parameters'] - self.feature_parameters = config['freqai']['feature_parameters'] - self.full_path = (str(config['user_data_dir'])+ - "/models/"+self.freqai_info['full_timerange']+ - '-'+self.freqai_info['identifier']) - self.metadata = {} - self.data = {} + self.freqai_info = config["freqai"] + self.data_split_parameters = config["freqai"]["data_split_parameters"] + self.model_training_parameters = config["freqai"]["model_training_parameters"] + self.feature_parameters = config["freqai"]["feature_parameters"] + self.full_path = Path( + config["user_data_dir"] + / "models" + / str(self.freqai_info["full_timerange"] + self.freqai_info["identifier"]) + ) + self.time_last_trained = None self.current_time = None self.model = None self.predictions = None - if not os.path.exists(self.full_path): - os.mkdir(self.full_path) - shutil.copy(self.config['config_files'][0],self.full_path+"/"+self.config['config_files'][0]) + if not self.full_path.is_dir(): + self.full_path.mkdir(parents=True, exist_ok=True) + shutil.copy( + self.config["config_files"][0], + Path(self.full_path / self.config["config_files"][0]), + ) def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ - Entry point to the FreqaiModel, it will train a new model if + Entry point to the FreqaiModel, it will train a new model if necesssary before making the prediction. The backtesting and training paradigm is a sliding training window with a following backtest window. Both windows slide according to the - length of the backtest window. This function is not intended to be - overridden by children of IFreqaiModel, but technically, it can be + length of the backtest window. This function is not intended to be + overridden by children of IFreqaiModel, but technically, it can be if the user wishes to make deeper changes to the sliding window logic. :params: :dataframe: Full dataframe coming from strategy - it contains entire - backtesting timerange + additional historical data necessary to train + backtesting timerange + additional historical data necessary to train the model. - :metadata: pair metadataa coming from strategy. + :metadata: pair metadataa coming from strategy. """ - self.pair = metadata['pair'] - self.dh = DataHandler(self.config, dataframe, self.data) + self.pair = metadata["pair"] + self.dh = DataHandler(self.config, dataframe) - print('going to train',len(self.dh.training_timeranges), - 'timeranges:',self.dh.training_timeranges) + print( + "going to train", + len(self.dh.training_timeranges), + "timeranges:", + self.dh.training_timeranges, + ) # Loop enforcing the sliding window training/backtesting paragigm # tr_train is the training time range e.g. 1 historical month - # tr_backtest is the backtesting time range e.g. the week directly - # following tr_train. Both of these windows slide through the + # tr_backtest is the backtesting time range e.g. the week directly + # following tr_train. Both of these windows slide through the # entire backtest - for tr_train, tr_backtest in zip(self.dh.training_timeranges, - self.dh.backtesting_timeranges): + for tr_train, tr_backtest in zip( + self.dh.training_timeranges, self.dh.backtesting_timeranges + ): gc.collect() - #self.config['timerange'] = tr_train - self.dh.data = {} # clean the pair specific data between models - self.freqai_info['training_timerange'] = tr_train + # self.config['timerange'] = tr_train + self.dh.data = {} # clean the pair specific data between models + self.freqai_info["training_timerange"] = tr_train dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) - print("training",self.pair,"for",tr_train) - self.dh.model_path = self.full_path+"/"+ 'sub-train'+'-'+str(tr_train)+'/' + print("training", self.pair, "for", tr_train) + # self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/" + self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train))) if not self.model_exists(self.pair, training_timerange=tr_train): self.model = self.train(dataframe_train, metadata) self.dh.save_data(self.model) @@ -86,8 +99,8 @@ class IFreqaiModel(ABC): preds, do_preds = self.predict(dataframe_backtest) - self.dh.append_predictions(preds,do_preds,len(dataframe_backtest)) - + self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) + self.dh.fill_predictions(len(dataframe)) return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std @@ -107,7 +120,7 @@ class IFreqaiModel(ABC): for storing, saving, loading, and analyzing the data. :params: :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. + :metadata: pair metadata from strategy. :returns: :model: Trained model which can be used to inference (self.predict) """ @@ -116,40 +129,40 @@ class IFreqaiModel(ABC): def fit(self) -> Any: """ - Most regressors use the same function names and arguments e.g. user + Most regressors use the same function names and arguments e.g. user can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. :params: - :data_dictionary: the dictionary constructed by DataHandler to hold + :data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ - return None - - def predict(self) -> Optional[Tuple[DataFrame, DataFrame]]: + return Any + + def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. - :return: + :return: :predictions: np.array of predictions :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove data (NaNs) or felt uncertain about data (PCA and DI index) """ - return None + return np.array([]), np.array([]) - def model_exists(self, pair: str, training_timerange: str = None) -> bool: + def model_exists(self, pair: str, training_timerange: str) -> bool: """ Given a pair and path, check if a model already exists :param pair: pair e.g. BTC/USD :param path: path to model """ - coin,_ = pair.split('/') - self.dh.model_filename = f"cb_"+coin.lower()+"_"+training_timerange - file_exists = os.path.isfile(self.dh.model_path+ - self.dh.model_filename+"_model.joblib") + coin, _ = pair.split("/") + self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange + path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib")) + file_exists = path_to_modelfile.is_file() if file_exists: - print("Found model at", self.dh.model_path+self.dh.model_filename) - else: print("Could not find model at", - self.dh.model_path+self.dh.model_filename) + print("Found model at", self.dh.model_path / self.dh.model_filename) + else: + print("Could not find model at", self.dh.model_path / self.dh.model_filename) return file_exists diff --git a/freqtrade/freqai/strategy_bridge.py b/freqtrade/freqai/strategy_bridge.py index c336e3c84..bb43084a0 100644 --- a/freqtrade/freqai/strategy_bridge.py +++ b/freqtrade/freqai/strategy_bridge.py @@ -3,10 +3,10 @@ from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver class CustomModel: """ - A bridge between the user defined IFreqaiModel class + A bridge between the user defined IFreqaiModel class and the strategy. """ - def __init__(self,config): + def __init__(self, config): self.bridge = FreqaiModelResolver.load_freqaimodel(config) diff --git a/freqtrade/resolvers/freqaimodel_resolver.py b/freqtrade/resolvers/freqaimodel_resolver.py index 9545afd24..2ba6b3e8a 100644 --- a/freqtrade/resolvers/freqaimodel_resolver.py +++ b/freqtrade/resolvers/freqaimodel_resolver.py @@ -12,6 +12,7 @@ from freqtrade.exceptions import OperationalException from freqtrade.freqai.freqai_interface import IFreqaiModel from freqtrade.resolvers import IResolver + logger = logging.getLogger(__name__) @@ -19,10 +20,11 @@ class FreqaiModelResolver(IResolver): """ This class contains all the logic to load custom hyperopt loss class """ + object_type = IFreqaiModel object_type_str = "FreqaiModel" user_subdir = USERPATH_FREQAIMODELS - initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve() + initial_search_path = Path(__file__).parent.parent.joinpath("optimize").resolve() @staticmethod def load_freqaimodel(config: Dict) -> IFreqaiModel: @@ -31,15 +33,17 @@ class FreqaiModelResolver(IResolver): :param config: configuration dictionary """ - freqaimodel_name = config.get('freqaimodel') + freqaimodel_name = config.get("freqaimodel") if not freqaimodel_name: raise OperationalException( "No freqaimodel set. Please use `--freqaimodel` to " "specify the FreqaiModel class to use.\n" ) - freqaimodel = FreqaiModelResolver.load_object(freqaimodel_name, - config, kwargs={'config': config}, - extra_dir=config.get('freqaimodel_path')) - + freqaimodel = FreqaiModelResolver.load_object( + freqaimodel_name, + config, + kwargs={"config": config}, + extra_dir=config.get("freqaimodel_path"), + ) return freqaimodel diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index a5370b5ac..feeed11a9 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -1,15 +1,17 @@ -import numpy as np +from typing import Any, Dict, Tuple + import pandas as pd from catboost import CatBoostRegressor, Pool from pandas import DataFrame -from typing import Any, Dict, Tuple + from freqtrade.freqai.freqai_interface import IFreqaiModel + class ExamplePredictionModel(IFreqaiModel): """ User created prediction model. The class needs to override three necessary functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + has its own DataHandler where data is held, saved, loaded, and managed. """ def make_labels(self, dataframe: DataFrame) -> DataFrame: @@ -19,15 +21,20 @@ class ExamplePredictionModel(IFreqaiModel): :dataframe: the full dataframe for the present training period """ - dataframe['s'] = (dataframe['close'].shift(-self.feature_parameters['period']).rolling( - self.feature_parameters['period']).max() / dataframe['close'] - 1) - self.dh.data['s_mean'] = dataframe['s'].mean() - self.dh.data['s_std'] = dataframe['s'].std() + dataframe["s"] = ( + dataframe["close"] + .shift(-self.feature_parameters["period"]) + .rolling(self.feature_parameters["period"]) + .max() + / dataframe["close"] + - 1 + ) + self.dh.data["s_mean"] = dataframe["s"].mean() + self.dh.data["s_std"] = dataframe["s"].std() - print('label mean',self.dh.data['s_mean'],'label std',self.dh.data['s_std']) - - return dataframe['s'] + print("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) + return dataframe["s"] def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: """ @@ -35,7 +42,7 @@ class ExamplePredictionModel(IFreqaiModel): for storing, saving, loading, and managed. :params: :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. + :metadata: pair metadata from strategy. :returns: :model: Trained model which can be used to inference (self.predict) """ @@ -46,8 +53,12 @@ class ExamplePredictionModel(IFreqaiModel): unfiltered_labels = self.make_labels(unfiltered_dataframe) # filter the features requested by user in the configuration file and elegantly handle NaNs - features_filtered, labels_filtered = self.dh.filter_features(unfiltered_dataframe, - self.dh.training_features_list, unfiltered_labels, training_filter=True) + features_filtered, labels_filtered = self.dh.filter_features( + unfiltered_dataframe, + self.dh.training_features_list, + unfiltered_labels, + training_filter=True, + ) # split data into train/test data. data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered) @@ -55,46 +66,47 @@ class ExamplePredictionModel(IFreqaiModel): data_dictionary = self.dh.standardize_data(data_dictionary) # optional additional data cleaning - if self.feature_parameters['principal_component_analysis']: + if self.feature_parameters["principal_component_analysis"]: self.dh.principal_component_analysis() if self.feature_parameters["remove_outliers"]: self.dh.remove_outliers(predict=False) - if self.feature_parameters['DI_threshold']: - self.dh.data['avg_mean_dist'] = self.dh.compute_distances() + if self.feature_parameters["DI_threshold"]: + self.dh.data["avg_mean_dist"] = self.dh.compute_distances() - print("length of train data", len(data_dictionary['train_features'])) + print("length of train data", len(data_dictionary["train_features"])) model = self.fit(data_dictionary) - print('Finished training') + print("Finished training") print(f'--------------------done training {metadata["pair"]}--------------------') return model def fit(self, data_dictionary: Dict) -> Any: """ - Most regressors use the same function names and arguments e.g. user + Most regressors use the same function names and arguments e.g. user can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. :params: - :data_dictionary: the dictionary constructed by DataHandler to hold + :data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ train_data = Pool( - data=data_dictionary['train_features'], - label=data_dictionary['train_labels'], - weight=data_dictionary['train_weights'] + data=data_dictionary["train_features"], + label=data_dictionary["train_labels"], + weight=data_dictionary["train_weights"], ) test_data = Pool( - data=data_dictionary['test_features'], - label=data_dictionary['test_labels'], - weight=data_dictionary['test_weights'] + data=data_dictionary["test_features"], + label=data_dictionary["test_labels"], + weight=data_dictionary["test_weights"], ) - model = CatBoostRegressor(verbose=100, early_stopping_rounds=400, - **self.model_training_parameters) + model = CatBoostRegressor( + verbose=100, early_stopping_rounds=400, **self.model_training_parameters + ) model.fit(X=train_data, eval_set=test_data) return model @@ -103,7 +115,7 @@ class ExamplePredictionModel(IFreqaiModel): """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. - :return: + :return: :predictions: np.array of predictions :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove data (NaNs) or felt uncertain about data (PCA and DI index) @@ -112,27 +124,31 @@ class ExamplePredictionModel(IFreqaiModel): print("--------------------Starting prediction--------------------") original_feature_list = self.dh.build_feature_list(self.config) - filtered_dataframe, _ = self.dh.filter_features(unfiltered_dataframe, original_feature_list, training_filter=False) + filtered_dataframe, _ = self.dh.filter_features( + unfiltered_dataframe, original_feature_list, training_filter=False + ) filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) - self.dh.data_dictionary['prediction_features'] = filtered_dataframe + self.dh.data_dictionary["prediction_features"] = filtered_dataframe - # optional additional data cleaning - if self.feature_parameters['principal_component_analysis']: + # optional additional data cleaning + if self.feature_parameters["principal_component_analysis"]: pca_components = self.dh.pca.transform(filtered_dataframe) - self.dh.data_dictionary['prediction_features'] = pd.DataFrame(data=pca_components, - columns = ['PC'+str(i) for i in range(0,self.dh.data['n_kept_components'])], - index = filtered_dataframe.index) - + self.dh.data_dictionary["prediction_features"] = pd.DataFrame( + data=pca_components, + columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])], + index=filtered_dataframe.index, + ) + if self.feature_parameters["remove_outliers"]: - self.dh.remove_outliers(predict=True) # creates dropped index + self.dh.remove_outliers(predict=True) # creates dropped index - if self.feature_parameters['DI_threshold']: - self.dh.check_if_pred_in_training_spaces() # sets do_predict + if self.feature_parameters["DI_threshold"]: + self.dh.check_if_pred_in_training_spaces() # sets do_predict - predictions = self.model.predict(self.dh.data_dictionary['prediction_features']) + predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) # compute the non-standardized predictions - predictions = predictions * self.dh.data['labels_std'] + self.dh.data['labels_mean'] + predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] print("--------------------Finished prediction--------------------") diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index d6b1295ec..873b31115 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -1,61 +1,59 @@ import logging +from functools import reduce + +import numpy as np +import pandas as pd import talib.abstract as ta from pandas import DataFrame -import pandas as pd from technical import qtpylib -import numpy as np -from freqtrade.strategy import (merge_informative_pair) -from freqtrade.strategy.interface import IStrategy + from freqtrade.freqai.strategy_bridge import CustomModel -from functools import reduce +from freqtrade.strategy import merge_informative_pair +from freqtrade.strategy.interface import IStrategy + + logger = logging.getLogger(__name__) + class FreqaiExampleStrategy(IStrategy): """ - Example strategy showing how the user connects their own + Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) self.model.bridge.start(dataframe, metadata) - to make predictions on their data. populate_any_indicators() automatically + to make predictions on their data. populate_any_indicators() automatically generates the variety of features indicated by the user in the canonical freqtrade configuration file under config['freqai']. """ - minimal_roi = { - "0": 0.01, - "240": -1 - } + minimal_roi = {"0": 0.01, "240": -1} plot_config = { - 'main_plot': { + "main_plot": {}, + "subplots": { + "prediction": {"prediction": {"color": "blue"}}, + "target_roi": { + "target_roi": {"color": "brown"}, + }, + "do_predict": { + "do_predict": {"color": "brown"}, + }, }, - 'subplots': { - "prediction":{ - 'prediction':{'color':'blue'} - }, - "target_roi":{ - 'target_roi':{'color':'brown'}, - }, - "do_predict":{ - 'do_predict':{'color':'brown'}, - }, - } } stoploss = -0.05 use_sell_signal = True - startup_candle_count: int = 1000 - + startup_candle_count: int = 1000 def informative_pairs(self): - pairs = self.freqai_info['corr_pairlist'] + pairs = self.freqai_info["corr_pairlist"] informative_pairs = [] for tf in self.timeframes: informative_pairs.append([(pair, tf) for pair in pairs]) return informative_pairs - def populate_any_indicators(self, pair, df, tf, informative=None,coin=''): + def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): """ Function designed to automatically generate, name and merge features from user indicated timeframes in the configuration file. User can add @@ -70,110 +68,116 @@ class FreqaiExampleStrategy(IStrategy): if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) - informative[coin+'rsi'] = ta.RSI(informative, timeperiod=14) - informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) - informative[coin+'adx'] = ta.ADX(informative, window=20) + informative[coin + "rsi"] = ta.RSI(informative, timeperiod=14) + informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25) + informative[coin + "adx"] = ta.ADX(informative, window=20) - informative[coin+'20sma'] = ta.SMA(informative,timeperiod=20) - informative[coin+'21ema'] = ta.EMA(informative,timeperiod=21) - informative[coin+'bmsb'] = np.where(informative[coin+'20sma'].lt(informative[coin+'21ema']),1,0) - informative[coin+'close_over_20sma'] = informative['close']/informative[coin+'20sma'] + informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20) + informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21) + informative[coin + "bmsb"] = np.where( + informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0 + ) + informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"] - informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) + informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25) - informative[coin+'ema21'] = ta.EMA(informative, timeperiod=21) - informative[coin+'sma20'] = ta.SMA(informative, timeperiod=20) + informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21) + informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20) stoch = ta.STOCHRSI(informative, 15, 20, 2, 2) - informative[coin+'srsi-fk'] = stoch['fastk'] - informative[coin+'srsi-fd'] = stoch['fastd'] + informative[coin + "srsi-fk"] = stoch["fastk"] + informative[coin + "srsi-fd"] = stoch["fastd"] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) - informative[coin+'bb_lowerband'] = bollinger['lower'] - informative[coin+'bb_middleband'] = bollinger['mid'] - informative[coin+'bb_upperband'] = bollinger['upper'] - informative[coin+'bb_width'] = ((informative[coin+"bb_upperband"] - informative[coin+"bb_lowerband"]) / informative[coin+"bb_middleband"]) - informative[coin+'close-bb_lower'] = informative['close'] / informative[coin+'bb_lowerband'] + informative[coin + "bb_lowerband"] = bollinger["lower"] + informative[coin + "bb_middleband"] = bollinger["mid"] + informative[coin + "bb_upperband"] = bollinger["upper"] + informative[coin + "bb_width"] = ( + informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"] + ) / informative[coin + "bb_middleband"] + informative[coin + "close-bb_lower"] = ( + informative["close"] / informative[coin + "bb_lowerband"] + ) - informative[coin+'roc'] = ta.ROC(informative, timeperiod=3) - informative[coin+'adx'] = ta.ADX(informative, window=14) + informative[coin + "roc"] = ta.ROC(informative, timeperiod=3) + informative[coin + "adx"] = ta.ADX(informative, window=14) macd = ta.MACD(informative) - informative[coin+'macd'] = macd['macd'] - informative[coin+'pct-change'] = informative['close'].pct_change() - informative[coin+'relative_volume'] = informative['volume'] / informative['volume'].rolling(10).mean() + informative[coin + "macd"] = macd["macd"] + informative[coin + "pct-change"] = informative["close"].pct_change() + informative[coin + "relative_volume"] = ( + informative["volume"] / informative["volume"].rolling(10).mean() + ) - informative[coin+'pct-change'] = informative['close'].pct_change() + informative[coin + "pct-change"] = informative["close"].pct_change() indicators = [col for col in informative if col.startswith(coin)] - for n in range(self.freqai_info['feature_parameters']['shift']+1): - if n==0: continue + for n in range(self.freqai_info["feature_parameters"]["shift"] + 1): + if n == 0: + continue informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix('_shift-'+str(n)) - informative = pd.concat((informative,informative_shift),axis=1) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config['timeframe'], tf, ffill=True) - skip_columns = [(s + '_'+tf) for s in - ['date', 'open', 'high', 'low', 'close', 'volume']] + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]] df = df.drop(columns=skip_columns) return df - def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # the configuration file parameters are stored here - self.freqai_info = self.config['freqai'] + self.freqai_info = self.config["freqai"] # the model is instantiated here self.model = CustomModel(self.config) - print('Populating indicators...') + print("Populating indicators...") - # the following loops are necessary for building the features + # the following loops are necessary for building the features # indicated by the user in the configuration file. - for tf in self.freqai_info['timeframes']: - dataframe = self.populate_any_indicators(metadata['pair'], - dataframe.copy(), tf) - for i in self.freqai_info['corr_pairlist']: - dataframe = self.populate_any_indicators(i, - dataframe.copy(), tf, coin=i.split("/")[0]+'-') + for tf in self.freqai_info["timeframes"]: + dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf) + for i in self.freqai_info["corr_pairlist"]: + dataframe = self.populate_any_indicators( + i, dataframe.copy(), tf, coin=i.split("/")[0] + "-" + ) - # the model will return 4 values, its prediction, an indication of whether or not the prediction - # should be accepted, the target mean/std values from the labels used during each training period. - (dataframe['prediction'], dataframe['do_predict'], - dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata) + # the model will return 4 values, its prediction, an indication of whether or not the + # prediction should be accepted, the target mean/std values from the labels used during + # each training period. + ( + dataframe["prediction"], + dataframe["do_predict"], + dataframe["target_mean"], + dataframe["target_std"], + ) = self.model.bridge.start(dataframe, metadata) - dataframe['target_roi'] = dataframe['target_mean']+dataframe['target_std']*0.5 - dataframe['sell_roi'] = dataframe['target_mean']-dataframe['target_std']*1.5 + dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5 + dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5 return dataframe - def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: buy_conditions = [ - (dataframe['prediction'] > dataframe['target_roi']) - & - (dataframe['do_predict'] == 1) + (dataframe["prediction"] > dataframe["target_roi"]) & (dataframe["do_predict"] == 1) ] if buy_conditions: - dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'buy'] = 1 + dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1 return dataframe - def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value') + # sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value') sell_conditions = [ - (dataframe['prediction'] < dataframe['sell_roi']) - & - (dataframe['do_predict'] == 1) + (dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1) ] if sell_conditions: - dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), 'sell'] = 1 + dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1 return dataframe def get_ticker_indicator(self): - return int(self.config['timeframe'][:-1]) + return int(self.config["timeframe"][:-1]) From 29c2d1d1891f7e804a133908702f435ff4fd8f32 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 4 May 2022 17:53:40 +0200 Subject: [PATCH 006/518] use logger in favor of print --- freqtrade/freqai/data_handler.py | 23 +++++++++++-------- freqtrade/freqai/freqai_interface.py | 15 ++++++++---- freqtrade/templates/ExamplePredictionModel.py | 18 +++++++++------ 3 files changed, 34 insertions(+), 22 deletions(-) diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py index 7264c6fab..9ab47d223 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_handler.py @@ -1,6 +1,7 @@ import copy import datetime import json +import logging import pickle as pk from pathlib import Path from typing import Any, Dict, List, Tuple @@ -17,6 +18,8 @@ from freqtrade.configuration import TimeRange SECONDS_IN_DAY = 86400 +logger = logging.getLogger(__name__) + class DataHandler: """ @@ -175,7 +178,7 @@ class DataHandler: labels = labels[ (drop_index == 0) & (drop_index_labels == 0) ] # assuming the labels depend entirely on the dataframe here. - print( + logger.info( "dropped", len(unfiltered_dataframe) - len(filtered_dataframe), "training data points due to NaNs, ensure you have downloaded", @@ -193,7 +196,7 @@ class DataHandler: # that was based on a single NaN is ultimately protected from buys with do_predict drop_index = ~drop_index self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) - print( + logger.info( "dropped", len(self.do_predict) - self.do_predict.sum(), "of", @@ -350,8 +353,8 @@ class DataHandler: pca2 = PCA(n_components=n_keep_components) self.data["n_kept_components"] = n_keep_components pca2 = pca2.fit(self.data_dictionary["train_features"]) - print("reduced feature dimension by", n_components - n_keep_components) - print("explained variance", np.sum(pca2.explained_variance_ratio_)) + logger.info("reduced feature dimension by", n_components - n_keep_components) + logger.info("explained variance", np.sum(pca2.explained_variance_ratio_)) train_components = pca2.transform(self.data_dictionary["train_features"]) test_components = pca2.transform(self.data_dictionary["test_features"]) @@ -377,10 +380,10 @@ class DataHandler: return None def compute_distances(self) -> float: - print("computing average mean distance for all training points") + logger.info("computing average mean distance for all training points") pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1) avg_mean_dist = pairwise.mean(axis=1).mean() - print("avg_mean_dist", avg_mean_dist) + logger.info("avg_mean_dist", avg_mean_dist) return avg_mean_dist @@ -407,7 +410,7 @@ class DataHandler: drop_index = ~drop_index do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) - print( + logger.info( "remove_outliers() tossed", len(do_predict) - do_predict.sum(), "predictions because they were beyond 3 std deviations from training data.", @@ -472,7 +475,7 @@ class DataHandler: for p in config["freqai"]["corr_pairlist"]: features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - print("number of features", len(features)) + logger.info("number of features", len(features)) return features def check_if_pred_in_training_spaces(self) -> None: @@ -483,7 +486,7 @@ class DataHandler: from the training data set. """ - print("checking if prediction features are in AOA") + logger.info("checking if prediction features are in AOA") distance = pairwise_distances( self.data_dictionary["train_features"], self.data_dictionary["prediction_features"], @@ -497,7 +500,7 @@ class DataHandler: 0, ) - print( + logger.info( "Distance checker tossed", len(do_predict) - do_predict.sum(), "predictions for being too far from training data", diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 9f04b09cd..05a0594f3 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -1,6 +1,7 @@ import gc +import logging import shutil -from abc import ABC +from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, Tuple @@ -12,6 +13,7 @@ from freqtrade.freqai.data_handler import DataHandler pd.options.mode.chained_assignment = None +logger = logging.getLogger(__name__) class IFreqaiModel(ABC): @@ -67,7 +69,7 @@ class IFreqaiModel(ABC): self.pair = metadata["pair"] self.dh = DataHandler(self.config, dataframe) - print( + logger.info( "going to train", len(self.dh.training_timeranges), "timeranges:", @@ -88,7 +90,7 @@ class IFreqaiModel(ABC): self.freqai_info["training_timerange"] = tr_train dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) - print("training", self.pair, "for", tr_train) + logger.info("training", self.pair, "for", tr_train) # self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/" self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train))) if not self.model_exists(self.pair, training_timerange=tr_train): @@ -114,6 +116,7 @@ class IFreqaiModel(ABC): return dataframe + @abstractmethod def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: """ Filter the training data and train a model to it. Train makes heavy use of the datahandler @@ -127,6 +130,7 @@ class IFreqaiModel(ABC): return Any + @abstractmethod def fit(self) -> Any: """ Most regressors use the same function names and arguments e.g. user @@ -139,6 +143,7 @@ class IFreqaiModel(ABC): return Any + @abstractmethod def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]: """ Filter the prediction features data and predict with it. @@ -162,7 +167,7 @@ class IFreqaiModel(ABC): path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib")) file_exists = path_to_modelfile.is_file() if file_exists: - print("Found model at", self.dh.model_path / self.dh.model_filename) + logger.info("Found model at", self.dh.model_path / self.dh.model_filename) else: - print("Could not find model at", self.dh.model_path / self.dh.model_filename) + logger.info("Could not find model at", self.dh.model_path / self.dh.model_filename) return file_exists diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index feeed11a9..4906b8c04 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -1,3 +1,4 @@ +import logging from typing import Any, Dict, Tuple import pandas as pd @@ -7,6 +8,9 @@ from pandas import DataFrame from freqtrade.freqai.freqai_interface import IFreqaiModel +logger = logging.getLogger(__name__) + + class ExamplePredictionModel(IFreqaiModel): """ User created prediction model. The class needs to override three necessary @@ -32,7 +36,7 @@ class ExamplePredictionModel(IFreqaiModel): self.dh.data["s_mean"] = dataframe["s"].mean() self.dh.data["s_std"] = dataframe["s"].std() - print("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) + logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) return dataframe["s"] @@ -46,7 +50,7 @@ class ExamplePredictionModel(IFreqaiModel): :returns: :model: Trained model which can be used to inference (self.predict) """ - print("--------------------Starting training--------------------") + logger.info("--------------------Starting training--------------------") # create the full feature list based on user config info self.dh.training_features_list = self.dh.build_feature_list(self.config) @@ -73,12 +77,12 @@ class ExamplePredictionModel(IFreqaiModel): if self.feature_parameters["DI_threshold"]: self.dh.data["avg_mean_dist"] = self.dh.compute_distances() - print("length of train data", len(data_dictionary["train_features"])) + logger.info("length of train data", len(data_dictionary["train_features"])) model = self.fit(data_dictionary) - print("Finished training") - print(f'--------------------done training {metadata["pair"]}--------------------') + logger.info("Finished training") + logger.info(f'--------------------done training {metadata["pair"]}--------------------') return model @@ -121,7 +125,7 @@ class ExamplePredictionModel(IFreqaiModel): data (NaNs) or felt uncertain about data (PCA and DI index) """ - print("--------------------Starting prediction--------------------") + logger.info("--------------------Starting prediction--------------------") original_feature_list = self.dh.build_feature_list(self.config) filtered_dataframe, _ = self.dh.filter_features( @@ -150,6 +154,6 @@ class ExamplePredictionModel(IFreqaiModel): # compute the non-standardized predictions predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] - print("--------------------Finished prediction--------------------") + logger.info("--------------------Finished prediction--------------------") return (predictions, self.dh.do_predict) From 764f9449b428be8b744ebea989ee81b81330aae9 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 5 May 2022 14:37:37 +0200 Subject: [PATCH 007/518] fix logger, debug some flake8 appeasements --- freqtrade/constants.py | 837 ++++++++++-------- freqtrade/freqai/data_handler.py | 29 +- freqtrade/freqai/freqai_interface.py | 15 +- freqtrade/optimize/backtesting.py | 8 +- freqtrade/templates/ExamplePredictionModel.py | 5 +- 5 files changed, 479 insertions(+), 415 deletions(-) diff --git a/freqtrade/constants.py b/freqtrade/constants.py index f8a9dc06d..d9664cff8 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -8,88 +8,133 @@ from typing import List, Literal, Tuple from freqtrade.enums import CandleType -DEFAULT_CONFIG = 'config.json' -DEFAULT_EXCHANGE = 'bittrex' +DEFAULT_CONFIG = "config.json" +DEFAULT_EXCHANGE = "bittrex" PROCESS_THROTTLE_SECS = 5 # sec HYPEROPT_EPOCH = 100 # epochs RETRY_TIMEOUT = 30 # sec -TIMEOUT_UNITS = ['minutes', 'seconds'] -EXPORT_OPTIONS = ['none', 'trades', 'signals'] -DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite' -DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite' -UNLIMITED_STAKE_AMOUNT = 'unlimited' +TIMEOUT_UNITS = ["minutes", "seconds"] +EXPORT_OPTIONS = ["none", "trades", "signals"] +DEFAULT_DB_PROD_URL = "sqlite:///tradesv3.sqlite" +DEFAULT_DB_DRYRUN_URL = "sqlite:///tradesv3.dryrun.sqlite" +UNLIMITED_STAKE_AMOUNT = "unlimited" DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05 -REQUIRED_ORDERTIF = ['entry', 'exit'] -REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange'] -PRICING_SIDES = ['ask', 'bid', 'same', 'other'] -ORDERTYPE_POSSIBILITIES = ['limit', 'market'] -ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc'] -HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss', - 'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily', - 'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily', - 'CalmarHyperOptLoss', - 'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss', - 'ProfitDrawDownHyperOptLoss'] -AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', - 'AgeFilter', 'OffsetFilter', 'PerformanceFilter', - 'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter', - 'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter'] -AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard'] -AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5'] -BACKTEST_BREAKDOWNS = ['day', 'week', 'month'] -BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month'] -BACKTEST_CACHE_DEFAULT = 'day' +REQUIRED_ORDERTIF = ["entry", "exit"] +REQUIRED_ORDERTYPES = ["entry", "exit", "stoploss", "stoploss_on_exchange"] +PRICING_SIDES = ["ask", "bid", "same", "other"] +ORDERTYPE_POSSIBILITIES = ["limit", "market"] +ORDERTIF_POSSIBILITIES = ["gtc", "fok", "ioc"] +HYPEROPT_LOSS_BUILTIN = [ + "ShortTradeDurHyperOptLoss", + "OnlyProfitHyperOptLoss", + "SharpeHyperOptLoss", + "SharpeHyperOptLossDaily", + "SortinoHyperOptLoss", + "SortinoHyperOptLossDaily", + "CalmarHyperOptLoss", + "MaxDrawDownHyperOptLoss", + "MaxDrawDownRelativeHyperOptLoss", + "ProfitDrawDownHyperOptLoss", +] +AVAILABLE_PAIRLISTS = [ + "StaticPairList", + "VolumePairList", + "AgeFilter", + "OffsetFilter", + "PerformanceFilter", + "PrecisionFilter", + "PriceFilter", + "RangeStabilityFilter", + "ShuffleFilter", + "SpreadFilter", + "VolatilityFilter", +] +AVAILABLE_PROTECTIONS = ["CooldownPeriod", "LowProfitPairs", "MaxDrawdown", "StoplossGuard"] +AVAILABLE_DATAHANDLERS = ["json", "jsongz", "hdf5"] +BACKTEST_BREAKDOWNS = ["day", "week", "month"] +BACKTEST_CACHE_AGE = ["none", "day", "week", "month"] +BACKTEST_CACHE_DEFAULT = "day" DRY_RUN_WALLET = 1000 -DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S' +DATETIME_PRINT_FORMAT = "%Y-%m-%d %H:%M:%S" MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons -DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume'] +DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"] # Don't modify sequence of DEFAULT_TRADES_COLUMNS # it has wide consequences for stored trades files -DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost'] -TRADING_MODES = ['spot', 'margin', 'futures'] -MARGIN_MODES = ['cross', 'isolated', ''] +DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"] +TRADING_MODES = ["spot", "margin", "futures"] +MARGIN_MODES = ["cross", "isolated", ""] -LAST_BT_RESULT_FN = '.last_result.json' -FTHYPT_FILEVERSION = 'fthypt_fileversion' +LAST_BT_RESULT_FN = ".last_result.json" +FTHYPT_FILEVERSION = "fthypt_fileversion" -USERPATH_HYPEROPTS = 'hyperopts' -USERPATH_STRATEGIES = 'strategies' -USERPATH_NOTEBOOKS = 'notebooks' -USERPATH_FREQAIMODELS = 'freqaimodels' +USERPATH_HYPEROPTS = "hyperopts" +USERPATH_STRATEGIES = "strategies" +USERPATH_NOTEBOOKS = "notebooks" +USERPATH_FREQAIMODELS = "freqaimodels" -TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent'] -WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw'] +TELEGRAM_SETTING_OPTIONS = ["on", "off", "silent"] +WEBHOOK_FORMAT_OPTIONS = ["form", "json", "raw"] -ENV_VAR_PREFIX = 'FREQTRADE__' +ENV_VAR_PREFIX = "FREQTRADE__" -NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired') +NON_OPEN_EXCHANGE_STATES = ("cancelled", "canceled", "closed", "expired") # Define decimals per coin for outputs # Only used for outputs. DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's DECIMALS_PER_COIN = { - 'BTC': 8, - 'ETH': 5, + "BTC": 8, + "ETH": 5, } -DUST_PER_COIN = { - 'BTC': 0.0001, - 'ETH': 0.01 -} +DUST_PER_COIN = {"BTC": 0.0001, "ETH": 0.01} # Source files with destination directories within user-directory USER_DATA_FILES = { - 'sample_strategy.py': USERPATH_STRATEGIES, - 'sample_hyperopt_loss.py': USERPATH_HYPEROPTS, - 'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS, + "sample_strategy.py": USERPATH_STRATEGIES, + "sample_hyperopt_loss.py": USERPATH_HYPEROPTS, + "strategy_analysis_example.ipynb": USERPATH_NOTEBOOKS, } SUPPORTED_FIAT = [ - "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", - "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", - "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", - "RUB", "UAH", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", - "USD", "BTC", "ETH", "XRP", "LTC", "BCH" + "AUD", + "BRL", + "CAD", + "CHF", + "CLP", + "CNY", + "CZK", + "DKK", + "EUR", + "GBP", + "HKD", + "HUF", + "IDR", + "ILS", + "INR", + "JPY", + "KRW", + "MXN", + "MYR", + "NOK", + "NZD", + "PHP", + "PKR", + "PLN", + "RUB", + "UAH", + "SEK", + "SGD", + "THB", + "TRY", + "TWD", + "ZAR", + "USD", + "BTC", + "ETH", + "XRP", + "LTC", + "BCH", ] MINIMAL_CONFIG = { @@ -100,380 +145,416 @@ MINIMAL_CONFIG = { "key": "", "secret": "", "pair_whitelist": [], - "ccxt_async_config": { - } - } + "ccxt_async_config": {}, + }, } # Required json-schema for user specified config CONF_SCHEMA = { - 'type': 'object', - 'properties': { - 'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1}, - 'new_pairs_days': {'type': 'integer', 'default': 30}, - 'timeframe': {'type': 'string'}, - 'stake_currency': {'type': 'string'}, - 'stake_amount': { - 'type': ['number', 'string'], - 'minimum': 0.0001, - 'pattern': UNLIMITED_STAKE_AMOUNT + "type": "object", + "properties": { + "max_open_trades": {"type": ["integer", "number"], "minimum": -1}, + "new_pairs_days": {"type": "integer", "default": 30}, + "timeframe": {"type": "string"}, + "stake_currency": {"type": "string"}, + "stake_amount": { + "type": ["number", "string"], + "minimum": 0.0001, + "pattern": UNLIMITED_STAKE_AMOUNT, }, - 'tradable_balance_ratio': { - 'type': 'number', - 'minimum': 0.0, - 'maximum': 1, - 'default': 0.99 + "tradable_balance_ratio": {"type": "number", "minimum": 0.0, "maximum": 1, "default": 0.99}, + "available_capital": { + "type": "number", + "minimum": 0, }, - 'available_capital': { - 'type': 'number', - 'minimum': 0, + "amend_last_stake_amount": {"type": "boolean", "default": False}, + "last_stake_amount_min_ratio": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.5, }, - 'amend_last_stake_amount': {'type': 'boolean', 'default': False}, - 'last_stake_amount_min_ratio': { - 'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5 + "fiat_display_currency": {"type": "string", "enum": SUPPORTED_FIAT}, + "dry_run": {"type": "boolean"}, + "dry_run_wallet": {"type": "number", "default": DRY_RUN_WALLET}, + "cancel_open_orders_on_exit": {"type": "boolean", "default": False}, + "process_only_new_candles": {"type": "boolean"}, + "minimal_roi": { + "type": "object", + "patternProperties": {"^[0-9.]+$": {"type": "number"}}, + "minProperties": 1, }, - 'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT}, - 'dry_run': {'type': 'boolean'}, - 'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET}, - 'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False}, - 'process_only_new_candles': {'type': 'boolean'}, - 'minimal_roi': { - 'type': 'object', - 'patternProperties': { - '^[0-9.]+$': {'type': 'number'} + "amount_reserve_percent": {"type": "number", "minimum": 0.0, "maximum": 0.5}, + "stoploss": {"type": "number", "maximum": 0, "exclusiveMaximum": True, "minimum": -1}, + "trailing_stop": {"type": "boolean"}, + "trailing_stop_positive": {"type": "number", "minimum": 0, "maximum": 1}, + "trailing_stop_positive_offset": {"type": "number", "minimum": 0, "maximum": 1}, + "trailing_only_offset_is_reached": {"type": "boolean"}, + "use_exit_signal": {"type": "boolean"}, + "exit_profit_only": {"type": "boolean"}, + "exit_profit_offset": {"type": "number"}, + "ignore_roi_if_entry_signal": {"type": "boolean"}, + "ignore_buying_expired_candle_after": {"type": "number"}, + "trading_mode": {"type": "string", "enum": TRADING_MODES}, + "margin_mode": {"type": "string", "enum": MARGIN_MODES}, + "liquidation_buffer": {"type": "number", "minimum": 0.0, "maximum": 0.99}, + "backtest_breakdown": { + "type": "array", + "items": {"type": "string", "enum": BACKTEST_BREAKDOWNS}, + }, + "bot_name": {"type": "string"}, + "unfilledtimeout": { + "type": "object", + "properties": { + "entry": {"type": "number", "minimum": 1}, + "exit": {"type": "number", "minimum": 1}, + "exit_timeout_count": {"type": "number", "minimum": 0, "default": 0}, + "unit": {"type": "string", "enum": TIMEOUT_UNITS, "default": "minutes"}, }, - 'minProperties': 1 }, - 'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5}, - 'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1}, - 'trailing_stop': {'type': 'boolean'}, - 'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1}, - 'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1}, - 'trailing_only_offset_is_reached': {'type': 'boolean'}, - 'use_exit_signal': {'type': 'boolean'}, - 'exit_profit_only': {'type': 'boolean'}, - 'exit_profit_offset': {'type': 'number'}, - 'ignore_roi_if_entry_signal': {'type': 'boolean'}, - 'ignore_buying_expired_candle_after': {'type': 'number'}, - 'trading_mode': {'type': 'string', 'enum': TRADING_MODES}, - 'margin_mode': {'type': 'string', 'enum': MARGIN_MODES}, - 'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99}, - 'backtest_breakdown': { - 'type': 'array', - 'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS} - }, - 'bot_name': {'type': 'string'}, - 'unfilledtimeout': { - 'type': 'object', - 'properties': { - 'entry': {'type': 'number', 'minimum': 1}, - 'exit': {'type': 'number', 'minimum': 1}, - 'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0}, - 'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'} - } - }, - 'entry_pricing': { - 'type': 'object', - 'properties': { - 'price_last_balance': { - 'type': 'number', - 'minimum': 0, - 'maximum': 1, - 'exclusiveMaximum': False, + "entry_pricing": { + "type": "object", + "properties": { + "price_last_balance": { + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": False, }, - 'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'}, - 'use_order_book': {'type': 'boolean'}, - 'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, }, - 'check_depth_of_market': { - 'type': 'object', - 'properties': { - 'enabled': {'type': 'boolean'}, - 'bids_to_ask_delta': {'type': 'number', 'minimum': 0}, - } + "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, + "use_order_book": {"type": "boolean"}, + "order_book_top": { + "type": "integer", + "minimum": 1, + "maximum": 50, + }, + "check_depth_of_market": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "bids_to_ask_delta": {"type": "number", "minimum": 0}, + }, }, }, - 'required': ['price_side'] + "required": ["price_side"], }, - 'exit_pricing': { - 'type': 'object', - 'properties': { - 'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'}, - 'price_last_balance': { - 'type': 'number', - 'minimum': 0, - 'maximum': 1, - 'exclusiveMaximum': False, + "exit_pricing": { + "type": "object", + "properties": { + "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, + "price_last_balance": { + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": False, }, - 'use_order_book': {'type': 'boolean'}, - 'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, }, - }, - 'required': ['price_side'] - }, - 'custom_price_max_distance_ratio': { - 'type': 'number', 'minimum': 0.0 - }, - 'order_types': { - 'type': 'object', - 'properties': { - 'entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, - 'exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, - 'force_exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, - 'force_entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, - 'emergency_exit': { - 'type': 'string', - 'enum': ORDERTYPE_POSSIBILITIES, - 'default': 'market'}, - 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, - 'stoploss_on_exchange': {'type': 'boolean'}, - 'stoploss_on_exchange_interval': {'type': 'number'}, - 'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0, - 'maximum': 1.0} - }, - 'required': ['entry', 'exit', 'stoploss', 'stoploss_on_exchange'] - }, - 'order_time_in_force': { - 'type': 'object', - 'properties': { - 'entry': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}, - 'exit': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES} - }, - 'required': REQUIRED_ORDERTIF - }, - 'exchange': {'$ref': '#/definitions/exchange'}, - 'edge': {'$ref': '#/definitions/edge'}, - 'experimental': { - 'type': 'object', - 'properties': { - 'block_bad_exchanges': {'type': 'boolean'} - } - }, - 'pairlists': { - 'type': 'array', - 'items': { - 'type': 'object', - 'properties': { - 'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS}, + "use_order_book": {"type": "boolean"}, + "order_book_top": { + "type": "integer", + "minimum": 1, + "maximum": 50, }, - 'required': ['method'], - } + }, + "required": ["price_side"], }, - 'protections': { - 'type': 'array', - 'items': { - 'type': 'object', - 'properties': { - 'method': {'type': 'string', 'enum': AVAILABLE_PROTECTIONS}, - 'stop_duration': {'type': 'number', 'minimum': 0.0}, - 'stop_duration_candles': {'type': 'number', 'minimum': 0}, - 'trade_limit': {'type': 'number', 'minimum': 1}, - 'lookback_period': {'type': 'number', 'minimum': 1}, - 'lookback_period_candles': {'type': 'number', 'minimum': 1}, + "custom_price_max_distance_ratio": {"type": "number", "minimum": 0.0}, + "order_types": { + "type": "object", + "properties": { + "entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, + "exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, + "force_exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, + "force_entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, + "emergency_exit": { + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + "default": "market", }, - 'required': ['method'], - } + "stoploss": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, + "stoploss_on_exchange": {"type": "boolean"}, + "stoploss_on_exchange_interval": {"type": "number"}, + "stoploss_on_exchange_limit_ratio": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + }, + }, + "required": ["entry", "exit", "stoploss", "stoploss_on_exchange"], }, - 'telegram': { - 'type': 'object', - 'properties': { - 'enabled': {'type': 'boolean'}, - 'token': {'type': 'string'}, - 'chat_id': {'type': 'string'}, - 'balance_dust_level': {'type': 'number', 'minimum': 0.0}, - 'notification_settings': { - 'type': 'object', - 'default': {}, - 'properties': { - 'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'entry_fill': {'type': 'string', - 'enum': TELEGRAM_SETTING_OPTIONS, - 'default': 'off' - }, - 'exit': { - 'type': ['string', 'object'], - 'additionalProperties': { - 'type': 'string', - 'enum': TELEGRAM_SETTING_OPTIONS - } + "order_time_in_force": { + "type": "object", + "properties": { + "entry": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, + "exit": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, + }, + "required": REQUIRED_ORDERTIF, + }, + "exchange": {"$ref": "#/definitions/exchange"}, + "edge": {"$ref": "#/definitions/edge"}, + "experimental": { + "type": "object", + "properties": {"block_bad_exchanges": {"type": "boolean"}}, + }, + "pairlists": { + "type": "array", + "items": { + "type": "object", + "properties": { + "method": {"type": "string", "enum": AVAILABLE_PAIRLISTS}, + }, + "required": ["method"], + }, + }, + "protections": { + "type": "array", + "items": { + "type": "object", + "properties": { + "method": {"type": "string", "enum": AVAILABLE_PROTECTIONS}, + "stop_duration": {"type": "number", "minimum": 0.0}, + "stop_duration_candles": {"type": "number", "minimum": 0}, + "trade_limit": {"type": "number", "minimum": 1}, + "lookback_period": {"type": "number", "minimum": 1}, + "lookback_period_candles": {"type": "number", "minimum": 1}, + }, + "required": ["method"], + }, + }, + "telegram": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "token": {"type": "string"}, + "chat_id": {"type": "string"}, + "balance_dust_level": {"type": "number", "minimum": 0.0}, + "notification_settings": { + "type": "object", + "default": {}, + "properties": { + "status": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "warning": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "startup": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "entry": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "entry_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "entry_fill": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "off", }, - 'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, - 'exit_fill': { - 'type': 'string', - 'enum': TELEGRAM_SETTING_OPTIONS, - 'default': 'off' + "exit": { + "type": ["string", "object"], + "additionalProperties": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, }, - 'protection_trigger': { - 'type': 'string', - 'enum': TELEGRAM_SETTING_OPTIONS, - 'default': 'off' + "exit_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, + "exit_fill": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "off", }, - 'protection_trigger_global': { - 'type': 'string', - 'enum': TELEGRAM_SETTING_OPTIONS, + "protection_trigger": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "off", }, - } + "protection_trigger_global": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + }, }, - 'reload': {'type': 'boolean'}, + "reload": {"type": "boolean"}, }, - 'required': ['enabled', 'token', 'chat_id'], + "required": ["enabled", "token", "chat_id"], }, - 'webhook': { - 'type': 'object', - 'properties': { - 'enabled': {'type': 'boolean'}, - 'url': {'type': 'string'}, - 'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'}, - 'retries': {'type': 'integer', 'minimum': 0}, - 'retry_delay': {'type': 'number', 'minimum': 0}, - 'webhookentry': {'type': 'object'}, - 'webhookentrycancel': {'type': 'object'}, - 'webhookentryfill': {'type': 'object'}, - 'webhookexit': {'type': 'object'}, - 'webhookexitcancel': {'type': 'object'}, - 'webhookexitfill': {'type': 'object'}, - 'webhookstatus': {'type': 'object'}, + "webhook": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "url": {"type": "string"}, + "format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"}, + "retries": {"type": "integer", "minimum": 0}, + "retry_delay": {"type": "number", "minimum": 0}, + "webhookentry": {"type": "object"}, + "webhookentrycancel": {"type": "object"}, + "webhookentryfill": {"type": "object"}, + "webhookexit": {"type": "object"}, + "webhookexitcancel": {"type": "object"}, + "webhookexitfill": {"type": "object"}, + "webhookstatus": {"type": "object"}, }, }, - 'api_server': { - 'type': 'object', - 'properties': { - 'enabled': {'type': 'boolean'}, - 'listen_ip_address': {'format': 'ipv4'}, - 'listen_port': { - 'type': 'integer', - 'minimum': 1024, - 'maximum': 65535 - }, - 'username': {'type': 'string'}, - 'password': {'type': 'string'}, - 'jwt_secret_key': {'type': 'string'}, - 'CORS_origins': {'type': 'array', 'items': {'type': 'string'}}, - 'verbosity': {'type': 'string', 'enum': ['error', 'info']}, + "api_server": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "listen_ip_address": {"format": "ipv4"}, + "listen_port": {"type": "integer", "minimum": 1024, "maximum": 65535}, + "username": {"type": "string"}, + "password": {"type": "string"}, + "jwt_secret_key": {"type": "string"}, + "CORS_origins": {"type": "array", "items": {"type": "string"}}, + "verbosity": {"type": "string", "enum": ["error", "info"]}, }, - 'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password'] + "required": ["enabled", "listen_ip_address", "listen_port", "username", "password"], }, - 'db_url': {'type': 'string'}, - 'export': {'type': 'string', 'enum': EXPORT_OPTIONS, 'default': 'trades'}, - 'disableparamexport': {'type': 'boolean'}, - 'initial_state': {'type': 'string', 'enum': ['running', 'stopped']}, - 'force_entry_enable': {'type': 'boolean'}, - 'disable_dataframe_checks': {'type': 'boolean'}, - 'internals': { - 'type': 'object', - 'default': {}, - 'properties': { - 'process_throttle_secs': {'type': 'integer'}, - 'interval': {'type': 'integer'}, - 'sd_notify': {'type': 'boolean'}, - } + "db_url": {"type": "string"}, + "export": {"type": "string", "enum": EXPORT_OPTIONS, "default": "trades"}, + "disableparamexport": {"type": "boolean"}, + "initial_state": {"type": "string", "enum": ["running", "stopped"]}, + "force_entry_enable": {"type": "boolean"}, + "disable_dataframe_checks": {"type": "boolean"}, + "internals": { + "type": "object", + "default": {}, + "properties": { + "process_throttle_secs": {"type": "integer"}, + "interval": {"type": "integer"}, + "sd_notify": {"type": "boolean"}, + }, }, - 'dataformat_ohlcv': { - 'type': 'string', - 'enum': AVAILABLE_DATAHANDLERS, - 'default': 'json' + "dataformat_ohlcv": {"type": "string", "enum": AVAILABLE_DATAHANDLERS, "default": "json"}, + "dataformat_trades": { + "type": "string", + "enum": AVAILABLE_DATAHANDLERS, + "default": "jsongz", }, - 'dataformat_trades': { - 'type': 'string', - 'enum': AVAILABLE_DATAHANDLERS, - 'default': 'jsongz' - }, - 'position_adjustment_enable': {'type': 'boolean'}, - 'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1}, + "position_adjustment_enable": {"type": "boolean"}, + "max_entry_position_adjustment": {"type": ["integer", "number"], "minimum": -1}, }, - 'definitions': { - 'exchange': { - 'type': 'object', - 'properties': { - 'name': {'type': 'string'}, - 'sandbox': {'type': 'boolean', 'default': False}, - 'key': {'type': 'string', 'default': ''}, - 'secret': {'type': 'string', 'default': ''}, - 'password': {'type': 'string', 'default': ''}, - 'uid': {'type': 'string'}, - 'pair_whitelist': { - 'type': 'array', - 'items': { - 'type': 'string', + "definitions": { + "exchange": { + "type": "object", + "properties": { + "name": {"type": "string"}, + "sandbox": {"type": "boolean", "default": False}, + "key": {"type": "string", "default": ""}, + "secret": {"type": "string", "default": ""}, + "password": {"type": "string", "default": ""}, + "uid": {"type": "string"}, + "pair_whitelist": { + "type": "array", + "items": { + "type": "string", }, - 'uniqueItems': True + "uniqueItems": True, }, - 'pair_blacklist': { - 'type': 'array', - 'items': { - 'type': 'string', + "pair_blacklist": { + "type": "array", + "items": { + "type": "string", }, - 'uniqueItems': True + "uniqueItems": True, }, - 'unknown_fee_rate': {'type': 'number'}, - 'outdated_offset': {'type': 'integer', 'minimum': 1}, - 'markets_refresh_interval': {'type': 'integer'}, - 'ccxt_config': {'type': 'object'}, - 'ccxt_async_config': {'type': 'object'} + "unknown_fee_rate": {"type": "number"}, + "outdated_offset": {"type": "integer", "minimum": 1}, + "markets_refresh_interval": {"type": "integer"}, + "ccxt_config": {"type": "object"}, + "ccxt_async_config": {"type": "object"}, }, - 'required': ['name'] + "required": ["name"], }, - 'edge': { - 'type': 'object', - 'properties': { - 'enabled': {'type': 'boolean'}, - 'process_throttle_secs': {'type': 'integer', 'minimum': 600}, - 'calculate_since_number_of_days': {'type': 'integer'}, - 'allowed_risk': {'type': 'number'}, - 'stoploss_range_min': {'type': 'number'}, - 'stoploss_range_max': {'type': 'number'}, - 'stoploss_range_step': {'type': 'number'}, - 'minimum_winrate': {'type': 'number'}, - 'minimum_expectancy': {'type': 'number'}, - 'min_trade_number': {'type': 'number'}, - 'max_trade_duration_minute': {'type': 'integer'}, - 'remove_pumps': {'type': 'boolean'} + "edge": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "process_throttle_secs": {"type": "integer", "minimum": 600}, + "calculate_since_number_of_days": {"type": "integer"}, + "allowed_risk": {"type": "number"}, + "stoploss_range_min": {"type": "number"}, + "stoploss_range_max": {"type": "number"}, + "stoploss_range_step": {"type": "number"}, + "minimum_winrate": {"type": "number"}, + "minimum_expectancy": {"type": "number"}, + "min_trade_number": {"type": "number"}, + "max_trade_duration_minute": {"type": "integer"}, + "remove_pumps": {"type": "boolean"}, }, - 'required': ['process_throttle_secs', 'allowed_risk'] - } + "required": ["process_throttle_secs", "allowed_risk"], + }, + "freqai": { + "type": "object", + "properties": { + "timeframes": {"type": "list"}, + "full_timerange": {"type": "str"}, + "train_period": {"type": "integer", "default": 0}, + "backtest_period": {"type": "integer", "default": 7}, + "identifier": {"type": "str", "default": "example"}, + "base_features": {"type": "list"}, + "corr_pairlist": {"type": "list"}, + "training_timerange": {"type": "string", "default": None}, + "feature_parameters": { + "type": "object", + "properties": { + "period": {"type": "integer"}, + "shift": {"type": "integer", "default": 0}, + "DI_threshold": {"type": "integer", "default": 0}, + "weight_factor": {"type": "number", "default": 0}, + "principal_component_analysis": {"type": "boolean", "default": False}, + "remove_outliers": {"type": "boolean", "default": False}, + }, + }, + "data_split_parameters": { + "type": "object", + "properties": { + "test_size": {"type": "number"}, + "random_state": {"type": "integer"}, + }, + }, + "model_training_parameters": { + "type": "object", + "properties": { + "n_estimators": {"type": "integer", "default": 2000}, + "random_state": {"type": "integer", "default": 1}, + "learning_rate": {"type": "number", "default": 0.02}, + "task_type": {"type": "string", "default": "CPU"}, + }, + }, + }, + }, }, } SCHEMA_TRADE_REQUIRED = [ - 'exchange', - 'timeframe', - 'max_open_trades', - 'stake_currency', - 'stake_amount', - 'tradable_balance_ratio', - 'last_stake_amount_min_ratio', - 'dry_run', - 'dry_run_wallet', - 'exit_pricing', - 'entry_pricing', - 'stoploss', - 'minimal_roi', - 'internals', - 'dataformat_ohlcv', - 'dataformat_trades', + "exchange", + "timeframe", + "max_open_trades", + "stake_currency", + "stake_amount", + "tradable_balance_ratio", + "last_stake_amount_min_ratio", + "dry_run", + "dry_run_wallet", + "exit_pricing", + "entry_pricing", + "stoploss", + "minimal_roi", + "internals", + "dataformat_ohlcv", + "dataformat_trades", ] SCHEMA_BACKTEST_REQUIRED = [ - 'exchange', - 'max_open_trades', - 'stake_currency', - 'stake_amount', - 'dry_run_wallet', - 'dataformat_ohlcv', - 'dataformat_trades', + "exchange", + "max_open_trades", + "stake_currency", + "stake_amount", + "dry_run_wallet", + "dataformat_ohlcv", + "dataformat_trades", ] SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [ - 'stoploss', - 'minimal_roi', + "stoploss", + "minimal_roi", ] SCHEMA_MINIMAL_REQUIRED = [ - 'exchange', - 'dry_run', - 'dataformat_ohlcv', - 'dataformat_trades', + "exchange", + "dry_run", + "dataformat_ohlcv", + "dataformat_trades", ] CANCEL_REASON = { diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py index 9ab47d223..94df869a1 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_handler.py @@ -36,6 +36,7 @@ class DataHandler: config["freqai"]["backtest_period"], ) self.data: Dict[Any, Any] = {} + self.data_dictionary: Dict[Any, Any] = {} self.config = config self.freq_config = config["freqai"] self.predictions = np.array([]) @@ -58,10 +59,6 @@ class DataHandler: save_path = Path(self.model_path) - # if not os.path.exists(self.model_path): - # os.mkdir(self.model_path) - # save_path = self.model_path + self.model_filename - # Save the trained model dump(model, save_path / str(self.model_filename + "_model.joblib")) self.data["model_path"] = self.model_path @@ -179,10 +176,8 @@ class DataHandler: (drop_index == 0) & (drop_index_labels == 0) ] # assuming the labels depend entirely on the dataframe here. logger.info( - "dropped", + "dropped %s training points due to NaNs, ensure all historical data downloaded", len(unfiltered_dataframe) - len(filtered_dataframe), - "training data points due to NaNs, ensure you have downloaded", - "all historical training data", ) self.data["filter_drop_index_training"] = drop_index @@ -197,12 +192,9 @@ class DataHandler: drop_index = ~drop_index self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) logger.info( - "dropped", + "dropped %s of %s prediction data points due to NaNs.", len(self.do_predict) - self.do_predict.sum(), - "of", len(filtered_dataframe), - "prediction data points due to NaNs. These are protected from prediction", - "with do_predict vector returned to strategy.", ) return filtered_dataframe, labels @@ -353,8 +345,8 @@ class DataHandler: pca2 = PCA(n_components=n_keep_components) self.data["n_kept_components"] = n_keep_components pca2 = pca2.fit(self.data_dictionary["train_features"]) - logger.info("reduced feature dimension by", n_components - n_keep_components) - logger.info("explained variance", np.sum(pca2.explained_variance_ratio_)) + logger.info("reduced feature dimension by %s", n_components - n_keep_components) + logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_)) train_components = pca2.transform(self.data_dictionary["train_features"]) test_components = pca2.transform(self.data_dictionary["test_features"]) @@ -383,7 +375,7 @@ class DataHandler: logger.info("computing average mean distance for all training points") pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1) avg_mean_dist = pairwise.mean(axis=1).mean() - logger.info("avg_mean_dist", avg_mean_dist) + logger.info("avg_mean_dist %s", avg_mean_dist) return avg_mean_dist @@ -411,9 +403,8 @@ class DataHandler: do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) logger.info( - "remove_outliers() tossed", + "remove_outliers() tossed %s predictions", len(do_predict) - do_predict.sum(), - "predictions because they were beyond 3 std deviations from training data.", ) self.do_predict += do_predict self.do_predict -= 1 @@ -475,7 +466,7 @@ class DataHandler: for p in config["freqai"]["corr_pairlist"]: features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - logger.info("number of features", len(features)) + logger.info("number of features %s", len(features)) return features def check_if_pred_in_training_spaces(self) -> None: @@ -486,7 +477,6 @@ class DataHandler: from the training data set. """ - logger.info("checking if prediction features are in AOA") distance = pairwise_distances( self.data_dictionary["train_features"], self.data_dictionary["prediction_features"], @@ -501,9 +491,8 @@ class DataHandler: ) logger.info( - "Distance checker tossed", + "Distance checker tossed %s predictions for being too far from training data", len(do_predict) - do_predict.sum(), - "predictions for being too far from training data", ) self.do_predict += do_predict diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 05a0594f3..368ed1635 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -69,12 +69,7 @@ class IFreqaiModel(ABC): self.pair = metadata["pair"] self.dh = DataHandler(self.config, dataframe) - logger.info( - "going to train", - len(self.dh.training_timeranges), - "timeranges:", - self.dh.training_timeranges, - ) + logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) # Loop enforcing the sliding window training/backtesting paragigm # tr_train is the training time range e.g. 1 historical month @@ -90,14 +85,14 @@ class IFreqaiModel(ABC): self.freqai_info["training_timerange"] = tr_train dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) - logger.info("training", self.pair, "for", tr_train) + logger.info("training %s for %s", self.pair, tr_train) # self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/" self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train))) if not self.model_exists(self.pair, training_timerange=tr_train): self.model = self.train(dataframe_train, metadata) self.dh.save_data(self.model) else: - self.model = self.dh.load_data(self.dh.model_path) + self.model = self.dh.load_data() preds, do_preds = self.predict(dataframe_backtest) @@ -167,7 +162,7 @@ class IFreqaiModel(ABC): path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib")) file_exists = path_to_modelfile.is_file() if file_exists: - logger.info("Found model at", self.dh.model_path / self.dh.model_filename) + logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename) else: - logger.info("Could not find model at", self.dh.model_path / self.dh.model_filename) + logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename) return file_exists diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 5051a8db0..f976b1238 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -204,12 +204,12 @@ class Backtesting: """ self.progress.init_step(BacktestState.DATALOAD, 1) - if self.config['freqaimodel']: - self.required_startup += int((self.config['freqai']['train_period']*86400) / - timeframe_to_seconds(self.config['timeframe'])) + if self.config['freqai']['train_period'] > 0: + self.required_startup += int((self.config['freqai']['train_period'] * 86400) / + timeframe_to_seconds(self.config['timeframe'])) + logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup) self.config['startup_candle_count'] = self.required_startup - data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index 4906b8c04..35f25775a 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -36,7 +36,7 @@ class ExamplePredictionModel(IFreqaiModel): self.dh.data["s_mean"] = dataframe["s"].mean() self.dh.data["s_std"] = dataframe["s"].std() - logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) + # logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) return dataframe["s"] @@ -77,11 +77,10 @@ class ExamplePredictionModel(IFreqaiModel): if self.feature_parameters["DI_threshold"]: self.dh.data["avg_mean_dist"] = self.dh.compute_distances() - logger.info("length of train data", len(data_dictionary["train_features"])) + logger.info("length of train data %s", len(data_dictionary["train_features"])) model = self.fit(data_dictionary) - logger.info("Finished training") logger.info(f'--------------------done training {metadata["pair"]}--------------------') return model From def71a0afedf18378bebed478decc3bb785efad5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 5 May 2022 15:35:51 +0200 Subject: [PATCH 008/518] auto build full_timerange and self manage training_timerange --- config_examples/config_freqai.example.json | 3 -- freqtrade/constants.py | 2 -- freqtrade/freqai/data_handler.py | 41 ++++++++++++++++++---- freqtrade/freqai/freqai_interface.py | 19 ++-------- 4 files changed, 38 insertions(+), 27 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 47109ff31..5bd4de6c4 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -49,12 +49,10 @@ } ], "freqai": { - "btc_pair": "BTC/USDT", "timeframes": [ "5m", "15m" ], - "full_timerange": "20210601-20210901", "train_period": 30, "backtest_period": 7, "identifier": "example", @@ -74,7 +72,6 @@ "LINK/USDT", "DOT/USDT" ], - "training_timerange": "20211220-20220117", "feature_parameters": { "period": 12, "shift": 1, diff --git a/freqtrade/constants.py b/freqtrade/constants.py index d9664cff8..d988a164e 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -478,13 +478,11 @@ CONF_SCHEMA = { "type": "object", "properties": { "timeframes": {"type": "list"}, - "full_timerange": {"type": "str"}, "train_period": {"type": "integer", "default": 0}, "backtest_period": {"type": "integer", "default": 7}, "identifier": {"type": "str", "default": "example"}, "base_features": {"type": "list"}, "corr_pairlist": {"type": "list"}, - "training_timerange": {"type": "string", "default": None}, "feature_parameters": { "type": "object", "properties": { diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_handler.py index 94df869a1..e58575970 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_handler.py @@ -3,6 +3,7 @@ import datetime import json import logging import pickle as pk +import shutil from pathlib import Path from typing import Any, Dict, List, Tuple @@ -30,15 +31,10 @@ class DataHandler: def __init__(self, config: Dict[str, Any], dataframe: DataFrame): self.full_dataframe = dataframe - (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( - config["freqai"]["full_timerange"], - config["freqai"]["train_period"], - config["freqai"]["backtest_period"], - ) self.data: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {} self.config = config - self.freq_config = config["freqai"] + self.freqai_config = config["freqai"] self.predictions = np.array([]) self.do_predict = np.array([]) self.target_mean = np.array([]) @@ -46,6 +42,16 @@ class DataHandler: self.model_path = Path() self.model_filename = "" + self.full_timerange = self.create_fulltimerange( + self.config["timerange"], self.freqai_config["train_period"] + ) + + (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( + self.full_timerange, + config["freqai"]["train_period"], + config["freqai"]["backtest_period"], + ) + def save_data(self, model: Any) -> None: """ Saves all data associated with a model for a single sub-train time range @@ -539,6 +545,29 @@ class DataHandler: return + def create_fulltimerange(self, backtest_tr: str, backtest_period: int) -> str: + backtest_timerange = TimeRange.parse_timerange(backtest_tr) + + backtest_timerange.startts = backtest_timerange.startts - backtest_period * SECONDS_IN_DAY + start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts) + stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts) + full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") + + self.full_path = Path( + self.config["user_data_dir"] + / "models" + / str(full_timerange + self.freqai_config["identifier"]) + ) + + if not self.full_path.is_dir(): + self.full_path.mkdir(parents=True, exist_ok=True) + shutil.copy( + Path(self.config["config_files"][0]).name, + Path(self.full_path / self.config["config_files"][0]), + ) + + return full_timerange + def np_encoder(self, object): if isinstance(object, np.generic): return object.item() diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 368ed1635..62779a4e1 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -1,6 +1,5 @@ import gc import logging -import shutil from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, Tuple @@ -32,24 +31,13 @@ class IFreqaiModel(ABC): self.data_split_parameters = config["freqai"]["data_split_parameters"] self.model_training_parameters = config["freqai"]["model_training_parameters"] self.feature_parameters = config["freqai"]["feature_parameters"] - self.full_path = Path( - config["user_data_dir"] - / "models" - / str(self.freqai_info["full_timerange"] + self.freqai_info["identifier"]) - ) + self.backtest_timerange = config["timerange"] self.time_last_trained = None self.current_time = None self.model = None self.predictions = None - if not self.full_path.is_dir(): - self.full_path.mkdir(parents=True, exist_ok=True) - shutil.copy( - self.config["config_files"][0], - Path(self.full_path / self.config["config_files"][0]), - ) - def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Entry point to the FreqaiModel, it will train a new model if @@ -82,12 +70,11 @@ class IFreqaiModel(ABC): gc.collect() # self.config['timerange'] = tr_train self.dh.data = {} # clean the pair specific data between models - self.freqai_info["training_timerange"] = tr_train + self.training_timerange = tr_train dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) logger.info("training %s for %s", self.pair, tr_train) - # self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/" - self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train))) + self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train))) if not self.model_exists(self.pair, training_timerange=tr_train): self.model = self.train(dataframe_train, metadata) self.dh.save_data(self.model) From 66715c5ba4316be461632777e7f4a68569b52857 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 5 May 2022 15:49:19 +0200 Subject: [PATCH 009/518] update doc --- docs/freqai.md | 43 ++++++++++++------------------------------- 1 file changed, 12 insertions(+), 31 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 6bc1e9365..844881613 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -49,9 +49,8 @@ config setup includes: ```json "freqai": { "timeframes" : ["5m","15m","4h"], - "full_timerange" : "20211220-20220220", - "train_period" : "month", - "backtest_period" : "week", + "train_period" : 30, + "backtest_period" : 7, "identifier" : "unique-id", "base_features": [ "rsi", @@ -63,18 +62,18 @@ config setup includes: "LINK/USD", "BNB/USD" ], - "train_params" : { + "feature_parameters" : { "period": 24, "shift": 2, "drop_features": false, "DI_threshold": 1, "weight_factor": 0, }, - "SPLIT_PARAMS" : { + "data_split_parameters" : { "test_size": 0.25, "random_state": 42 }, - "CLASSIFIER_PARAMS" : { + "model_training_parameters" : { "n_estimators": 100, "random_state": 42, "learning_rate": 0.02, @@ -110,11 +109,11 @@ no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_ ### Deciding the sliding training window and backtesting duration -`full_timerange` lets the user set the full backtesting range to train and -backtest through. Meanwhile `train_period` is the sliding training window and -`backtest_period` is the sliding backtesting window. In the present example, -the user is asking Freqai to train and backtest the range of `20211220-20220220` (`month`). -The user wishes to backtest each `week` with a newly trained model. This means that +Users define the backtesting timerange with the typical `--timerange` parameter in the user +configuration file. `train_period` is the duration of the sliding training window, while +`backtest_period` is the sliding backtesting window, both in number of days. In the present example, +the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days. +This means that if the user sets `--timerange 20210501-20210701`, Freqai will train 8 separate models (because the full range comprises 8 weeks), and then backtest the subsequent week associated with each of the 8 training data set timerange months. Users can think of this as a "sliding window" which @@ -128,7 +127,7 @@ month of data. The freqai training/backtesting module can be executed with the following command: ```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel --timerange 20210501-20210701 ``` where the user needs to have a FreqaiExampleStrategy that fits to the requirements outlined @@ -178,19 +177,7 @@ and `make_labels()` to let them customize various aspects of their training proc ### Running the model live -After the user has designed a desirable featureset, Freqai can be run in dry/live -using the typical trade command: - -```bash -freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --training_timerange '20211220-20220120' -``` - -Where the user has now specified exactly which of the models from the sliding window -that they wish to run live using `--training_timerange` (typically this would be the most -recent model trained). As of right now, freqai will -not automatically retain itself, so the user needs to manually retrain and then -reload the config file with a new `--training_timerange` in order to update the -model. +TODO: Freqai is not automated for live yet. ## Data anylsis techniques @@ -208,12 +195,6 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._ -`drop_features` tells Freqai to train the model on the user defined features, -followed by a feature importance evaluation where it drops the top and bottom -performing features (there is evidence to suggest the top features may not be -helpful in equity/crypto trading since the ultimate objective is to predict low -frequency patterns, source: numerai)._ - Finally, `period` defines the offset used for the `labels`. In the present example, the user is asking for `labels` that are 24 candles in the future. From 00ff0c9b917a28f55d676976736cd4e0e25a7bc9 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 12:54:49 +0200 Subject: [PATCH 010/518] ensure user defined timerange truncates final backtest so that we arent mismatching data lengths upon return to strategy. Rename DataHandler class to FreqaiDataKitchen --- .../{data_handler.py => data_kitchen.py} | 24 +++++++++++++++---- freqtrade/freqai/freqai_interface.py | 4 ++-- 2 files changed, 22 insertions(+), 6 deletions(-) rename freqtrade/freqai/{data_handler.py => data_kitchen.py} (96%) diff --git a/freqtrade/freqai/data_handler.py b/freqtrade/freqai/data_kitchen.py similarity index 96% rename from freqtrade/freqai/data_handler.py rename to freqtrade/freqai/data_kitchen.py index e58575970..1bea97697 100644 --- a/freqtrade/freqai/data_handler.py +++ b/freqtrade/freqai/data_kitchen.py @@ -22,7 +22,7 @@ SECONDS_IN_DAY = 86400 logger = logging.getLogger(__name__) -class DataHandler: +class FreqaiDataKitchen: """ Class designed to handle all the data for the IFreqaiModel class model. Functionalities include holding, saving, loading, and analyzing the data. @@ -291,32 +291,48 @@ class DataHandler: bt_period = bt_split * SECONDS_IN_DAY full_timerange = TimeRange.parse_timerange(tr) + config_timerange = TimeRange.parse_timerange(self.config["timerange"]) timerange_train = copy.deepcopy(full_timerange) timerange_backtest = copy.deepcopy(full_timerange) tr_training_list = [] tr_backtesting_list = [] first = True + # within_config_timerange = True while True: if not first: timerange_train.startts = timerange_train.startts + bt_period timerange_train.stopts = timerange_train.startts + train_period - # if a full training period doesnt fit, we stop - if timerange_train.stopts > full_timerange.stopts: - break + # make sure we finish with a full backtest + # if timerange_train.stopts > config_timerange.stopts - bt_period: + # within_config_timerange = False + # timerange_train.stopts = config_timerange.stopts - bt_period + # # if a full training period doesnt fit, we stop + # if timerange_train.stopts > full_timerange.stopts: + # break first = False start = datetime.datetime.utcfromtimestamp(timerange_train.startts) stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts) tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) # associated backtest period + if timerange_backtest.stopts > config_timerange.stopts: + timerange_backtest.stopts = config_timerange.stopts + timerange_backtest.startts = timerange_train.stopts timerange_backtest.stopts = timerange_backtest.startts + bt_period + start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) + # ensure we are predicting on exactly same amount of data as requested by user defined + # --timerange + if timerange_backtest.stopts == config_timerange.stopts: + break + + print(tr_training_list, tr_backtesting_list) return tr_training_list, tr_backtesting_list def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 62779a4e1..63e94383c 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd from pandas import DataFrame -from freqtrade.freqai.data_handler import DataHandler +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen pd.options.mode.chained_assignment = None @@ -55,7 +55,7 @@ class IFreqaiModel(ABC): :metadata: pair metadataa coming from strategy. """ self.pair = metadata["pair"] - self.dh = DataHandler(self.config, dataframe) + self.dh = FreqaiDataKitchen(self.config, dataframe) logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) From 3020218096593105e38ce8993068431bda4e3dac Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 13:06:54 +0200 Subject: [PATCH 011/518] fix bug on backtest timerange --- freqtrade/freqai/data_kitchen.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 1bea97697..38d518e23 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -317,11 +317,14 @@ class FreqaiDataKitchen: tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) # associated backtest period + + timerange_backtest.startts = timerange_train.stopts + if timerange_backtest.stopts > config_timerange.stopts: timerange_backtest.stopts = config_timerange.stopts - timerange_backtest.startts = timerange_train.stopts - timerange_backtest.stopts = timerange_backtest.startts + bt_period + else: + timerange_backtest.stopts = timerange_backtest.startts + bt_period start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) From e9a7b68bc121a13131e6f08aed4477b993cb48e0 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 13:10:47 +0200 Subject: [PATCH 012/518] revert constants.py and add changes --- freqtrade/constants.py | 782 +++++++++++++++++++---------------------- 1 file changed, 370 insertions(+), 412 deletions(-) diff --git a/freqtrade/constants.py b/freqtrade/constants.py index d988a164e..653fa8c67 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -8,133 +8,87 @@ from typing import List, Literal, Tuple from freqtrade.enums import CandleType -DEFAULT_CONFIG = "config.json" -DEFAULT_EXCHANGE = "bittrex" +DEFAULT_CONFIG = 'config.json' +DEFAULT_EXCHANGE = 'bittrex' PROCESS_THROTTLE_SECS = 5 # sec HYPEROPT_EPOCH = 100 # epochs RETRY_TIMEOUT = 30 # sec -TIMEOUT_UNITS = ["minutes", "seconds"] -EXPORT_OPTIONS = ["none", "trades", "signals"] -DEFAULT_DB_PROD_URL = "sqlite:///tradesv3.sqlite" -DEFAULT_DB_DRYRUN_URL = "sqlite:///tradesv3.dryrun.sqlite" -UNLIMITED_STAKE_AMOUNT = "unlimited" +TIMEOUT_UNITS = ['minutes', 'seconds'] +EXPORT_OPTIONS = ['none', 'trades', 'signals'] +DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite' +DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite' +UNLIMITED_STAKE_AMOUNT = 'unlimited' DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05 -REQUIRED_ORDERTIF = ["entry", "exit"] -REQUIRED_ORDERTYPES = ["entry", "exit", "stoploss", "stoploss_on_exchange"] -PRICING_SIDES = ["ask", "bid", "same", "other"] -ORDERTYPE_POSSIBILITIES = ["limit", "market"] -ORDERTIF_POSSIBILITIES = ["gtc", "fok", "ioc"] -HYPEROPT_LOSS_BUILTIN = [ - "ShortTradeDurHyperOptLoss", - "OnlyProfitHyperOptLoss", - "SharpeHyperOptLoss", - "SharpeHyperOptLossDaily", - "SortinoHyperOptLoss", - "SortinoHyperOptLossDaily", - "CalmarHyperOptLoss", - "MaxDrawDownHyperOptLoss", - "MaxDrawDownRelativeHyperOptLoss", - "ProfitDrawDownHyperOptLoss", -] -AVAILABLE_PAIRLISTS = [ - "StaticPairList", - "VolumePairList", - "AgeFilter", - "OffsetFilter", - "PerformanceFilter", - "PrecisionFilter", - "PriceFilter", - "RangeStabilityFilter", - "ShuffleFilter", - "SpreadFilter", - "VolatilityFilter", -] -AVAILABLE_PROTECTIONS = ["CooldownPeriod", "LowProfitPairs", "MaxDrawdown", "StoplossGuard"] -AVAILABLE_DATAHANDLERS = ["json", "jsongz", "hdf5"] -BACKTEST_BREAKDOWNS = ["day", "week", "month"] -BACKTEST_CACHE_AGE = ["none", "day", "week", "month"] -BACKTEST_CACHE_DEFAULT = "day" +REQUIRED_ORDERTIF = ['entry', 'exit'] +REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange'] +PRICING_SIDES = ['ask', 'bid', 'same', 'other'] +ORDERTYPE_POSSIBILITIES = ['limit', 'market'] +ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc'] +HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss', + 'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily', + 'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily', + 'CalmarHyperOptLoss', + 'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss', + 'ProfitDrawDownHyperOptLoss'] +AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', + 'AgeFilter', 'OffsetFilter', 'PerformanceFilter', + 'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter', + 'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter'] +AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard'] +AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5'] +BACKTEST_BREAKDOWNS = ['day', 'week', 'month'] +BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month'] +BACKTEST_CACHE_DEFAULT = 'day' DRY_RUN_WALLET = 1000 -DATETIME_PRINT_FORMAT = "%Y-%m-%d %H:%M:%S" +DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S' MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons -DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"] +DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume'] # Don't modify sequence of DEFAULT_TRADES_COLUMNS # it has wide consequences for stored trades files -DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"] -TRADING_MODES = ["spot", "margin", "futures"] -MARGIN_MODES = ["cross", "isolated", ""] +DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost'] +TRADING_MODES = ['spot', 'margin', 'futures'] +MARGIN_MODES = ['cross', 'isolated', ''] -LAST_BT_RESULT_FN = ".last_result.json" -FTHYPT_FILEVERSION = "fthypt_fileversion" +LAST_BT_RESULT_FN = '.last_result.json' +FTHYPT_FILEVERSION = 'fthypt_fileversion' -USERPATH_HYPEROPTS = "hyperopts" -USERPATH_STRATEGIES = "strategies" -USERPATH_NOTEBOOKS = "notebooks" -USERPATH_FREQAIMODELS = "freqaimodels" +USERPATH_HYPEROPTS = 'hyperopts' +USERPATH_STRATEGIES = 'strategies' +USERPATH_NOTEBOOKS = 'notebooks' -TELEGRAM_SETTING_OPTIONS = ["on", "off", "silent"] -WEBHOOK_FORMAT_OPTIONS = ["form", "json", "raw"] +TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent'] +WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw'] -ENV_VAR_PREFIX = "FREQTRADE__" +ENV_VAR_PREFIX = 'FREQTRADE__' -NON_OPEN_EXCHANGE_STATES = ("cancelled", "canceled", "closed", "expired") +NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired') # Define decimals per coin for outputs # Only used for outputs. DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's DECIMALS_PER_COIN = { - "BTC": 8, - "ETH": 5, + 'BTC': 8, + 'ETH': 5, } -DUST_PER_COIN = {"BTC": 0.0001, "ETH": 0.01} +DUST_PER_COIN = { + 'BTC': 0.0001, + 'ETH': 0.01 +} # Source files with destination directories within user-directory USER_DATA_FILES = { - "sample_strategy.py": USERPATH_STRATEGIES, - "sample_hyperopt_loss.py": USERPATH_HYPEROPTS, - "strategy_analysis_example.ipynb": USERPATH_NOTEBOOKS, + 'sample_strategy.py': USERPATH_STRATEGIES, + 'sample_hyperopt_loss.py': USERPATH_HYPEROPTS, + 'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS, } SUPPORTED_FIAT = [ - "AUD", - "BRL", - "CAD", - "CHF", - "CLP", - "CNY", - "CZK", - "DKK", - "EUR", - "GBP", - "HKD", - "HUF", - "IDR", - "ILS", - "INR", - "JPY", - "KRW", - "MXN", - "MYR", - "NOK", - "NZD", - "PHP", - "PKR", - "PLN", - "RUB", - "UAH", - "SEK", - "SGD", - "THB", - "TRY", - "TWD", - "ZAR", - "USD", - "BTC", - "ETH", - "XRP", - "LTC", - "BCH", + "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", + "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", + "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", + "RUB", "UAH", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", + "USD", "BTC", "ETH", "XRP", "LTC", "BCH" ] MINIMAL_CONFIG = { @@ -145,334 +99,338 @@ MINIMAL_CONFIG = { "key": "", "secret": "", "pair_whitelist": [], - "ccxt_async_config": {}, - }, + "ccxt_async_config": { + } + } } # Required json-schema for user specified config CONF_SCHEMA = { - "type": "object", - "properties": { - "max_open_trades": {"type": ["integer", "number"], "minimum": -1}, - "new_pairs_days": {"type": "integer", "default": 30}, - "timeframe": {"type": "string"}, - "stake_currency": {"type": "string"}, - "stake_amount": { - "type": ["number", "string"], - "minimum": 0.0001, - "pattern": UNLIMITED_STAKE_AMOUNT, + 'type': 'object', + 'properties': { + 'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1}, + 'new_pairs_days': {'type': 'integer', 'default': 30}, + 'timeframe': {'type': 'string'}, + 'stake_currency': {'type': 'string'}, + 'stake_amount': { + 'type': ['number', 'string'], + 'minimum': 0.0001, + 'pattern': UNLIMITED_STAKE_AMOUNT }, - "tradable_balance_ratio": {"type": "number", "minimum": 0.0, "maximum": 1, "default": 0.99}, - "available_capital": { - "type": "number", - "minimum": 0, + 'tradable_balance_ratio': { + 'type': 'number', + 'minimum': 0.0, + 'maximum': 1, + 'default': 0.99 }, - "amend_last_stake_amount": {"type": "boolean", "default": False}, - "last_stake_amount_min_ratio": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0, - "default": 0.5, + 'available_capital': { + 'type': 'number', + 'minimum': 0, }, - "fiat_display_currency": {"type": "string", "enum": SUPPORTED_FIAT}, - "dry_run": {"type": "boolean"}, - "dry_run_wallet": {"type": "number", "default": DRY_RUN_WALLET}, - "cancel_open_orders_on_exit": {"type": "boolean", "default": False}, - "process_only_new_candles": {"type": "boolean"}, - "minimal_roi": { - "type": "object", - "patternProperties": {"^[0-9.]+$": {"type": "number"}}, - "minProperties": 1, + 'amend_last_stake_amount': {'type': 'boolean', 'default': False}, + 'last_stake_amount_min_ratio': { + 'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5 }, - "amount_reserve_percent": {"type": "number", "minimum": 0.0, "maximum": 0.5}, - "stoploss": {"type": "number", "maximum": 0, "exclusiveMaximum": True, "minimum": -1}, - "trailing_stop": {"type": "boolean"}, - "trailing_stop_positive": {"type": "number", "minimum": 0, "maximum": 1}, - "trailing_stop_positive_offset": {"type": "number", "minimum": 0, "maximum": 1}, - "trailing_only_offset_is_reached": {"type": "boolean"}, - "use_exit_signal": {"type": "boolean"}, - "exit_profit_only": {"type": "boolean"}, - "exit_profit_offset": {"type": "number"}, - "ignore_roi_if_entry_signal": {"type": "boolean"}, - "ignore_buying_expired_candle_after": {"type": "number"}, - "trading_mode": {"type": "string", "enum": TRADING_MODES}, - "margin_mode": {"type": "string", "enum": MARGIN_MODES}, - "liquidation_buffer": {"type": "number", "minimum": 0.0, "maximum": 0.99}, - "backtest_breakdown": { - "type": "array", - "items": {"type": "string", "enum": BACKTEST_BREAKDOWNS}, - }, - "bot_name": {"type": "string"}, - "unfilledtimeout": { - "type": "object", - "properties": { - "entry": {"type": "number", "minimum": 1}, - "exit": {"type": "number", "minimum": 1}, - "exit_timeout_count": {"type": "number", "minimum": 0, "default": 0}, - "unit": {"type": "string", "enum": TIMEOUT_UNITS, "default": "minutes"}, + 'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT}, + 'dry_run': {'type': 'boolean'}, + 'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET}, + 'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False}, + 'process_only_new_candles': {'type': 'boolean'}, + 'minimal_roi': { + 'type': 'object', + 'patternProperties': { + '^[0-9.]+$': {'type': 'number'} }, + 'minProperties': 1 }, - "entry_pricing": { - "type": "object", - "properties": { - "price_last_balance": { - "type": "number", - "minimum": 0, - "maximum": 1, - "exclusiveMaximum": False, + 'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5}, + 'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1}, + 'trailing_stop': {'type': 'boolean'}, + 'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1}, + 'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1}, + 'trailing_only_offset_is_reached': {'type': 'boolean'}, + 'use_exit_signal': {'type': 'boolean'}, + 'exit_profit_only': {'type': 'boolean'}, + 'exit_profit_offset': {'type': 'number'}, + 'ignore_roi_if_entry_signal': {'type': 'boolean'}, + 'ignore_buying_expired_candle_after': {'type': 'number'}, + 'trading_mode': {'type': 'string', 'enum': TRADING_MODES}, + 'margin_mode': {'type': 'string', 'enum': MARGIN_MODES}, + 'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99}, + 'backtest_breakdown': { + 'type': 'array', + 'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS} + }, + 'bot_name': {'type': 'string'}, + 'unfilledtimeout': { + 'type': 'object', + 'properties': { + 'entry': {'type': 'number', 'minimum': 1}, + 'exit': {'type': 'number', 'minimum': 1}, + 'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0}, + 'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'} + } + }, + 'entry_pricing': { + 'type': 'object', + 'properties': { + 'price_last_balance': { + 'type': 'number', + 'minimum': 0, + 'maximum': 1, + 'exclusiveMaximum': False, }, - "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, - "use_order_book": {"type": "boolean"}, - "order_book_top": { - "type": "integer", - "minimum": 1, - "maximum": 50, - }, - "check_depth_of_market": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "bids_to_ask_delta": {"type": "number", "minimum": 0}, - }, + 'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'}, + 'use_order_book': {'type': 'boolean'}, + 'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, }, + 'check_depth_of_market': { + 'type': 'object', + 'properties': { + 'enabled': {'type': 'boolean'}, + 'bids_to_ask_delta': {'type': 'number', 'minimum': 0}, + } }, }, - "required": ["price_side"], + 'required': ['price_side'] }, - "exit_pricing": { - "type": "object", - "properties": { - "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, - "price_last_balance": { - "type": "number", - "minimum": 0, - "maximum": 1, - "exclusiveMaximum": False, - }, - "use_order_book": {"type": "boolean"}, - "order_book_top": { - "type": "integer", - "minimum": 1, - "maximum": 50, + 'exit_pricing': { + 'type': 'object', + 'properties': { + 'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'}, + 'price_last_balance': { + 'type': 'number', + 'minimum': 0, + 'maximum': 1, + 'exclusiveMaximum': False, }, + 'use_order_book': {'type': 'boolean'}, + 'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, }, }, - "required": ["price_side"], + 'required': ['price_side'] }, - "custom_price_max_distance_ratio": {"type": "number", "minimum": 0.0}, - "order_types": { - "type": "object", - "properties": { - "entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "force_exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "force_entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "emergency_exit": { - "type": "string", - "enum": ORDERTYPE_POSSIBILITIES, - "default": "market", - }, - "stoploss": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "stoploss_on_exchange": {"type": "boolean"}, - "stoploss_on_exchange_interval": {"type": "number"}, - "stoploss_on_exchange_limit_ratio": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0, - }, + 'custom_price_max_distance_ratio': { + 'type': 'number', 'minimum': 0.0 + }, + 'order_types': { + 'type': 'object', + 'properties': { + 'entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, + 'exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, + 'force_exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, + 'force_entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, + 'emergency_exit': { + 'type': 'string', + 'enum': ORDERTYPE_POSSIBILITIES, + 'default': 'market'}, + 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}, + 'stoploss_on_exchange': {'type': 'boolean'}, + 'stoploss_on_exchange_interval': {'type': 'number'}, + 'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0, + 'maximum': 1.0} }, - "required": ["entry", "exit", "stoploss", "stoploss_on_exchange"], + 'required': ['entry', 'exit', 'stoploss', 'stoploss_on_exchange'] }, - "order_time_in_force": { - "type": "object", - "properties": { - "entry": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, - "exit": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, + 'order_time_in_force': { + 'type': 'object', + 'properties': { + 'entry': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}, + 'exit': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES} }, - "required": REQUIRED_ORDERTIF, + 'required': REQUIRED_ORDERTIF }, - "exchange": {"$ref": "#/definitions/exchange"}, - "edge": {"$ref": "#/definitions/edge"}, - "experimental": { - "type": "object", - "properties": {"block_bad_exchanges": {"type": "boolean"}}, + 'exchange': {'$ref': '#/definitions/exchange'}, + 'edge': {'$ref': '#/definitions/edge'}, + 'experimental': { + 'type': 'object', + 'properties': { + 'block_bad_exchanges': {'type': 'boolean'} + } }, - "pairlists": { - "type": "array", - "items": { - "type": "object", - "properties": { - "method": {"type": "string", "enum": AVAILABLE_PAIRLISTS}, + 'pairlists': { + 'type': 'array', + 'items': { + 'type': 'object', + 'properties': { + 'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS}, }, - "required": ["method"], - }, + 'required': ['method'], + } }, - "protections": { - "type": "array", - "items": { - "type": "object", - "properties": { - "method": {"type": "string", "enum": AVAILABLE_PROTECTIONS}, - "stop_duration": {"type": "number", "minimum": 0.0}, - "stop_duration_candles": {"type": "number", "minimum": 0}, - "trade_limit": {"type": "number", "minimum": 1}, - "lookback_period": {"type": "number", "minimum": 1}, - "lookback_period_candles": {"type": "number", "minimum": 1}, + 'protections': { + 'type': 'array', + 'items': { + 'type': 'object', + 'properties': { + 'method': {'type': 'string', 'enum': AVAILABLE_PROTECTIONS}, + 'stop_duration': {'type': 'number', 'minimum': 0.0}, + 'stop_duration_candles': {'type': 'number', 'minimum': 0}, + 'trade_limit': {'type': 'number', 'minimum': 1}, + 'lookback_period': {'type': 'number', 'minimum': 1}, + 'lookback_period_candles': {'type': 'number', 'minimum': 1}, }, - "required": ["method"], - }, + 'required': ['method'], + } }, - "telegram": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "token": {"type": "string"}, - "chat_id": {"type": "string"}, - "balance_dust_level": {"type": "number", "minimum": 0.0}, - "notification_settings": { - "type": "object", - "default": {}, - "properties": { - "status": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "warning": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "startup": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "entry": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "entry_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "entry_fill": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "off", + 'telegram': { + 'type': 'object', + 'properties': { + 'enabled': {'type': 'boolean'}, + 'token': {'type': 'string'}, + 'chat_id': {'type': 'string'}, + 'balance_dust_level': {'type': 'number', 'minimum': 0.0}, + 'notification_settings': { + 'type': 'object', + 'default': {}, + 'properties': { + 'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'entry_fill': {'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS, + 'default': 'off' + }, + 'exit': { + 'type': ['string', 'object'], + 'additionalProperties': { + 'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS + } }, - "exit": { - "type": ["string", "object"], - "additionalProperties": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - }, + 'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}, + 'exit_fill': { + 'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS, + 'default': 'off' }, - "exit_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "exit_fill": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "off", + 'protection_trigger': { + 'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS, + 'default': 'off' }, - "protection_trigger": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "off", + 'protection_trigger_global': { + 'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS, }, - "protection_trigger_global": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - }, - }, + } }, - "reload": {"type": "boolean"}, + 'reload': {'type': 'boolean'}, }, - "required": ["enabled", "token", "chat_id"], + 'required': ['enabled', 'token', 'chat_id'], }, - "webhook": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "url": {"type": "string"}, - "format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"}, - "retries": {"type": "integer", "minimum": 0}, - "retry_delay": {"type": "number", "minimum": 0}, - "webhookentry": {"type": "object"}, - "webhookentrycancel": {"type": "object"}, - "webhookentryfill": {"type": "object"}, - "webhookexit": {"type": "object"}, - "webhookexitcancel": {"type": "object"}, - "webhookexitfill": {"type": "object"}, - "webhookstatus": {"type": "object"}, + 'webhook': { + 'type': 'object', + 'properties': { + 'enabled': {'type': 'boolean'}, + 'url': {'type': 'string'}, + 'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'}, + 'retries': {'type': 'integer', 'minimum': 0}, + 'retry_delay': {'type': 'number', 'minimum': 0}, + 'webhookentry': {'type': 'object'}, + 'webhookentrycancel': {'type': 'object'}, + 'webhookentryfill': {'type': 'object'}, + 'webhookexit': {'type': 'object'}, + 'webhookexitcancel': {'type': 'object'}, + 'webhookexitfill': {'type': 'object'}, + 'webhookstatus': {'type': 'object'}, }, }, - "api_server": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "listen_ip_address": {"format": "ipv4"}, - "listen_port": {"type": "integer", "minimum": 1024, "maximum": 65535}, - "username": {"type": "string"}, - "password": {"type": "string"}, - "jwt_secret_key": {"type": "string"}, - "CORS_origins": {"type": "array", "items": {"type": "string"}}, - "verbosity": {"type": "string", "enum": ["error", "info"]}, + 'api_server': { + 'type': 'object', + 'properties': { + 'enabled': {'type': 'boolean'}, + 'listen_ip_address': {'format': 'ipv4'}, + 'listen_port': { + 'type': 'integer', + 'minimum': 1024, + 'maximum': 65535 + }, + 'username': {'type': 'string'}, + 'password': {'type': 'string'}, + 'jwt_secret_key': {'type': 'string'}, + 'CORS_origins': {'type': 'array', 'items': {'type': 'string'}}, + 'verbosity': {'type': 'string', 'enum': ['error', 'info']}, }, - "required": ["enabled", "listen_ip_address", "listen_port", "username", "password"], + 'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password'] }, - "db_url": {"type": "string"}, - "export": {"type": "string", "enum": EXPORT_OPTIONS, "default": "trades"}, - "disableparamexport": {"type": "boolean"}, - "initial_state": {"type": "string", "enum": ["running", "stopped"]}, - "force_entry_enable": {"type": "boolean"}, - "disable_dataframe_checks": {"type": "boolean"}, - "internals": { - "type": "object", - "default": {}, - "properties": { - "process_throttle_secs": {"type": "integer"}, - "interval": {"type": "integer"}, - "sd_notify": {"type": "boolean"}, - }, + 'db_url': {'type': 'string'}, + 'export': {'type': 'string', 'enum': EXPORT_OPTIONS, 'default': 'trades'}, + 'disableparamexport': {'type': 'boolean'}, + 'initial_state': {'type': 'string', 'enum': ['running', 'stopped']}, + 'force_entry_enable': {'type': 'boolean'}, + 'disable_dataframe_checks': {'type': 'boolean'}, + 'internals': { + 'type': 'object', + 'default': {}, + 'properties': { + 'process_throttle_secs': {'type': 'integer'}, + 'interval': {'type': 'integer'}, + 'sd_notify': {'type': 'boolean'}, + } }, - "dataformat_ohlcv": {"type": "string", "enum": AVAILABLE_DATAHANDLERS, "default": "json"}, - "dataformat_trades": { - "type": "string", - "enum": AVAILABLE_DATAHANDLERS, - "default": "jsongz", + 'dataformat_ohlcv': { + 'type': 'string', + 'enum': AVAILABLE_DATAHANDLERS, + 'default': 'json' }, - "position_adjustment_enable": {"type": "boolean"}, - "max_entry_position_adjustment": {"type": ["integer", "number"], "minimum": -1}, + 'dataformat_trades': { + 'type': 'string', + 'enum': AVAILABLE_DATAHANDLERS, + 'default': 'jsongz' + }, + 'position_adjustment_enable': {'type': 'boolean'}, + 'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1}, }, - "definitions": { - "exchange": { - "type": "object", - "properties": { - "name": {"type": "string"}, - "sandbox": {"type": "boolean", "default": False}, - "key": {"type": "string", "default": ""}, - "secret": {"type": "string", "default": ""}, - "password": {"type": "string", "default": ""}, - "uid": {"type": "string"}, - "pair_whitelist": { - "type": "array", - "items": { - "type": "string", + 'definitions': { + 'exchange': { + 'type': 'object', + 'properties': { + 'name': {'type': 'string'}, + 'sandbox': {'type': 'boolean', 'default': False}, + 'key': {'type': 'string', 'default': ''}, + 'secret': {'type': 'string', 'default': ''}, + 'password': {'type': 'string', 'default': ''}, + 'uid': {'type': 'string'}, + 'pair_whitelist': { + 'type': 'array', + 'items': { + 'type': 'string', }, - "uniqueItems": True, + 'uniqueItems': True }, - "pair_blacklist": { - "type": "array", - "items": { - "type": "string", + 'pair_blacklist': { + 'type': 'array', + 'items': { + 'type': 'string', }, - "uniqueItems": True, + 'uniqueItems': True }, - "unknown_fee_rate": {"type": "number"}, - "outdated_offset": {"type": "integer", "minimum": 1}, - "markets_refresh_interval": {"type": "integer"}, - "ccxt_config": {"type": "object"}, - "ccxt_async_config": {"type": "object"}, + 'unknown_fee_rate': {'type': 'number'}, + 'outdated_offset': {'type': 'integer', 'minimum': 1}, + 'markets_refresh_interval': {'type': 'integer'}, + 'ccxt_config': {'type': 'object'}, + 'ccxt_async_config': {'type': 'object'} }, - "required": ["name"], + 'required': ['name'] }, - "edge": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "process_throttle_secs": {"type": "integer", "minimum": 600}, - "calculate_since_number_of_days": {"type": "integer"}, - "allowed_risk": {"type": "number"}, - "stoploss_range_min": {"type": "number"}, - "stoploss_range_max": {"type": "number"}, - "stoploss_range_step": {"type": "number"}, - "minimum_winrate": {"type": "number"}, - "minimum_expectancy": {"type": "number"}, - "min_trade_number": {"type": "number"}, - "max_trade_duration_minute": {"type": "integer"}, - "remove_pumps": {"type": "boolean"}, + 'edge': { + 'type': 'object', + 'properties': { + 'enabled': {'type': 'boolean'}, + 'process_throttle_secs': {'type': 'integer', 'minimum': 600}, + 'calculate_since_number_of_days': {'type': 'integer'}, + 'allowed_risk': {'type': 'number'}, + 'stoploss_range_min': {'type': 'number'}, + 'stoploss_range_max': {'type': 'number'}, + 'stoploss_range_step': {'type': 'number'}, + 'minimum_winrate': {'type': 'number'}, + 'minimum_expectancy': {'type': 'number'}, + 'min_trade_number': {'type': 'number'}, + 'max_trade_duration_minute': {'type': 'integer'}, + 'remove_pumps': {'type': 'boolean'} }, - "required": ["process_throttle_secs", "allowed_risk"], + 'required': ['process_throttle_secs', 'allowed_risk'] }, "freqai": { "type": "object", @@ -516,43 +474,43 @@ CONF_SCHEMA = { } SCHEMA_TRADE_REQUIRED = [ - "exchange", - "timeframe", - "max_open_trades", - "stake_currency", - "stake_amount", - "tradable_balance_ratio", - "last_stake_amount_min_ratio", - "dry_run", - "dry_run_wallet", - "exit_pricing", - "entry_pricing", - "stoploss", - "minimal_roi", - "internals", - "dataformat_ohlcv", - "dataformat_trades", + 'exchange', + 'timeframe', + 'max_open_trades', + 'stake_currency', + 'stake_amount', + 'tradable_balance_ratio', + 'last_stake_amount_min_ratio', + 'dry_run', + 'dry_run_wallet', + 'exit_pricing', + 'entry_pricing', + 'stoploss', + 'minimal_roi', + 'internals', + 'dataformat_ohlcv', + 'dataformat_trades', ] SCHEMA_BACKTEST_REQUIRED = [ - "exchange", - "max_open_trades", - "stake_currency", - "stake_amount", - "dry_run_wallet", - "dataformat_ohlcv", - "dataformat_trades", + 'exchange', + 'max_open_trades', + 'stake_currency', + 'stake_amount', + 'dry_run_wallet', + 'dataformat_ohlcv', + 'dataformat_trades', ] SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [ - "stoploss", - "minimal_roi", + 'stoploss', + 'minimal_roi', ] SCHEMA_MINIMAL_REQUIRED = [ - "exchange", - "dry_run", - "dataformat_ohlcv", - "dataformat_trades", + 'exchange', + 'dry_run', + 'dataformat_ohlcv', + 'dataformat_trades', ] CANCEL_REASON = { @@ -576,4 +534,4 @@ TradeList = List[List] LongShort = Literal['long', 'short'] EntryExit = Literal['entry', 'exit'] -BuySell = Literal['buy', 'sell'] +BuySell = Literal['buy', 'sell'] \ No newline at end of file From b03c7b514ddca28f4b0f49807d0ac42d717d1f0c Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 13:15:06 +0200 Subject: [PATCH 013/518] optional style for interfacing freqai with backtesting --- freqtrade/freqai/data_kitchen.py | 9 +-------- freqtrade/optimize/backtesting.py | 2 +- 2 files changed, 2 insertions(+), 9 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 38d518e23..600f82e21 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -304,13 +304,6 @@ class FreqaiDataKitchen: timerange_train.startts = timerange_train.startts + bt_period timerange_train.stopts = timerange_train.startts + train_period - # make sure we finish with a full backtest - # if timerange_train.stopts > config_timerange.stopts - bt_period: - # within_config_timerange = False - # timerange_train.stopts = config_timerange.stopts - bt_period - # # if a full training period doesnt fit, we stop - # if timerange_train.stopts > full_timerange.stopts: - # break first = False start = datetime.datetime.utcfromtimestamp(timerange_train.startts) stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts) @@ -335,7 +328,7 @@ class FreqaiDataKitchen: if timerange_backtest.stopts == config_timerange.stopts: break - print(tr_training_list, tr_backtesting_list) + # print(tr_training_list, tr_backtesting_list) return tr_training_list, tr_backtesting_list def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index f976b1238..a753a3b07 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -204,7 +204,7 @@ class Backtesting: """ self.progress.init_step(BacktestState.DATALOAD, 1) - if self.config['freqai']['train_period'] > 0: + if self.config.get('freqai', {}).get('train_period') > 0: self.required_startup += int((self.config['freqai']['train_period'] * 86400) / timeframe_to_seconds(self.config['timeframe'])) logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup) From b08c0888bbfde75171057004dffd1c871930a536 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 13:35:01 +0200 Subject: [PATCH 014/518] add USERPATH_FREQAIMODELS, remove return values from @abstract methods --- freqtrade/constants.py | 1 + freqtrade/freqai/freqai_interface.py | 6 ------ 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 653fa8c67..c19a71c61 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -55,6 +55,7 @@ FTHYPT_FILEVERSION = 'fthypt_fileversion' USERPATH_HYPEROPTS = 'hyperopts' USERPATH_STRATEGIES = 'strategies' USERPATH_NOTEBOOKS = 'notebooks' +USERPATH_FREQAIMODELS = 'freqaimodels' TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent'] WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw'] diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 63e94383c..081e69de4 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -110,8 +110,6 @@ class IFreqaiModel(ABC): :model: Trained model which can be used to inference (self.predict) """ - return Any - @abstractmethod def fit(self) -> Any: """ @@ -123,8 +121,6 @@ class IFreqaiModel(ABC): all the training and test data/labels. """ - return Any - @abstractmethod def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]: """ @@ -136,8 +132,6 @@ class IFreqaiModel(ABC): data (NaNs) or felt uncertain about data (PCA and DI index) """ - return np.array([]), np.array([]) - def model_exists(self, pair: str, training_timerange: str) -> bool: """ Given a pair and path, check if a model already exists From f653ace24bda2d0b900fdfad3a810242cd98cc48 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 15:10:11 +0200 Subject: [PATCH 015/518] another attempt at fixing datalength bug --- freqtrade/freqai/data_kitchen.py | 29 +++++++++++++++------------- freqtrade/freqai/freqai_interface.py | 5 ++++- 2 files changed, 20 insertions(+), 14 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 600f82e21..eac5eac30 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -39,6 +39,10 @@ class FreqaiDataKitchen: self.do_predict = np.array([]) self.target_mean = np.array([]) self.target_std = np.array([]) + self.full_predictions = np.array([]) + self.full_do_predict = np.array([]) + self.full_target_mean = np.array([]) + self.full_target_std = np.array([]) self.model_path = Path() self.model_filename = "" @@ -313,12 +317,11 @@ class FreqaiDataKitchen: timerange_backtest.startts = timerange_train.stopts + timerange_backtest.stopts = timerange_backtest.startts + bt_period + if timerange_backtest.stopts > config_timerange.stopts: timerange_backtest.stopts = config_timerange.stopts - else: - timerange_backtest.stopts = timerange_backtest.startts + bt_period - start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) @@ -328,7 +331,7 @@ class FreqaiDataKitchen: if timerange_backtest.stopts == config_timerange.stopts: break - # print(tr_training_list, tr_backtesting_list) + print(tr_training_list, tr_backtesting_list) return tr_training_list, tr_backtesting_list def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: @@ -536,10 +539,10 @@ class FreqaiDataKitchen: ones = np.ones(len_dataframe) s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"] - self.predictions = np.append(self.predictions, predictions) - self.do_predict = np.append(self.do_predict, do_predict) - self.target_mean = np.append(self.target_mean, s_mean) - self.target_std = np.append(self.target_std, s_std) + self.full_predictions = np.append(self.full_predictions, predictions) + self.full_do_predict = np.append(self.full_do_predict, do_predict) + self.full_target_mean = np.append(self.full_target_mean, s_mean) + self.full_target_std = np.append(self.full_target_std, s_std) return @@ -549,11 +552,11 @@ class FreqaiDataKitchen: when it goes back to the strategy. These rows are not included in the backtest. """ - filler = np.zeros(len_dataframe - len(self.predictions)) # startup_candle_count - self.predictions = np.append(filler, self.predictions) - self.do_predict = np.append(filler, self.do_predict) - self.target_mean = np.append(filler, self.target_mean) - self.target_std = np.append(filler, self.target_std) + filler = np.zeros(len_dataframe - len(self.full_predictions)) # startup_candle_count + self.full_predictions = np.append(filler, self.full_predictions) + self.full_do_predict = np.append(filler, self.full_do_predict) + self.full_target_mean = np.append(filler, self.full_target_mean) + self.full_target_std = np.append(filler, self.full_target_std) return diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 081e69de4..002596fee 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -84,10 +84,13 @@ class IFreqaiModel(ABC): preds, do_preds = self.predict(dataframe_backtest) self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) + print('predictions', len(self.dh.full_predictions), + 'do_predict', len(self.dh.full_do_predict)) self.dh.fill_predictions(len(dataframe)) - return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std + return (self.dh.full_predictions, self.dh.full_do_predict, + self.dh.full_target_mean, self.dh.full_target_std) def make_labels(self, dataframe: DataFrame) -> DataFrame: """ From aae233bd6c19e80f8c23a930e3913b2781c455f3 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 15:24:45 +0200 Subject: [PATCH 016/518] try passing the check tests --- freqtrade/optimize/backtesting.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index a753a3b07..add864a67 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -204,9 +204,9 @@ class Backtesting: """ self.progress.init_step(BacktestState.DATALOAD, 1) - if self.config.get('freqai', {}).get('train_period') > 0: - self.required_startup += int((self.config['freqai']['train_period'] * 86400) / - timeframe_to_seconds(self.config['timeframe'])) + if self.config.get('freqai') is not None: + self.required_startup += int((self.config.get('freqai', {}).get('train_period') * + 86400) / timeframe_to_seconds(self.config['timeframe'])) logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup) self.config['startup_candle_count'] = self.required_startup From a4f5811a5b1cdb030af85ac63135e331d4ac83fe Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 15:36:15 +0200 Subject: [PATCH 017/518] fix flake8 issue in arguments.py --- freqtrade/commands/arguments.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index f47748502..bdd6e4fb4 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -12,7 +12,8 @@ from freqtrade.constants import DEFAULT_CONFIG ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"] -ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel", "freqaimodel_path"] +ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel", + "freqaimodel_path"] ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"] From 178c2014b044632c87c9295b37c7ef861fb0cf37 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 6 May 2022 16:20:52 +0200 Subject: [PATCH 018/518] appease mypy --- freqtrade/freqai/data_kitchen.py | 23 +++++++++++++---------- freqtrade/freqai/freqai_interface.py | 16 ++++++++++++---- 2 files changed, 25 insertions(+), 14 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index eac5eac30..b2ea71984 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -8,6 +8,7 @@ from pathlib import Path from typing import Any, Dict, List, Tuple import numpy as np +import numpy.typing as npt import pandas as pd from joblib import dump, load from pandas import DataFrame @@ -35,14 +36,14 @@ class FreqaiDataKitchen: self.data_dictionary: Dict[Any, Any] = {} self.config = config self.freqai_config = config["freqai"] - self.predictions = np.array([]) - self.do_predict = np.array([]) - self.target_mean = np.array([]) - self.target_std = np.array([]) - self.full_predictions = np.array([]) - self.full_do_predict = np.array([]) - self.full_target_mean = np.array([]) - self.full_target_std = np.array([]) + self.predictions: npt.ArrayLike = np.array([]) + self.do_predict: npt.ArrayLike = np.array([]) + self.target_mean: npt.ArrayLike = np.array([]) + self.target_std: npt.ArrayLike = np.array([]) + self.full_predictions: npt.ArrayLike = np.array([]) + self.full_do_predict: npt.ArrayLike = np.array([]) + self.full_target_mean: npt.ArrayLike = np.array([]) + self.full_target_std: npt.ArrayLike = np.array([]) self.model_path = Path() self.model_filename = "" @@ -123,6 +124,7 @@ class FreqaiDataKitchen: :labels: cleaned labels ready to be split. """ + weights: npt.ArrayLike if self.config["freqai"]["feature_parameters"]["weight_factor"] > 0: weights = self.set_weights_higher_recent(len(filtered_dataframe)) else: @@ -519,12 +521,13 @@ class FreqaiDataKitchen: self.do_predict += do_predict self.do_predict -= 1 - def set_weights_higher_recent(self, num_weights: int) -> int: + def set_weights_higher_recent(self, num_weights: int) -> npt.ArrayLike: """ Set weights so that recent data is more heavily weighted during training than older data. """ - weights = np.zeros(num_weights) + + weights = np.zeros_like(num_weights) for i in range(1, len(weights)): weights[len(weights) - i] = np.exp( -i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 002596fee..16b6fd9f9 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -4,10 +4,12 @@ from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, Tuple -import numpy as np +import numpy.typing as npt import pandas as pd from pandas import DataFrame +from freqtrade.data.dataprovider import DataProvider +from freqtrade.enums import RunMode from freqtrade.freqai.data_kitchen import FreqaiDataKitchen @@ -37,8 +39,9 @@ class IFreqaiModel(ABC): self.current_time = None self.model = None self.predictions = None + self.live_trained_timerange = None - def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + def start(self, dataframe: DataFrame, metadata: dict, dp: DataProvider) -> DataFrame: """ Entry point to the FreqaiModel, it will train a new model if necesssary before making the prediction. @@ -57,6 +60,9 @@ class IFreqaiModel(ABC): self.pair = metadata["pair"] self.dh = FreqaiDataKitchen(self.config, dataframe) + if dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE): + logger.info('testing live') + logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) # Loop enforcing the sliding window training/backtesting paragigm @@ -99,7 +105,7 @@ class IFreqaiModel(ABC): :dataframe: the full dataframe for the present training period """ - return dataframe + return @abstractmethod def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: @@ -124,8 +130,10 @@ class IFreqaiModel(ABC): all the training and test data/labels. """ + return + @abstractmethod - def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]: + def predict(self, dataframe: DataFrame) -> Tuple[npt.ArrayLike, npt.ArrayLike]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. From 22bd5556ed21c6483e2b5ebb542e4ea0efa9df0f Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 9 May 2022 15:25:00 +0200 Subject: [PATCH 019/518] add self-retraining functionality for live/dry --- config_examples/config_freqai.example.json | 7 +- freqtrade/constants.py | 2 + freqtrade/freqai/data_kitchen.py | 150 +++++++++++++++--- freqtrade/freqai/freqai_interface.py | 58 ++++++- freqtrade/strategy/interface.py | 16 ++ freqtrade/templates/ExamplePredictionModel.py | 8 +- freqtrade/templates/FreqaiExampleStrategy.py | 21 ++- 7 files changed, 218 insertions(+), 44 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 5bd4de6c4..351585d17 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -13,7 +13,7 @@ "exit": 30 }, "exchange": { - "name": "ftx", + "name": "binance", "key": "", "secret": "", "ccxt_config": { @@ -55,7 +55,9 @@ ], "train_period": 30, "backtest_period": 7, - "identifier": "example", + "identifier": "livetest5", + "live_trained_timerange": "20220330-20220429", + "live_full_backtestrange": "20220302-20220501", "base_features": [ "rsi", "close_over_20sma", @@ -68,6 +70,7 @@ "macd" ], "corr_pairlist": [ + "BTC/USDT", "ETH/USDT", "LINK/USDT", "DOT/USDT" diff --git a/freqtrade/constants.py b/freqtrade/constants.py index c19a71c61..0dc355914 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -440,6 +440,8 @@ CONF_SCHEMA = { "train_period": {"type": "integer", "default": 0}, "backtest_period": {"type": "integer", "default": 7}, "identifier": {"type": "str", "default": "example"}, + "live_trained_timerange": {"type": "str"}, + "live_full_backtestrange": {"type": "str"}, "base_features": {"type": "list"}, "corr_pairlist": {"type": "list"}, "feature_parameters": { diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b2ea71984..7b6a65a59 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -16,6 +16,10 @@ from sklearn.metrics.pairwise import pairwise_distances from sklearn.model_selection import train_test_split from freqtrade.configuration import TimeRange +from freqtrade.data.history import load_pair_history +from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data +from freqtrade.resolvers import ExchangeResolver +from freqtrade.strategy.interface import IStrategy SECONDS_IN_DAY = 86400 @@ -30,7 +34,7 @@ class FreqaiDataKitchen: author: Robert Caulk, rob.caulk@gmail.com """ - def __init__(self, config: Dict[str, Any], dataframe: DataFrame): + def __init__(self, config: Dict[str, Any], dataframe: DataFrame, live: bool = False): self.full_dataframe = dataframe self.data: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {} @@ -45,17 +49,29 @@ class FreqaiDataKitchen: self.full_target_mean: npt.ArrayLike = np.array([]) self.full_target_std: npt.ArrayLike = np.array([]) self.model_path = Path() - self.model_filename = "" + self.model_filename: str = "" - self.full_timerange = self.create_fulltimerange( - self.config["timerange"], self.freqai_config["train_period"] - ) + if not live: + self.full_timerange = self.create_fulltimerange(self.config["timerange"], + self.freqai_config["train_period"] + ) - (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( - self.full_timerange, - config["freqai"]["train_period"], - config["freqai"]["backtest_period"], - ) + (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( + self.full_timerange, + config["freqai"]["train_period"], + config["freqai"]["backtest_period"], + ) + + def set_paths(self) -> None: + self.full_path = Path(self.config['user_data_dir'] / + "models" / + str(self.freqai_config['live_full_backtestrange'] + + self.freqai_config['identifier'])) + + self.model_path = Path(self.full_path / str("sub-train" + "-" + + str(self.freqai_config['live_trained_timerange']))) + + return def save_data(self, model: Any) -> None: """ @@ -187,10 +203,10 @@ class FreqaiDataKitchen: labels = labels[ (drop_index == 0) & (drop_index_labels == 0) ] # assuming the labels depend entirely on the dataframe here. - logger.info( - "dropped %s training points due to NaNs, ensure all historical data downloaded", - len(unfiltered_dataframe) - len(filtered_dataframe), - ) + # logger.info( + # "dropped %s training points due to NaNs, ensure all historical data downloaded", + # len(unfiltered_dataframe) - len(filtered_dataframe), + # ) self.data["filter_drop_index_training"] = drop_index else: @@ -485,11 +501,11 @@ class FreqaiDataKitchen: shift = "" if n > 0: shift = "_shift-" + str(n) - features.append(ft + shift + "_" + tf) + # features.append(ft + shift + "_" + tf) for p in config["freqai"]["corr_pairlist"]: features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - logger.info("number of features %s", len(features)) + # logger.info("number of features %s", len(features)) return features def check_if_pred_in_training_spaces(self) -> None: @@ -513,10 +529,10 @@ class FreqaiDataKitchen: 0, ) - logger.info( - "Distance checker tossed %s predictions for being too far from training data", - len(do_predict) - do_predict.sum(), - ) + # logger.info( + # "Distance checker tossed %s predictions for being too far from training data", + # len(do_predict) - do_predict.sum(), + # ) self.do_predict += do_predict self.do_predict -= 1 @@ -577,15 +593,105 @@ class FreqaiDataKitchen: / str(full_timerange + self.freqai_config["identifier"]) ) + config_path = Path(self.config["config_files"][0]) + if not self.full_path.is_dir(): self.full_path.mkdir(parents=True, exist_ok=True) shutil.copy( - Path(self.config["config_files"][0]).name, - Path(self.full_path / self.config["config_files"][0]), + config_path.name, + Path(self.full_path / config_path.parts[-1]), ) return full_timerange + def check_if_new_training_required(self, training_timerange: str, + metadata: dict) -> Tuple[bool, str]: + + time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() + + trained_timerange = TimeRange.parse_timerange(training_timerange) + + elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY + + trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + start = datetime.datetime.utcfromtimestamp(trained_timerange.startts) + stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts) + + new_trained_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") + + retrain = elapsed_time > self.freqai_config['backtest_period'] + + if retrain: + coin, _ = metadata['pair'].split("/") + # set the new model_path + self.model_path = Path(self.full_path / str("sub-train" + "-" + + str(new_trained_timerange))) + + self.model_filename = "cb_" + coin.lower() + "_" + new_trained_timerange + # this is not persistent at the moment TODO + self.freqai_config['live_trained_timerange'] = new_trained_timerange + # enables persistence, but not fully implemented into save/load data yer + self.data['live_trained_timerange'] = new_trained_timerange + + return retrain, new_trained_timerange + + def download_new_data_for_retraining(self, new_timerange: str, metadata: dict) -> None: + + exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], + self.config, validate=False) + pairs = self.freqai_config['corr_pairlist'] + [metadata['pair']] + timerange = TimeRange.parse_timerange(new_timerange) + # data_handler = get_datahandler(datadir, data_format) + + refresh_backtest_ohlcv_data( + exchange, pairs=pairs, timeframes=self.freqai_config['timeframes'], + datadir=self.config['datadir'], timerange=timerange, + new_pairs_days=self.config['new_pairs_days'], + erase=False, data_format=self.config['dataformat_ohlcv'], + trading_mode=self.config.get('trading_mode', 'spot'), + prepend=self.config.get('prepend_data', False) + ) + + def load_pairs_histories(self, new_timerange: str, metadata: dict) -> Tuple[Dict[Any, Any], + DataFrame]: + corr_dataframes: Dict[Any, Any] = {} + # pair_dataframes: Dict[Any, Any] = {} + pairs = self.freqai_config['corr_pairlist'] # + [metadata['pair']] + timerange = TimeRange.parse_timerange(new_timerange) + + for p in pairs: + corr_dataframes[p] = {} + for tf in self.freqai_config['timeframes']: + corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], + timeframe=tf, + pair=p, timerange=timerange) + + base_dataframe = [dataframe for key, dataframe in corr_dataframes.items() + if metadata['pair'] in key] + + # [0] indexes the lowest tf for the basepair + return corr_dataframes, base_dataframe[0][self.config['timeframe']] + + def use_strategy_to_populate_indicators(self, strategy: IStrategy, metadata: dict, + corr_dataframes: dict, + dataframe: DataFrame) -> DataFrame: + + # dataframe = pair_dataframes[0] # this is the base tf pair df + + for tf in self.freqai_config["timeframes"]: + # dataframe = strategy.populate_any_indicators(metadata["pair"], dataframe.copy, + # tf, pair_dataframes[tf]) + for i in self.freqai_config["corr_pairlist"]: + dataframe = strategy.populate_any_indicators(i, + dataframe.copy(), + tf, + corr_dataframes[i][tf], + coin=i.split("/")[0] + "-" + ) + + return dataframe + def np_encoder(self, object): if isinstance(object, np.generic): return object.item() diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 16b6fd9f9..222061e2a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -8,9 +8,9 @@ import numpy.typing as npt import pandas as pd from pandas import DataFrame -from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import RunMode from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.strategy.interface import IStrategy pd.options.mode.chained_assignment = None @@ -33,15 +33,14 @@ class IFreqaiModel(ABC): self.data_split_parameters = config["freqai"]["data_split_parameters"] self.model_training_parameters = config["freqai"]["model_training_parameters"] self.feature_parameters = config["freqai"]["feature_parameters"] - self.backtest_timerange = config["timerange"] + # self.backtest_timerange = config["timerange"] self.time_last_trained = None self.current_time = None self.model = None self.predictions = None - self.live_trained_timerange = None - def start(self, dataframe: DataFrame, metadata: dict, dp: DataProvider) -> DataFrame: + def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ Entry point to the FreqaiModel, it will train a new model if necesssary before making the prediction. @@ -57,11 +56,18 @@ class IFreqaiModel(ABC): the model. :metadata: pair metadataa coming from strategy. """ - self.pair = metadata["pair"] - self.dh = FreqaiDataKitchen(self.config, dataframe) - if dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE): - logger.info('testing live') + live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) + + self.pair = metadata["pair"] + self.dh = FreqaiDataKitchen(self.config, dataframe, live) + + if live: + # logger.info('testing live') + self.start_live(dataframe, metadata, strategy) + + return (self.dh.full_predictions, self.dh.full_do_predict, + self.dh.full_target_mean, self.dh.full_target_std) logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) @@ -98,6 +104,42 @@ class IFreqaiModel(ABC): return (self.dh.full_predictions, self.dh.full_do_predict, self.dh.full_target_mean, self.dh.full_target_std) + def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None: + + self.dh.set_paths() + + file_exists = self.model_exists(metadata['pair'], + training_timerange=self.freqai_info[ + 'live_trained_timerange']) + + (retrain, + new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ + 'live_trained_timerange'], + metadata) + + if retrain or not file_exists: + self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) + # dataframe = download-data + corr_dataframes, pair_dataframes = self.dh.load_pairs_histories(new_trained_timerange, + metadata) + + unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, + metadata, + corr_dataframes, + pair_dataframes) + + self.model = self.train(unfiltered_dataframe, metadata) + self.dh.save_data(self.model) + + self.freqai_info + + self.model = self.dh.load_data() + preds, do_preds = self.predict(dataframe) + self.dh.append_predictions(preds, do_preds, len(dataframe)) + # dataframe should have len 1 here + + return + def make_labels(self, dataframe: DataFrame) -> DataFrame: """ User defines the labels here (target values). diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 57afbf32a..e681d70bd 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -532,6 +532,22 @@ class IStrategy(ABC, HyperStrategyMixin): """ return None + def populate_any_indicators(self, pair: str, df: DataFrame, tf: str, + informative: DataFrame = None, coin: str = "") -> DataFrame: + """ + Function designed to automatically generate, name and merge features + from user indicated timeframes in the configuration file. User can add + additional features here, but must follow the naming convention. + Defined in IStrategy because Freqai needs to know it exists. + :params: + :pair: pair to be used as informative + :df: strategy dataframe which will receive merges from informatives + :tf: timeframe of the dataframe which will modify the feature names + :informative: the dataframe associated with the informative pair + :coin: the name of the coin which will modify the feature names. + """ + return df + ### # END - Intended to be overridden by strategy ### diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index 35f25775a..08f9d2ba9 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -124,7 +124,7 @@ class ExamplePredictionModel(IFreqaiModel): data (NaNs) or felt uncertain about data (PCA and DI index) """ - logger.info("--------------------Starting prediction--------------------") + # logger.info("--------------------Starting prediction--------------------") original_feature_list = self.dh.build_feature_list(self.config) filtered_dataframe, _ = self.dh.filter_features( @@ -151,8 +151,8 @@ class ExamplePredictionModel(IFreqaiModel): predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) # compute the non-standardized predictions - predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] + self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] - logger.info("--------------------Finished prediction--------------------") + # logger.info("--------------------Finished prediction--------------------") - return (predictions, self.dh.do_predict) + return (self.dh.predictions, self.dh.do_predict) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 873b31115..13309d8c3 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -44,13 +44,16 @@ class FreqaiExampleStrategy(IStrategy): stoploss = -0.05 use_sell_signal = True - startup_candle_count: int = 1000 + startup_candle_count: int = 300 def informative_pairs(self): - pairs = self.freqai_info["corr_pairlist"] + pairs = self.config["freqai"]["corr_pairlist"] informative_pairs = [] - for tf in self.timeframes: - informative_pairs.append([(pair, tf) for pair in pairs]) + for tf in self.config["freqai"]["timeframes"]: + # informative_pairs.append((self.pair, tf)) + # informative_pairs.append([(pair, tf) for pair in pairs]) + for pair in pairs: + informative_pairs.append((pair, tf)) return informative_pairs def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): @@ -129,6 +132,7 @@ class FreqaiExampleStrategy(IStrategy): # the configuration file parameters are stored here self.freqai_info = self.config["freqai"] + self.pair = metadata['pair'] # the model is instantiated here self.model = CustomModel(self.config) @@ -138,12 +142,13 @@ class FreqaiExampleStrategy(IStrategy): # the following loops are necessary for building the features # indicated by the user in the configuration file. for tf in self.freqai_info["timeframes"]: - dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf) - for i in self.freqai_info["corr_pairlist"]: + # dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf) + for pair in self.freqai_info["corr_pairlist"]: dataframe = self.populate_any_indicators( - i, dataframe.copy(), tf, coin=i.split("/")[0] + "-" + pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) + print('dataframe_built') # the model will return 4 values, its prediction, an indication of whether or not the # prediction should be accepted, the target mean/std values from the labels used during # each training period. @@ -152,7 +157,7 @@ class FreqaiExampleStrategy(IStrategy): dataframe["do_predict"], dataframe["target_mean"], dataframe["target_std"], - ) = self.model.bridge.start(dataframe, metadata) + ) = self.model.bridge.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5 dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5 From 9b3e5faebeb2235358ccc9ca003014fb89d64567 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 9 May 2022 17:01:49 +0200 Subject: [PATCH 020/518] create more flexible whitelist, avoid duplicating whitelist features into corr_pairlist, update docs --- config_examples/config_freqai.example.json | 5 +- docs/freqai.md | 82 ++++++++++++++++--- freqtrade/freqai/data_kitchen.py | 47 +++++++---- freqtrade/freqai/freqai_interface.py | 12 +-- freqtrade/templates/ExamplePredictionModel.py | 7 +- freqtrade/templates/FreqaiExampleStrategy.py | 5 +- 6 files changed, 119 insertions(+), 39 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 351585d17..d89c835b1 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -24,7 +24,8 @@ "rateLimit": 200 }, "pair_whitelist": [ - "BTC/USDT" + "BTC/USDT", + "ETH/USDT" ], "pair_blacklist": [] }, @@ -55,7 +56,7 @@ ], "train_period": 30, "backtest_period": 7, - "identifier": "livetest5", + "identifier": "new_corrlist", "live_trained_timerange": "20220330-20220429", "live_full_backtestrange": "20220302-20220501", "base_features": [ diff --git a/docs/freqai.md b/docs/freqai.md index 844881613..431705dd9 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -65,8 +65,6 @@ config setup includes: "feature_parameters" : { "period": 24, "shift": 2, - "drop_features": false, - "DI_threshold": 1, "weight_factor": 0, }, "data_split_parameters" : { @@ -79,8 +77,7 @@ config setup includes: "learning_rate": 0.02, "task_type": "CPU", }, - }, - + } ``` ### Building the feature set @@ -153,8 +150,6 @@ The Freqai strategy requires the user to include the following lines of code in # the following loops are necessary for building the features # indicated by the user in the configuration file. for tf in self.freqai_info['timeframes']: - dataframe = self.populate_any_indicators(metadata['pair'], - dataframe.copy(), tf) for i in self.freqai_info['corr_pairlist']: dataframe = self.populate_any_indicators(i, dataframe.copy(), tf, coin=i.split("/")[0]+'-') @@ -177,8 +172,36 @@ and `make_labels()` to let them customize various aspects of their training proc ### Running the model live -TODO: Freqai is not automated for live yet. +Freqai can be run dry/live using the following command +```bash +freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel +``` + +By default, Freqai will not find find any existing models and will start by training a new one +given the user configuration settings. Following training, it will use that model to predict for the +duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain +a new model, and begin making predictions with the updated model. + +If the user wishes to start dry/live from a saved model, the following configuration +parameters need to be set: + +```json + "freqai": { + "identifier": "example", + "live_trained_timerange": "20220330-20220429", + "live_full_backtestrange": "20220302-20220501" + } +``` + +Where the `identifier` is the same identifier which was set during the backtesting/training. Meanwhile, +the `live_trained_timerange` is the sub-trained timerange (the training window) which was set +during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`. +`live_full_backtestrange` was the full data range assocaited with the backtest/training (the full time +window that the training window and backtesting windows slide through). These values can be located +inside the `user_data/models/` directory. In this case, although Freqai will initiate with a +pretrained model, if a full `backtest_period` has elapsed since the end of the user set +`live_trained_timerange`, it will self retrain. ## Data anylsis techniques ### Controlling the model learning process @@ -226,12 +249,49 @@ $$ DI_k = d_k/\overline{d} $$ Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The dissimilarity index allows predictions which are outliers and not existent in the model feature space, to be thrown out due -to low levels of certainty. The user can tweak the DI with `DI_threshold` to increase -or decrease the extrapolation of the trained model. +to low levels of certainty. Activating the Dissimilarity Index can be achieved with: + +```json + "freqai": { + "feature_parameters" : { + "DI_threshold": 1 + } + } +``` + +The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the +trained model. ### Reducing data dimensionality with Principal Component Analysis -TO BE WRITTEN +Users can reduce the dimensionality of their features by activating the `principal_component_analysis`: + +```json + "freqai": { + "feature_parameters" : { + "principal_component_analysis": true + } + } +``` + +Which will perform PCA on the features and reduce the dimensionality of the data so that the explained +variance of the data set is >= 0.999. + +### Removing outliers based on feature statistical distributions + +The user can tell Freqai to remove outlier data points from the trainig/test data sets by setting: + +```json + "freqai": { + "feature_parameters" : { + "remove_outliers": true + } + } +``` + +Freqai will check the statistical distributions of each feature (or component if the user activated +`principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away +from the mean. ## Additional information ### Feature standardization @@ -242,5 +302,5 @@ data only. This includes all test data and unseen prediction data (dry/live/back ### File structure `user_data_dir/models/` contains all the data associated with the trainings and -backtestings. This file structure is heavily controlled and read by the `DataHandler()` +backtestings. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` and should thus not be modified. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 7b6a65a59..961f26e57 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -485,7 +485,7 @@ class FreqaiDataKitchen: return - def build_feature_list(self, config: dict) -> list: + def build_feature_list(self, config: dict, metadata: dict) -> list: """ Build the list of features that will be used to filter the full dataframe. Feature list is construced from the @@ -501,8 +501,10 @@ class FreqaiDataKitchen: shift = "" if n > 0: shift = "_shift-" + str(n) - # features.append(ft + shift + "_" + tf) + features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf) for p in config["freqai"]["corr_pairlist"]: + if metadata['pair'] in p: + continue # avoid duplicate features features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) # logger.info("number of features %s", len(features)) @@ -640,9 +642,10 @@ class FreqaiDataKitchen: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config, validate=False) - pairs = self.freqai_config['corr_pairlist'] + [metadata['pair']] + pairs = self.freqai_config['corr_pairlist'] + if metadata['pair'] not in pairs: + pairs += metadata['pair'] # dont include pair twice timerange = TimeRange.parse_timerange(new_timerange) - # data_handler = get_datahandler(datadir, data_format) refresh_backtest_ohlcv_data( exchange, pairs=pairs, timeframes=self.freqai_config['timeframes'], @@ -656,33 +659,45 @@ class FreqaiDataKitchen: def load_pairs_histories(self, new_timerange: str, metadata: dict) -> Tuple[Dict[Any, Any], DataFrame]: corr_dataframes: Dict[Any, Any] = {} - # pair_dataframes: Dict[Any, Any] = {} + base_dataframes: Dict[Any, Any] = {} pairs = self.freqai_config['corr_pairlist'] # + [metadata['pair']] timerange = TimeRange.parse_timerange(new_timerange) - for p in pairs: - corr_dataframes[p] = {} - for tf in self.freqai_config['timeframes']: + for tf in self.freqai_config['timeframes']: + base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'], + timeframe=tf, + pair=metadata['pair'], timerange=timerange) + for p in pairs: + if metadata['pair'] in p: + continue # dont repeat anything from whitelist + corr_dataframes[p] = {} corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, pair=p, timerange=timerange) - base_dataframe = [dataframe for key, dataframe in corr_dataframes.items() - if metadata['pair'] in key] + # base_dataframe = [dataframe for key, dataframe in corr_dataframes.items() + # if metadata['pair'] in key] # [0] indexes the lowest tf for the basepair - return corr_dataframes, base_dataframe[0][self.config['timeframe']] + return corr_dataframes, base_dataframes - def use_strategy_to_populate_indicators(self, strategy: IStrategy, metadata: dict, + def use_strategy_to_populate_indicators(self, strategy: IStrategy, corr_dataframes: dict, - dataframe: DataFrame) -> DataFrame: + base_dataframes: dict, + metadata: dict) -> DataFrame: - # dataframe = pair_dataframes[0] # this is the base tf pair df + dataframe = base_dataframes[self.config['timeframe']] for tf in self.freqai_config["timeframes"]: - # dataframe = strategy.populate_any_indicators(metadata["pair"], dataframe.copy, - # tf, pair_dataframes[tf]) + dataframe = strategy.populate_any_indicators(metadata['pair'], + dataframe.copy(), + tf, + base_dataframes[tf], + coin=metadata['pair'].split("/")[0] + "-" + ) for i in self.freqai_config["corr_pairlist"]: + if metadata['pair'] in i: + continue # dont repeat anything from whitelist dataframe = strategy.populate_any_indicators(i, dataframe.copy(), tf, diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 222061e2a..e019eb842 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -93,7 +93,7 @@ class IFreqaiModel(ABC): else: self.model = self.dh.load_data() - preds, do_preds = self.predict(dataframe_backtest) + preds, do_preds = self.predict(dataframe_backtest, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) print('predictions', len(self.dh.full_predictions), @@ -120,13 +120,13 @@ class IFreqaiModel(ABC): if retrain or not file_exists: self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) # dataframe = download-data - corr_dataframes, pair_dataframes = self.dh.load_pairs_histories(new_trained_timerange, + corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, metadata) unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, - metadata, corr_dataframes, - pair_dataframes) + base_dataframes, + metadata) self.model = self.train(unfiltered_dataframe, metadata) self.dh.save_data(self.model) @@ -134,7 +134,7 @@ class IFreqaiModel(ABC): self.freqai_info self.model = self.dh.load_data() - preds, do_preds = self.predict(dataframe) + preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) # dataframe should have len 1 here @@ -175,7 +175,7 @@ class IFreqaiModel(ABC): return @abstractmethod - def predict(self, dataframe: DataFrame) -> Tuple[npt.ArrayLike, npt.ArrayLike]: + def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index 08f9d2ba9..3db8d3aeb 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -53,7 +53,7 @@ class ExamplePredictionModel(IFreqaiModel): logger.info("--------------------Starting training--------------------") # create the full feature list based on user config info - self.dh.training_features_list = self.dh.build_feature_list(self.config) + self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata) unfiltered_labels = self.make_labels(unfiltered_dataframe) # filter the features requested by user in the configuration file and elegantly handle NaNs @@ -114,7 +114,8 @@ class ExamplePredictionModel(IFreqaiModel): return model - def predict(self, unfiltered_dataframe: DataFrame) -> Tuple[DataFrame, DataFrame]: + def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, + DataFrame]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. @@ -126,7 +127,7 @@ class ExamplePredictionModel(IFreqaiModel): # logger.info("--------------------Starting prediction--------------------") - original_feature_list = self.dh.build_feature_list(self.config) + original_feature_list = self.dh.build_feature_list(self.config, metadata) filtered_dataframe, _ = self.dh.filter_features( unfiltered_dataframe, original_feature_list, training_filter=False ) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 13309d8c3..45526e2ac 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -142,8 +142,11 @@ class FreqaiExampleStrategy(IStrategy): # the following loops are necessary for building the features # indicated by the user in the configuration file. for tf in self.freqai_info["timeframes"]: - # dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf) + dataframe = self.populate_any_indicators(self.pair, dataframe.copy(), tf, + coin=self.pair.split("/")[0] + "-") for pair in self.freqai_info["corr_pairlist"]: + if metadata['pair'] in pair: + continue # do not include whitelisted pair twice if it is in corr_pairlist dataframe = self.populate_any_indicators( pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) From a7029e35b51404424f872e1663d9602f29695607 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 10 May 2022 11:39:01 +0200 Subject: [PATCH 021/518] ensure informative pairs includes any combination of whitelist - corr_pairlist --- freqtrade/templates/FreqaiExampleStrategy.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 45526e2ac..e2bb6e041 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -47,12 +47,15 @@ class FreqaiExampleStrategy(IStrategy): startup_candle_count: int = 300 def informative_pairs(self): - pairs = self.config["freqai"]["corr_pairlist"] + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["corr_pairlist"] informative_pairs = [] for tf in self.config["freqai"]["timeframes"]: - # informative_pairs.append((self.pair, tf)) - # informative_pairs.append([(pair, tf) for pair in pairs]) - for pair in pairs: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication informative_pairs.append((pair, tf)) return informative_pairs From a8022c104a23f0a25e2fc4f6b65c15746be83824 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 14:01:53 +0200 Subject: [PATCH 022/518] give beta testers more information in the doc --- docs/freqai.md | 17 +++++++++++++++++ freqtrade/freqai/data_kitchen.py | 2 -- freqtrade/templates/ExamplePredictionModel.py | 4 ++-- 3 files changed, 19 insertions(+), 4 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 431705dd9..0fb1fa4c6 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -41,6 +41,23 @@ in the model. intermediate performance of the model during training. This data does not directly influence nodal weights within the model. +## Install prerequisites + +Use `pip` to install the prerequisities with: + +`pip install -r requirements-freqai.txt` + +## Running from the example files + +An example strategy, example prediction model, and example config can all be found in +`freqtrade/templates/ExampleFreqaiStrategy.py`, `freqtrade/templates/ExamplePredictionModel.py`, +`config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded +the necessary data, Freqai can be executed from these templates with: + +`freqtrade backtesting --config config_examples/config_freqai.example.json--strategy +ExampleFreqaiStrategy --freqaimodel ExamplePredictionModel +--freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates` + ## Configuring the bot ### Example config file The user interface is isolated to the typical config file. A typical Freqai diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 961f26e57..364b503e9 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -113,8 +113,6 @@ class FreqaiDataKitchen: with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) self.training_features_list = self.data["training_features_list"] - # if self.data.get("training_features_list"): - # self.training_features_list = [*self.data.get("training_features_list")] self.data_dictionary["train_features"] = pd.read_pickle( self.model_path / str(self.model_filename + "_trained_df.pkl") diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index 3db8d3aeb..796fb23ed 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -42,8 +42,8 @@ class ExamplePredictionModel(IFreqaiModel): def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: """ - Filter the training data and train a model to it. Train makes heavy use of the datahandler - for storing, saving, loading, and managed. + Filter the training data and train a model to it. Train makes heavy use of the datahkitchen + for storing, saving, loading, and analyzing the data. :params: :unfiltered_dataframe: Full dataframe for the current training period :metadata: pair metadata from strategy. From 717df891b1ecc7ce6c148149c32d51609af47147 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 14:05:19 +0200 Subject: [PATCH 023/518] use bash visual in doc --- docs/freqai.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 0fb1fa4c6..fd84dffc6 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -54,9 +54,11 @@ An example strategy, example prediction model, and example config can all be fou `config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with: -`freqtrade backtesting --config config_examples/config_freqai.example.json--strategy +```bash +freqtrade backtesting --config config_examples/config_freqai.example.json--strategy ExampleFreqaiStrategy --freqaimodel ExamplePredictionModel ---freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates` +--freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates +``` ## Configuring the bot ### Example config file From f4296173e938fe5f32421f1750fa15274f1a1fd9 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 14:31:26 +0200 Subject: [PATCH 024/518] use bash visual in doc --- docs/freqai.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/freqai.md b/docs/freqai.md index fd84dffc6..11ef972c7 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -56,7 +56,7 @@ the necessary data, Freqai can be executed from these templates with: ```bash freqtrade backtesting --config config_examples/config_freqai.example.json--strategy -ExampleFreqaiStrategy --freqaimodel ExamplePredictionModel +FreqaiExampleStrategy --freqaimodel ExamplePredictionModel --freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates ``` From e5759d950b111b60c3650e5a69285718db566645 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 14:32:17 +0200 Subject: [PATCH 025/518] fix typo --- docs/freqai.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/freqai.md b/docs/freqai.md index 11ef972c7..9e9155826 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -55,7 +55,7 @@ An example strategy, example prediction model, and example config can all be fou the necessary data, Freqai can be executed from these templates with: ```bash -freqtrade backtesting --config config_examples/config_freqai.example.json--strategy +freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel ExamplePredictionModel --freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates ``` From 9e94d28860515018e472e8079d9d6e1f9b39886b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 14:33:39 +0200 Subject: [PATCH 026/518] add timerange to backtest commnad --- docs/freqai.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/freqai.md b/docs/freqai.md index 9e9155826..6b7a4d5c8 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -57,7 +57,7 @@ the necessary data, Freqai can be executed from these templates with: ```bash freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel ExamplePredictionModel ---freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates +--freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates --timerange 20220101-220201 ``` ## Configuring the bot From 80dcd88abf33f42510f97d75de6998ce397a2f50 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 15 May 2022 15:26:09 +0200 Subject: [PATCH 027/518] allow user to run config from anywhere on their system --- freqtrade/freqai/data_kitchen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 364b503e9..c9d518418 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -598,7 +598,7 @@ class FreqaiDataKitchen: if not self.full_path.is_dir(): self.full_path.mkdir(parents=True, exist_ok=True) shutil.copy( - config_path.name, + config_path.resolve(), Path(self.full_path / config_path.parts[-1]), ) From 8664e8f9a3f57a1ec19a48c74fdd6ac07236bfe2 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 17 May 2022 17:13:38 +0200 Subject: [PATCH 028/518] create a prediction_models folder where basic prediction models can live (similar to optimize/hyperopt-loss. Update resolver/docs/and gitignore to accommodate change --- .gitignore | 2 + docs/freqai.md | 11 +- freqtrade/freqai/freqai_interface.py | 9 +- .../CatboostPredictionModel.py | 159 ++++++++++++++++++ freqtrade/resolvers/freqaimodel_resolver.py | 3 +- user_data/freqaimodels/.gitkeep | 0 6 files changed, 174 insertions(+), 10 deletions(-) create mode 100644 freqtrade/freqai/prediction_models/CatboostPredictionModel.py create mode 100644 user_data/freqaimodels/.gitkeep diff --git a/.gitignore b/.gitignore index 17823f642..dc87402b4 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,8 @@ user_data/* !user_data/strategy/sample_strategy.py !user_data/notebooks !user_data/models +!user_data/freqaimodels +user_data/freqaimodels/* user_data/models/* user_data/notebooks/* freqtrade-plot.html diff --git a/docs/freqai.md b/docs/freqai.md index 6b7a4d5c8..b5aa587bf 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -49,15 +49,16 @@ Use `pip` to install the prerequisities with: ## Running from the example files -An example strategy, example prediction model, and example config can all be found in -`freqtrade/templates/ExampleFreqaiStrategy.py`, `freqtrade/templates/ExamplePredictionModel.py`, +An example strategy, an example prediction model, and example config can all be found in +`freqtrade/templates/ExampleFreqaiStrategy.py`, +`freqtrade/freqai/prediction_models/CatboostPredictionModel.py`, `config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with: ```bash freqtrade backtesting --config config_examples/config_freqai.example.json --strategy -FreqaiExampleStrategy --freqaimodel ExamplePredictionModel ---freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates --timerange 20220101-220201 +FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freqtrade/templates +--timerange 20220101-220201 ``` ## Configuring the bot @@ -185,7 +186,7 @@ the feature set with a proper naming convention for the IFreqaiModel to use late ### Building an IFreqaiModel -Freqai has a base example model in `templates/ExamplePredictionModel.py`, but users can customize and create +Freqai has an example prediction model based on the popular `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.py`). However, users can customize and create their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()`, `predict()`, and `make_labels()` to let them customize various aspects of their training procedures. diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index e019eb842..ae05ae33a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -105,6 +105,11 @@ class IFreqaiModel(ABC): self.dh.full_target_mean, self.dh.full_target_std) def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None: + """ + The main broad execution for dry/live. This function will check if a retraining should be + performed, and if so, retrain and reset the model. + + """ self.dh.set_paths() @@ -119,7 +124,6 @@ class IFreqaiModel(ABC): if retrain or not file_exists: self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) - # dataframe = download-data corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, metadata) @@ -131,12 +135,9 @@ class IFreqaiModel(ABC): self.model = self.train(unfiltered_dataframe, metadata) self.dh.save_data(self.model) - self.freqai_info - self.model = self.dh.load_data() preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) - # dataframe should have len 1 here return diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py new file mode 100644 index 000000000..fecfc2220 --- /dev/null +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -0,0 +1,159 @@ +import logging +from typing import Any, Dict, Tuple + +import pandas as pd +from catboost import CatBoostRegressor, Pool +from pandas import DataFrame + +from freqtrade.freqai.freqai_interface import IFreqaiModel + + +logger = logging.getLogger(__name__) + + +class CatboostPredictionModel(IFreqaiModel): + """ + User created prediction model. The class needs to override three necessary + functions, predict(), train(), fit(). The class inherits ModelHandler which + has its own DataHandler where data is held, saved, loaded, and managed. + """ + + def make_labels(self, dataframe: DataFrame) -> DataFrame: + """ + User defines the labels here (target values). + :params: + :dataframe: the full dataframe for the present training period + """ + + dataframe["s"] = ( + dataframe["close"] + .shift(-self.feature_parameters["period"]) + .rolling(self.feature_parameters["period"]) + .max() + / dataframe["close"] + - 1 + ) + self.dh.data["s_mean"] = dataframe["s"].mean() + self.dh.data["s_std"] = dataframe["s"].std() + + # logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) + + return dataframe["s"] + + def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: + """ + Filter the training data and train a model to it. Train makes heavy use of the datahkitchen + for storing, saving, loading, and analyzing the data. + :params: + :unfiltered_dataframe: Full dataframe for the current training period + :metadata: pair metadata from strategy. + :returns: + :model: Trained model which can be used to inference (self.predict) + """ + logger.info("--------------------Starting training--------------------") + + # create the full feature list based on user config info + self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata) + unfiltered_labels = self.make_labels(unfiltered_dataframe) + + # filter the features requested by user in the configuration file and elegantly handle NaNs + features_filtered, labels_filtered = self.dh.filter_features( + unfiltered_dataframe, + self.dh.training_features_list, + unfiltered_labels, + training_filter=True, + ) + + # split data into train/test data. + data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered) + # standardize all data based on train_dataset only + data_dictionary = self.dh.standardize_data(data_dictionary) + + # optional additional data cleaning + if self.feature_parameters["principal_component_analysis"]: + self.dh.principal_component_analysis() + if self.feature_parameters["remove_outliers"]: + self.dh.remove_outliers(predict=False) + if self.feature_parameters["DI_threshold"]: + self.dh.data["avg_mean_dist"] = self.dh.compute_distances() + + logger.info("length of train data %s", len(data_dictionary["train_features"])) + + model = self.fit(data_dictionary) + + logger.info(f'--------------------done training {metadata["pair"]}--------------------') + + return model + + def fit(self, data_dictionary: Dict) -> Any: + """ + Most regressors use the same function names and arguments e.g. user + can drop in LGBMRegressor in place of CatBoostRegressor and all data + management will be properly handled by Freqai. + :params: + :data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + """ + + train_data = Pool( + data=data_dictionary["train_features"], + label=data_dictionary["train_labels"], + weight=data_dictionary["train_weights"], + ) + + test_data = Pool( + data=data_dictionary["test_features"], + label=data_dictionary["test_labels"], + weight=data_dictionary["test_weights"], + ) + + model = CatBoostRegressor( + verbose=100, early_stopping_rounds=400, **self.model_training_parameters + ) + model.fit(X=train_data, eval_set=test_data) + + return model + + def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, + DataFrame]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :predictions: np.array of predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + # logger.info("--------------------Starting prediction--------------------") + + original_feature_list = self.dh.build_feature_list(self.config, metadata) + filtered_dataframe, _ = self.dh.filter_features( + unfiltered_dataframe, original_feature_list, training_filter=False + ) + filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) + self.dh.data_dictionary["prediction_features"] = filtered_dataframe + + # optional additional data cleaning + if self.feature_parameters["principal_component_analysis"]: + pca_components = self.dh.pca.transform(filtered_dataframe) + self.dh.data_dictionary["prediction_features"] = pd.DataFrame( + data=pca_components, + columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])], + index=filtered_dataframe.index, + ) + + if self.feature_parameters["remove_outliers"]: + self.dh.remove_outliers(predict=True) # creates dropped index + + if self.feature_parameters["DI_threshold"]: + self.dh.check_if_pred_in_training_spaces() # sets do_predict + + predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) + + # compute the non-standardized predictions + self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] + + # logger.info("--------------------Finished prediction--------------------") + + return (self.dh.predictions, self.dh.do_predict) diff --git a/freqtrade/resolvers/freqaimodel_resolver.py b/freqtrade/resolvers/freqaimodel_resolver.py index 2ba6b3e8a..e666b462c 100644 --- a/freqtrade/resolvers/freqaimodel_resolver.py +++ b/freqtrade/resolvers/freqaimodel_resolver.py @@ -24,7 +24,8 @@ class FreqaiModelResolver(IResolver): object_type = IFreqaiModel object_type_str = "FreqaiModel" user_subdir = USERPATH_FREQAIMODELS - initial_search_path = Path(__file__).parent.parent.joinpath("optimize").resolve() + initial_search_path = Path(__file__).parent.parent.joinpath( + "freqai/prediction_models").resolve() @staticmethod def load_freqaimodel(config: Dict) -> IFreqaiModel: diff --git a/user_data/freqaimodels/.gitkeep b/user_data/freqaimodels/.gitkeep new file mode 100644 index 000000000..e69de29bb From d1d451c27e496bf2546993d9060efa1abb6e96b9 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 17 May 2022 18:15:03 +0200 Subject: [PATCH 029/518] auto populate features based on a prepended % in the strategy (remove feature assignment from config). Update doc/constants/example strategy to reflect change --- config_examples/config_freqai.example.json | 13 +---- docs/freqai.md | 35 ++++++++---- freqtrade/constants.py | 3 +- freqtrade/freqai/data_kitchen.py | 53 +++++++++++-------- .../CatboostPredictionModel.py | 5 +- freqtrade/templates/FreqaiExampleStrategy.py | 40 +++++++------- 6 files changed, 80 insertions(+), 69 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index d89c835b1..648f36917 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -56,20 +56,9 @@ ], "train_period": 30, "backtest_period": 7, - "identifier": "new_corrlist", + "identifier": "example", "live_trained_timerange": "20220330-20220429", "live_full_backtestrange": "20220302-20220501", - "base_features": [ - "rsi", - "close_over_20sma", - "relative_volume", - "bb_width", - "mfi", - "roc", - "pct-change", - "adx", - "macd" - ], "corr_pairlist": [ "BTC/USDT", "ETH/USDT", diff --git a/docs/freqai.md b/docs/freqai.md index b5aa587bf..29a45d042 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -72,11 +72,6 @@ config setup includes: "train_period" : 30, "backtest_period" : 7, "identifier" : "unique-id", - "base_features": [ - "rsi", - "mfi", - "roc", - ], "corr_pairlist": [ "ETH/USD", "LINK/USD", @@ -102,11 +97,31 @@ config setup includes: ### Building the feature set -Most of these parameters are controlling the feature data set. The `base_features` -indicates the basic indicators the user wishes to include in the feature set. -The `timeframes` are the timeframes of each base_feature that the user wishes to -include in the feature set. In the present case, the user is asking for the -`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, etc. to be included +Most of these parameters are controlling the feature data set. Features are added by the user +inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`: + +```python + def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): + informative['%-''%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) + informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) + informative['%-' + coin + "adx"] = ta.ADX(informative, window=20) + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) + informative[coin + "bb_lowerband"] = bollinger["lower"] + informative[coin + "bb_middleband"] = bollinger["mid"] + informative[coin + "bb_upperband"] = bollinger["upper"] + informative['%-' + coin + "bb_width"] = ( + informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"] + ) / informative[coin + "bb_middleband"] +``` +The user of the present example does not want to pass the `bb_lowerband` as a feature to the model, +and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the +model for training/prediction and has therfore prepended it with `%`._ + +(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) + +The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()` + included metric for inclusion in the feature set. In the present case, the user is asking for the +`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. In addition, the user can ask for each of these features to be included from diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 0dc355914..686991e2c 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -442,7 +442,6 @@ CONF_SCHEMA = { "identifier": {"type": "str", "default": "example"}, "live_trained_timerange": {"type": "str"}, "live_full_backtestrange": {"type": "str"}, - "base_features": {"type": "list"}, "corr_pairlist": {"type": "list"}, "feature_parameters": { "type": "object", @@ -537,4 +536,4 @@ TradeList = List[List] LongShort = Literal['long', 'short'] EntryExit = Literal['entry', 'exit'] -BuySell = Literal['buy', 'sell'] \ No newline at end of file +BuySell = Literal['buy', 'sell'] diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index c9d518418..cfdbac5f5 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -483,31 +483,38 @@ class FreqaiDataKitchen: return - def build_feature_list(self, config: dict, metadata: dict) -> list: - """ - Build the list of features that will be used to filter - the full dataframe. Feature list is construced from the - user configuration file. - :params: - :config: Canonical freqtrade config file containing all - user defined input in config['freqai] dictionary. - """ - features = [] - for tf in config["freqai"]["timeframes"]: - for ft in config["freqai"]["base_features"]: - for n in range(config["freqai"]["feature_parameters"]["shift"] + 1): - shift = "" - if n > 0: - shift = "_shift-" + str(n) - features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf) - for p in config["freqai"]["corr_pairlist"]: - if metadata['pair'] in p: - continue # avoid duplicate features - features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - - # logger.info("number of features %s", len(features)) + def find_features(self, dataframe: DataFrame) -> list: + column_names = dataframe.columns + features = [c for c in column_names if '%' in c] + assert features, ("Could not find any features!") return features + # def build_feature_list(self, config: dict, metadata: dict) -> list: + # """ + # SUPERCEDED BY self.find_features() + # Build the list of features that will be used to filter + # the full dataframe. Feature list is construced from the + # user configuration file. + # :params: + # :config: Canonical freqtrade config file containing all + # user defined input in config['freqai] dictionary. + # """ + # features = [] + # for tf in config["freqai"]["timeframes"]: + # for ft in config["freqai"]["base_features"]: + # for n in range(config["freqai"]["feature_parameters"]["shift"] + 1): + # shift = "" + # if n > 0: + # shift = "_shift-" + str(n) + # features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf) + # for p in config["freqai"]["corr_pairlist"]: + # if metadata['pair'] in p: + # continue # avoid duplicate features + # features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) + + # # logger.info("number of features %s", len(features)) + # return features + def check_if_pred_in_training_spaces(self) -> None: """ Compares the distance from each prediction point to each training data diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index fecfc2220..e2ba6bd29 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -53,9 +53,8 @@ class CatboostPredictionModel(IFreqaiModel): logger.info("--------------------Starting training--------------------") # create the full feature list based on user config info - self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata) + self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe) unfiltered_labels = self.make_labels(unfiltered_dataframe) - # filter the features requested by user in the configuration file and elegantly handle NaNs features_filtered, labels_filtered = self.dh.filter_features( unfiltered_dataframe, @@ -127,7 +126,7 @@ class CatboostPredictionModel(IFreqaiModel): # logger.info("--------------------Starting prediction--------------------") - original_feature_list = self.dh.build_feature_list(self.config, metadata) + original_feature_list = self.dh.find_features(unfiltered_dataframe) filtered_dataframe, _ = self.dh.filter_features( unfiltered_dataframe, original_feature_list, training_filter=False ) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index e2bb6e041..f478dd332 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -62,8 +62,11 @@ class FreqaiExampleStrategy(IStrategy): def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): """ Function designed to automatically generate, name and merge features - from user indicated timeframes in the configuration file. User can add - additional features here, but must follow the naming convention. + from user indicated timeframes in the configuration file. User controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e. user should not prepend any supporting metrics + (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. :params: :pair: pair to be used as informative :df: strategy dataframe which will receive merges from informatives @@ -74,49 +77,50 @@ class FreqaiExampleStrategy(IStrategy): if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) - informative[coin + "rsi"] = ta.RSI(informative, timeperiod=14) - informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25) - informative[coin + "adx"] = ta.ADX(informative, window=20) + informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) + informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) + informative['%-' + coin + "adx"] = ta.ADX(informative, window=20) informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20) informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21) - informative[coin + "bmsb"] = np.where( + informative['%-' + coin + "bmsb"] = np.where( informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0 ) - informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"] + informative['%-' + coin + "close_over_20sma"] = informative["close"] / informative[ + coin + "20sma"] - informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25) + informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21) informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20) stoch = ta.STOCHRSI(informative, 15, 20, 2, 2) - informative[coin + "srsi-fk"] = stoch["fastk"] - informative[coin + "srsi-fd"] = stoch["fastd"] + informative['%-' + coin + "srsi-fk"] = stoch["fastk"] + informative['%-' + coin + "srsi-fd"] = stoch["fastd"] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) informative[coin + "bb_lowerband"] = bollinger["lower"] informative[coin + "bb_middleband"] = bollinger["mid"] informative[coin + "bb_upperband"] = bollinger["upper"] - informative[coin + "bb_width"] = ( + informative['%-' + coin + "bb_width"] = ( informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"] ) / informative[coin + "bb_middleband"] - informative[coin + "close-bb_lower"] = ( + informative['%-' + coin + "close-bb_lower"] = ( informative["close"] / informative[coin + "bb_lowerband"] ) - informative[coin + "roc"] = ta.ROC(informative, timeperiod=3) - informative[coin + "adx"] = ta.ADX(informative, window=14) + informative['%-' + coin + "roc"] = ta.ROC(informative, timeperiod=3) + informative['%-' + coin + "adx"] = ta.ADX(informative, window=14) macd = ta.MACD(informative) - informative[coin + "macd"] = macd["macd"] + informative['%-' + coin + "macd"] = macd["macd"] informative[coin + "pct-change"] = informative["close"].pct_change() - informative[coin + "relative_volume"] = ( + informative['%-' + coin + "relative_volume"] = ( informative["volume"] / informative["volume"].rolling(10).mean() ) informative[coin + "pct-change"] = informative["close"].pct_change() - indicators = [col for col in informative if col.startswith(coin)] + indicators = [col for col in informative if col.startswith('%')] for n in range(self.freqai_info["feature_parameters"]["shift"] + 1): if n == 0: @@ -154,7 +158,6 @@ class FreqaiExampleStrategy(IStrategy): pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) - print('dataframe_built') # the model will return 4 values, its prediction, an indication of whether or not the # prediction should be accepted, the target mean/std values from the labels used during # each training period. @@ -181,7 +184,6 @@ class FreqaiExampleStrategy(IStrategy): return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value') sell_conditions = [ (dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1) ] From db66b82f6fc432bc16cdb07b4269deb3163a06e5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 17 May 2022 19:50:06 +0200 Subject: [PATCH 030/518] accept open-ended timeranges from user --- freqtrade/freqai/data_kitchen.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index cfdbac5f5..8ccb95dbe 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -312,6 +312,9 @@ class FreqaiDataKitchen: full_timerange = TimeRange.parse_timerange(tr) config_timerange = TimeRange.parse_timerange(self.config["timerange"]) + if config_timerange.stopts == 0: + config_timerange.stopts = int(datetime.datetime.now( + tz=datetime.timezone.utc).timestamp()) timerange_train = copy.deepcopy(full_timerange) timerange_backtest = copy.deepcopy(full_timerange) @@ -589,6 +592,10 @@ class FreqaiDataKitchen: def create_fulltimerange(self, backtest_tr: str, backtest_period: int) -> str: backtest_timerange = TimeRange.parse_timerange(backtest_tr) + if backtest_timerange.stopts == 0: + backtest_timerange.stopts = int(datetime.datetime.now( + tz=datetime.timezone.utc).timestamp()) + backtest_timerange.startts = backtest_timerange.startts - backtest_period * SECONDS_IN_DAY start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts) stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts) From c81b9607915bbf3fc916a5f0e0a7fbbd0e0834f2 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 15 May 2022 16:25:08 +0200 Subject: [PATCH 031/518] Fix some typos --- docs/freqai.md | 35 +++++++++++-------- freqtrade/freqai/freqai_interface.py | 6 ++-- freqtrade/templates/ExamplePredictionModel.py | 2 +- mkdocs.yml | 2 +- 4 files changed, 26 insertions(+), 19 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 29a45d042..730f353c8 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -4,8 +4,9 @@ Freqai is still experimental, and should be used at the user's own discretion. Freqai is a module designed to automate a variety of tasks associated with -training a regressor to predict signals based on input features. Among the -the features includes: +training a regressor to predict signals based on input features. + +Among the the features included: * Easy large feature set construction based on simple user input * Sweep model training and backtesting to simulate consistent model retraining through time @@ -16,6 +17,7 @@ the features includes: * Cleaning of NaNs from the data set before training and prediction. TODO: + * live is not automated, still some architectural work to be done ## Background and vocabulary @@ -43,7 +45,7 @@ directly influence nodal weights within the model. ## Install prerequisites -Use `pip` to install the prerequisities with: +Use `pip` to install the prerequisites with: `pip install -r requirements-freqai.txt` @@ -62,7 +64,9 @@ FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freq ``` ## Configuring the bot + ### Example config file + The user interface is isolated to the typical config file. A typical Freqai config setup includes: @@ -152,8 +156,8 @@ data set timerange months. Users can think of this as a "sliding window" which emulates Freqai retraining itself once per week in live using the previous month of data. - ## Running Freqai + ### Training and backtesting The freqai training/backtesting module can be executed with the following command: @@ -196,6 +200,7 @@ The Freqai strategy requires the user to include the following lines of code in return dataframe ``` + The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds the feature set with a proper naming convention for the IFreqaiModel to use later. @@ -216,7 +221,7 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example. By default, Freqai will not find find any existing models and will start by training a new one given the user configuration settings. Following training, it will use that model to predict for the duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain -a new model, and begin making predictions with the updated model. +a new model, and begin making predictions with the updated model. If the user wishes to start dry/live from a saved model, the following configuration parameters need to be set: @@ -232,13 +237,14 @@ parameters need to be set: Where the `identifier` is the same identifier which was set during the backtesting/training. Meanwhile, the `live_trained_timerange` is the sub-trained timerange (the training window) which was set during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`. -`live_full_backtestrange` was the full data range assocaited with the backtest/training (the full time +`live_full_backtestrange` was the full data range associated with the backtest/training (the full time window that the training window and backtesting windows slide through). These values can be located inside the `user_data/models/` directory. In this case, although Freqai will initiate with a -pretrained model, if a full `backtest_period` has elapsed since the end of the user set -`live_trained_timerange`, it will self retrain. +pre-trained model, if a full `backtest_period` has elapsed since the end of the user set +`live_trained_timerange`, it will self retrain. ## Data anylsis techniques + ### Controlling the model learning process The user can define model settings for the data split `data_split_parameters` and learning parameters @@ -258,7 +264,7 @@ the user is asking for `labels` that are 24 candles in the future. ### Removing outliers with the Dissimilarity Index -The Dissimilarity Index (DI) aims to quantiy the uncertainty associated with each +The Dissimilarity Index (DI) aims to quantity the uncertainty associated with each prediction by the model. To do so, Freqai measures the distance between each training data point and all other training data points: @@ -310,11 +316,11 @@ Users can reduce the dimensionality of their features by activating the `princip ``` Which will perform PCA on the features and reduce the dimensionality of the data so that the explained -variance of the data set is >= 0.999. +variance of the data set is >= 0.999. ### Removing outliers based on feature statistical distributions -The user can tell Freqai to remove outlier data points from the trainig/test data sets by setting: +The user can tell Freqai to remove outlier data points from the training/test data sets by setting: ```json "freqai": { @@ -326,9 +332,10 @@ The user can tell Freqai to remove outlier data points from the trainig/test dat Freqai will check the statistical distributions of each feature (or component if the user activated `principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away -from the mean. +from the mean. ## Additional information + ### Feature standardization The feature set created by the user is automatically standardized to the training @@ -337,5 +344,5 @@ data only. This includes all test data and unseen prediction data (dry/live/back ### File structure `user_data_dir/models/` contains all the data associated with the trainings and -backtestings. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` -and should thus not be modified. +backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` +and should thus not be modified. diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index ae05ae33a..b7c879ff0 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -43,7 +43,7 @@ class IFreqaiModel(ABC): def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ Entry point to the FreqaiModel, it will train a new model if - necesssary before making the prediction. + necessary before making the prediction. The backtesting and training paradigm is a sliding training window with a following backtest window. Both windows slide according to the length of the backtest window. This function is not intended to be @@ -54,7 +54,7 @@ class IFreqaiModel(ABC): :dataframe: Full dataframe coming from strategy - it contains entire backtesting timerange + additional historical data necessary to train the model. - :metadata: pair metadataa coming from strategy. + :metadata: pair metadata coming from strategy. """ live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) @@ -71,7 +71,7 @@ class IFreqaiModel(ABC): logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) - # Loop enforcing the sliding window training/backtesting paragigm + # Loop enforcing the sliding window training/backtesting paradigm # tr_train is the training time range e.g. 1 historical month # tr_backtest is the backtesting time range e.g. the week directly # following tr_train. Both of these windows slide through the diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py index 796fb23ed..3d2b7a808 100644 --- a/freqtrade/templates/ExamplePredictionModel.py +++ b/freqtrade/templates/ExamplePredictionModel.py @@ -42,7 +42,7 @@ class ExamplePredictionModel(IFreqaiModel): def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: """ - Filter the training data and train a model to it. Train makes heavy use of the datahkitchen + Filter the training data and train a model to it. Train makes heavy use of the datakitchen for storing, saving, loading, and analyzing the data. :params: :unfiltered_dataframe: Full dataframe for the current training period diff --git a/mkdocs.yml b/mkdocs.yml index 64d78363d..18744e0d5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -35,8 +35,8 @@ nav: - Edge Positioning: edge.md - Advanced Strategy: strategy-advanced.md - Advanced Hyperopt: advanced-hyperopt.md - - Sandbox Testing: sandbox-testing.md - Freqai: freqai.md + - Sandbox Testing: sandbox-testing.md - FAQ: faq.md - SQL Cheat-sheet: sql_cheatsheet.md - Strategy migration: strategy_migration.md From c708dd3186a577fdab5ad5afd7a6c9b6b42e718b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 17 May 2022 20:41:42 +0200 Subject: [PATCH 032/518] doc update thanks matthias --- docs/freqai.md | 35 ++++++++++++++++++++++++----------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 730f353c8..df41846a4 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -4,7 +4,7 @@ Freqai is still experimental, and should be used at the user's own discretion. Freqai is a module designed to automate a variety of tasks associated with -training a regressor to predict signals based on input features. +training a predictive model to provide signals based on input features. Among the the features included: @@ -15,10 +15,16 @@ Among the the features included: * Automatic file management for storage of models to be reused during live * Smart and safe data standardization * Cleaning of NaNs from the data set before training and prediction. +* Automated live retraining (still VERY experimental. Proceed with caution.) -TODO: +## General approach -* live is not automated, still some architectural work to be done +The user provides FreqAI with a set of custom indicators (created inside the strategy the same way +a typical Freqtrade strategy is created) as well as a target value (typically some price change into +the future). FreqAI trains a model to predict the target value based on the input of custom indicators. +FreqAI will train and save a new model for each pair in the config whitelist. +Users employ FreqAI to backtest a strategy (emulate reality with retraining a model as new data is +introduced) and run the model live to generate buy and sell signals. ## Background and vocabulary @@ -58,9 +64,7 @@ An example strategy, an example prediction model, and example config can all be the necessary data, Freqai can be executed from these templates with: ```bash -freqtrade backtesting --config config_examples/config_freqai.example.json --strategy -FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freqtrade/templates ---timerange 20220101-220201 +freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freqtrade/templates --timerange 20220101-20220201 ``` ## Configuring the bot @@ -163,12 +167,21 @@ month of data. The freqai training/backtesting module can be executed with the following command: ```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel --timerange 20210501-20210701 +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel CatboostPredictionModel --timerange 20210501-20210701 ``` -where the user needs to have a FreqaiExampleStrategy that fits to the requirements outlined -below. The ExamplePredictionModel is a user built class which lets users design their -own training procedures and data analysis. +If this command has never been executed with the existing config file, then it will train a new model +for each pair, for each backtesting window within the bigger `--timerange`._ + +--- +**NOTE** +Once the training is completed, the user can execute this again with the same config file and +FreqAI will find the trained models and load them instead of spending time training. This is useful +if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user +*wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`. +This way, the user can return to using any model they wish by simply changing the `identifier`. + +--- ### Building a freqai strategy @@ -264,7 +277,7 @@ the user is asking for `labels` that are 24 candles in the future. ### Removing outliers with the Dissimilarity Index -The Dissimilarity Index (DI) aims to quantity the uncertainty associated with each +The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction by the model. To do so, Freqai measures the distance between each training data point and all other training data points: From 89eacf2f47fd02526027400022db7b5c487be23d Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 19 May 2022 17:15:50 +0200 Subject: [PATCH 033/518] Retrain model if FreqAI found a pretrained model but user strategy is not passing the expected features (user has changed the features in the strategy but has passed a the same config[freqai][identifier]). Logger warning output to user. --- freqtrade/freqai/freqai_interface.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index b7c879ff0..6a1d97470 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -92,6 +92,11 @@ class IFreqaiModel(ABC): self.dh.save_data(self.model) else: self.model = self.dh.load_data() + strategy_provided_features = self.dh.find_features(dataframe_train) + if strategy_provided_features != self.dh.training_features_list: + logger.info("User changed input features, retraining model.") + self.model = self.train(dataframe_train, metadata) + self.dh.save_data(self.model) preds, do_preds = self.predict(dataframe_backtest, metadata) From 67eb94c69d88f7b53a1bf9d980faf5dcd4f4b037 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 19 May 2022 17:55:00 +0200 Subject: [PATCH 034/518] download-data will now check if freqai is active in config, and if so will also download data for corr_pairlist --- freqtrade/commands/data_commands.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index a2e2a100a..4588bf67b 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -50,7 +50,13 @@ def start_download_data(args: Dict[str, Any]) -> None: exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False) markets = [p for p, m in exchange.markets.items() if market_is_active(m) or config.get('include_inactive')] - expanded_pairs = expand_pairlist(config['pairs'], markets) + if config.get('freqai') is not None: + assert config['freqai'].get('corr_pairlist'), "No corr_pairlist found in config." + full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist'] + if pair not in config['pairs']] + expanded_pairs = expand_pairlist(full_pairs, markets) + else: + expanded_pairs = expand_pairlist(config['pairs'], markets) # Manual validations of relevant settings if not config['exchange'].get('skip_pair_validation', False): From 1fae6c9ef794a014c3e8f1a692bda8b66b46b960 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 19 May 2022 19:27:38 +0200 Subject: [PATCH 035/518] keep model accessible in memory to avoid loading objects from disk during live/dry --- freqtrade/freqai/data_kitchen.py | 30 +++++++++++++++----- freqtrade/templates/FreqaiExampleStrategy.py | 6 ++-- 2 files changed, 26 insertions(+), 10 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 8ccb95dbe..e35243f6a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -50,8 +50,9 @@ class FreqaiDataKitchen: self.full_target_std: npt.ArrayLike = np.array([]) self.model_path = Path() self.model_filename: str = "" - - if not live: + self.model_dictionary: Dict[Any, Any] = {} + self.live = live + if not self.live: self.full_timerange = self.create_fulltimerange(self.config["timerange"], self.freqai_config["train_period"] ) @@ -88,8 +89,8 @@ class FreqaiDataKitchen: # Save the trained model dump(model, save_path / str(self.model_filename + "_model.joblib")) - self.data["model_path"] = self.model_path - self.data["model_filename"] = self.model_filename + self.data["model_path"] = str(self.model_path) + self.data["model_filename"] = str(self.model_filename) self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns) # store the metadata with open(save_path / str(self.model_filename + "_metadata.json"), "w") as fp: @@ -100,6 +101,9 @@ class FreqaiDataKitchen: save_path / str(self.model_filename + "_trained_df.pkl") ) + if self.live: + self.model_dictionary[self.model_filename] = model + return def load_data(self) -> Any: @@ -108,7 +112,6 @@ class FreqaiDataKitchen: :returns: :model: User trained model which can be inferenced for new predictions """ - model = load(self.model_path / str(self.model_filename + "_model.joblib")) with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) @@ -118,8 +121,20 @@ class FreqaiDataKitchen: self.model_path / str(self.model_filename + "_trained_df.pkl") ) - self.model_path = self.data["model_path"] + self.model_path = Path(self.data["model_path"]) self.model_filename = self.data["model_filename"] + + # try to access model in memory instead of loading object from disk to save time + if self.live and self.model_filename in self.model_dictionary: + model = self.model_dictionary[self.model_filename] + else: + model = load(self.model_path / str(self.model_filename + "_model.joblib")) + + assert model, ( + f"Unable to load model, ensure model exists at " + f"{self.model_path} " + ) + if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: self.pca = pk.load( open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "rb") @@ -682,7 +697,8 @@ class FreqaiDataKitchen: for p in pairs: if metadata['pair'] in p: continue # dont repeat anything from whitelist - corr_dataframes[p] = {} + if p not in corr_dataframes: + corr_dataframes[p] = {} corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, pair=p, timerange=timerange) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index f478dd332..6478ca167 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -59,6 +59,9 @@ class FreqaiExampleStrategy(IStrategy): informative_pairs.append((pair, tf)) return informative_pairs + def bot_start(self): + self.model = CustomModel(self.config) + def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): """ Function designed to automatically generate, name and merge features @@ -141,9 +144,6 @@ class FreqaiExampleStrategy(IStrategy): self.freqai_info = self.config["freqai"] self.pair = metadata['pair'] - # the model is instantiated here - self.model = CustomModel(self.config) - print("Populating indicators...") # the following loops are necessary for building the features From c5ecf941770727908774ba928a96a7a3c1ec32b6 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 19 May 2022 21:15:58 +0200 Subject: [PATCH 036/518] move live retraining to separate thread. --- freqtrade/freqai/freqai_interface.py | 89 ++++++++++++++++---- freqtrade/templates/FreqaiExampleStrategy.py | 2 - 2 files changed, 74 insertions(+), 17 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 6a1d97470..2523cd561 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -1,5 +1,8 @@ +# import contextlib import gc import logging +# import sys +import threading from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, Tuple @@ -16,6 +19,24 @@ from freqtrade.strategy.interface import IStrategy pd.options.mode.chained_assignment = None logger = logging.getLogger(__name__) +# FIXME: suppress stdout for background training +# class DummyFile(object): +# def write(self, x): pass + + +# @contextlib.contextmanager +# def nostdout(): +# save_stdout = sys.stdout +# sys.stdout = DummyFile() +# yield +# sys.stdout = save_stdout + + +def threaded(fn): + def wrapper(*args, **kwargs): + threading.Thread(target=fn, args=args, kwargs=kwargs).start() + return wrapper + class IFreqaiModel(ABC): """ @@ -39,6 +60,8 @@ class IFreqaiModel(ABC): self.current_time = None self.model = None self.predictions = None + self.training_on_separate_thread = False + self.retrain = False def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ @@ -122,25 +145,26 @@ class IFreqaiModel(ABC): training_timerange=self.freqai_info[ 'live_trained_timerange']) - (retrain, - new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ + if not self.training_on_separate_thread: + # this will also prevent other pairs from trying to train simultaneously. + (self.retrain, + new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ 'live_trained_timerange'], - metadata) + metadata) + else: + logger.info("FreqAI training a new model on background thread.") + self.retrain = False - if retrain or not file_exists: - self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) - corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, - metadata) - - unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, - corr_dataframes, - base_dataframes, - metadata) - - self.model = self.train(unfiltered_dataframe, metadata) - self.dh.save_data(self.model) + if self.retrain or not file_exists: + self.training_on_separate_thread = True # acts like a lock + self.retrain_model_on_separate_thread(new_trained_timerange, metadata, strategy) self.model = self.dh.load_data() + + strategy_provided_features = self.dh.find_features(dataframe) + if strategy_provided_features != self.dh.training_features_list: + self.train_model_in_series(new_trained_timerange, metadata, strategy) + preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) @@ -206,3 +230,38 @@ class IFreqaiModel(ABC): else: logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename) return file_exists + + @threaded + def retrain_model_on_separate_thread(self, new_trained_timerange: str, metadata: dict, + strategy: IStrategy): + + # with nostdout(): + self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) + corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, + metadata) + + unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, + corr_dataframes, + base_dataframes, + metadata) + + self.model = self.train(unfiltered_dataframe, metadata) + self.dh.save_data(self.model) + + self.training_on_separate_thread = False + self.retrain = False + + def train_model_in_series(self, new_trained_timerange: str, metadata: dict, + strategy: IStrategy): + + self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) + corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, + metadata) + + unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, + corr_dataframes, + base_dataframes, + metadata) + + self.model = self.train(unfiltered_dataframe, metadata) + self.dh.save_data(self.model) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 6478ca167..c8befebcf 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -144,8 +144,6 @@ class FreqaiExampleStrategy(IStrategy): self.freqai_info = self.config["freqai"] self.pair = metadata['pair'] - print("Populating indicators...") - # the following loops are necessary for building the features # indicated by the user in the configuration file. for tf in self.freqai_info["timeframes"]: From 42d95af829738dbb4efc1f69ba9606c01e811230 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 22 May 2022 17:51:49 +0200 Subject: [PATCH 037/518] Aggregated commit. Adding support vector machine for outlier detection, improve user interface to dry/live, better standardization, fix various other bugs --- config_examples/config_freqai.example.json | 11 +- docs/freqai.md | 10 +- freqtrade/freqai/data_kitchen.py | 380 +++++++++++++----- freqtrade/freqai/freqai_interface.py | 56 ++- .../CatboostPredictionModel.py | 82 ++-- freqtrade/templates/ExamplePredictionModel.py | 159 -------- freqtrade/templates/FreqaiExampleStrategy.py | 6 +- 7 files changed, 404 insertions(+), 300 deletions(-) delete mode 100644 freqtrade/templates/ExamplePredictionModel.py diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 648f36917..a895a7341 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -57,8 +57,8 @@ "train_period": 30, "backtest_period": 7, "identifier": "example", - "live_trained_timerange": "20220330-20220429", - "live_full_backtestrange": "20220302-20220501", + "live_trained_timerange": "", + "live_full_backtestrange": "", "corr_pairlist": [ "BTC/USDT", "ETH/USDT", @@ -68,20 +68,19 @@ "feature_parameters": { "period": 12, "shift": 1, - "drop_features": false, "DI_threshold": 1, "weight_factor": 0, "principal_component_analysis": false, - "remove_outliers": false + "use_SVM_to_remove_outliers": false }, "data_split_parameters": { "test_size": 0.25, "random_state": 1 }, "model_training_parameters": { - "n_estimators": 2000, + "n_estimators": 1000, "random_state": 1, - "learning_rate": 0.02, + "learning_rate": 0.1, "task_type": "CPU" } }, diff --git a/docs/freqai.md b/docs/freqai.md index df41846a4..8a37e7d66 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -331,21 +331,21 @@ Users can reduce the dimensionality of their features by activating the `princip Which will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. -### Removing outliers based on feature statistical distributions +### Removing outliers using a Support Vector Machine (SVM) The user can tell Freqai to remove outlier data points from the training/test data sets by setting: ```json "freqai": { "feature_parameters" : { - "remove_outliers": true + "use_SVM_to_remove_outliers: true } } ``` -Freqai will check the statistical distributions of each feature (or component if the user activated -`principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away -from the mean. +Freqai will train an SVM on the training data (or components if the user activated +`principal_component_analysis`) and remove any data point that it deems to be sit beyond the +feature space. ## Additional information diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index e35243f6a..f589a1c89 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -10,8 +10,9 @@ from typing import Any, Dict, List, Tuple import numpy as np import numpy.typing as npt import pandas as pd -from joblib import dump, load +from joblib import dump, load # , Parallel, delayed # used for auto distribution assignment from pandas import DataFrame +from sklearn import linear_model from sklearn.metrics.pairwise import pairwise_distances from sklearn.model_selection import train_test_split @@ -22,6 +23,9 @@ from freqtrade.resolvers import ExchangeResolver from freqtrade.strategy.interface import IStrategy +# import scipy as spy # used for auto distribution assignment + + SECONDS_IN_DAY = 86400 logger = logging.getLogger(__name__) @@ -52,6 +56,7 @@ class FreqaiDataKitchen: self.model_filename: str = "" self.model_dictionary: Dict[Any, Any] = {} self.live = live + self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: self.full_timerange = self.create_fulltimerange(self.config["timerange"], self.freqai_config["train_period"] @@ -89,6 +94,10 @@ class FreqaiDataKitchen: # Save the trained model dump(model, save_path / str(self.model_filename + "_model.joblib")) + + if self.svm_model is not None: + dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib")) + self.data["model_path"] = str(self.model_path) self.data["model_filename"] = str(self.model_filename) self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns) @@ -104,6 +113,19 @@ class FreqaiDataKitchen: if self.live: self.model_dictionary[self.model_filename] = model + # TODO add a helper function to let user save/load any data they are custom adding. We + # do not want them having to edit the default save/load methods here. Below is an example + # of what we do NOT want. + + # if self.freqai_config['feature_parameters']['determine_statistical_distributions']: + # self.data_dictionary["upper_quantiles"].to_pickle( + # save_path / str(self.model_filename + "_upper_quantiles.pkl") + # ) + + # self.data_dictionary["lower_quantiles"].to_pickle( + # save_path / str(self.model_filename + "_lower_quantiles.pkl") + # ) + return def load_data(self) -> Any: @@ -121,6 +143,19 @@ class FreqaiDataKitchen: self.model_path / str(self.model_filename + "_trained_df.pkl") ) + # TODO add a helper function to let user save/load any data they are custom adding. We + # do not want them having to edit the default save/load methods here. Below is an example + # of what we do NOT want. + + # if self.freqai_config['feature_parameters']['determine_statistical_distributions']: + # self.data_dictionary["upper_quantiles"] = pd.read_pickle( + # self.model_path / str(self.model_filename + "_upper_quantiles.pkl") + # ) + + # self.data_dictionary["lower_quantiles"] = pd.read_pickle( + # self.model_path / str(self.model_filename + "_lower_quantiles.pkl") + # ) + self.model_path = Path(self.data["model_path"]) self.model_filename = self.data["model_filename"] @@ -130,6 +165,10 @@ class FreqaiDataKitchen: else: model = load(self.model_path / str(self.model_filename + "_model.joblib")) + if Path(self.model_path / str(self.model_filename + + "_svm_model.joblib")).resolve().exists(): + self.svm_model = load(self.model_path / str(self.model_filename + "_svm_model.joblib")) + assert model, ( f"Unable to load model, ensure model exists at " f"{self.model_path} " @@ -159,6 +198,12 @@ class FreqaiDataKitchen: else: weights = np.ones(len(filtered_dataframe)) + if self.config["freqai"]["feature_parameters"]["stratify"] > 0: + stratification = np.zeros(len(filtered_dataframe)) + for i in range(1, len(stratification)): + if i % self.config["freqai"]["feature_parameters"]["stratify"] == 0: + stratification[i] = 1 + ( train_features, test_features, @@ -170,6 +215,8 @@ class FreqaiDataKitchen: filtered_dataframe[: filtered_dataframe.shape[0]], labels, weights, + stratify=stratification, + # shuffle=False, **self.config["freqai"]["data_split_parameters"] ) @@ -261,9 +308,9 @@ class FreqaiDataKitchen: return self.data_dictionary - def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: + def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: """ - Standardize all data in the data_dictionary according to the training dataset + Normalize all data in the data_dictionary according to the training dataset :params: :data_dictionary: dictionary containing the cleaned and split training/test data/labels :returns: @@ -297,6 +344,42 @@ class FreqaiDataKitchen: return data_dictionary + def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: + """ + Standardize all data in the data_dictionary according to the training dataset + :params: + :data_dictionary: dictionary containing the cleaned and split training/test data/labels + :returns: + :data_dictionary: updated dictionary with standardized values. + """ + # standardize the data by training stats + train_max = data_dictionary["train_features"].max() + train_min = data_dictionary["train_features"].min() + data_dictionary["train_features"] = 2 * ( + data_dictionary["train_features"] - train_min + ) / (train_max - train_min) - 1 + data_dictionary["test_features"] = 2 * ( + data_dictionary["test_features"] - train_min + ) / (train_max - train_min) - 1 + + train_labels_max = data_dictionary["train_labels"].max() + train_labels_min = data_dictionary["train_labels"].min() + data_dictionary["train_labels"] = 2 * ( + data_dictionary["train_labels"] - train_labels_min + ) / (train_labels_max - train_labels_min) - 1 + data_dictionary["test_labels"] = 2 * ( + data_dictionary["test_labels"] - train_labels_min + ) / (train_labels_max - train_labels_min) - 1 + + for item in train_max.keys(): + self.data[item + "_max"] = train_max[item] + self.data[item + "_min"] = train_min[item] + + self.data["labels_max"] = train_labels_max + self.data["labels_min"] = train_labels_min + + return data_dictionary + def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: """ Standardizes a set of data using the mean and standard deviation from @@ -305,6 +388,20 @@ class FreqaiDataKitchen: :df: Dataframe to be standardized """ + for item in df.keys(): + df[item] = 2 * (df[item] - self.data[item + "_min"]) / (self.data[item + "_max"] - + self.data[item + '_min']) - 1 + + return df + + def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame: + """ + Normalizes a set of data using the mean and standard deviation from + the associated training data. + :params: + :df: Dataframe to be standardized + """ + for item in df.keys(): df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"] @@ -420,6 +517,8 @@ class FreqaiDataKitchen: self.data["n_kept_components"] = n_keep_components self.pca = pca2 + logger.info(f'PCA reduced total features from {n_components} to {n_keep_components}') + if not self.model_path.is_dir(): self.model_path.mkdir(parents=True, exist_ok=True) pk.dump(pca2, open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "wb")) @@ -434,70 +533,53 @@ class FreqaiDataKitchen: return avg_mean_dist - def remove_outliers(self, predict: bool) -> None: - """ - Remove data that looks like an outlier based on the distribution of each - variable. - :params: - :predict: boolean which tells the function if this is prediction data or - training data coming in. - """ - - lower_quantile = self.data_dictionary["train_features"].quantile(0.001) - upper_quantile = self.data_dictionary["train_features"].quantile(0.999) + def use_SVM_to_remove_outliers(self, predict: bool) -> None: if predict: - - df = self.data_dictionary["prediction_features"][ - (self.data_dictionary["prediction_features"] < upper_quantile) - & (self.data_dictionary["prediction_features"] > lower_quantile) - ] - drop_index = pd.isnull(df).any(1) - self.data_dictionary["prediction_features"].fillna(0, inplace=True) - drop_index = ~drop_index - do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) + assert self.svm_model, "No svm model available for outlier removal" + y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"]) + do_predict = np.where(y_pred == -1, 0, y_pred) logger.info( - "remove_outliers() tossed %s predictions", - len(do_predict) - do_predict.sum(), + f'svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions' ) self.do_predict += do_predict self.do_predict -= 1 else: + # use SGDOneClassSVM to increase speed? + self.svm_model = linear_model.SGDOneClassSVM(nu=0.1).fit( + self.data_dictionary["train_features"] + ) + y_pred = self.svm_model.predict(self.data_dictionary["train_features"]) + dropped_points = np.where(y_pred == -1, 0, y_pred) + # keep_index = np.where(y_pred == 1) + self.data_dictionary["train_features"] = self.data_dictionary[ + "train_features"][(y_pred == 1)] + self.data_dictionary["train_labels"] = self.data_dictionary[ + "train_labels"][(y_pred == 1)] + self.data_dictionary["train_weights"] = self.data_dictionary[ + "train_weights"][(y_pred == 1)] - filter_train_df = self.data_dictionary["train_features"][ - (self.data_dictionary["train_features"] < upper_quantile) - & (self.data_dictionary["train_features"] > lower_quantile) - ] - drop_index = pd.isnull(filter_train_df).any(1) - drop_index = drop_index.replace(True, 1).replace(False, 0) - self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ - (drop_index == 0) - ] - self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][ - (drop_index == 0) - ] - self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][ - (drop_index == 0) - ] + logger.info( + f'svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}' + f' train points from {len(y_pred)}' + ) - # do the same for the test data - filter_test_df = self.data_dictionary["test_features"][ - (self.data_dictionary["test_features"] < upper_quantile) - & (self.data_dictionary["test_features"] > lower_quantile) - ] - drop_index = pd.isnull(filter_test_df).any(1) - drop_index = drop_index.replace(True, 1).replace(False, 0) - self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][ - (drop_index == 0) - ] - self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ - (drop_index == 0) - ] - self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ - (drop_index == 0) - ] + # same for test data + y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) + dropped_points = np.where(y_pred == -1, 0, y_pred) + self.data_dictionary["test_features"] = self.data_dictionary[ + "test_features"][(y_pred == 1)] + self.data_dictionary["test_labels"] = self.data_dictionary[ + "test_labels"][(y_pred == 1)] + self.data_dictionary["test_weights"] = self.data_dictionary[ + "test_weights"][(y_pred == 1)] + + logger.info( + f'svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}' + f' test points from {len(y_pred)}' + ) return @@ -507,32 +589,6 @@ class FreqaiDataKitchen: assert features, ("Could not find any features!") return features - # def build_feature_list(self, config: dict, metadata: dict) -> list: - # """ - # SUPERCEDED BY self.find_features() - # Build the list of features that will be used to filter - # the full dataframe. Feature list is construced from the - # user configuration file. - # :params: - # :config: Canonical freqtrade config file containing all - # user defined input in config['freqai] dictionary. - # """ - # features = [] - # for tf in config["freqai"]["timeframes"]: - # for ft in config["freqai"]["base_features"]: - # for n in range(config["freqai"]["feature_parameters"]["shift"] + 1): - # shift = "" - # if n > 0: - # shift = "_shift-" + str(n) - # features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf) - # for p in config["freqai"]["corr_pairlist"]: - # if metadata['pair'] in p: - # continue # avoid duplicate features - # features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) - - # # logger.info("number of features %s", len(features)) - # return features - def check_if_pred_in_training_spaces(self) -> None: """ Compares the distance from each prediction point to each training data @@ -568,7 +624,7 @@ class FreqaiDataKitchen: training than older data. """ - weights = np.zeros_like(num_weights) + weights = np.zeros(num_weights) for i in range(1, len(weights)): weights[len(weights) - i] = np.exp( -i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights) @@ -638,19 +694,23 @@ class FreqaiDataKitchen: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - trained_timerange = TimeRange.parse_timerange(training_timerange) + if training_timerange: # user passed no live_trained_timerange in config + trained_timerange = TimeRange.parse_timerange(training_timerange) + elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY + trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + retrain = elapsed_time > self.freqai_config['backtest_period'] + else: + trained_timerange = TimeRange.parse_timerange("20000101-20000201") + trained_timerange.startts = int(time - self.freqai_config['train_period'] * + SECONDS_IN_DAY) + trained_timerange.stopts = int(time) + retrain = True - elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY - - trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY - trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY start = datetime.datetime.utcfromtimestamp(trained_timerange.startts) stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts) - new_trained_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") - retrain = elapsed_time > self.freqai_config['backtest_period'] - if retrain: coin, _ = metadata['pair'].split("/") # set the new model_path @@ -738,3 +798,141 @@ class FreqaiDataKitchen: def np_encoder(self, object): if isinstance(object, np.generic): return object.item() + + # Functions containing useful data manpulation examples. but not actively in use. + + # def build_feature_list(self, config: dict, metadata: dict) -> list: + # """ + # SUPERCEDED BY self.find_features() + # Build the list of features that will be used to filter + # the full dataframe. Feature list is construced from the + # user configuration file. + # :params: + # :config: Canonical freqtrade config file containing all + # user defined input in config['freqai] dictionary. + # """ + # features = [] + # for tf in config["freqai"]["timeframes"]: + # for ft in config["freqai"]["base_features"]: + # for n in range(config["freqai"]["feature_parameters"]["shift"] + 1): + # shift = "" + # if n > 0: + # shift = "_shift-" + str(n) + # features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf) + # for p in config["freqai"]["corr_pairlist"]: + # if metadata['pair'] in p: + # continue # avoid duplicate features + # features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf) + + # # logger.info("number of features %s", len(features)) + # return features + + # Possibly phasing these outlier removal methods below out in favor of + # use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance). + # But these have good data manipulation examples, so keep them commented here for now. + + # def determine_statistical_distributions(self) -> None: + # from fitter import Fitter + + # logger.info('Determining best model for all features, may take some time') + + # def compute_quantiles(ft): + # f = Fitter(self.data_dictionary["train_features"][ft], + # distributions=['gamma', 'cauchy', 'laplace', + # 'beta', 'uniform', 'lognorm']) + # f.fit() + # # f.summary() + # dist = list(f.get_best().items())[0][0] + # params = f.get_best()[dist] + # upper_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.999, **params) + # lower_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.001, **params) + + # return ft, upper_q, lower_q, dist + + # quantiles_tuple = Parallel(n_jobs=-1)( + # delayed(compute_quantiles)(ft) for ft in self.data_dictionary[ + # 'train_features'].columns) + + # df = pd.DataFrame(quantiles_tuple, columns=['features', 'upper_quantiles', + # 'lower_quantiles', 'dist']) + # self.data_dictionary['upper_quantiles'] = df['upper_quantiles'] + # self.data_dictionary['lower_quantiles'] = df['lower_quantiles'] + + # return + + # def remove_outliers(self, predict: bool) -> None: + # """ + # Remove data that looks like an outlier based on the distribution of each + # variable. + # :params: + # :predict: boolean which tells the function if this is prediction data or + # training data coming in. + # """ + + # lower_quantile = self.data_dictionary["lower_quantiles"].to_numpy() + # upper_quantile = self.data_dictionary["upper_quantiles"].to_numpy() + + # if predict: + + # df = self.data_dictionary["prediction_features"][ + # (self.data_dictionary["prediction_features"] < upper_quantile) + # & (self.data_dictionary["prediction_features"] > lower_quantile) + # ] + # drop_index = pd.isnull(df).any(1) + # self.data_dictionary["prediction_features"].fillna(0, inplace=True) + # drop_index = ~drop_index + # do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) + + # logger.info( + # "remove_outliers() tossed %s predictions", + # len(do_predict) - do_predict.sum(), + # ) + # self.do_predict += do_predict + # self.do_predict -= 1 + + # else: + + # filter_train_df = self.data_dictionary["train_features"][ + # (self.data_dictionary["train_features"] < upper_quantile) + # & (self.data_dictionary["train_features"] > lower_quantile) + # ] + # drop_index = pd.isnull(filter_train_df).any(1) + # drop_index = drop_index.replace(True, 1).replace(False, 0) + # self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ + # (drop_index == 0) + # ] + # self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][ + # (drop_index == 0) + # ] + # self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][ + # (drop_index == 0) + # ] + + # logger.info( + # f'remove_outliers() tossed {drop_index.sum()}' + # f' training points from {len(filter_train_df)}' + # ) + + # # do the same for the test data + # filter_test_df = self.data_dictionary["test_features"][ + # (self.data_dictionary["test_features"] < upper_quantile) + # & (self.data_dictionary["test_features"] > lower_quantile) + # ] + # drop_index = pd.isnull(filter_test_df).any(1) + # drop_index = drop_index.replace(True, 1).replace(False, 0) + # self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][ + # (drop_index == 0) + # ] + # self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ + # (drop_index == 0) + # ] + # self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ + # (drop_index == 0) + # ] + + # logger.info( + # f'remove_outliers() tossed {drop_index.sum()}' + # f' test points from {len(filter_test_df)}' + # ) + + # return diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 2523cd561..f1dd5550a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -62,6 +62,7 @@ class IFreqaiModel(ABC): self.predictions = None self.training_on_separate_thread = False self.retrain = False + self.first = True def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ @@ -80,12 +81,12 @@ class IFreqaiModel(ABC): :metadata: pair metadata coming from strategy. """ - live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) + self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) self.pair = metadata["pair"] - self.dh = FreqaiDataKitchen(self.config, dataframe, live) + self.dh = FreqaiDataKitchen(self.config, dataframe, self.live) - if live: + if self.live: # logger.info('testing live') self.start_live(dataframe, metadata, strategy) @@ -115,11 +116,12 @@ class IFreqaiModel(ABC): self.dh.save_data(self.model) else: self.model = self.dh.load_data() - strategy_provided_features = self.dh.find_features(dataframe_train) - if strategy_provided_features != self.dh.training_features_list: - logger.info("User changed input features, retraining model.") - self.model = self.train(dataframe_train, metadata) - self.dh.save_data(self.model) + # strategy_provided_features = self.dh.find_features(dataframe_train) + # # TOFIX doesnt work with PCA + # if strategy_provided_features != self.dh.training_features_list: + # logger.info("User changed input features, retraining model.") + # self.model = self.train(dataframe_train, metadata) + # self.dh.save_data(self.model) preds, do_preds = self.predict(dataframe_backtest, metadata) @@ -148,7 +150,7 @@ class IFreqaiModel(ABC): if not self.training_on_separate_thread: # this will also prevent other pairs from trying to train simultaneously. (self.retrain, - new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ + self.new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ 'live_trained_timerange'], metadata) else: @@ -156,14 +158,19 @@ class IFreqaiModel(ABC): self.retrain = False if self.retrain or not file_exists: - self.training_on_separate_thread = True # acts like a lock - self.retrain_model_on_separate_thread(new_trained_timerange, metadata, strategy) + if self.first: + self.train_model_in_series(self.new_trained_timerange, metadata, strategy) + self.first = False + else: + self.training_on_separate_thread = True # acts like a lock + self.retrain_model_on_separate_thread(self.new_trained_timerange, + metadata, strategy) self.model = self.dh.load_data() strategy_provided_features = self.dh.find_features(dataframe) if strategy_provided_features != self.dh.training_features_list: - self.train_model_in_series(new_trained_timerange, metadata, strategy) + self.train_model_in_series(self.new_trained_timerange, metadata, strategy) preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) @@ -215,12 +222,36 @@ class IFreqaiModel(ABC): data (NaNs) or felt uncertain about data (PCA and DI index) """ + @abstractmethod + def data_cleaning_train(self) -> None: + """ + User can add data analysis and cleaning here. + Any function inside this method should drop training data points from the filtered_dataframe + based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example + of how outlier data points are dropped from the dataframe used for training. + """ + + @abstractmethod + def data_cleaning_predict(self) -> None: + """ + User can add data analysis and cleaning here. + These functions each modify self.dh.do_predict, which is a dataframe with equal length + to the number of candles coming from and returning to the strategy. Inside do_predict, + 1 allows prediction and < 0 signals to the strategy that the model is not confident in + the prediction. + See FreqaiDataKitchen::remove_outliers() for an example + of how the do_predict vector is modified. do_predict is ultimately passed back to strategy + for buy signals. + """ + def model_exists(self, pair: str, training_timerange: str) -> bool: """ Given a pair and path, check if a model already exists :param pair: pair e.g. BTC/USD :param path: path to model """ + if self.live and training_timerange is None: + return False coin, _ = pair.split("/") self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib")) @@ -265,3 +296,4 @@ class IFreqaiModel(ABC): self.model = self.train(unfiltered_dataframe, metadata) self.dh.save_data(self.model) + self.retrain = False diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index e2ba6bd29..8550f3f15 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -29,7 +29,7 @@ class CatboostPredictionModel(IFreqaiModel): dataframe["close"] .shift(-self.feature_parameters["period"]) .rolling(self.feature_parameters["period"]) - .max() + .mean() / dataframe["close"] - 1 ) @@ -68,15 +68,11 @@ class CatboostPredictionModel(IFreqaiModel): # standardize all data based on train_dataset only data_dictionary = self.dh.standardize_data(data_dictionary) - # optional additional data cleaning - if self.feature_parameters["principal_component_analysis"]: - self.dh.principal_component_analysis() - if self.feature_parameters["remove_outliers"]: - self.dh.remove_outliers(predict=False) - if self.feature_parameters["DI_threshold"]: - self.dh.data["avg_mean_dist"] = self.dh.compute_distances() + # optional additional data cleaning/analysis + self.data_cleaning_train() - logger.info("length of train data %s", len(data_dictionary["train_features"])) + logger.info(f'Training model on {len(self.dh.training_features_list)} features') + logger.info(f'Training model on {len(data_dictionary["train_features"])} data points') model = self.fit(data_dictionary) @@ -86,9 +82,7 @@ class CatboostPredictionModel(IFreqaiModel): def fit(self, data_dictionary: Dict) -> Any: """ - Most regressors use the same function names and arguments e.g. user - can drop in LGBMRegressor in place of CatBoostRegressor and all data - management will be properly handled by Freqai. + User sets up the training and test data to fit their desired model here :params: :data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. @@ -133,7 +127,51 @@ class CatboostPredictionModel(IFreqaiModel): filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) self.dh.data_dictionary["prediction_features"] = filtered_dataframe - # optional additional data cleaning + # optional additional data cleaning/analysis + self.data_cleaning_predict(filtered_dataframe) + + predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) + + # compute the non-standardized predictions + self.dh.predictions = (predictions + 1) * (self.dh.data["labels_max"] - + self.dh.data["labels_min"]) / 2 + self.dh.data[ + "labels_min"] + + # logger.info("--------------------Finished prediction--------------------") + + return (self.dh.predictions, self.dh.do_predict) + + def data_cleaning_train(self) -> None: + """ + User can add data analysis and cleaning here. + Any function inside this method should drop training data points from the filtered_dataframe + based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example + of how outlier data points are dropped from the dataframe used for training. + """ + if self.feature_parameters["principal_component_analysis"]: + self.dh.principal_component_analysis() + + # if self.feature_parameters["determine_statistical_distributions"]: + # self.dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # self.dh.remove_outliers(predict=False) + + if self.feature_parameters["use_SVM_to_remove_outliers"]: + self.dh.use_SVM_to_remove_outliers(predict=False) + if self.feature_parameters["DI_threshold"]: + self.dh.data["avg_mean_dist"] = self.dh.compute_distances() + + def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None: + """ + User can add data analysis and cleaning here. + These functions each modify self.dh.do_predict, which is a dataframe with equal length + to the number of candles coming from and returning to the strategy. Inside do_predict, + 1 allows prediction and < 0 signals to the strategy that the model is not confident in + the prediction. + See FreqaiDataKitchen::remove_outliers() for an example + of how the do_predict vector is modified. do_predict is ultimately passed back to strategy + for buy signals. + """ if self.feature_parameters["principal_component_analysis"]: pca_components = self.dh.pca.transform(filtered_dataframe) self.dh.data_dictionary["prediction_features"] = pd.DataFrame( @@ -142,17 +180,13 @@ class CatboostPredictionModel(IFreqaiModel): index=filtered_dataframe.index, ) - if self.feature_parameters["remove_outliers"]: - self.dh.remove_outliers(predict=True) # creates dropped index + # if self.feature_parameters["determine_statistical_distributions"]: + # self.dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # self.dh.remove_outliers(predict=True) # creates dropped index + + if self.feature_parameters["use_SVM_to_remove_outliers"]: + self.dh.use_SVM_to_remove_outliers(predict=True) if self.feature_parameters["DI_threshold"]: self.dh.check_if_pred_in_training_spaces() # sets do_predict - - predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) - - # compute the non-standardized predictions - self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] - - # logger.info("--------------------Finished prediction--------------------") - - return (self.dh.predictions, self.dh.do_predict) diff --git a/freqtrade/templates/ExamplePredictionModel.py b/freqtrade/templates/ExamplePredictionModel.py deleted file mode 100644 index 3d2b7a808..000000000 --- a/freqtrade/templates/ExamplePredictionModel.py +++ /dev/null @@ -1,159 +0,0 @@ -import logging -from typing import Any, Dict, Tuple - -import pandas as pd -from catboost import CatBoostRegressor, Pool -from pandas import DataFrame - -from freqtrade.freqai.freqai_interface import IFreqaiModel - - -logger = logging.getLogger(__name__) - - -class ExamplePredictionModel(IFreqaiModel): - """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. - """ - - def make_labels(self, dataframe: DataFrame) -> DataFrame: - """ - User defines the labels here (target values). - :params: - :dataframe: the full dataframe for the present training period - """ - - dataframe["s"] = ( - dataframe["close"] - .shift(-self.feature_parameters["period"]) - .rolling(self.feature_parameters["period"]) - .max() - / dataframe["close"] - - 1 - ) - self.dh.data["s_mean"] = dataframe["s"].mean() - self.dh.data["s_std"] = dataframe["s"].std() - - # logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) - - return dataframe["s"] - - def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: - """ - Filter the training data and train a model to it. Train makes heavy use of the datakitchen - for storing, saving, loading, and analyzing the data. - :params: - :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. - :returns: - :model: Trained model which can be used to inference (self.predict) - """ - logger.info("--------------------Starting training--------------------") - - # create the full feature list based on user config info - self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata) - unfiltered_labels = self.make_labels(unfiltered_dataframe) - - # filter the features requested by user in the configuration file and elegantly handle NaNs - features_filtered, labels_filtered = self.dh.filter_features( - unfiltered_dataframe, - self.dh.training_features_list, - unfiltered_labels, - training_filter=True, - ) - - # split data into train/test data. - data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered) - # standardize all data based on train_dataset only - data_dictionary = self.dh.standardize_data(data_dictionary) - - # optional additional data cleaning - if self.feature_parameters["principal_component_analysis"]: - self.dh.principal_component_analysis() - if self.feature_parameters["remove_outliers"]: - self.dh.remove_outliers(predict=False) - if self.feature_parameters["DI_threshold"]: - self.dh.data["avg_mean_dist"] = self.dh.compute_distances() - - logger.info("length of train data %s", len(data_dictionary["train_features"])) - - model = self.fit(data_dictionary) - - logger.info(f'--------------------done training {metadata["pair"]}--------------------') - - return model - - def fit(self, data_dictionary: Dict) -> Any: - """ - Most regressors use the same function names and arguments e.g. user - can drop in LGBMRegressor in place of CatBoostRegressor and all data - management will be properly handled by Freqai. - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. - """ - - train_data = Pool( - data=data_dictionary["train_features"], - label=data_dictionary["train_labels"], - weight=data_dictionary["train_weights"], - ) - - test_data = Pool( - data=data_dictionary["test_features"], - label=data_dictionary["test_labels"], - weight=data_dictionary["test_weights"], - ) - - model = CatBoostRegressor( - verbose=100, early_stopping_rounds=400, **self.model_training_parameters - ) - model.fit(X=train_data, eval_set=test_data) - - return model - - def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, - DataFrame]: - """ - Filter the prediction features data and predict with it. - :param: unfiltered_dataframe: Full dataframe for the current backtest period. - :return: - :predictions: np.array of predictions - :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove - data (NaNs) or felt uncertain about data (PCA and DI index) - """ - - # logger.info("--------------------Starting prediction--------------------") - - original_feature_list = self.dh.build_feature_list(self.config, metadata) - filtered_dataframe, _ = self.dh.filter_features( - unfiltered_dataframe, original_feature_list, training_filter=False - ) - filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) - self.dh.data_dictionary["prediction_features"] = filtered_dataframe - - # optional additional data cleaning - if self.feature_parameters["principal_component_analysis"]: - pca_components = self.dh.pca.transform(filtered_dataframe) - self.dh.data_dictionary["prediction_features"] = pd.DataFrame( - data=pca_components, - columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])], - index=filtered_dataframe.index, - ) - - if self.feature_parameters["remove_outliers"]: - self.dh.remove_outliers(predict=True) # creates dropped index - - if self.feature_parameters["DI_threshold"]: - self.dh.check_if_pred_in_training_spaces() # sets do_predict - - predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) - - # compute the non-standardized predictions - self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"] - - # logger.info("--------------------Finished prediction--------------------") - - return (self.dh.predictions, self.dh.do_predict) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index c8befebcf..a76ea2303 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -166,8 +166,8 @@ class FreqaiExampleStrategy(IStrategy): dataframe["target_std"], ) = self.model.bridge.start(dataframe, metadata, self) - dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5 - dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5 + dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.5 + dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1 return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: @@ -183,7 +183,7 @@ class FreqaiExampleStrategy(IStrategy): def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: sell_conditions = [ - (dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1) + (dataframe["do_predict"] <= 0) ] if sell_conditions: dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1 From af0cc21af919a503ce1a7fd2854ca2ce50935fca Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 00:06:26 +0200 Subject: [PATCH 038/518] Enable hourly/minute retraining in live/dry. Suppress catboost folder output. Update config + constants + docs to reflect updates. --- config_examples/config_freqai.example.json | 3 +- docs/freqai.md | 19 ++++++- freqtrade/constants.py | 4 +- freqtrade/freqai/data_kitchen.py | 54 ++++++++++--------- freqtrade/freqai/freqai_interface.py | 20 ++++--- .../CatboostPredictionModel.py | 1 + 6 files changed, 66 insertions(+), 35 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index a895a7341..ed3782775 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -71,7 +71,8 @@ "DI_threshold": 1, "weight_factor": 0, "principal_component_analysis": false, - "use_SVM_to_remove_outliers": false + "use_SVM_to_remove_outliers": false, + "stratify": 0 }, "data_split_parameters": { "test_size": 0.25, diff --git a/docs/freqai.md b/docs/freqai.md index 8a37e7d66..606b88912 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -151,7 +151,8 @@ no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_ Users define the backtesting timerange with the typical `--timerange` parameter in the user configuration file. `train_period` is the duration of the sliding training window, while -`backtest_period` is the sliding backtesting window, both in number of days. In the present example, +`backtest_period` is the sliding backtesting window, both in number of days (backtest_period can be +a float to indicate sub daily retraining in live/dry mode). In the present example, the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days. This means that if the user sets `--timerange 20210501-20210701`, Freqai will train 8 separate models (because the full range comprises 8 weeks), @@ -347,6 +348,22 @@ Freqai will train an SVM on the training data (or components if the user activat `principal_component_analysis`) and remove any data point that it deems to be sit beyond the feature space. +## Stratifying the data + +The user can stratify the training/testing data using: + +```json + "freqai": { + "feature_parameters" : { + "stratify": 3 + } + } +``` + +which will split the data chronolocially so that every X data points is a testing data point. In the +present example, the user is asking for every third data point in the dataframe to be used for +testing, the other points are used for training. + ## Additional information ### Feature standardization diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 686991e2c..05581cc3a 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -438,7 +438,7 @@ CONF_SCHEMA = { "properties": { "timeframes": {"type": "list"}, "train_period": {"type": "integer", "default": 0}, - "backtest_period": {"type": "integer", "default": 7}, + "backtest_period": {"type": "float", "default": 7}, "identifier": {"type": "str", "default": "example"}, "live_trained_timerange": {"type": "str"}, "live_full_backtestrange": {"type": "str"}, @@ -451,7 +451,7 @@ CONF_SCHEMA = { "DI_threshold": {"type": "integer", "default": 0}, "weight_factor": {"type": "number", "default": 0}, "principal_component_analysis": {"type": "boolean", "default": False}, - "remove_outliers": {"type": "boolean", "default": False}, + "use_SVM_to_remove_outliers": {"type": "boolean", "default": False}, }, }, "data_split_parameters": { diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index f589a1c89..e09a2d0d5 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -689,50 +689,58 @@ class FreqaiDataKitchen: return full_timerange - def check_if_new_training_required(self, training_timerange: str, - metadata: dict) -> Tuple[bool, str]: + def check_if_new_training_required(self, trained_timerange: TimeRange, + metadata: dict, + timestamp: int = 0) -> Tuple[bool, TimeRange, int]: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - if training_timerange: # user passed no live_trained_timerange in config - trained_timerange = TimeRange.parse_timerange(training_timerange) + if trained_timerange.startts != 0: + # trained_timerange = TimeRange.parse_timerange(training_timerange) + # keep hour available incase user wants to train multiple times per day + # training_timerange is a str for day range only, so we add the extra hours + # original_stop_seconds = trained_timerange.stopts + # trained_timerange.stopts += int(timestamp - original_stop_seconds) + # trained_timerange.startts += int(timestamp - original_stop_seconds) elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY - trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY - trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY retrain = elapsed_time > self.freqai_config['backtest_period'] - else: - trained_timerange = TimeRange.parse_timerange("20000101-20000201") + if retrain: + trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + else: # user passed no live_trained_timerange in config + trained_timerange = TimeRange.parse_timerange("20000101-20000201") # arbitrary date trained_timerange.startts = int(time - self.freqai_config['train_period'] * SECONDS_IN_DAY) trained_timerange.stopts = int(time) retrain = True - start = datetime.datetime.utcfromtimestamp(trained_timerange.startts) - stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts) - new_trained_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") + timestamp = trained_timerange.stopts + # start = datetime.datetime.utcfromtimestamp(trained_timerange.startts) + # stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts) + # new_trained_timerange_str = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") if retrain: coin, _ = metadata['pair'].split("/") # set the new model_path self.model_path = Path(self.full_path / str("sub-train" + "-" + - str(new_trained_timerange))) + str(timestamp))) - self.model_filename = "cb_" + coin.lower() + "_" + new_trained_timerange + self.model_filename = "cb_" + coin.lower() + "_" + str(timestamp) # this is not persistent at the moment TODO - self.freqai_config['live_trained_timerange'] = new_trained_timerange + self.freqai_config['live_trained_timerange'] = str(timestamp) # enables persistence, but not fully implemented into save/load data yer - self.data['live_trained_timerange'] = new_trained_timerange + self.data['live_trained_timerange'] = str(timestamp) - return retrain, new_trained_timerange + return retrain, trained_timerange, timestamp - def download_new_data_for_retraining(self, new_timerange: str, metadata: dict) -> None: + def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config, validate=False) pairs = self.freqai_config['corr_pairlist'] if metadata['pair'] not in pairs: pairs += metadata['pair'] # dont include pair twice - timerange = TimeRange.parse_timerange(new_timerange) + # timerange = TimeRange.parse_timerange(new_timerange) refresh_backtest_ohlcv_data( exchange, pairs=pairs, timeframes=self.freqai_config['timeframes'], @@ -743,12 +751,12 @@ class FreqaiDataKitchen: prepend=self.config.get('prepend_data', False) ) - def load_pairs_histories(self, new_timerange: str, metadata: dict) -> Tuple[Dict[Any, Any], - DataFrame]: + def load_pairs_histories(self, timerange: TimeRange, metadata: dict) -> Tuple[Dict[Any, Any], + DataFrame]: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} pairs = self.freqai_config['corr_pairlist'] # + [metadata['pair']] - timerange = TimeRange.parse_timerange(new_timerange) + # timerange = TimeRange.parse_timerange(new_timerange) for tf in self.freqai_config['timeframes']: base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'], @@ -763,10 +771,6 @@ class FreqaiDataKitchen: timeframe=tf, pair=p, timerange=timerange) - # base_dataframe = [dataframe for key, dataframe in corr_dataframes.items() - # if metadata['pair'] in key] - - # [0] indexes the lowest tf for the basepair return corr_dataframes, base_dataframes def use_strategy_to_populate_indicators(self, strategy: IStrategy, diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index f1dd5550a..6e597531b 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -11,6 +11,7 @@ import numpy.typing as npt import pandas as pd from pandas import DataFrame +from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.strategy.interface import IStrategy @@ -63,6 +64,12 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False self.first = True + self.timestamp = 0 + if self.freqai_info['live_trained_timerange']: + self.new_trained_timerange = TimeRange.parse_timerange( + self.freqai_info['live_trained_timerange']) + else: + self.new_trained_timerange = TimeRange() def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ @@ -150,9 +157,10 @@ class IFreqaiModel(ABC): if not self.training_on_separate_thread: # this will also prevent other pairs from trying to train simultaneously. (self.retrain, - self.new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[ - 'live_trained_timerange'], - metadata) + self.new_trained_timerange, + self.timestamp) = self.dh.check_if_new_training_required(self.new_trained_timerange, + metadata, + timestamp=self.timestamp) else: logger.info("FreqAI training a new model on background thread.") self.retrain = False @@ -250,7 +258,7 @@ class IFreqaiModel(ABC): :param pair: pair e.g. BTC/USD :param path: path to model """ - if self.live and training_timerange is None: + if self.live and training_timerange == "": return False coin, _ = pair.split("/") self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange @@ -263,7 +271,7 @@ class IFreqaiModel(ABC): return file_exists @threaded - def retrain_model_on_separate_thread(self, new_trained_timerange: str, metadata: dict, + def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict, strategy: IStrategy): # with nostdout(): @@ -282,7 +290,7 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False - def train_model_in_series(self, new_trained_timerange: str, metadata: dict, + def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict, strategy: IStrategy): self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 8550f3f15..3dad6add6 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -101,6 +101,7 @@ class CatboostPredictionModel(IFreqaiModel): ) model = CatBoostRegressor( + allow_writing_files=False, verbose=100, early_stopping_rounds=400, **self.model_training_parameters ) model.fit(X=train_data, eval_set=test_data) From 3587bd82e16b2781e1f12f926114dfde88927cee Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 00:10:36 +0200 Subject: [PATCH 039/518] cleanup superceded code --- freqtrade/freqai/data_kitchen.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index e09a2d0d5..da0d7e4df 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -696,28 +696,19 @@ class FreqaiDataKitchen: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() if trained_timerange.startts != 0: - # trained_timerange = TimeRange.parse_timerange(training_timerange) - # keep hour available incase user wants to train multiple times per day - # training_timerange is a str for day range only, so we add the extra hours - # original_stop_seconds = trained_timerange.stopts - # trained_timerange.stopts += int(timestamp - original_stop_seconds) - # trained_timerange.startts += int(timestamp - original_stop_seconds) elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY retrain = elapsed_time > self.freqai_config['backtest_period'] if retrain: trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY else: # user passed no live_trained_timerange in config - trained_timerange = TimeRange.parse_timerange("20000101-20000201") # arbitrary date + trained_timerange = TimeRange() trained_timerange.startts = int(time - self.freqai_config['train_period'] * SECONDS_IN_DAY) trained_timerange.stopts = int(time) retrain = True timestamp = trained_timerange.stopts - # start = datetime.datetime.utcfromtimestamp(trained_timerange.startts) - # stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts) - # new_trained_timerange_str = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") if retrain: coin, _ = metadata['pair'].split("/") From ee3cdd0ffec87bc89ec0700196609998ae2a1d21 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 09:55:58 +0200 Subject: [PATCH 040/518] more cleanup --- freqtrade/freqai/data_kitchen.py | 15 ++++++--------- freqtrade/freqai/freqai_interface.py | 8 +++----- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index da0d7e4df..3347bbe97 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -690,8 +690,7 @@ class FreqaiDataKitchen: return full_timerange def check_if_new_training_required(self, trained_timerange: TimeRange, - metadata: dict, - timestamp: int = 0) -> Tuple[bool, TimeRange, int]: + metadata: dict) -> Tuple[bool, TimeRange]: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() @@ -708,21 +707,19 @@ class FreqaiDataKitchen: trained_timerange.stopts = int(time) retrain = True - timestamp = trained_timerange.stopts - if retrain: coin, _ = metadata['pair'].split("/") # set the new model_path self.model_path = Path(self.full_path / str("sub-train" + "-" + - str(timestamp))) + str(int(trained_timerange.stopts)))) - self.model_filename = "cb_" + coin.lower() + "_" + str(timestamp) + self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) # this is not persistent at the moment TODO - self.freqai_config['live_trained_timerange'] = str(timestamp) + self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) # enables persistence, but not fully implemented into save/load data yer - self.data['live_trained_timerange'] = str(timestamp) + self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) - return retrain, trained_timerange, timestamp + return retrain, trained_timerange def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 6e597531b..3ff98b8ee 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -64,7 +64,6 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False self.first = True - self.timestamp = 0 if self.freqai_info['live_trained_timerange']: self.new_trained_timerange = TimeRange.parse_timerange( self.freqai_info['live_trained_timerange']) @@ -157,10 +156,9 @@ class IFreqaiModel(ABC): if not self.training_on_separate_thread: # this will also prevent other pairs from trying to train simultaneously. (self.retrain, - self.new_trained_timerange, - self.timestamp) = self.dh.check_if_new_training_required(self.new_trained_timerange, - metadata, - timestamp=self.timestamp) + self.new_trained_timerange) = self.dh.check_if_new_training_required( + self.new_trained_timerange, + metadata) else: logger.info("FreqAI training a new model on background thread.") self.retrain = False From dede12864899094dc9f487fc41edbf96d069a7b0 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 10:15:59 +0200 Subject: [PATCH 041/518] set process_only_new_candles to true in example strat --- freqtrade/templates/FreqaiExampleStrategy.py | 1 + 1 file changed, 1 insertion(+) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index a76ea2303..690532c10 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -42,6 +42,7 @@ class FreqaiExampleStrategy(IStrategy): }, } + process_only_new_candles = False stoploss = -0.05 use_sell_signal = True startup_candle_count: int = 300 From e1c068ca662ce7f35160659edbf8502f27083a6d Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 12:07:09 +0200 Subject: [PATCH 042/518] add config asserts, use .get method with default values for optional functionality, move data_cleaning_* to freqai_interface (away from user custom pred model) since it is controlled by config params. --- freqtrade/freqai/data_kitchen.py | 105 ++++++++++----- freqtrade/freqai/freqai_interface.py | 127 ++++++++++++------ .../CatboostPredictionModel.py | 21 ++- freqtrade/optimize/backtesting.py | 2 +- 4 files changed, 162 insertions(+), 93 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 3347bbe97..148efd5dd 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -43,6 +43,7 @@ class FreqaiDataKitchen: self.data: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {} self.config = config + self.assert_config(self.config, live) self.freqai_config = config["freqai"] self.predictions: npt.ArrayLike = np.array([]) self.do_predict: npt.ArrayLike = np.array([]) @@ -59,7 +60,7 @@ class FreqaiDataKitchen: self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: self.full_timerange = self.create_fulltimerange(self.config["timerange"], - self.freqai_config["train_period"] + self.freqai_config.get("train_period") ) (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange( @@ -68,14 +69,33 @@ class FreqaiDataKitchen: config["freqai"]["backtest_period"], ) + def assert_config(self, config: Dict[str, Any], live: bool) -> None: + assert config.get('freqai'), "No Freqai parameters found in config file." + assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in" + "config file.") + assert type(config.get('freqai', {}) + .get('train_period')) is int, ('Can only train on full day period.' + 'No fractional days permitted.') + assert config.get('freqai', {}).get('backtest_period'), ("No Freqai backtest_period found" + "in config file.") + if not live: + assert type(config.get('freqai', {}) + .get('backtest_period')) is int, ('Can only backtest on full day' + 'backtest_period. Only live/dry mode' + 'allows fractions of days') + assert config.get('freqai', {}).get('identifier'), ("No Freqai identifier found in config" + "file.") + assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" + "found in config file.") + def set_paths(self) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / - str(self.freqai_config['live_full_backtestrange'] + - self.freqai_config['identifier'])) + str(self.freqai_config.get('live_full_backtestrange') + + self.freqai_config.get('identifier'))) self.model_path = Path(self.full_path / str("sub-train" + "-" + - str(self.freqai_config['live_trained_timerange']))) + str(self.freqai_config.get('live_trained_timerange')))) return @@ -117,7 +137,7 @@ class FreqaiDataKitchen: # do not want them having to edit the default save/load methods here. Below is an example # of what we do NOT want. - # if self.freqai_config['feature_parameters']['determine_statistical_distributions']: + # if self.freqai_config.get('feature_parameters','determine_statistical_distributions'): # self.data_dictionary["upper_quantiles"].to_pickle( # save_path / str(self.model_filename + "_upper_quantiles.pkl") # ) @@ -147,7 +167,7 @@ class FreqaiDataKitchen: # do not want them having to edit the default save/load methods here. Below is an example # of what we do NOT want. - # if self.freqai_config['feature_parameters']['determine_statistical_distributions']: + # if self.freqai_config.get('feature_parameters','determine_statistical_distributions'): # self.data_dictionary["upper_quantiles"] = pd.read_pickle( # self.model_path / str(self.model_filename + "_upper_quantiles.pkl") # ) @@ -193,15 +213,15 @@ class FreqaiDataKitchen: """ weights: npt.ArrayLike - if self.config["freqai"]["feature_parameters"]["weight_factor"] > 0: + if self.freqai_config["feature_parameters"].get("weight_factor", 0) > 0: weights = self.set_weights_higher_recent(len(filtered_dataframe)) else: weights = np.ones(len(filtered_dataframe)) - if self.config["freqai"]["feature_parameters"]["stratify"] > 0: + if self.freqai_config["feature_parameters"].get("stratify", 0) > 0: stratification = np.zeros(len(filtered_dataframe)) for i in range(1, len(stratification)): - if i % self.config["freqai"]["feature_parameters"]["stratify"] == 0: + if i % self.freqai_config.get("feature_parameters", {}).get("stratify", 0) == 0: stratification[i] = 1 ( @@ -525,6 +545,14 @@ class FreqaiDataKitchen: return None + def pca_transform(self, filtered_dataframe: DataFrame) -> None: + pca_components = self.pca.transform(filtered_dataframe) + self.data_dictionary["prediction_features"] = pd.DataFrame( + data=pca_components, + columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])], + index=filtered_dataframe.index, + ) + def compute_distances(self) -> float: logger.info("computing average mean distance for all training points") pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1) @@ -675,7 +703,7 @@ class FreqaiDataKitchen: self.full_path = Path( self.config["user_data_dir"] / "models" - / str(full_timerange + self.freqai_config["identifier"]) + / str(full_timerange + self.freqai_config.get("identifier")) ) config_path = Path(self.config["config_files"][0]) @@ -696,13 +724,15 @@ class FreqaiDataKitchen: if trained_timerange.startts != 0: elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY - retrain = elapsed_time > self.freqai_config['backtest_period'] + retrain = elapsed_time > self.freqai_config.get('backtest_period') if retrain: - trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY - trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY + trained_timerange.startts += self.freqai_config.get( + 'backtest_period', 0) * SECONDS_IN_DAY + trained_timerange.stopts += self.freqai_config.get( + 'backtest_period', 0) * SECONDS_IN_DAY else: # user passed no live_trained_timerange in config trained_timerange = TimeRange() - trained_timerange.startts = int(time - self.freqai_config['train_period'] * + trained_timerange.startts = int(time - self.freqai_config.get('train_period') * SECONDS_IN_DAY) trained_timerange.stopts = int(time) retrain = True @@ -725,13 +755,13 @@ class FreqaiDataKitchen: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config, validate=False) - pairs = self.freqai_config['corr_pairlist'] + pairs = self.freqai_config.get('corr_pairlist', []) if metadata['pair'] not in pairs: pairs += metadata['pair'] # dont include pair twice # timerange = TimeRange.parse_timerange(new_timerange) refresh_backtest_ohlcv_data( - exchange, pairs=pairs, timeframes=self.freqai_config['timeframes'], + exchange, pairs=pairs, timeframes=self.freqai_config.get('timeframes'), datadir=self.config['datadir'], timerange=timerange, new_pairs_days=self.config['new_pairs_days'], erase=False, data_format=self.config['dataformat_ohlcv'], @@ -743,21 +773,22 @@ class FreqaiDataKitchen: DataFrame]: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} - pairs = self.freqai_config['corr_pairlist'] # + [metadata['pair']] + pairs = self.freqai_config.get('corr_pairlist', []) # + [metadata['pair']] # timerange = TimeRange.parse_timerange(new_timerange) - for tf in self.freqai_config['timeframes']: + for tf in self.freqai_config.get('timeframes'): base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, pair=metadata['pair'], timerange=timerange) - for p in pairs: - if metadata['pair'] in p: - continue # dont repeat anything from whitelist - if p not in corr_dataframes: - corr_dataframes[p] = {} - corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], - timeframe=tf, - pair=p, timerange=timerange) + if pairs: + for p in pairs: + if metadata['pair'] in p: + continue # dont repeat anything from whitelist + if p not in corr_dataframes: + corr_dataframes[p] = {} + corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], + timeframe=tf, + pair=p, timerange=timerange) return corr_dataframes, base_dataframes @@ -767,23 +798,25 @@ class FreqaiDataKitchen: metadata: dict) -> DataFrame: dataframe = base_dataframes[self.config['timeframe']] + pairs = self.freqai_config.get("corr_pairlist", []) - for tf in self.freqai_config["timeframes"]: + for tf in self.freqai_config.get("timeframes"): dataframe = strategy.populate_any_indicators(metadata['pair'], dataframe.copy(), tf, base_dataframes[tf], coin=metadata['pair'].split("/")[0] + "-" ) - for i in self.freqai_config["corr_pairlist"]: - if metadata['pair'] in i: - continue # dont repeat anything from whitelist - dataframe = strategy.populate_any_indicators(i, - dataframe.copy(), - tf, - corr_dataframes[i][tf], - coin=i.split("/")[0] + "-" - ) + if pairs: + for i in pairs: + if metadata['pair'] in i: + continue # dont repeat anything from whitelist + dataframe = strategy.populate_any_indicators(i, + dataframe.copy(), + tf, + corr_dataframes[i][tf], + coin=i.split("/")[0] + "-" + ) return dataframe diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 3ff98b8ee..2b3addab3 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -20,7 +20,7 @@ from freqtrade.strategy.interface import IStrategy pd.options.mode.chained_assignment = None logger = logging.getLogger(__name__) -# FIXME: suppress stdout for background training +# FIXME: suppress stdout for background training? # class DummyFile(object): # def write(self, x): pass @@ -51,6 +51,7 @@ class IFreqaiModel(ABC): def __init__(self, config: Dict[str, Any]) -> None: self.config = config + self.assert_config(self.config) self.freqai_info = config["freqai"] self.data_split_parameters = config["freqai"]["data_split_parameters"] self.model_training_parameters = config["freqai"]["model_training_parameters"] @@ -64,12 +65,25 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False self.first = True - if self.freqai_info['live_trained_timerange']: + if self.freqai_info.get('live_trained_timerange'): self.new_trained_timerange = TimeRange.parse_timerange( self.freqai_info['live_trained_timerange']) else: self.new_trained_timerange = TimeRange() + def assert_config(self, config: Dict[str, Any]) -> None: + + assert config.get('freqai'), "No Freqai parameters found in config file." + assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai" + "data_split_parameters" + "in config file.") + assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai" + "modeltrainingparameters" + "found in config file.") + assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai" + "feature_parameters found in" + "config file.") + def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ Entry point to the FreqaiModel, it will train a new model if @@ -192,55 +206,30 @@ class IFreqaiModel(ABC): return - @abstractmethod - def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: - """ - Filter the training data and train a model to it. Train makes heavy use of the datahandler - for storing, saving, loading, and analyzing the data. - :params: - :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. - :returns: - :model: Trained model which can be used to inference (self.predict) - """ - - @abstractmethod - def fit(self) -> Any: - """ - Most regressors use the same function names and arguments e.g. user - can drop in LGBMRegressor in place of CatBoostRegressor and all data - management will be properly handled by Freqai. - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. - """ - - return - - @abstractmethod - def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]: - """ - Filter the prediction features data and predict with it. - :param: unfiltered_dataframe: Full dataframe for the current backtest period. - :return: - :predictions: np.array of predictions - :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove - data (NaNs) or felt uncertain about data (PCA and DI index) - """ - - @abstractmethod def data_cleaning_train(self) -> None: """ - User can add data analysis and cleaning here. + Base data cleaning method for train Any function inside this method should drop training data points from the filtered_dataframe based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example of how outlier data points are dropped from the dataframe used for training. """ + if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): + self.dh.principal_component_analysis() - @abstractmethod - def data_cleaning_predict(self) -> None: + # if self.feature_parameters["determine_statistical_distributions"]: + # self.dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # self.dh.remove_outliers(predict=False) + + if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): + self.dh.use_SVM_to_remove_outliers(predict=False) + + if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): + self.dh.data["avg_mean_dist"] = self.dh.compute_distances() + + def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None: """ - User can add data analysis and cleaning here. + Base data cleaning method for predict. These functions each modify self.dh.do_predict, which is a dataframe with equal length to the number of candles coming from and returning to the strategy. Inside do_predict, 1 allows prediction and < 0 signals to the strategy that the model is not confident in @@ -249,6 +238,19 @@ class IFreqaiModel(ABC): of how the do_predict vector is modified. do_predict is ultimately passed back to strategy for buy signals. """ + if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): + self.dh.pca_transform() + + # if self.feature_parameters["determine_statistical_distributions"]: + # self.dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # self.dh.remove_outliers(predict=True) # creates dropped index + + if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): + self.dh.use_SVM_to_remove_outliers(predict=True) + + if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): + self.dh.check_if_pred_in_training_spaces() # sets do_predict def model_exists(self, pair: str, training_timerange: str) -> bool: """ @@ -303,3 +305,42 @@ class IFreqaiModel(ABC): self.model = self.train(unfiltered_dataframe, metadata) self.dh.save_data(self.model) self.retrain = False + + # Methods which are overridden by user made prediction models. + # See freqai/prediction_models/CatboostPredictionModlel.py for an example. + + @abstractmethod + def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: + """ + Filter the training data and train a model to it. Train makes heavy use of the datahandler + for storing, saving, loading, and analyzing the data. + :params: + :unfiltered_dataframe: Full dataframe for the current training period + :metadata: pair metadata from strategy. + :returns: + :model: Trained model which can be used to inference (self.predict) + """ + + @abstractmethod + def fit(self) -> Any: + """ + Most regressors use the same function names and arguments e.g. user + can drop in LGBMRegressor in place of CatBoostRegressor and all data + management will be properly handled by Freqai. + :params: + :data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + """ + + return + + @abstractmethod + def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :predictions: np.array of predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 3dad6add6..d09554e3e 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -1,7 +1,6 @@ import logging from typing import Any, Dict, Tuple -import pandas as pd from catboost import CatBoostRegressor, Pool from pandas import DataFrame @@ -149,7 +148,7 @@ class CatboostPredictionModel(IFreqaiModel): based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example of how outlier data points are dropped from the dataframe used for training. """ - if self.feature_parameters["principal_component_analysis"]: + if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): self.dh.principal_component_analysis() # if self.feature_parameters["determine_statistical_distributions"]: @@ -157,9 +156,10 @@ class CatboostPredictionModel(IFreqaiModel): # if self.feature_parameters["remove_outliers"]: # self.dh.remove_outliers(predict=False) - if self.feature_parameters["use_SVM_to_remove_outliers"]: + if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): self.dh.use_SVM_to_remove_outliers(predict=False) - if self.feature_parameters["DI_threshold"]: + + if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): self.dh.data["avg_mean_dist"] = self.dh.compute_distances() def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None: @@ -173,21 +173,16 @@ class CatboostPredictionModel(IFreqaiModel): of how the do_predict vector is modified. do_predict is ultimately passed back to strategy for buy signals. """ - if self.feature_parameters["principal_component_analysis"]: - pca_components = self.dh.pca.transform(filtered_dataframe) - self.dh.data_dictionary["prediction_features"] = pd.DataFrame( - data=pca_components, - columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])], - index=filtered_dataframe.index, - ) + if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): + self.dh.pca_transform() # if self.feature_parameters["determine_statistical_distributions"]: # self.dh.determine_statistical_distributions() # if self.feature_parameters["remove_outliers"]: # self.dh.remove_outliers(predict=True) # creates dropped index - if self.feature_parameters["use_SVM_to_remove_outliers"]: + if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): self.dh.use_SVM_to_remove_outliers(predict=True) - if self.feature_parameters["DI_threshold"]: + if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): self.dh.check_if_pred_in_training_spaces() # sets do_predict diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index add864a67..3996dd08d 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -207,7 +207,7 @@ class Backtesting: if self.config.get('freqai') is not None: self.required_startup += int((self.config.get('freqai', {}).get('train_period') * 86400) / timeframe_to_seconds(self.config['timeframe'])) - logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup) + logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}') self.config['startup_candle_count'] = self.required_startup data = history.load_data( From b0d2d13eb19a5a64a4bec8b5314d36544ec21a38 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 23 May 2022 21:05:05 +0200 Subject: [PATCH 043/518] improve data persistence/mapping for live/dry. This accommodates quick reloads after crash and handles multi-pair cleanly --- freqtrade/freqai/data_drawer.py | 59 +++++++++ freqtrade/freqai/data_kitchen.py | 123 ++++++++++-------- freqtrade/freqai/freqai_interface.py | 102 ++++++++++----- .../CatboostPredictionModel.py | 46 ------- 4 files changed, 199 insertions(+), 131 deletions(-) create mode 100644 freqtrade/freqai/data_drawer.py diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py new file mode 100644 index 000000000..a27a4b67f --- /dev/null +++ b/freqtrade/freqai/data_drawer.py @@ -0,0 +1,59 @@ + +import json +import logging +from pathlib import Path +from typing import Any, Dict, Tuple + +# import pickle as pk +import numpy as np + + +logger = logging.getLogger(__name__) + + +class FreqaiDataDrawer: + """ + Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving + /loading to/from disk. + This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is + reinstantiated for each coin. + """ + def __init__(self, full_path: Path): + + # dictionary holding all pair metadata necessary to load in from disk + self.pair_dict: Dict[str, Any] = {} + # dictionary holding all actively inferenced models in memory given a model filename + self.model_dictionary: Dict[str, Any] = {} + self.full_path = full_path + self.load_drawer_from_disk() + + def load_drawer_from_disk(self): + exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists() + if exists: + with open(self.full_path / str('pair_dictionary.json'), "r") as fp: + self.pair_dict = json.load(fp) + else: + logger.info("Could not find existing datadrawer, starting from scratch") + return exists + + def save_drawer_to_disk(self): + with open(self.full_path / str('pair_dictionary.json'), "w") as fp: + json.dump(self.pair_dict, fp, default=self.np_encoder) + + def np_encoder(self, object): + if isinstance(object, np.generic): + return object.item() + + def get_pair_dict_info(self, metadata: dict) -> Tuple[str, int, bool]: + pair_in_dict = self.pair_dict.get(metadata['pair']) + if pair_in_dict: + model_filename = self.pair_dict[metadata['pair']]['model_filename'] + trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] + coin_first = self.pair_dict[metadata['pair']]['first'] + else: + self.pair_dict[metadata['pair']] = {} + model_filename = self.pair_dict[metadata['pair']]['model_filename'] = '' + coin_first = self.pair_dict[metadata['pair']]['first'] = True + trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 + + return model_filename, trained_timestamp, coin_first diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 148efd5dd..f5ddf8462 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -19,6 +19,7 @@ from sklearn.model_selection import train_test_split from freqtrade.configuration import TimeRange from freqtrade.data.history import load_pair_history from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data +from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.resolvers import ExchangeResolver from freqtrade.strategy.interface import IStrategy @@ -33,13 +34,13 @@ logger = logging.getLogger(__name__) class FreqaiDataKitchen: """ - Class designed to handle all the data for the IFreqaiModel class model. + Class designed to analyze data for a single pair. Employed by the IFreqaiModel class. Functionalities include holding, saving, loading, and analyzing the data. author: Robert Caulk, rob.caulk@gmail.com """ - def __init__(self, config: Dict[str, Any], dataframe: DataFrame, live: bool = False): - self.full_dataframe = dataframe + def __init__(self, config: Dict[str, Any], data_drawer: FreqaiDataDrawer, live: bool = False, + pair: str = ''): self.data: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {} self.config = config @@ -53,10 +54,10 @@ class FreqaiDataKitchen: self.full_do_predict: npt.ArrayLike = np.array([]) self.full_target_mean: npt.ArrayLike = np.array([]) self.full_target_std: npt.ArrayLike = np.array([]) - self.model_path = Path() + self.data_path = Path() self.model_filename: str = "" - self.model_dictionary: Dict[Any, Any] = {} self.live = live + self.pair = pair self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: self.full_timerange = self.create_fulltimerange(self.config["timerange"], @@ -69,6 +70,8 @@ class FreqaiDataKitchen: config["freqai"]["backtest_period"], ) + self.data_drawer = data_drawer + def assert_config(self, config: Dict[str, Any], live: bool) -> None: assert config.get('freqai'), "No Freqai parameters found in config file." assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in" @@ -88,18 +91,18 @@ class FreqaiDataKitchen: assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" "found in config file.") - def set_paths(self) -> None: + def set_paths(self, trained_timestamp: int = None) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / str(self.freqai_config.get('live_full_backtestrange') + self.freqai_config.get('identifier'))) - self.model_path = Path(self.full_path / str("sub-train" + "-" + - str(self.freqai_config.get('live_trained_timerange')))) + self.data_path = Path(self.full_path / str("sub-train" + "-" + self.pair.split("/")[0] + + str(trained_timestamp))) return - def save_data(self, model: Any) -> None: + def save_data(self, model: Any, coin: str = '') -> None: """ Saves all data associated with a model for a single sub-train time range :params: @@ -107,10 +110,10 @@ class FreqaiDataKitchen: predictions """ - if not self.model_path.is_dir(): - self.model_path.mkdir(parents=True, exist_ok=True) + if not self.data_path.is_dir(): + self.data_path.mkdir(parents=True, exist_ok=True) - save_path = Path(self.model_path) + save_path = Path(self.data_path) # Save the trained model dump(model, save_path / str(self.model_filename + "_model.joblib")) @@ -118,7 +121,7 @@ class FreqaiDataKitchen: if self.svm_model is not None: dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib")) - self.data["model_path"] = str(self.model_path) + self.data["data_path"] = str(self.data_path) self.data["model_filename"] = str(self.model_filename) self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns) # store the metadata @@ -131,7 +134,10 @@ class FreqaiDataKitchen: ) if self.live: - self.model_dictionary[self.model_filename] = model + self.data_drawer.model_dictionary[self.model_filename] = model + self.data_drawer.pair_dict[coin]['model_filename'] = self.model_filename + self.data_drawer.pair_dict[coin]['data_path'] = str(self.data_path) + self.data_drawer.save_drawer_to_disk() # TODO add a helper function to let user save/load any data they are custom adding. We # do not want them having to edit the default save/load methods here. Below is an example @@ -148,19 +154,23 @@ class FreqaiDataKitchen: return - def load_data(self) -> Any: + def load_data(self, coin: str = '') -> Any: """ loads all data required to make a prediction on a sub-train time range :returns: :model: User trained model which can be inferenced for new predictions """ - with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp: + if self.live: + self.model_filename = self.data_drawer.pair_dict[coin]['model_filename'] + self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path']) + + with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) self.training_features_list = self.data["training_features_list"] self.data_dictionary["train_features"] = pd.read_pickle( - self.model_path / str(self.model_filename + "_trained_df.pkl") + self.data_path / str(self.model_filename + "_trained_df.pkl") ) # TODO add a helper function to let user save/load any data they are custom adding. We @@ -169,34 +179,34 @@ class FreqaiDataKitchen: # if self.freqai_config.get('feature_parameters','determine_statistical_distributions'): # self.data_dictionary["upper_quantiles"] = pd.read_pickle( - # self.model_path / str(self.model_filename + "_upper_quantiles.pkl") + # self.data_path / str(self.model_filename + "_upper_quantiles.pkl") # ) # self.data_dictionary["lower_quantiles"] = pd.read_pickle( - # self.model_path / str(self.model_filename + "_lower_quantiles.pkl") + # self.data_path / str(self.model_filename + "_lower_quantiles.pkl") # ) - self.model_path = Path(self.data["model_path"]) - self.model_filename = self.data["model_filename"] + # self.data_path = Path(self.data["data_path"]) + # self.model_filename = self.data["model_filename"] # try to access model in memory instead of loading object from disk to save time - if self.live and self.model_filename in self.model_dictionary: - model = self.model_dictionary[self.model_filename] + if self.live and self.model_filename in self.data_drawer.model_dictionary: + model = self.data_drawer.model_dictionary[self.model_filename] else: - model = load(self.model_path / str(self.model_filename + "_model.joblib")) + model = load(self.data_path / str(self.model_filename + "_model.joblib")) - if Path(self.model_path / str(self.model_filename + + if Path(self.data_path / str(self.model_filename + "_svm_model.joblib")).resolve().exists(): - self.svm_model = load(self.model_path / str(self.model_filename + "_svm_model.joblib")) + self.svm_model = load(self.data_path / str(self.model_filename + "_svm_model.joblib")) assert model, ( f"Unable to load model, ensure model exists at " - f"{self.model_path} " + f"{self.data_path} " ) if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: self.pca = pk.load( - open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "rb") + open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "rb") ) return model @@ -539,9 +549,9 @@ class FreqaiDataKitchen: logger.info(f'PCA reduced total features from {n_components} to {n_keep_components}') - if not self.model_path.is_dir(): - self.model_path.mkdir(parents=True, exist_ok=True) - pk.dump(pca2, open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "wb")) + if not self.data_path.is_dir(): + self.data_path.mkdir(parents=True, exist_ok=True) + pk.dump(pca2, open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "wb")) return None @@ -717,40 +727,51 @@ class FreqaiDataKitchen: return full_timerange - def check_if_new_training_required(self, trained_timerange: TimeRange, - metadata: dict) -> Tuple[bool, TimeRange]: + def check_if_new_training_required(self, trained_timestamp: int) -> Tuple[bool, TimeRange]: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - - if trained_timerange.startts != 0: - elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY + trained_timerange = TimeRange() + if trained_timestamp != 0: + elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY retrain = elapsed_time > self.freqai_config.get('backtest_period') if retrain: - trained_timerange.startts += self.freqai_config.get( - 'backtest_period', 0) * SECONDS_IN_DAY - trained_timerange.stopts += self.freqai_config.get( - 'backtest_period', 0) * SECONDS_IN_DAY + trained_timerange.startts = int(time - self.freqai_config.get( + 'backtest_period', 0) * SECONDS_IN_DAY) + trained_timerange.stopts = int(time) else: # user passed no live_trained_timerange in config - trained_timerange = TimeRange() trained_timerange.startts = int(time - self.freqai_config.get('train_period') * SECONDS_IN_DAY) trained_timerange.stopts = int(time) retrain = True - if retrain: - coin, _ = metadata['pair'].split("/") - # set the new model_path - self.model_path = Path(self.full_path / str("sub-train" + "-" + - str(int(trained_timerange.stopts)))) + # if retrain: + # coin, _ = metadata['pair'].split("/") + # # set the new data_path + # self.data_path = Path(self.full_path / str("sub-train" + "-" + + # str(int(trained_timerange.stopts)))) - self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) - # this is not persistent at the moment TODO - self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) - # enables persistence, but not fully implemented into save/load data yer - self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) + # self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) + # # this is not persistent at the moment TODO + # self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) + # # enables persistence, but not fully implemented into save/load data yer + # self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) return retrain, trained_timerange + def set_new_model_names(self, metadata: dict, trained_timerange: TimeRange): + + coin, _ = metadata['pair'].split("/") + # set the new data_path + self.data_path = Path(self.full_path / str("sub-train" + "-" + + metadata['pair'].split("/")[0] + + str(int(trained_timerange.stopts)))) + + self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) + # this is not persistent at the moment TODO + self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) + # enables persistence, but not fully implemented into save/load data yer + self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) + def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 2b3addab3..0b1fb3b86 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -13,6 +13,7 @@ from pandas import DataFrame from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode +from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.strategy.interface import IStrategy @@ -65,11 +66,14 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False self.first = True - if self.freqai_info.get('live_trained_timerange'): - self.new_trained_timerange = TimeRange.parse_timerange( - self.freqai_info['live_trained_timerange']) - else: - self.new_trained_timerange = TimeRange() + # if self.freqai_info.get('live_trained_timerange'): + # self.new_trained_timerange = TimeRange.parse_timerange( + # self.freqai_info['live_trained_timerange']) + # else: + # self.new_trained_timerange = TimeRange() + + self.set_full_path() + self.data_drawer = FreqaiDataDrawer(Path(self.full_path)) def assert_config(self, config: Dict[str, Any]) -> None: @@ -86,7 +90,7 @@ class IFreqaiModel(ABC): def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ - Entry point to the FreqaiModel, it will train a new model if + Entry point to the FreqaiModel from a specific pair, it will train a new model if necessary before making the prediction. The backtesting and training paradigm is a sliding training window with a following backtest window. Both windows slide according to the @@ -103,8 +107,8 @@ class IFreqaiModel(ABC): self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) - self.pair = metadata["pair"] - self.dh = FreqaiDataKitchen(self.config, dataframe, self.live) + # FreqaiDataKitchen is reinstantiated for each coin + self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) if self.live: # logger.info('testing live') @@ -113,7 +117,7 @@ class IFreqaiModel(ABC): return (self.dh.full_predictions, self.dh.full_do_predict, self.dh.full_target_mean, self.dh.full_target_std) - logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) + logger.info(f'Training {len(self.dh.training_timeranges)} timeranges') # Loop enforcing the sliding window training/backtesting paradigm # tr_train is the training time range e.g. 1 historical month @@ -129,9 +133,12 @@ class IFreqaiModel(ABC): self.training_timerange = tr_train dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) - logger.info("training %s for %s", self.pair, tr_train) - self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train))) - if not self.model_exists(self.pair, training_timerange=tr_train): + logger.info("training %s for %s", metadata["pair"], tr_train) + trained_timestamp = TimeRange.parse_timerange(tr_train) + self.dh.data_path = Path(self.dh.full_path / + str("sub-train" + "-" + metadata['pair'].split("/")[0] + + str(int(trained_timestamp.stopts)))) + if not self.model_exists(metadata["pair"], trained_timestamp=trained_timestamp.stopts): self.model = self.train(dataframe_train, metadata) self.dh.save_data(self.model) else: @@ -161,36 +168,40 @@ class IFreqaiModel(ABC): """ - self.dh.set_paths() + (model_filename, + trained_timestamp, + coin_first) = self.data_drawer.get_pair_dict_info(metadata) - file_exists = self.model_exists(metadata['pair'], - training_timerange=self.freqai_info[ - 'live_trained_timerange']) + if trained_timestamp != 0: + self.dh.set_paths(trained_timestamp) + # data_drawer thinks the file eixts, verify here + file_exists = self.model_exists(metadata['pair'], + trained_timestamp=trained_timestamp, + model_filename=model_filename) if not self.training_on_separate_thread: # this will also prevent other pairs from trying to train simultaneously. (self.retrain, - self.new_trained_timerange) = self.dh.check_if_new_training_required( - self.new_trained_timerange, - metadata) + new_trained_timerange) = self.dh.check_if_new_training_required( + trained_timestamp) + self.dh.set_paths(new_trained_timerange.stopts) else: logger.info("FreqAI training a new model on background thread.") self.retrain = False if self.retrain or not file_exists: - if self.first: - self.train_model_in_series(self.new_trained_timerange, metadata, strategy) - self.first = False + if coin_first: + self.train_model_in_series(new_trained_timerange, metadata, strategy) else: self.training_on_separate_thread = True # acts like a lock - self.retrain_model_on_separate_thread(self.new_trained_timerange, + self.retrain_model_on_separate_thread(new_trained_timerange, metadata, strategy) - self.model = self.dh.load_data() + self.model = self.dh.load_data(coin=metadata['pair']) strategy_provided_features = self.dh.find_features(dataframe) if strategy_provided_features != self.dh.training_features_list: - self.train_model_in_series(self.new_trained_timerange, metadata, strategy) + self.train_model_in_series(new_trained_timerange, metadata, strategy) preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) @@ -252,24 +263,34 @@ class IFreqaiModel(ABC): if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): self.dh.check_if_pred_in_training_spaces() # sets do_predict - def model_exists(self, pair: str, training_timerange: str) -> bool: + def model_exists(self, pair: str, trained_timestamp: int = None, + model_filename: str = '') -> bool: """ Given a pair and path, check if a model already exists :param pair: pair e.g. BTC/USD :param path: path to model """ - if self.live and training_timerange == "": - return False coin, _ = pair.split("/") - self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange - path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib")) + + if self.live and trained_timestamp is None: + self.dh.model_filename = model_filename + else: + self.dh.model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp) + + path_to_modelfile = Path(self.dh.data_path / str(self.dh.model_filename + "_model.joblib")) file_exists = path_to_modelfile.is_file() if file_exists: - logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename) + logger.info("Found model at %s", self.dh.data_path / self.dh.model_filename) else: - logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename) + logger.info("Could not find model at %s", self.dh.data_path / self.dh.model_filename) return file_exists + def set_full_path(self) -> None: + self.full_path = Path(self.config['user_data_dir'] / + "models" / + str(self.freqai_info.get('live_full_backtestrange') + + self.freqai_info.get('identifier'))) + @threaded def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict, strategy: IStrategy): @@ -285,7 +306,13 @@ class IFreqaiModel(ABC): metadata) self.model = self.train(unfiltered_dataframe, metadata) - self.dh.save_data(self.model) + + self.data_drawer.pair_dict[metadata['pair']][ + 'trained_timestamp'] = new_trained_timerange.stopts + + self.dh.set_new_model_names(metadata, new_trained_timerange) + + self.dh.save_data(self.model, coin=metadata['pair']) self.training_on_separate_thread = False self.retrain = False @@ -303,7 +330,14 @@ class IFreqaiModel(ABC): metadata) self.model = self.train(unfiltered_dataframe, metadata) - self.dh.save_data(self.model) + + self.data_drawer.pair_dict[metadata['pair']][ + 'trained_timestamp'] = new_trained_timerange.stopts + + self.dh.set_new_model_names(metadata, new_trained_timerange) + + self.data_drawer.pair_dict[metadata['pair']]['first'] = False + self.dh.save_data(self.model, coin=metadata['pair']) self.retrain = False # Methods which are overridden by user made prediction models. diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index d09554e3e..6349174ad 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -140,49 +140,3 @@ class CatboostPredictionModel(IFreqaiModel): # logger.info("--------------------Finished prediction--------------------") return (self.dh.predictions, self.dh.do_predict) - - def data_cleaning_train(self) -> None: - """ - User can add data analysis and cleaning here. - Any function inside this method should drop training data points from the filtered_dataframe - based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example - of how outlier data points are dropped from the dataframe used for training. - """ - if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): - self.dh.principal_component_analysis() - - # if self.feature_parameters["determine_statistical_distributions"]: - # self.dh.determine_statistical_distributions() - # if self.feature_parameters["remove_outliers"]: - # self.dh.remove_outliers(predict=False) - - if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): - self.dh.use_SVM_to_remove_outliers(predict=False) - - if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): - self.dh.data["avg_mean_dist"] = self.dh.compute_distances() - - def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None: - """ - User can add data analysis and cleaning here. - These functions each modify self.dh.do_predict, which is a dataframe with equal length - to the number of candles coming from and returning to the strategy. Inside do_predict, - 1 allows prediction and < 0 signals to the strategy that the model is not confident in - the prediction. - See FreqaiDataKitchen::remove_outliers() for an example - of how the do_predict vector is modified. do_predict is ultimately passed back to strategy - for buy signals. - """ - if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): - self.dh.pca_transform() - - # if self.feature_parameters["determine_statistical_distributions"]: - # self.dh.determine_statistical_distributions() - # if self.feature_parameters["remove_outliers"]: - # self.dh.remove_outliers(predict=True) # creates dropped index - - if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): - self.dh.use_SVM_to_remove_outliers(predict=True) - - if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): - self.dh.check_if_pred_in_training_spaces() # sets do_predict From 059c28542548374ed4e2402f0f9e5c945a8295e8 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 24 May 2022 12:01:01 +0200 Subject: [PATCH 044/518] paying closer attention to managing live retraining on separate thread without affecting prediction of other coins on master thread --- freqtrade/freqai/data_drawer.py | 1 + freqtrade/freqai/data_kitchen.py | 7 +- freqtrade/freqai/freqai_interface.py | 198 ++++++++++-------- .../CatboostPredictionModel.py | 51 ++--- 4 files changed, 139 insertions(+), 118 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index a27a4b67f..51f56fae6 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -24,6 +24,7 @@ class FreqaiDataDrawer: self.pair_dict: Dict[str, Any] = {} # dictionary holding all actively inferenced models in memory given a model filename self.model_dictionary: Dict[str, Any] = {} + self.pair_data_dict: Dict[str, Any] = {} self.full_path = full_path self.load_drawer_from_disk() diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index f5ddf8462..a4867d7eb 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -91,14 +91,15 @@ class FreqaiDataKitchen: assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" "found in config file.") - def set_paths(self, trained_timestamp: int = None) -> None: + def set_paths(self, metadata: dict, trained_timestamp: int = None,) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / str(self.freqai_config.get('live_full_backtestrange') + self.freqai_config.get('identifier'))) - self.data_path = Path(self.full_path / str("sub-train" + "-" + self.pair.split("/")[0] + - str(trained_timestamp))) + self.data_path = Path(self.full_path / str("sub-train" + "-" + + metadata['pair'].split("/")[0] + + str(trained_timestamp))) return diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 0b1fb3b86..19b7dbb27 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -108,14 +108,22 @@ class IFreqaiModel(ABC): self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) # FreqaiDataKitchen is reinstantiated for each coin - self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) - if self.live: - # logger.info('testing live') - self.start_live(dataframe, metadata, strategy) + if not self.training_on_separate_thread: + self.dh = FreqaiDataKitchen(self.config, self.data_drawer, + self.live, metadata["pair"]) + dh = self.start_live(dataframe, metadata, strategy, self.dh) + else: + # we will have at max 2 separate instances of the kitchen at once. + self.dh_fg = FreqaiDataKitchen(self.config, self.data_drawer, + self.live, metadata["pair"]) + dh = self.start_live(dataframe, metadata, strategy, self.dh_fg) - return (self.dh.full_predictions, self.dh.full_do_predict, - self.dh.full_target_mean, self.dh.full_target_std) + return (dh.full_predictions, dh.full_do_predict, + dh.full_target_mean, dh.full_target_std) + + # Backtesting only + self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) logger.info(f'Training {len(self.dh.training_timeranges)} timeranges') @@ -138,8 +146,9 @@ class IFreqaiModel(ABC): self.dh.data_path = Path(self.dh.full_path / str("sub-train" + "-" + metadata['pair'].split("/")[0] + str(int(trained_timestamp.stopts)))) - if not self.model_exists(metadata["pair"], trained_timestamp=trained_timestamp.stopts): - self.model = self.train(dataframe_train, metadata) + if not self.model_exists(metadata["pair"], self.dh, + trained_timestamp=trained_timestamp.stopts): + self.model = self.train(dataframe_train, metadata, self.dh) self.dh.save_data(self.model) else: self.model = self.dh.load_data() @@ -150,7 +159,7 @@ class IFreqaiModel(ABC): # self.model = self.train(dataframe_train, metadata) # self.dh.save_data(self.model) - preds, do_preds = self.predict(dataframe_backtest, metadata) + preds, do_preds = self.predict(dataframe_backtest, self.dh) self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) print('predictions', len(self.dh.full_predictions), @@ -161,7 +170,8 @@ class IFreqaiModel(ABC): return (self.dh.full_predictions, self.dh.full_do_predict, self.dh.full_target_mean, self.dh.full_target_std) - def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None: + def start_live(self, dataframe: DataFrame, metadata: dict, + strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen: """ The main broad execution for dry/live. This function will check if a retraining should be performed, and if so, retrain and reset the model. @@ -172,52 +182,49 @@ class IFreqaiModel(ABC): trained_timestamp, coin_first) = self.data_drawer.get_pair_dict_info(metadata) - if trained_timestamp != 0: - self.dh.set_paths(trained_timestamp) - # data_drawer thinks the file eixts, verify here - file_exists = self.model_exists(metadata['pair'], - trained_timestamp=trained_timestamp, - model_filename=model_filename) - if not self.training_on_separate_thread: + file_exists = False + + if trained_timestamp != 0: + dh.set_paths(metadata, trained_timestamp) + # data_drawer thinks the file eixts, verify here + file_exists = self.model_exists(metadata['pair'], + dh, + trained_timestamp=trained_timestamp, + model_filename=model_filename) + + # if not self.training_on_separate_thread: # this will also prevent other pairs from trying to train simultaneously. (self.retrain, - new_trained_timerange) = self.dh.check_if_new_training_required( - trained_timestamp) - self.dh.set_paths(new_trained_timerange.stopts) + new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp) + dh.set_paths(metadata, new_trained_timerange.stopts) + # if self.training_on_separate_thread: + # logger.info("FreqAI training a new model on background thread.") + # self.retrain = False + + if self.retrain or not file_exists: + if coin_first: + self.train_model_in_series(new_trained_timerange, metadata, strategy, dh) + else: + self.training_on_separate_thread = True # acts like a lock + self.retrain_model_on_separate_thread(new_trained_timerange, + metadata, strategy, dh) + else: logger.info("FreqAI training a new model on background thread.") - self.retrain = False - if self.retrain or not file_exists: - if coin_first: - self.train_model_in_series(new_trained_timerange, metadata, strategy) - else: - self.training_on_separate_thread = True # acts like a lock - self.retrain_model_on_separate_thread(new_trained_timerange, - metadata, strategy) + self.model = dh.load_data(coin=metadata['pair']) - self.model = self.dh.load_data(coin=metadata['pair']) + # strategy_provided_features = dh.find_features(dataframe) + # if strategy_provided_features != dh.training_features_list: + # self.train_model_in_series(new_trained_timerange, metadata, strategy) - strategy_provided_features = self.dh.find_features(dataframe) - if strategy_provided_features != self.dh.training_features_list: - self.train_model_in_series(new_trained_timerange, metadata, strategy) + preds, do_preds = self.predict(dataframe, dh) + dh.append_predictions(preds, do_preds, len(dataframe)) - preds, do_preds = self.predict(dataframe, metadata) - self.dh.append_predictions(preds, do_preds, len(dataframe)) + return dh - return - - def make_labels(self, dataframe: DataFrame) -> DataFrame: - """ - User defines the labels here (target values). - :params: - :dataframe: the full dataframe for the present training period - """ - - return - - def data_cleaning_train(self) -> None: + def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None: """ Base data cleaning method for train Any function inside this method should drop training data points from the filtered_dataframe @@ -225,23 +232,23 @@ class IFreqaiModel(ABC): of how outlier data points are dropped from the dataframe used for training. """ if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): - self.dh.principal_component_analysis() + dh.principal_component_analysis() # if self.feature_parameters["determine_statistical_distributions"]: - # self.dh.determine_statistical_distributions() + # dh.determine_statistical_distributions() # if self.feature_parameters["remove_outliers"]: - # self.dh.remove_outliers(predict=False) + # dh.remove_outliers(predict=False) if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): - self.dh.use_SVM_to_remove_outliers(predict=False) + dh.use_SVM_to_remove_outliers(predict=False) if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): - self.dh.data["avg_mean_dist"] = self.dh.compute_distances() + dh.data["avg_mean_dist"] = dh.compute_distances() - def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None: + def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None: """ Base data cleaning method for predict. - These functions each modify self.dh.do_predict, which is a dataframe with equal length + These functions each modify dh.do_predict, which is a dataframe with equal length to the number of candles coming from and returning to the strategy. Inside do_predict, 1 allows prediction and < 0 signals to the strategy that the model is not confident in the prediction. @@ -250,20 +257,20 @@ class IFreqaiModel(ABC): for buy signals. """ if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): - self.dh.pca_transform() + dh.pca_transform() # if self.feature_parameters["determine_statistical_distributions"]: - # self.dh.determine_statistical_distributions() + # dh.determine_statistical_distributions() # if self.feature_parameters["remove_outliers"]: - # self.dh.remove_outliers(predict=True) # creates dropped index + # dh.remove_outliers(predict=True) # creates dropped index if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): - self.dh.use_SVM_to_remove_outliers(predict=True) + dh.use_SVM_to_remove_outliers(predict=True) if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): - self.dh.check_if_pred_in_training_spaces() # sets do_predict + dh.check_if_pred_in_training_spaces() # sets do_predict - def model_exists(self, pair: str, trained_timestamp: int = None, + def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None, model_filename: str = '') -> bool: """ Given a pair and path, check if a model already exists @@ -272,17 +279,17 @@ class IFreqaiModel(ABC): """ coin, _ = pair.split("/") - if self.live and trained_timestamp is None: - self.dh.model_filename = model_filename - else: - self.dh.model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp) + # if self.live and trained_timestamp == 0: + # dh.model_filename = model_filename + if not self.live: + dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp) - path_to_modelfile = Path(self.dh.data_path / str(self.dh.model_filename + "_model.joblib")) + path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib")) file_exists = path_to_modelfile.is_file() if file_exists: - logger.info("Found model at %s", self.dh.data_path / self.dh.model_filename) + logger.info("Found model at %s", dh.data_path / dh.model_filename) else: - logger.info("Could not find model at %s", self.dh.data_path / self.dh.model_filename) + logger.info("Could not find model at %s", dh.data_path / dh.model_filename) return file_exists def set_full_path(self) -> None: @@ -293,58 +300,58 @@ class IFreqaiModel(ABC): @threaded def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict, - strategy: IStrategy): + strategy: IStrategy, dh: FreqaiDataKitchen): # with nostdout(): - self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) - corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, - metadata) + dh.download_new_data_for_retraining(new_trained_timerange, metadata) + corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange, + metadata) - unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, - corr_dataframes, - base_dataframes, - metadata) + unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy, + corr_dataframes, + base_dataframes, + metadata) - self.model = self.train(unfiltered_dataframe, metadata) + self.model = self.train(unfiltered_dataframe, metadata, dh) self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts - self.dh.set_new_model_names(metadata, new_trained_timerange) + dh.set_new_model_names(metadata, new_trained_timerange) - self.dh.save_data(self.model, coin=metadata['pair']) + dh.save_data(self.model, coin=metadata['pair']) self.training_on_separate_thread = False self.retrain = False def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict, - strategy: IStrategy): + strategy: IStrategy, dh: FreqaiDataKitchen): - self.dh.download_new_data_for_retraining(new_trained_timerange, metadata) - corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange, - metadata) + dh.download_new_data_for_retraining(new_trained_timerange, metadata) + corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange, + metadata) - unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy, - corr_dataframes, - base_dataframes, - metadata) + unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy, + corr_dataframes, + base_dataframes, + metadata) - self.model = self.train(unfiltered_dataframe, metadata) + self.model = self.train(unfiltered_dataframe, metadata, dh) self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts - self.dh.set_new_model_names(metadata, new_trained_timerange) + dh.set_new_model_names(metadata, new_trained_timerange) self.data_drawer.pair_dict[metadata['pair']]['first'] = False - self.dh.save_data(self.model, coin=metadata['pair']) + dh.save_data(self.model, coin=metadata['pair']) self.retrain = False # Methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModlel.py for an example. @abstractmethod - def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any: + def train(self, unfiltered_dataframe: DataFrame, metadata: dict, dh: FreqaiDataKitchen) -> Any: """ Filter the training data and train a model to it. Train makes heavy use of the datahandler for storing, saving, loading, and analyzing the data. @@ -369,7 +376,8 @@ class IFreqaiModel(ABC): return @abstractmethod - def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]: + def predict(self, dataframe: DataFrame, + dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. @@ -378,3 +386,13 @@ class IFreqaiModel(ABC): :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove data (NaNs) or felt uncertain about data (PCA and DI index) """ + + @abstractmethod + def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame: + """ + User defines the labels here (target values). + :params: + :dataframe: the full dataframe for the present training period + """ + + return diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 6349174ad..87ddfdb66 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -4,6 +4,7 @@ from typing import Any, Dict, Tuple from catboost import CatBoostRegressor, Pool from pandas import DataFrame +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel @@ -17,7 +18,7 @@ class CatboostPredictionModel(IFreqaiModel): has its own DataHandler where data is held, saved, loaded, and managed. """ - def make_labels(self, dataframe: DataFrame) -> DataFrame: + def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame: """ User defines the labels here (target values). :params: @@ -32,14 +33,15 @@ class CatboostPredictionModel(IFreqaiModel): / dataframe["close"] - 1 ) - self.dh.data["s_mean"] = dataframe["s"].mean() - self.dh.data["s_std"] = dataframe["s"].std() + dh.data["s_mean"] = dataframe["s"].mean() + dh.data["s_std"] = dataframe["s"].std() - # logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"]) + # logger.info("label mean", dh.data["s_mean"], "label std", dh.data["s_std"]) return dataframe["s"] - def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: + def train(self, unfiltered_dataframe: DataFrame, + metadata: dict, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]: """ Filter the training data and train a model to it. Train makes heavy use of the datahkitchen for storing, saving, loading, and analyzing the data. @@ -52,25 +54,25 @@ class CatboostPredictionModel(IFreqaiModel): logger.info("--------------------Starting training--------------------") # create the full feature list based on user config info - self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe) - unfiltered_labels = self.make_labels(unfiltered_dataframe) + dh.training_features_list = dh.find_features(unfiltered_dataframe) + unfiltered_labels = self.make_labels(unfiltered_dataframe, dh) # filter the features requested by user in the configuration file and elegantly handle NaNs - features_filtered, labels_filtered = self.dh.filter_features( + features_filtered, labels_filtered = dh.filter_features( unfiltered_dataframe, - self.dh.training_features_list, + dh.training_features_list, unfiltered_labels, training_filter=True, ) # split data into train/test data. - data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered) + data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered) # standardize all data based on train_dataset only - data_dictionary = self.dh.standardize_data(data_dictionary) + data_dictionary = dh.standardize_data(data_dictionary) # optional additional data cleaning/analysis - self.data_cleaning_train() + self.data_cleaning_train(dh) - logger.info(f'Training model on {len(self.dh.training_features_list)} features') + logger.info(f'Training model on {len(dh.training_features_list)} features') logger.info(f'Training model on {len(data_dictionary["train_features"])} data points') model = self.fit(data_dictionary) @@ -107,8 +109,8 @@ class CatboostPredictionModel(IFreqaiModel): return model - def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, - DataFrame]: + def predict(self, unfiltered_dataframe: DataFrame, + dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. @@ -120,23 +122,22 @@ class CatboostPredictionModel(IFreqaiModel): # logger.info("--------------------Starting prediction--------------------") - original_feature_list = self.dh.find_features(unfiltered_dataframe) - filtered_dataframe, _ = self.dh.filter_features( + original_feature_list = dh.find_features(unfiltered_dataframe) + filtered_dataframe, _ = dh.filter_features( unfiltered_dataframe, original_feature_list, training_filter=False ) - filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe) - self.dh.data_dictionary["prediction_features"] = filtered_dataframe + filtered_dataframe = dh.standardize_data_from_metadata(filtered_dataframe) + dh.data_dictionary["prediction_features"] = filtered_dataframe # optional additional data cleaning/analysis - self.data_cleaning_predict(filtered_dataframe) + self.data_cleaning_predict(dh) - predictions = self.model.predict(self.dh.data_dictionary["prediction_features"]) + predictions = self.model.predict(dh.data_dictionary["prediction_features"]) # compute the non-standardized predictions - self.dh.predictions = (predictions + 1) * (self.dh.data["labels_max"] - - self.dh.data["labels_min"]) / 2 + self.dh.data[ - "labels_min"] + dh.predictions = (predictions + 1) * (dh.data["labels_max"] - + dh.data["labels_min"]) / 2 + dh.data["labels_min"] # logger.info("--------------------Finished prediction--------------------") - return (self.dh.predictions, self.dh.do_predict) + return (dh.predictions, dh.do_predict) From 255d35976e2737573fc09aabee65a5e776f18f09 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 24 May 2022 12:58:53 +0200 Subject: [PATCH 045/518] add priority metadata to pairs to avoid a sync of train time + train period --- freqtrade/freqai/data_drawer.py | 13 +++++++++++++ freqtrade/freqai/freqai_interface.py | 10 +++++++++- .../prediction_models/CatboostPredictionModel.py | 4 +++- 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 51f56fae6..a47ff6ec2 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -56,5 +56,18 @@ class FreqaiDataDrawer: model_filename = self.pair_dict[metadata['pair']]['model_filename'] = '' coin_first = self.pair_dict[metadata['pair']]['first'] = True trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 + self.pair_dict[metadata['pair']]['priority'] = 1 return model_filename, trained_timestamp, coin_first + + def set_pair_dict_info(self, metadata: dict) -> None: + pair_in_dict = self.pair_dict.get(metadata['pair']) + if pair_in_dict: + return + else: + self.pair_dict[metadata['pair']] = {} + self.pair_dict[metadata['pair']]['model_filename'] = '' + self.pair_dict[metadata['pair']]['first'] = True + self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 + self.pair_dict[metadata['pair']]['priority'] = 1 + return diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 19b7dbb27..3fdd379dc 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -109,7 +109,10 @@ class IFreqaiModel(ABC): # FreqaiDataKitchen is reinstantiated for each coin if self.live: - if not self.training_on_separate_thread: + self.data_drawer.set_pair_dict_info(metadata) + if (not self.training_on_separate_thread and + self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1): + self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) dh = self.start_live(dataframe, metadata, strategy, self.dh) @@ -212,6 +215,7 @@ class IFreqaiModel(ABC): else: logger.info("FreqAI training a new model on background thread.") + self.data_drawer.pair_dict[metadata['pair']]['priority'] = 1 self.model = dh.load_data(coin=metadata['pair']) @@ -319,6 +323,9 @@ class IFreqaiModel(ABC): dh.set_new_model_names(metadata, new_trained_timerange) + # set this coin to lower priority to allow other coins in white list to get trained + self.data_drawer.pair_dict[metadata['pair']]['priority'] = 0 + dh.save_data(self.model, coin=metadata['pair']) self.training_on_separate_thread = False @@ -344,6 +351,7 @@ class IFreqaiModel(ABC): dh.set_new_model_names(metadata, new_trained_timerange) self.data_drawer.pair_dict[metadata['pair']]['first'] = False + dh.save_data(self.model, coin=metadata['pair']) self.retrain = False diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 87ddfdb66..73ea46032 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -51,7 +51,9 @@ class CatboostPredictionModel(IFreqaiModel): :returns: :model: Trained model which can be used to inference (self.predict) """ - logger.info("--------------------Starting training--------------------") + + logger.info('--------------------Starting training' + f'{metadata["pair"]} --------------------') # create the full feature list based on user config info dh.training_features_list = dh.find_features(unfiltered_dataframe) From 31ae2b30606f3c52fb7ee1e3d170e59a8bc06b31 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 24 May 2022 14:46:16 +0200 Subject: [PATCH 046/518] alleviate FutureWarning in sklearn about ensuring svm model features are passed with identical order --- docs/freqai.md | 41 ++++++++++++++++++-- freqtrade/freqai/data_kitchen.py | 8 +++- freqtrade/strategy/interface.py | 2 +- freqtrade/templates/FreqaiExampleStrategy.py | 21 +++++++--- 4 files changed, 61 insertions(+), 11 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 606b88912..27d393d0a 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -105,11 +105,11 @@ config setup includes: ### Building the feature set -Most of these parameters are controlling the feature data set. Features are added by the user -inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`: +Features are added by the user inside the `populate_any_indicators()` method of the strategy +by prepending indicators with `%`: ```python - def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""): informative['%-''%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) informative['%-' + coin + "adx"] = ta.ADX(informative, window=20) @@ -120,11 +120,46 @@ inside the `populate_any_indicators()` method of the strategy by prepending indi informative['%-' + coin + "bb_width"] = ( informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"] ) / informative[coin + "bb_middleband"] + + + + # The following code automatically adds features according to the `shift` parameter passed + # in the config. Do not remove + indicators = [col for col in informative if col.startswith('%')] + for n in range(self.freqai_info["feature_parameters"]["shift"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + # The following code safely merges into the base timeframe. + # Do not remove. + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]] + df = df.drop(columns=skip_columns) ``` The user of the present example does not want to pass the `bb_lowerband` as a feature to the model, and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the model for training/prediction and has therfore prepended it with `%`._ +Note: features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()` +will fail in live/dry. If the user wishes to add generalized features that are not associated with +a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` +(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`: + +```python + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""): + + + # Add generalized indicators here (because in live, it will call only this function to populate + # indicators for retraining). Notice how we ensure not to add them multiple times by associating + # these generalized indicators to the basepair/timeframe + if pair == metadata['pair'] and tf == self.timeframe: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + + (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()` diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index a4867d7eb..765c58a37 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -823,7 +823,9 @@ class FreqaiDataKitchen: pairs = self.freqai_config.get("corr_pairlist", []) for tf in self.freqai_config.get("timeframes"): - dataframe = strategy.populate_any_indicators(metadata['pair'], + dataframe = strategy.populate_any_indicators( + metadata, + metadata['pair'], dataframe.copy(), tf, base_dataframes[tf], @@ -833,7 +835,9 @@ class FreqaiDataKitchen: for i in pairs: if metadata['pair'] in i: continue # dont repeat anything from whitelist - dataframe = strategy.populate_any_indicators(i, + dataframe = strategy.populate_any_indicators( + metadata, + i, dataframe.copy(), tf, corr_dataframes[i][tf], diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index e681d70bd..6237e3397 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -532,7 +532,7 @@ class IStrategy(ABC, HyperStrategyMixin): """ return None - def populate_any_indicators(self, pair: str, df: DataFrame, tf: str, + def populate_any_indicators(self, metadata: dict, pair: str, df: DataFrame, tf: str, informative: DataFrame = None, coin: str = "") -> DataFrame: """ Function designed to automatically generate, name and merge features diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 690532c10..d2eb2c306 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -63,7 +63,7 @@ class FreqaiExampleStrategy(IStrategy): def bot_start(self): self.model = CustomModel(self.config) - def populate_any_indicators(self, pair, df, tf, informative=None, coin=""): + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""): """ Function designed to automatically generate, name and merge features from user indicated timeframes in the configuration file. User controls the indicators @@ -124,8 +124,9 @@ class FreqaiExampleStrategy(IStrategy): informative[coin + "pct-change"] = informative["close"].pct_change() + # The following code automatically adds features according to the `shift` parameter passed + # in the config. Do not remove indicators = [col for col in informative if col.startswith('%')] - for n in range(self.freqai_info["feature_parameters"]["shift"] + 1): if n == 0: continue @@ -133,28 +134,38 @@ class FreqaiExampleStrategy(IStrategy): informative_shift = informative_shift.add_suffix("_shift-" + str(n)) informative = pd.concat((informative, informative_shift), axis=1) + # The following code safely merges into the base timeframe. + # Do not remove. df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]] df = df.drop(columns=skip_columns) + # Add generalized indicators (not associated to any individual coin or timeframe) here + # because in live, it will call this function to populate + # indicators during training. Notice how we ensure not to add them multiple times + if pair == metadata['pair'] and tf == self.timeframe: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # the configuration file parameters are stored here self.freqai_info = self.config["freqai"] self.pair = metadata['pair'] # the following loops are necessary for building the features # indicated by the user in the configuration file. + # All indicators must be populated by populate_any_indicators() for live functionality + # to work correctly. for tf in self.freqai_info["timeframes"]: - dataframe = self.populate_any_indicators(self.pair, dataframe.copy(), tf, + dataframe = self.populate_any_indicators(metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-") for pair in self.freqai_info["corr_pairlist"]: if metadata['pair'] in pair: continue # do not include whitelisted pair twice if it is in corr_pairlist dataframe = self.populate_any_indicators( - pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" + metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) # the model will return 4 values, its prediction, an indication of whether or not the From 58b5abbaa6a3f937dc7c7cf13ebba248a2430219 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 24 May 2022 15:28:38 +0200 Subject: [PATCH 047/518] improve multithreaded training queue system --- freqtrade/freqai/data_drawer.py | 16 +++++++++++++++- freqtrade/freqai/freqai_interface.py | 11 +++++++---- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index a47ff6ec2..a5d8a2123 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -18,7 +18,7 @@ class FreqaiDataDrawer: This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is reinstantiated for each coin. """ - def __init__(self, full_path: Path): + def __init__(self, full_path: Path, pair_whitelist): # dictionary holding all pair metadata necessary to load in from disk self.pair_dict: Dict[str, Any] = {} @@ -27,6 +27,8 @@ class FreqaiDataDrawer: self.pair_data_dict: Dict[str, Any] = {} self.full_path = full_path self.load_drawer_from_disk() + self.training_queue: Dict[str, int] = {} + self.create_training_queue(pair_whitelist) def load_drawer_from_disk(self): exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists() @@ -71,3 +73,15 @@ class FreqaiDataDrawer: self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 self.pair_dict[metadata['pair']]['priority'] = 1 return + + def create_training_queue(self, pairs: list) -> None: + for i, pair in enumerate(pairs): + self.training_queue[pair] = i + 1 + + def pair_to_end_of_training_queue(self, pair: str) -> None: + # march all pairs up in the queue + for p in self.training_queue: + self.training_queue[p] -= 1 + + # send pair to end of queue + self.training_queue[pair] = len(self.training_queue) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 3fdd379dc..55733b844 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -73,7 +73,8 @@ class IFreqaiModel(ABC): # self.new_trained_timerange = TimeRange() self.set_full_path() - self.data_drawer = FreqaiDataDrawer(Path(self.full_path)) + self.data_drawer = FreqaiDataDrawer(Path(self.full_path), + self.config['exchange']['pair_whitelist']) def assert_config(self, config: Dict[str, Any]) -> None: @@ -110,8 +111,9 @@ class IFreqaiModel(ABC): # FreqaiDataKitchen is reinstantiated for each coin if self.live: self.data_drawer.set_pair_dict_info(metadata) + print('Current train queue:', self.data_drawer.training_queue) if (not self.training_on_separate_thread and - self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1): + self.data_drawer.training_queue == 1): self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) @@ -323,8 +325,9 @@ class IFreqaiModel(ABC): dh.set_new_model_names(metadata, new_trained_timerange) - # set this coin to lower priority to allow other coins in white list to get trained - self.data_drawer.pair_dict[metadata['pair']]['priority'] = 0 + # send the pair to the end of the queue so other coins can take on the background thread + # retraining + self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) dh.save_data(self.model, coin=metadata['pair']) From 35bed842cb5ce5ef780544c1a5f6cedb1d6b5532 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 11:31:03 +0200 Subject: [PATCH 048/518] cleanup, add clarity to comments and docstrings --- freqtrade/freqai/freqai_interface.py | 169 +++++++++++++-------------- 1 file changed, 81 insertions(+), 88 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 55733b844..d60f37ffb 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -21,18 +21,6 @@ from freqtrade.strategy.interface import IStrategy pd.options.mode.chained_assignment = None logger = logging.getLogger(__name__) -# FIXME: suppress stdout for background training? -# class DummyFile(object): -# def write(self, x): pass - - -# @contextlib.contextmanager -# def nostdout(): -# save_stdout = sys.stdout -# sys.stdout = DummyFile() -# yield -# sys.stdout = save_stdout - def threaded(fn): def wrapper(*args, **kwargs): @@ -57,8 +45,6 @@ class IFreqaiModel(ABC): self.data_split_parameters = config["freqai"]["data_split_parameters"] self.model_training_parameters = config["freqai"]["model_training_parameters"] self.feature_parameters = config["freqai"]["feature_parameters"] - # self.backtest_timerange = config["timerange"] - self.time_last_trained = None self.current_time = None self.model = None @@ -66,12 +52,6 @@ class IFreqaiModel(ABC): self.training_on_separate_thread = False self.retrain = False self.first = True - # if self.freqai_info.get('live_trained_timerange'): - # self.new_trained_timerange = TimeRange.parse_timerange( - # self.freqai_info['live_trained_timerange']) - # else: - # self.new_trained_timerange = TimeRange() - self.set_full_path() self.data_drawer = FreqaiDataDrawer(Path(self.full_path), self.config['exchange']['pair_whitelist']) @@ -93,12 +73,7 @@ class IFreqaiModel(ABC): """ Entry point to the FreqaiModel from a specific pair, it will train a new model if necessary before making the prediction. - The backtesting and training paradigm is a sliding training window - with a following backtest window. Both windows slide according to the - length of the backtest window. This function is not intended to be - overridden by children of IFreqaiModel, but technically, it can be - if the user wishes to make deeper changes to the sliding window - logic. + :params: :dataframe: Full dataframe coming from strategy - it contains entire backtesting timerange + additional historical data necessary to train @@ -108,10 +83,12 @@ class IFreqaiModel(ABC): self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) - # FreqaiDataKitchen is reinstantiated for each coin + # For live, we may be training new models on a separate thread while other pairs still need + # to inference their historical models. Here we use a training queue system to handle this + # and we keep the flag self.training_on_separate_threaad in the current object to help + # determine what the current pair will do if self.live: self.data_drawer.set_pair_dict_info(metadata) - print('Current train queue:', self.data_drawer.training_queue) if (not self.training_on_separate_thread and self.data_drawer.training_queue == 1): @@ -124,13 +101,38 @@ class IFreqaiModel(ABC): self.live, metadata["pair"]) dh = self.start_live(dataframe, metadata, strategy, self.dh_fg) - return (dh.full_predictions, dh.full_do_predict, - dh.full_target_mean, dh.full_target_std) + # return (dh.full_predictions, dh.full_do_predict, + # dh.full_target_mean, dh.full_target_std) - # Backtesting only - self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) + # For backtesting, each pair enters and then gets trained for each window along the + # sliding window defined by "train_period" (training window) and "backtest_period" + # (backtest window, i.e. window immediately following the training window). + # FreqAI slides the window and sequentially builds the backtesting results before returning + # the concatenated results for the full backtesting period back to the strategy. + else: + self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) + logger.info(f'Training {len(self.dh.training_timeranges)} timeranges') + dh = self.start_backtesting(dataframe, metadata, self.dh) - logger.info(f'Training {len(self.dh.training_timeranges)} timeranges') + return (dh.full_predictions, dh.full_do_predict, + dh.full_target_mean, dh.full_target_std) + + def start_backtesting(self, dataframe: DataFrame, metadata: dict, + dh: FreqaiDataKitchen) -> FreqaiDataKitchen: + """ + The main broad execution for backtesting. For backtesting, each pair enters and then gets + trained for each window along the sliding window defined by "train_period" (training window) + and "backtest_period" (backtest window, i.e. window immediately following the + training window). FreqAI slides the window and sequentially builds the backtesting results + before returning the concatenated results for the full backtesting period back to the + strategy. + :params: + dataframe: DataFrame = strategy passed dataframe + metadata: Dict = pair metadata + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + :returns: + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + """ # Loop enforcing the sliding window training/backtesting paradigm # tr_train is the training time range e.g. 1 historical month @@ -138,49 +140,54 @@ class IFreqaiModel(ABC): # following tr_train. Both of these windows slide through the # entire backtest for tr_train, tr_backtest in zip( - self.dh.training_timeranges, self.dh.backtesting_timeranges + dh.training_timeranges, dh.backtesting_timeranges ): gc.collect() - # self.config['timerange'] = tr_train - self.dh.data = {} # clean the pair specific data between models + dh.data = {} # clean the pair specific data between training window sliding self.training_timerange = tr_train - dataframe_train = self.dh.slice_dataframe(tr_train, dataframe) - dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe) + dataframe_train = dh.slice_dataframe(tr_train, dataframe) + dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe) logger.info("training %s for %s", metadata["pair"], tr_train) trained_timestamp = TimeRange.parse_timerange(tr_train) - self.dh.data_path = Path(self.dh.full_path / - str("sub-train" + "-" + metadata['pair'].split("/")[0] + - str(int(trained_timestamp.stopts)))) - if not self.model_exists(metadata["pair"], self.dh, + dh.data_path = Path(dh.full_path / + str("sub-train" + "-" + metadata['pair'].split("/")[0] + + str(int(trained_timestamp.stopts)))) + if not self.model_exists(metadata["pair"], dh, trained_timestamp=trained_timestamp.stopts): - self.model = self.train(dataframe_train, metadata, self.dh) - self.dh.save_data(self.model) + self.model = self.train(dataframe_train, metadata, dh) + dh.save_data(self.model) else: - self.model = self.dh.load_data() + self.model = dh.load_data() + # strategy_provided_features = self.dh.find_features(dataframe_train) - # # TOFIX doesnt work with PCA + # # FIXME doesnt work with PCA # if strategy_provided_features != self.dh.training_features_list: # logger.info("User changed input features, retraining model.") # self.model = self.train(dataframe_train, metadata) # self.dh.save_data(self.model) - preds, do_preds = self.predict(dataframe_backtest, self.dh) + preds, do_preds = self.predict(dataframe_backtest, dh) - self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) - print('predictions', len(self.dh.full_predictions), - 'do_predict', len(self.dh.full_do_predict)) + dh.append_predictions(preds, do_preds, len(dataframe_backtest)) + print('predictions', len(dh.full_predictions), + 'do_predict', len(dh.full_do_predict)) - self.dh.fill_predictions(len(dataframe)) + dh.fill_predictions(len(dataframe)) - return (self.dh.full_predictions, self.dh.full_do_predict, - self.dh.full_target_mean, self.dh.full_target_std) + return dh def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen: """ The main broad execution for dry/live. This function will check if a retraining should be performed, and if so, retrain and reset the model. - + :params: + dataframe: DataFrame = strategy passed dataframe + metadata: Dict = pair metadata + strategy: IStrategy = currently employed strategy + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + :returns: + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only """ (model_filename, @@ -190,22 +197,16 @@ class IFreqaiModel(ABC): if not self.training_on_separate_thread: file_exists = False - if trained_timestamp != 0: + if trained_timestamp != 0: # historical model available dh.set_paths(metadata, trained_timestamp) - # data_drawer thinks the file eixts, verify here file_exists = self.model_exists(metadata['pair'], dh, trained_timestamp=trained_timestamp, model_filename=model_filename) - # if not self.training_on_separate_thread: - # this will also prevent other pairs from trying to train simultaneously. (self.retrain, new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp) dh.set_paths(metadata, new_trained_timerange.stopts) - # if self.training_on_separate_thread: - # logger.info("FreqAI training a new model on background thread.") - # self.retrain = False if self.retrain or not file_exists: if coin_first: @@ -217,10 +218,10 @@ class IFreqaiModel(ABC): else: logger.info("FreqAI training a new model on background thread.") - self.data_drawer.pair_dict[metadata['pair']]['priority'] = 1 self.model = dh.load_data(coin=metadata['pair']) + # FIXME # strategy_provided_features = dh.find_features(dataframe) # if strategy_provided_features != dh.training_features_list: # self.train_model_in_series(new_trained_timerange, metadata, strategy) @@ -240,17 +241,17 @@ class IFreqaiModel(ABC): if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): dh.principal_component_analysis() - # if self.feature_parameters["determine_statistical_distributions"]: - # dh.determine_statistical_distributions() - # if self.feature_parameters["remove_outliers"]: - # dh.remove_outliers(predict=False) - if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): dh.use_SVM_to_remove_outliers(predict=False) if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): dh.data["avg_mean_dist"] = dh.compute_distances() + # if self.feature_parameters["determine_statistical_distributions"]: + # dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # dh.remove_outliers(predict=False) + def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None: """ Base data cleaning method for predict. @@ -265,16 +266,16 @@ class IFreqaiModel(ABC): if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): dh.pca_transform() - # if self.feature_parameters["determine_statistical_distributions"]: - # dh.determine_statistical_distributions() - # if self.feature_parameters["remove_outliers"]: - # dh.remove_outliers(predict=True) # creates dropped index - if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): dh.use_SVM_to_remove_outliers(predict=True) if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'): - dh.check_if_pred_in_training_spaces() # sets do_predict + dh.check_if_pred_in_training_spaces() + + # if self.feature_parameters["determine_statistical_distributions"]: + # dh.determine_statistical_distributions() + # if self.feature_parameters["remove_outliers"]: + # dh.remove_outliers(predict=True) # creates dropped index def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None, model_filename: str = '') -> bool: @@ -285,8 +286,6 @@ class IFreqaiModel(ABC): """ coin, _ = pair.split("/") - # if self.live and trained_timestamp == 0: - # dh.model_filename = model_filename if not self.live: dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp) @@ -312,7 +311,6 @@ class IFreqaiModel(ABC): dh.download_new_data_for_retraining(new_trained_timerange, metadata) corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange, metadata) - unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy, corr_dataframes, base_dataframes, @@ -322,13 +320,8 @@ class IFreqaiModel(ABC): self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts - dh.set_new_model_names(metadata, new_trained_timerange) - - # send the pair to the end of the queue so other coins can take on the background thread - # retraining self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) - dh.save_data(self.model, coin=metadata['pair']) self.training_on_separate_thread = False @@ -350,11 +343,8 @@ class IFreqaiModel(ABC): self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts - dh.set_new_model_names(metadata, new_trained_timerange) - self.data_drawer.pair_dict[metadata['pair']]['first'] = False - dh.save_data(self.model, coin=metadata['pair']) self.retrain = False @@ -380,7 +370,7 @@ class IFreqaiModel(ABC): can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. :params: - :data_dictionary: the dictionary constructed by DataHandler to hold + data_dictionary: Dict = the dictionary constructed by DataHandler to hold all the training and test data/labels. """ @@ -391,11 +381,13 @@ class IFreqaiModel(ABC): dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]: """ Filter the prediction features data and predict with it. - :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :param: + unfiltered_dataframe: Full dataframe for the current backtest period. + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only :return: :predictions: np.array of predictions :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove - data (NaNs) or felt uncertain about data (PCA and DI index) + data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index) """ @abstractmethod @@ -403,7 +395,8 @@ class IFreqaiModel(ABC): """ User defines the labels here (target values). :params: - :dataframe: the full dataframe for the present training period + dataframe: DataFrame = the full dataframe for the present training period + dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only """ return From 7ff32586075fdb4266ce53027e8f99054385388c Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 11:43:45 +0200 Subject: [PATCH 049/518] remove assertions, log error if user has not assigned freqai in config, fix stratify bug --- config_examples/config_freqai.example.json | 1 - freqtrade/freqai/data_kitchen.py | 39 ++++++++++++---------- freqtrade/freqai/freqai_interface.py | 23 +++++++------ 3 files changed, 34 insertions(+), 29 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index ed3782775..5f7f38373 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -62,7 +62,6 @@ "corr_pairlist": [ "BTC/USDT", "ETH/USDT", - "LINK/USDT", "DOT/USDT" ], "feature_parameters": { diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 765c58a37..b0eb8b40d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -73,23 +73,26 @@ class FreqaiDataKitchen: self.data_drawer = data_drawer def assert_config(self, config: Dict[str, Any], live: bool) -> None: - assert config.get('freqai'), "No Freqai parameters found in config file." - assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in" - "config file.") - assert type(config.get('freqai', {}) - .get('train_period')) is int, ('Can only train on full day period.' - 'No fractional days permitted.') - assert config.get('freqai', {}).get('backtest_period'), ("No Freqai backtest_period found" - "in config file.") - if not live: - assert type(config.get('freqai', {}) - .get('backtest_period')) is int, ('Can only backtest on full day' - 'backtest_period. Only live/dry mode' - 'allows fractions of days') - assert config.get('freqai', {}).get('identifier'), ("No Freqai identifier found in config" - "file.") - assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" - "found in config file.") + if not config.get('freqai'): + logger.error('No Freqai parameters found in config file.') + + # assert config.get('freqai'), "No Freqai parameters found in config file." + # assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in" + # "config file.") + # assert type(config.get('freqai', {}) + # .get('train_period')) is int, ('Can only train on full day period.' + # 'No fractional days permitted.') + # assert config.get('freqai', {}).get('backtest_period'), ("No Freqai backtest_period found" + # "in config file.") + # if not live: + # assert type(config.get('freqai', {}) + # .get('backtest_period')) is int, ('Can only backtest on full day' + # 'backtest_period. Only live/dry mode' + # 'allows fractions of days') + # assert config.get('freqai', {}).get('identifier'), ("No Freqai identifier found in config" + # "file.") + # assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" + # "found in config file.") def set_paths(self, metadata: dict, trained_timestamp: int = None,) -> None: self.full_path = Path(self.config['user_data_dir'] / @@ -234,6 +237,8 @@ class FreqaiDataKitchen: for i in range(1, len(stratification)): if i % self.freqai_config.get("feature_parameters", {}).get("stratify", 0) == 0: stratification[i] = 1 + else: + stratification = None ( train_features, diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index d60f37ffb..041343683 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -57,17 +57,18 @@ class IFreqaiModel(ABC): self.config['exchange']['pair_whitelist']) def assert_config(self, config: Dict[str, Any]) -> None: - - assert config.get('freqai'), "No Freqai parameters found in config file." - assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai" - "data_split_parameters" - "in config file.") - assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai" - "modeltrainingparameters" - "found in config file.") - assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai" - "feature_parameters found in" - "config file.") + if not config.get('freqai'): + logger.error('No Freqai parameters found in config file.') + # assert config.get('freqai'), "No Freqai parameters found in config file." + # assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai" + # "data_split_parameters" + # "in config file.") + # assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai" + # "modeltrainingparameters" + # "found in config file.") + # assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai" + # "feature_parameters found in" + # "config file.") def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ From 7486d9d9e2880b27591d3bb94498273019ce3f95 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 12:37:25 +0200 Subject: [PATCH 050/518] proper validation of freqai config parameters --- freqtrade/configuration/config_validation.py | 16 ++++++++++++++ freqtrade/constants.py | 11 ++++++++++ freqtrade/enums/runmode.py | 3 +-- freqtrade/freqai/data_kitchen.py | 23 -------------------- freqtrade/freqai/freqai_interface.py | 18 +++++---------- 5 files changed, 34 insertions(+), 37 deletions(-) diff --git a/freqtrade/configuration/config_validation.py b/freqtrade/configuration/config_validation.py index ee846e7e6..5f1f68554 100644 --- a/freqtrade/configuration/config_validation.py +++ b/freqtrade/configuration/config_validation.py @@ -85,6 +85,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False) _validate_unlimited_amount(conf) _validate_ask_orderbook(conf) validate_migrated_strategy_settings(conf) + _validate_freqai(conf) # validate configuration before returning logger.info('Validating configuration ...') @@ -163,6 +164,21 @@ def _validate_edge(conf: Dict[str, Any]) -> None: ) +def _validate_freqai(conf: Dict[str, Any]) -> None: + """ + Freqai param validator + """ + + if not conf.get('freqai', {}): + return + + for param in constants.SCHEMA_FREQAI_REQUIRED: + if param not in conf.get('freqai', {}): + raise OperationalException( + f'{param} not found in Freqai config' + ) + + def _validate_whitelist(conf: Dict[str, Any]) -> None: """ Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does. diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 05581cc3a..eea657d84 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -515,6 +515,17 @@ SCHEMA_MINIMAL_REQUIRED = [ 'dataformat_trades', ] +SCHEMA_FREQAI_REQUIRED = [ + 'timeframes', + 'train_period', + 'backtest_period', + 'identifier', + 'corr_pairlist', + 'feature_parameters', + 'data_split_parameters', + 'model_training_parameters' +] + CANCEL_REASON = { "TIMEOUT": "cancelled due to timeout", "PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open", diff --git a/freqtrade/enums/runmode.py b/freqtrade/enums/runmode.py index c280edf7c..6545aaec7 100644 --- a/freqtrade/enums/runmode.py +++ b/freqtrade/enums/runmode.py @@ -15,10 +15,9 @@ class RunMode(Enum): UTIL_NO_EXCHANGE = "util_no_exchange" PLOT = "plot" WEBSERVER = "webserver" - FREQAI = "freqai" OTHER = "other" TRADING_MODES = [RunMode.LIVE, RunMode.DRY_RUN] -OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT, RunMode.FREQAI] +OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT] NON_UTIL_MODES = TRADING_MODES + OPTIMIZE_MODES diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b0eb8b40d..c5f57bf86 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -44,7 +44,6 @@ class FreqaiDataKitchen: self.data: Dict[Any, Any] = {} self.data_dictionary: Dict[Any, Any] = {} self.config = config - self.assert_config(self.config, live) self.freqai_config = config["freqai"] self.predictions: npt.ArrayLike = np.array([]) self.do_predict: npt.ArrayLike = np.array([]) @@ -72,28 +71,6 @@ class FreqaiDataKitchen: self.data_drawer = data_drawer - def assert_config(self, config: Dict[str, Any], live: bool) -> None: - if not config.get('freqai'): - logger.error('No Freqai parameters found in config file.') - - # assert config.get('freqai'), "No Freqai parameters found in config file." - # assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in" - # "config file.") - # assert type(config.get('freqai', {}) - # .get('train_period')) is int, ('Can only train on full day period.' - # 'No fractional days permitted.') - # assert config.get('freqai', {}).get('backtest_period'), ("No Freqai backtest_period found" - # "in config file.") - # if not live: - # assert type(config.get('freqai', {}) - # .get('backtest_period')) is int, ('Can only backtest on full day' - # 'backtest_period. Only live/dry mode' - # 'allows fractions of days') - # assert config.get('freqai', {}).get('identifier'), ("No Freqai identifier found in config" - # "file.") - # assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters" - # "found in config file.") - def set_paths(self, metadata: dict, trained_timestamp: int = None,) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 041343683..71807ad19 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -13,6 +13,7 @@ from pandas import DataFrame from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode +from freqtrade.exceptions import OperationalException from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.strategy.interface import IStrategy @@ -57,18 +58,11 @@ class IFreqaiModel(ABC): self.config['exchange']['pair_whitelist']) def assert_config(self, config: Dict[str, Any]) -> None: - if not config.get('freqai'): - logger.error('No Freqai parameters found in config file.') - # assert config.get('freqai'), "No Freqai parameters found in config file." - # assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai" - # "data_split_parameters" - # "in config file.") - # assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai" - # "modeltrainingparameters" - # "found in config file.") - # assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai" - # "feature_parameters found in" - # "config file.") + + if not config.get('freqai', {}): + raise OperationalException( + "No freqai parameters found in configuration file." + ) def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: """ From b79d4e8876dc612b6f56db75ed8cf18d1c62d7c5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 14:40:32 +0200 Subject: [PATCH 051/518] Allow user to go live and start from pretrained models (after a completed backtest) by simply reusing the `identifier` config parameter while dry/live. --- config_examples/config_freqai.example.json | 5 ++-- docs/freqai.md | 23 ++++++++---------- freqtrade/constants.py | 4 +--- freqtrade/freqai/data_kitchen.py | 27 +++++++++++----------- freqtrade/freqai/freqai_interface.py | 13 +++++++---- 5 files changed, 33 insertions(+), 39 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 5f7f38373..7582afef0 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -55,10 +55,9 @@ "15m" ], "train_period": 30, - "backtest_period": 7, + "backtest_period": 10, "identifier": "example", - "live_trained_timerange": "", - "live_full_backtestrange": "", + "live_trained_timestamp": 0, "corr_pairlist": [ "BTC/USDT", "ETH/USDT", diff --git a/docs/freqai.md b/docs/freqai.md index 27d393d0a..1c6a4ec4a 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -158,7 +158,7 @@ a specific pair or timeframe, they should use the following structure inside `po if pair == metadata['pair'] and tf == self.timeframe: df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 - +``` (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) @@ -270,27 +270,22 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example. By default, Freqai will not find find any existing models and will start by training a new one given the user configuration settings. Following training, it will use that model to predict for the duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain -a new model, and begin making predictions with the updated model. +a new model, and begin making predictions with the updated model. FreqAI in live mode permits +the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent +retraining. -If the user wishes to start dry/live from a saved model, the following configuration -parameters need to be set: +If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse +the same `identifier` parameter ```json "freqai": { "identifier": "example", - "live_trained_timerange": "20220330-20220429", - "live_full_backtestrange": "20220302-20220501" } ``` -Where the `identifier` is the same identifier which was set during the backtesting/training. Meanwhile, -the `live_trained_timerange` is the sub-trained timerange (the training window) which was set -during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`. -`live_full_backtestrange` was the full data range associated with the backtest/training (the full time -window that the training window and backtesting windows slide through). These values can be located -inside the `user_data/models/` directory. In this case, although Freqai will initiate with a -pre-trained model, if a full `backtest_period` has elapsed since the end of the user set -`live_trained_timerange`, it will self retrain. +In this case, although Freqai will initiate with a +pre-trained model, it will still check to see how much time has elapsed since the model was trained, +and if a full `backtest_period` has elapsed since the end of the loaded model, FreqAI will self retrain. ## Data anylsis techniques diff --git a/freqtrade/constants.py b/freqtrade/constants.py index eea657d84..f6ef462c3 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -440,15 +440,13 @@ CONF_SCHEMA = { "train_period": {"type": "integer", "default": 0}, "backtest_period": {"type": "float", "default": 7}, "identifier": {"type": "str", "default": "example"}, - "live_trained_timerange": {"type": "str"}, - "live_full_backtestrange": {"type": "str"}, "corr_pairlist": {"type": "list"}, "feature_parameters": { "type": "object", "properties": { "period": {"type": "integer"}, "shift": {"type": "integer", "default": 0}, - "DI_threshold": {"type": "integer", "default": 0}, + "DI_threshold": {"type": "float", "default": 0}, "weight_factor": {"type": "number", "default": 0}, "principal_component_analysis": {"type": "boolean", "default": False}, "use_SVM_to_remove_outliers": {"type": "boolean", "default": False}, diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index c5f57bf86..eafb9cc46 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -74,8 +74,7 @@ class FreqaiDataKitchen: def set_paths(self, metadata: dict, trained_timestamp: int = None,) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / - str(self.freqai_config.get('live_full_backtestrange') + - self.freqai_config.get('identifier'))) + str(self.freqai_config.get('identifier'))) self.data_path = Path(self.full_path / str("sub-train" + "-" + metadata['pair'].split("/")[0] + @@ -114,11 +113,11 @@ class FreqaiDataKitchen: save_path / str(self.model_filename + "_trained_df.pkl") ) - if self.live: - self.data_drawer.model_dictionary[self.model_filename] = model - self.data_drawer.pair_dict[coin]['model_filename'] = self.model_filename - self.data_drawer.pair_dict[coin]['data_path'] = str(self.data_path) - self.data_drawer.save_drawer_to_disk() + # if self.live: + self.data_drawer.model_dictionary[self.model_filename] = model + self.data_drawer.pair_dict[coin]['model_filename'] = self.model_filename + self.data_drawer.pair_dict[coin]['data_path'] = str(self.data_path) + self.data_drawer.save_drawer_to_disk() # TODO add a helper function to let user save/load any data they are custom adding. We # do not want them having to edit the default save/load methods here. Below is an example @@ -142,9 +141,9 @@ class FreqaiDataKitchen: :model: User trained model which can be inferenced for new predictions """ - if self.live: - self.model_filename = self.data_drawer.pair_dict[coin]['model_filename'] - self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path']) + # if self.live: + self.model_filename = self.data_drawer.pair_dict[coin]['model_filename'] + self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path']) with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) @@ -696,7 +695,7 @@ class FreqaiDataKitchen: self.full_path = Path( self.config["user_data_dir"] / "models" - / str(full_timerange + self.freqai_config.get("identifier")) + / str(self.freqai_config.get("identifier")) ) config_path = Path(self.config["config_files"][0]) @@ -750,10 +749,10 @@ class FreqaiDataKitchen: str(int(trained_timerange.stopts)))) self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) - # this is not persistent at the moment TODO - self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) + + # self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts)) # enables persistence, but not fully implemented into save/load data yer - self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) + # self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 71807ad19..d7bbc549a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -77,13 +77,13 @@ class IFreqaiModel(ABC): """ self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) + self.data_drawer.set_pair_dict_info(metadata) # For live, we may be training new models on a separate thread while other pairs still need # to inference their historical models. Here we use a training queue system to handle this # and we keep the flag self.training_on_separate_threaad in the current object to help # determine what the current pair will do if self.live: - self.data_drawer.set_pair_dict_info(metadata) if (not self.training_on_separate_thread and self.data_drawer.training_queue == 1): @@ -137,6 +137,7 @@ class IFreqaiModel(ABC): for tr_train, tr_backtest in zip( dh.training_timeranges, dh.backtesting_timeranges ): + (_, _, _) = self.data_drawer.get_pair_dict_info(metadata) gc.collect() dh.data = {} # clean the pair specific data between training window sliding self.training_timerange = tr_train @@ -150,9 +151,12 @@ class IFreqaiModel(ABC): if not self.model_exists(metadata["pair"], dh, trained_timestamp=trained_timestamp.stopts): self.model = self.train(dataframe_train, metadata, dh) - dh.save_data(self.model) + self.data_drawer.pair_dict[metadata['pair']][ + 'trained_timestamp'] = trained_timestamp.stopts + dh.set_new_model_names(metadata, trained_timestamp) + dh.save_data(self.model, metadata['pair']) else: - self.model = dh.load_data() + self.model = dh.load_data(metadata['pair']) # strategy_provided_features = self.dh.find_features(dataframe_train) # # FIXME doesnt work with PCA @@ -295,8 +299,7 @@ class IFreqaiModel(ABC): def set_full_path(self) -> None: self.full_path = Path(self.config['user_data_dir'] / "models" / - str(self.freqai_info.get('live_full_backtestrange') + - self.freqai_info.get('identifier'))) + str(self.freqai_info.get('identifier'))) @threaded def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict, From 7593339c14c0f45011f88f6d7034e845ee6363f8 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 14:42:46 +0200 Subject: [PATCH 052/518] small cleanup --- docs/freqai.md | 2 +- freqtrade/commands/data_commands.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 1c6a4ec4a..403145525 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -110,7 +110,7 @@ by prepending indicators with `%`: ```python def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""): - informative['%-''%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) + informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) informative['%-' + coin + "adx"] = ta.ADX(informative, window=20) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 4588bf67b..c7e6a7b84 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -51,7 +51,6 @@ def start_download_data(args: Dict[str, Any]) -> None: markets = [p for p, m in exchange.markets.items() if market_is_active(m) or config.get('include_inactive')] if config.get('freqai') is not None: - assert config['freqai'].get('corr_pairlist'), "No corr_pairlist found in config." full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist'] if pair not in config['pairs']] expanded_pairs = expand_pairlist(full_pairs, markets) From d79983c791a3534d083960358d86631a80648d10 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 14:55:19 +0200 Subject: [PATCH 053/518] try to pass flake8 --- freqtrade/commands/data_commands.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index c7e6a7b84..2019413b5 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -50,7 +50,7 @@ def start_download_data(args: Dict[str, Any]) -> None: exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False) markets = [p for p, m in exchange.markets.items() if market_is_active(m) or config.get('include_inactive')] - if config.get('freqai') is not None: + if config.get('freqai', {}): full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist'] if pair not in config['pairs']] expanded_pairs = expand_pairlist(full_pairs, markets) From ff531c416f185ddaba02a38f0393f118e288afb4 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 25 May 2022 15:31:50 +0200 Subject: [PATCH 054/518] reduce `complexity` inside start_download_data() in an effort to appease flake8 --- freqtrade/commands/data_commands.py | 10 +++------- freqtrade/plugins/pairlist/pairlist_helpers.py | 11 +++++++++++ 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 2019413b5..21b7a3194 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -12,7 +12,7 @@ from freqtrade.enums import CandleType, RunMode, TradingMode from freqtrade.exceptions import OperationalException from freqtrade.exchange import timeframe_to_minutes from freqtrade.exchange.exchange import market_is_active -from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist +from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist from freqtrade.resolvers import ExchangeResolver @@ -50,12 +50,8 @@ def start_download_data(args: Dict[str, Any]) -> None: exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False) markets = [p for p, m in exchange.markets.items() if market_is_active(m) or config.get('include_inactive')] - if config.get('freqai', {}): - full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist'] - if pair not in config['pairs']] - expanded_pairs = expand_pairlist(full_pairs, markets) - else: - expanded_pairs = expand_pairlist(config['pairs'], markets) + + expanded_pairs = dynamic_expand_pairlist(config, markets) # Manual validations of relevant settings if not config['exchange'].get('skip_pair_validation', False): diff --git a/freqtrade/plugins/pairlist/pairlist_helpers.py b/freqtrade/plugins/pairlist/pairlist_helpers.py index 1de27fcbd..23233481a 100644 --- a/freqtrade/plugins/pairlist/pairlist_helpers.py +++ b/freqtrade/plugins/pairlist/pairlist_helpers.py @@ -40,3 +40,14 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str], except re.error as err: raise ValueError(f"Wildcard error in {pair_wc}, {err}") return result + + +def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]: + if config.get('freqai', {}): + full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist'] + if pair not in config['pairs']] + expanded_pairs = expand_pairlist(full_pairs, markets) + else: + expanded_pairs = expand_pairlist(config['pairs'], markets) + + return expanded_pairs From 6193205012f681d233933fee50df66f33b63ddcc Mon Sep 17 00:00:00 2001 From: robcaulk Date: Thu, 26 May 2022 21:07:50 +0200 Subject: [PATCH 055/518] fix bug for target_mean/std array merging in backtesting --- config_examples/config_freqai.example.json | 19 ++- docs/freqai.md | 106 +++++++++++--- freqtrade/freqai/data_kitchen.py | 131 ++++++++++-------- freqtrade/freqai/freqai_interface.py | 23 +-- .../CatboostPredictionModel.py | 13 +- freqtrade/templates/FreqaiExampleStrategy.py | 4 +- 6 files changed, 186 insertions(+), 110 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 7582afef0..b6c7ba7d8 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -1,7 +1,7 @@ { "max_open_trades": 1, "stake_currency": "USDT", - "stake_amount": 800, + "stake_amount": 900, "tradable_balance_ratio": 1, "fiat_display_currency": "USD", "dry_run": true, @@ -24,8 +24,7 @@ "rateLimit": 200 }, "pair_whitelist": [ - "BTC/USDT", - "ETH/USDT" + "BTC/USDT" ], "pair_blacklist": [] }, @@ -55,7 +54,7 @@ "15m" ], "train_period": 30, - "backtest_period": 10, + "backtest_period": 7, "identifier": "example", "live_trained_timestamp": 0, "corr_pairlist": [ @@ -64,16 +63,16 @@ "DOT/USDT" ], "feature_parameters": { - "period": 12, + "period": 24, "shift": 1, - "DI_threshold": 1, - "weight_factor": 0, + "DI_threshold": 0, + "weight_factor": 0.9, "principal_component_analysis": false, - "use_SVM_to_remove_outliers": false, - "stratify": 0 + "use_SVM_to_remove_outliers": true, + "stratify": 3 }, "data_split_parameters": { - "test_size": 0.25, + "test_size": 0.33, "random_state": 1 }, "model_training_parameters": { diff --git a/docs/freqai.md b/docs/freqai.md index 403145525..821f42258 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -221,33 +221,43 @@ This way, the user can return to using any model they wish by simply changing th ### Building a freqai strategy -The Freqai strategy requires the user to include the following lines of code in `populate_ any _indicators()` +The Freqai strategy requires the user to include the following lines of code in the strategy: ```python - from freqtrade.freqai.strategy_bridge import CustomModel + from freqtrade.freqai.strategy_bridge import CustomModel - def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # the configuration file parameters are stored here - self.freqai_info = self.config['freqai'] + def informative_pairs(self): + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["corr_pairlist"] + informative_pairs = [] + for tf in self.config["freqai"]["timeframes"]: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication + informative_pairs.append((pair, tf)) + return informative_pairs - # the model is instantiated here - self.model = CustomModel(self.config) + def bot_start(self): + self.model = CustomModel(self.config) - print('Populating indicators...') + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + self.freqai_info = self.config['freqai'] - # the following loops are necessary for building the features - # indicated by the user in the configuration file. - for tf in self.freqai_info['timeframes']: - for i in self.freqai_info['corr_pairlist']: - dataframe = self.populate_any_indicators(i, - dataframe.copy(), tf, coin=i.split("/")[0]+'-') + # the following loops are necessary for building the features + # indicated by the user in the configuration file. + for tf in self.freqai_info['timeframes']: + for i in self.freqai_info['corr_pairlist']: + dataframe = self.populate_any_indicators(i, + dataframe.copy(), tf, coin=i.split("/")[0]+'-') - # the model will return 4 values, its prediction, an indication of whether or not the prediction - # should be accepted, the target mean/std values from the labels used during each training period. - (dataframe['prediction'], dataframe['do_predict'], - dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata) + # the model will return 4 values, its prediction, an indication of whether or not the prediction + # should be accepted, the target mean/std values from the labels used during each training period. + (dataframe['prediction'], dataframe['do_predict'], + dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata) - return dataframe + return dataframe ``` The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds @@ -314,7 +324,7 @@ data point and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ -where $d_{ab}$ is the distance between the standardized points $a$ and $b$. $p$ +where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features i.e. the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: @@ -392,13 +402,63 @@ The user can stratify the training/testing data using: which will split the data chronolocially so that every X data points is a testing data point. In the present example, the user is asking for every third data point in the dataframe to be used for -testing, the other points are used for training. +testing, the other points are used for training. + + + + ## Additional information -### Feature standardization +### Feature normalization -The feature set created by the user is automatically standardized to the training +The feature set created by the user is automatically normalized to the training data only. This includes all test data and unseen prediction data (dry/live/backtest). ### File structure diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index eafb9cc46..b5f1f6edb 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -141,9 +141,9 @@ class FreqaiDataKitchen: :model: User trained model which can be inferenced for new predictions """ - # if self.live: - self.model_filename = self.data_drawer.pair_dict[coin]['model_filename'] - self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path']) + if self.live: + self.model_filename = self.data_drawer.pair_dict[coin]['model_filename'] + self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path']) with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp: self.data = json.load(fp) @@ -329,42 +329,6 @@ class FreqaiDataKitchen: :data_dictionary: updated dictionary with standardized values. """ # standardize the data by training stats - train_mean = data_dictionary["train_features"].mean() - train_std = data_dictionary["train_features"].std() - data_dictionary["train_features"] = ( - data_dictionary["train_features"] - train_mean - ) / train_std - data_dictionary["test_features"] = ( - data_dictionary["test_features"] - train_mean - ) / train_std - - train_labels_std = data_dictionary["train_labels"].std() - train_labels_mean = data_dictionary["train_labels"].mean() - data_dictionary["train_labels"] = ( - data_dictionary["train_labels"] - train_labels_mean - ) / train_labels_std - data_dictionary["test_labels"] = ( - data_dictionary["test_labels"] - train_labels_mean - ) / train_labels_std - - for item in train_std.keys(): - self.data[item + "_std"] = train_std[item] - self.data[item + "_mean"] = train_mean[item] - - self.data["labels_std"] = train_labels_std - self.data["labels_mean"] = train_labels_mean - - return data_dictionary - - def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: - """ - Standardize all data in the data_dictionary according to the training dataset - :params: - :data_dictionary: dictionary containing the cleaned and split training/test data/labels - :returns: - :data_dictionary: updated dictionary with standardized values. - """ - # standardize the data by training stats train_max = data_dictionary["train_features"].max() train_min = data_dictionary["train_features"].min() data_dictionary["train_features"] = 2 * ( @@ -392,9 +356,9 @@ class FreqaiDataKitchen: return data_dictionary - def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: + def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame: """ - Standardizes a set of data using the mean and standard deviation from + Normalize a set of data using the mean and standard deviation from the associated training data. :params: :df: Dataframe to be standardized @@ -406,19 +370,6 @@ class FreqaiDataKitchen: return df - def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame: - """ - Normalizes a set of data using the mean and standard deviation from - the associated training data. - :params: - :df: Dataframe to be standardized - """ - - for item in df.keys(): - df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"] - - return df - def split_timerange( self, tr: str, train_split: int = 28, bt_split: int = 7 ) -> Tuple[list, list]: @@ -657,12 +608,12 @@ class FreqaiDataKitchen: """ ones = np.ones(len_dataframe) - s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"] + target_mean, target_std = ones * self.data["target_mean"], ones * self.data["target_std"] self.full_predictions = np.append(self.full_predictions, predictions) self.full_do_predict = np.append(self.full_do_predict, do_predict) - self.full_target_mean = np.append(self.full_target_mean, s_mean) - self.full_target_std = np.append(self.full_target_std, s_std) + self.full_target_mean = np.append(self.full_target_mean, target_mean) + self.full_target_std = np.append(self.full_target_std, target_std) return @@ -827,6 +778,23 @@ class FreqaiDataKitchen: return dataframe + def fit_labels(self) -> None: + import scipy as spy + + f = spy.stats.norm.fit(self.data_dictionary["train_labels"]) + + # KEEPME incase we want to let user start to grab quantiles. + # upper_q = spy.stats.norm.ppf(self.freqai_config['feature_parameters'][ + # 'target_quantile'], *f) + # lower_q = spy.stats.norm.ppf(1 - self.freqai_config['feature_parameters'][ + # 'target_quantile'], *f) + + self.data["target_mean"], self.data["target_std"] = f[0], f[1] + # self.data["upper_quantile"] = upper_q + # self.data["lower_quantile"] = lower_q + + return + def np_encoder(self, object): if isinstance(object, np.generic): return object.item() @@ -968,3 +936,52 @@ class FreqaiDataKitchen: # ) # return + + # def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: + # """ + # standardize all data in the data_dictionary according to the training dataset + # :params: + # :data_dictionary: dictionary containing the cleaned and split training/test data/labels + # :returns: + # :data_dictionary: updated dictionary with standardized values. + # """ + # # standardize the data by training stats + # train_mean = data_dictionary["train_features"].mean() + # train_std = data_dictionary["train_features"].std() + # data_dictionary["train_features"] = ( + # data_dictionary["train_features"] - train_mean + # ) / train_std + # data_dictionary["test_features"] = ( + # data_dictionary["test_features"] - train_mean + # ) / train_std + + # train_labels_std = data_dictionary["train_labels"].std() + # train_labels_mean = data_dictionary["train_labels"].mean() + # data_dictionary["train_labels"] = ( + # data_dictionary["train_labels"] - train_labels_mean + # ) / train_labels_std + # data_dictionary["test_labels"] = ( + # data_dictionary["test_labels"] - train_labels_mean + # ) / train_labels_std + + # for item in train_std.keys(): + # self.data[item + "_std"] = train_std[item] + # self.data[item + "_mean"] = train_mean[item] + + # self.data["labels_std"] = train_labels_std + # self.data["labels_mean"] = train_labels_mean + + # return data_dictionary + + # def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: + # """ + # Normalizes a set of data using the mean and standard deviation from + # the associated training data. + # :params: + # :df: Dataframe to be standardized + # """ + + # for item in df.keys(): + # df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"] + + # return df diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index d7bbc549a..68d21ecdc 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -158,12 +158,7 @@ class IFreqaiModel(ABC): else: self.model = dh.load_data(metadata['pair']) - # strategy_provided_features = self.dh.find_features(dataframe_train) - # # FIXME doesnt work with PCA - # if strategy_provided_features != self.dh.training_features_list: - # logger.info("User changed input features, retraining model.") - # self.model = self.train(dataframe_train, metadata) - # self.dh.save_data(self.model) + self.check_if_feature_list_matches_strategy(dataframe_train, dh) preds, do_preds = self.predict(dataframe_backtest, dh) @@ -220,16 +215,23 @@ class IFreqaiModel(ABC): self.model = dh.load_data(coin=metadata['pair']) - # FIXME - # strategy_provided_features = dh.find_features(dataframe) - # if strategy_provided_features != dh.training_features_list: - # self.train_model_in_series(new_trained_timerange, metadata, strategy) + self.check_if_feature_list_matches_strategy(dataframe, dh) preds, do_preds = self.predict(dataframe, dh) dh.append_predictions(preds, do_preds, len(dataframe)) return dh + def check_if_feature_list_matches_strategy(self, dataframe: DataFrame, + dh: FreqaiDataKitchen) -> None: + strategy_provided_features = dh.find_features(dataframe) + if strategy_provided_features != dh.training_features_list: + raise OperationalException("Trying to access pretrained model with `identifier` " + "but found different features furnished by current strategy." + "Change `identifer` to train from scratch, or ensure the" + "strategy is furnishing the same features as the pretrained" + "model") + def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None: """ Base data cleaning method for train @@ -237,6 +239,7 @@ class IFreqaiModel(ABC): based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example of how outlier data points are dropped from the dataframe used for training. """ + if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): dh.principal_component_analysis() diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 73ea46032..3f70400d8 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -33,10 +33,6 @@ class CatboostPredictionModel(IFreqaiModel): / dataframe["close"] - 1 ) - dh.data["s_mean"] = dataframe["s"].mean() - dh.data["s_std"] = dataframe["s"].std() - - # logger.info("label mean", dh.data["s_mean"], "label std", dh.data["s_std"]) return dataframe["s"] @@ -68,8 +64,9 @@ class CatboostPredictionModel(IFreqaiModel): # split data into train/test data. data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered) - # standardize all data based on train_dataset only - data_dictionary = dh.standardize_data(data_dictionary) + dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy + # normalize all data based on train_dataset only + data_dictionary = dh.normalize_data(data_dictionary) # optional additional data cleaning/analysis self.data_cleaning_train(dh) @@ -128,7 +125,7 @@ class CatboostPredictionModel(IFreqaiModel): filtered_dataframe, _ = dh.filter_features( unfiltered_dataframe, original_feature_list, training_filter=False ) - filtered_dataframe = dh.standardize_data_from_metadata(filtered_dataframe) + filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe) dh.data_dictionary["prediction_features"] = filtered_dataframe # optional additional data cleaning/analysis @@ -136,7 +133,7 @@ class CatboostPredictionModel(IFreqaiModel): predictions = self.model.predict(dh.data_dictionary["prediction_features"]) - # compute the non-standardized predictions + # compute the non-normalized predictions dh.predictions = (predictions + 1) * (dh.data["labels_max"] - dh.data["labels_min"]) / 2 + dh.data["labels_min"] diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index d2eb2c306..ed7c828cc 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -178,8 +178,8 @@ class FreqaiExampleStrategy(IStrategy): dataframe["target_std"], ) = self.model.bridge.start(dataframe, metadata, self) - dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.5 - dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1 + dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] + dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: From 23c30dbc10703bda5b9a872d9c81ecf2155790c5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 00:43:52 +0200 Subject: [PATCH 056/518] add error for user trying to backtest with backtest_period<1 --- freqtrade/freqai/data_kitchen.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b5f1f6edb..1089797d1 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -19,6 +19,7 @@ from sklearn.model_selection import train_test_split from freqtrade.configuration import TimeRange from freqtrade.data.history import load_pair_history from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data +from freqtrade.exceptions import OperationalException from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.resolvers import ExchangeResolver from freqtrade.strategy.interface import IStrategy @@ -59,6 +60,11 @@ class FreqaiDataKitchen: self.pair = pair self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: + if config.get('freqai', {}).get('backtest_period') is not int: + raise OperationalException('backtest_period < 1,' + 'Can only backtest on full day increments' + 'backtest_period. Only live/dry mode' + 'allows fractions of days') self.full_timerange = self.create_fulltimerange(self.config["timerange"], self.freqai_config.get("train_period") ) From 8a501831d6626fd37302d156917bbd935c3d8531 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 01:15:55 +0200 Subject: [PATCH 057/518] fix the error logic on previous commit --- freqtrade/freqai/data_kitchen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 1089797d1..63420a52b 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -60,7 +60,7 @@ class FreqaiDataKitchen: self.pair = pair self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: - if config.get('freqai', {}).get('backtest_period') is not int: + if config.get('freqai', {}).get('backtest_period') < 1: raise OperationalException('backtest_period < 1,' 'Can only backtest on full day increments' 'backtest_period. Only live/dry mode' From c080571b7a98ad3c29fd5c89ef229983040c2583 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 12:23:32 +0200 Subject: [PATCH 058/518] help futures go dry/live with auto download feature --- docs/freqai.md | 2 +- freqtrade/freqai/data_kitchen.py | 7 +++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 821f42258..78e25a234 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -400,7 +400,7 @@ The user can stratify the training/testing data using: } ``` -which will split the data chronolocially so that every X data points is a testing data point. In the +which will split the data chronologically so that every X data points is a testing data point. In the present example, the user is asking for every third data point in the dataframe to be used for testing, the other points are used for training. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 63420a52b..45131f3d0 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -715,10 +715,9 @@ class FreqaiDataKitchen: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config, validate=False) - pairs = self.freqai_config.get('corr_pairlist', []) - if metadata['pair'] not in pairs: - pairs += metadata['pair'] # dont include pair twice - # timerange = TimeRange.parse_timerange(new_timerange) + pairs = copy.deepcopy(self.freqai_config.get('corr_pairlist', [])) + if str(metadata['pair']) not in pairs: + pairs.append(str(metadata['pair'])) refresh_backtest_ohlcv_data( exchange, pairs=pairs, timeframes=self.freqai_config.get('timeframes'), From 65fdebab75bd6238228620aa53172be01813d3fd Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 13:01:33 +0200 Subject: [PATCH 059/518] let load_pairs_histories load futures candles in live --- freqtrade/freqai/data_kitchen.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 45131f3d0..5aa84620c 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -738,7 +738,9 @@ class FreqaiDataKitchen: for tf in self.freqai_config.get('timeframes'): base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, - pair=metadata['pair'], timerange=timerange) + pair=metadata['pair'], timerange=timerange, + candle_type=self.config.get( + 'trading_mode', 'spot')) if pairs: for p in pairs: if metadata['pair'] in p: @@ -747,7 +749,9 @@ class FreqaiDataKitchen: corr_dataframes[p] = {} corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, - pair=p, timerange=timerange) + pair=p, timerange=timerange, + candle_type=self.config.get( + 'trading_mode', 'spot')) return corr_dataframes, base_dataframes From 891fb87712ae01adf2a578f457ff64251b4bcb4a Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 13:38:22 +0200 Subject: [PATCH 060/518] give load_cached_data_for_updating the right flags to avoid redownloading data in dry/live --- freqtrade/freqai/data_kitchen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 5aa84620c..9866ee33a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -669,7 +669,7 @@ class FreqaiDataKitchen: def check_if_new_training_required(self, trained_timestamp: int) -> Tuple[bool, TimeRange]: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - trained_timerange = TimeRange() + trained_timerange = TimeRange('date', 'date') if trained_timestamp != 0: elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY retrain = elapsed_time > self.freqai_config.get('backtest_period') From b8f9c3557bd03a87217b12a6ba824e45e26fcea7 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 27 May 2022 13:56:34 +0200 Subject: [PATCH 061/518] dirty dirty, dont look here (hacking a flag to avoid reloading leverage_tiers in dry/live) --- freqtrade/exchange/exchange.py | 4 ++-- freqtrade/freqai/data_kitchen.py | 2 +- freqtrade/resolvers/exchange_resolver.py | 6 ++++-- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 65b9fb628..348f9c395 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -86,7 +86,7 @@ class Exchange: # TradingMode.SPOT always supported and not required in this list ] - def __init__(self, config: Dict[str, Any], validate: bool = True) -> None: + def __init__(self, config: Dict[str, Any], validate: bool = True, freqai: bool = False) -> None: """ Initializes this module with the given config, it does basic validation whether the specified exchange and pairs are valid. @@ -196,7 +196,7 @@ class Exchange: self.markets_refresh_interval: int = exchange_config.get( "markets_refresh_interval", 60) * 60 - if self.trading_mode != TradingMode.SPOT: + if self.trading_mode != TradingMode.SPOT and freqai is False: self.fill_leverage_tiers() self.additional_exchange_init() diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 9866ee33a..93e7b74ad 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -714,7 +714,7 @@ class FreqaiDataKitchen: def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], - self.config, validate=False) + self.config, validate=False, freqai=True) pairs = copy.deepcopy(self.freqai_config.get('corr_pairlist', [])) if str(metadata['pair']) not in pairs: pairs.append(str(metadata['pair'])) diff --git a/freqtrade/resolvers/exchange_resolver.py b/freqtrade/resolvers/exchange_resolver.py index 4dfbf445b..c1ec8b69c 100644 --- a/freqtrade/resolvers/exchange_resolver.py +++ b/freqtrade/resolvers/exchange_resolver.py @@ -18,7 +18,8 @@ class ExchangeResolver(IResolver): object_type = Exchange @staticmethod - def load_exchange(exchange_name: str, config: dict, validate: bool = True) -> Exchange: + def load_exchange(exchange_name: str, config: dict, validate: bool = True, + freqai: bool = False) -> Exchange: """ Load the custom class from config parameter :param exchange_name: name of the Exchange to load @@ -31,7 +32,8 @@ class ExchangeResolver(IResolver): try: exchange = ExchangeResolver._load_exchange(exchange_name, kwargs={'config': config, - 'validate': validate}) + 'validate': validate, + 'freqai': freqai}) except ImportError: logger.info( f"No {exchange_name} specific subclass found. Using the generic class instead.") From c5a16e91fbb3052d01e4311ee72f739ba97fbf51 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 11:11:41 +0200 Subject: [PATCH 062/518] throw user error if user tries to load models but feeds the wrong features (while using PCA) --- docs/freqai.md | 2 ++ freqtrade/freqai/data_kitchen.py | 8 +++++++- freqtrade/freqai/freqai_interface.py | 10 +++++++--- .../prediction_models/CatboostPredictionModel.py | 5 +++-- 4 files changed, 19 insertions(+), 6 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 78e25a234..57ff8f897 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -313,6 +313,8 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._ +![weight-factor](assets/weights_factor.png) + Finally, `period` defines the offset used for the `labels`. In the present example, the user is asking for `labels` that are 24 candles in the future. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 93e7b74ad..58b14b9f1 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -477,6 +477,11 @@ class FreqaiDataKitchen: index=self.data_dictionary["train_features"].index, ) + # keeping a copy of the non-transformed features so we can check for errors during + # model load from disk + self.data['training_features_list_raw'] = copy.deepcopy(self.training_features_list) + self.training_features_list = self.data_dictionary["train_features"].columns + self.data_dictionary["test_features"] = pd.DataFrame( data=test_components, columns=["PC" + str(i) for i in range(0, n_keep_components)], @@ -563,7 +568,8 @@ class FreqaiDataKitchen: def find_features(self, dataframe: DataFrame) -> list: column_names = dataframe.columns features = [c for c in column_names if '%' in c] - assert features, ("Could not find any features!") + if not features: + raise OperationalException("Could not find any features!") return features def check_if_pred_in_training_spaces(self) -> None: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 68d21ecdc..ab2d37753 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -225,7 +225,11 @@ class IFreqaiModel(ABC): def check_if_feature_list_matches_strategy(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> None: strategy_provided_features = dh.find_features(dataframe) - if strategy_provided_features != dh.training_features_list: + if dh.data['training_features_list_raw']: + feature_list = dh.data['training_features_list_raw'] + else: + feature_list = dh.training_features_list + if strategy_provided_features != feature_list: raise OperationalException("Trying to access pretrained model with `identifier` " "but found different features furnished by current strategy." "Change `identifer` to train from scratch, or ensure the" @@ -254,7 +258,7 @@ class IFreqaiModel(ABC): # if self.feature_parameters["remove_outliers"]: # dh.remove_outliers(predict=False) - def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None: + def data_cleaning_predict(self, dh: FreqaiDataKitchen, dataframe: DataFrame) -> None: """ Base data cleaning method for predict. These functions each modify dh.do_predict, which is a dataframe with equal length @@ -266,7 +270,7 @@ class IFreqaiModel(ABC): for buy signals. """ if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'): - dh.pca_transform() + dh.pca_transform(dataframe) if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'): dh.use_SVM_to_remove_outliers(predict=True) diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 3f70400d8..5147faf0c 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -71,7 +71,8 @@ class CatboostPredictionModel(IFreqaiModel): # optional additional data cleaning/analysis self.data_cleaning_train(dh) - logger.info(f'Training model on {len(dh.training_features_list)} features') + logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}' + 'features') logger.info(f'Training model on {len(data_dictionary["train_features"])} data points') model = self.fit(data_dictionary) @@ -129,7 +130,7 @@ class CatboostPredictionModel(IFreqaiModel): dh.data_dictionary["prediction_features"] = filtered_dataframe # optional additional data cleaning/analysis - self.data_cleaning_predict(dh) + self.data_cleaning_predict(dh, filtered_dataframe) predictions = self.model.predict(dh.data_dictionary["prediction_features"]) From 0bf915054d95f82bd5bb5f313a825d25c2ad0219 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 11:22:32 +0200 Subject: [PATCH 063/518] handle key check correctly --- freqtrade/freqai/freqai_interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index ab2d37753..75c00988f 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -225,7 +225,7 @@ class IFreqaiModel(ABC): def check_if_feature_list_matches_strategy(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> None: strategy_provided_features = dh.find_features(dataframe) - if dh.data['training_features_list_raw']: + if 'training_features_list_raw' in dh.data: feature_list = dh.data['training_features_list_raw'] else: feature_list = dh.training_features_list From 7870a86e9acbc3962e63a2fbacef993ab00500d3 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 11:38:57 +0200 Subject: [PATCH 064/518] fix live retraining bug --- docs/assets/weights_factor.png | Bin 0 -> 129065 bytes freqtrade/freqai/data_kitchen.py | 2 +- freqtrade/freqai/freqai_interface.py | 3 ++- 3 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 docs/assets/weights_factor.png diff --git a/docs/assets/weights_factor.png b/docs/assets/weights_factor.png new file mode 100644 index 0000000000000000000000000000000000000000..1171a49ba4d9300c6828b766fc8d80aecc907cf5 GIT binary patch literal 129065 zcmeFZRZv`A*FV?xfeuei60)Zfli+xmpKwkJjAW*CDu;3lGiNy`@ 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b/freqtrade/freqai/data_kitchen.py @@ -675,7 +675,7 @@ class FreqaiDataKitchen: def check_if_new_training_required(self, trained_timestamp: int) -> Tuple[bool, TimeRange]: time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - trained_timerange = TimeRange('date', 'date') + trained_timerange = TimeRange() if trained_timestamp != 0: elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY retrain = elapsed_time > self.freqai_config.get('backtest_period') diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 75c00988f..3e737e5e7 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -187,7 +187,8 @@ class IFreqaiModel(ABC): (model_filename, trained_timestamp, coin_first) = self.data_drawer.get_pair_dict_info(metadata) - + if self.training_on_separate_thread: + print('debug_here') if not self.training_on_separate_thread: file_exists = False From 2a4d1e2d64df6584b5c54849f33b96e49eb21e60 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 12:23:26 +0200 Subject: [PATCH 065/518] fix bug in setting new timerange for retraining --- freqtrade/freqai/data_kitchen.py | 2 +- freqtrade/freqai/freqai_interface.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 69a311925..629019549 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -681,7 +681,7 @@ class FreqaiDataKitchen: retrain = elapsed_time > self.freqai_config.get('backtest_period') if retrain: trained_timerange.startts = int(time - self.freqai_config.get( - 'backtest_period', 0) * SECONDS_IN_DAY) + 'train_period', 0) * SECONDS_IN_DAY) trained_timerange.stopts = int(time) else: # user passed no live_trained_timerange in config trained_timerange.startts = int(time - self.freqai_config.get('train_period') * diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 3e737e5e7..75c00988f 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -187,8 +187,7 @@ class IFreqaiModel(ABC): (model_filename, trained_timestamp, coin_first) = self.data_drawer.get_pair_dict_info(metadata) - if self.training_on_separate_thread: - print('debug_here') + if not self.training_on_separate_thread: file_exists = False From e54614fa2f190970234fcf7e4b67299c0eacbe38 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 14:55:07 +0200 Subject: [PATCH 066/518] remove remnants of single threaded version, ensure pair queue priority is checked before retraining --- freqtrade/freqai/freqai_interface.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 75c00988f..1952cd234 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -85,7 +85,7 @@ class IFreqaiModel(ABC): # determine what the current pair will do if self.live: if (not self.training_on_separate_thread and - self.data_drawer.training_queue == 1): + self.data_drawer.training_queue[metadata['pair']] == 1): self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) @@ -321,16 +321,26 @@ class IFreqaiModel(ABC): base_dataframes, metadata) - self.model = self.train(unfiltered_dataframe, metadata, dh) + try: + model = self.train(unfiltered_dataframe, metadata, dh) + except ValueError: + logger.warning('Value error encountered during training') + self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) + self.training_on_separate_thread = False + self.retrain = False + return self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts dh.set_new_model_names(metadata, new_trained_timerange) - self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) - dh.save_data(self.model, coin=metadata['pair']) + logger.info('Training queue' + f'{sorted(self.data_drawer.training_queue.items(), key=lambda item: item[1])}') + dh.save_data(model, coin=metadata['pair']) + self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) self.training_on_separate_thread = False self.retrain = False + return def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict, strategy: IStrategy, dh: FreqaiDataKitchen): @@ -344,13 +354,13 @@ class IFreqaiModel(ABC): base_dataframes, metadata) - self.model = self.train(unfiltered_dataframe, metadata, dh) + model = self.train(unfiltered_dataframe, metadata, dh) self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts dh.set_new_model_names(metadata, new_trained_timerange) self.data_drawer.pair_dict[metadata['pair']]['first'] = False - dh.save_data(self.model, coin=metadata['pair']) + dh.save_data(model, coin=metadata['pair']) self.retrain = False # Methods which are overridden by user made prediction models. From 83dd45372356c62b17cd4789d377e2e25d7e1378 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 18:26:19 +0200 Subject: [PATCH 067/518] catch errors occuring on background thread, and make sure to keep the ball rolling. Improve pair retraining queue. --- freqtrade/freqai/data_drawer.py | 18 ++++++++--------- freqtrade/freqai/data_kitchen.py | 6 ++++-- freqtrade/freqai/freqai_interface.py | 29 ++++++++++++++++++---------- 3 files changed, 31 insertions(+), 22 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index a5d8a2123..477f45d84 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -28,7 +28,7 @@ class FreqaiDataDrawer: self.full_path = full_path self.load_drawer_from_disk() self.training_queue: Dict[str, int] = {} - self.create_training_queue(pair_whitelist) + # self.create_training_queue(pair_whitelist) def load_drawer_from_disk(self): exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists() @@ -58,7 +58,6 @@ class FreqaiDataDrawer: model_filename = self.pair_dict[metadata['pair']]['model_filename'] = '' coin_first = self.pair_dict[metadata['pair']]['first'] = True trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 - self.pair_dict[metadata['pair']]['priority'] = 1 return model_filename, trained_timestamp, coin_first @@ -71,17 +70,16 @@ class FreqaiDataDrawer: self.pair_dict[metadata['pair']]['model_filename'] = '' self.pair_dict[metadata['pair']]['first'] = True self.pair_dict[metadata['pair']]['trained_timestamp'] = 0 - self.pair_dict[metadata['pair']]['priority'] = 1 + self.pair_dict[metadata['pair']]['priority'] = len(self.pair_dict) return - def create_training_queue(self, pairs: list) -> None: - for i, pair in enumerate(pairs): - self.training_queue[pair] = i + 1 + # def create_training_queue(self, pairs: list) -> None: + # for i, pair in enumerate(pairs): + # self.training_queue[pair] = i + 1 def pair_to_end_of_training_queue(self, pair: str) -> None: # march all pairs up in the queue - for p in self.training_queue: - self.training_queue[p] -= 1 - + for p in self.pair_dict: + self.pair_dict[p]['priority'] -= 1 # send pair to end of queue - self.training_queue[pair] = len(self.training_queue) + self.pair_dict[pair]['priority'] = len(self.pair_dict) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 629019549..a1bda34bf 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -717,10 +717,12 @@ class FreqaiDataKitchen: # enables persistence, but not fully implemented into save/load data yer # self.data['live_trained_timerange'] = str(int(trained_timerange.stopts)) - def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None: + def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict, + strategy: IStrategy) -> None: exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config, validate=False, freqai=True) + # exchange = strategy.dp._exchange # closes ccxt session pairs = copy.deepcopy(self.freqai_config.get('corr_pairlist', [])) if str(metadata['pair']) not in pairs: pairs.append(str(metadata['pair'])) @@ -766,7 +768,7 @@ class FreqaiDataKitchen: base_dataframes: dict, metadata: dict) -> DataFrame: - dataframe = base_dataframes[self.config['timeframe']] + dataframe = base_dataframes[self.config['timeframe']].copy() pairs = self.freqai_config.get("corr_pairlist", []) for tf in self.freqai_config.get("timeframes"): diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 1952cd234..dfd2f0bbd 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -85,7 +85,7 @@ class IFreqaiModel(ABC): # determine what the current pair will do if self.live: if (not self.training_on_separate_thread and - self.data_drawer.training_queue[metadata['pair']] == 1): + self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1): self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"]) @@ -313,13 +313,22 @@ class IFreqaiModel(ABC): strategy: IStrategy, dh: FreqaiDataKitchen): # with nostdout(): - dh.download_new_data_for_retraining(new_trained_timerange, metadata) + dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy) corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange, metadata) - unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy, - corr_dataframes, - base_dataframes, - metadata) + + # protecting from common benign errors associated with grabbing new data from exchange: + try: + unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy, + corr_dataframes, + base_dataframes, + metadata) + except Exception: + logger.warning('Mismatched sizes encountered in strategy') + self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) + self.training_on_separate_thread = False + self.retrain = False + return try: model = self.train(unfiltered_dataframe, metadata, dh) @@ -333,8 +342,8 @@ class IFreqaiModel(ABC): self.data_drawer.pair_dict[metadata['pair']][ 'trained_timestamp'] = new_trained_timerange.stopts dh.set_new_model_names(metadata, new_trained_timerange) - logger.info('Training queue' - f'{sorted(self.data_drawer.training_queue.items(), key=lambda item: item[1])}') + # logger.info('Training queue' + # f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}') dh.save_data(model, coin=metadata['pair']) self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) @@ -345,7 +354,7 @@ class IFreqaiModel(ABC): def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict, strategy: IStrategy, dh: FreqaiDataKitchen): - dh.download_new_data_for_retraining(new_trained_timerange, metadata) + dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy) corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange, metadata) @@ -363,7 +372,7 @@ class IFreqaiModel(ABC): dh.save_data(model, coin=metadata['pair']) self.retrain = False - # Methods which are overridden by user made prediction models. + # Following methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModlel.py for an example. @abstractmethod From 4eb29c8810bad526f79206f2688eeaf7b5a2da1a Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 28 May 2022 18:34:26 +0200 Subject: [PATCH 068/518] Dont reset pair priority if it doesnt successfully train --- freqtrade/freqai/freqai_interface.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index dfd2f0bbd..ac0ad9d5a 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -325,7 +325,7 @@ class IFreqaiModel(ABC): metadata) except Exception: logger.warning('Mismatched sizes encountered in strategy') - self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) + # self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) self.training_on_separate_thread = False self.retrain = False return @@ -334,7 +334,7 @@ class IFreqaiModel(ABC): model = self.train(unfiltered_dataframe, metadata, dh) except ValueError: logger.warning('Value error encountered during training') - self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) + # self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) self.training_on_separate_thread = False self.retrain = False return From ce365eb9e3a12e83d21e696ff0c4c9033e7ce1a7 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 14:45:46 +0200 Subject: [PATCH 069/518] improve example strat so that it has dynamic buy and sell logic according to original prediction --- .gitignore | 3 +- .../config_freqai_futures.example.json | 93 ++++++++++++++++++ ...e.json => config_freqai_spot.example.json} | 17 ++-- freqtrade/freqai/freqai_interface.py | 1 + freqtrade/templates/FreqaiExampleStrategy.py | 98 ++++++++++++++++--- 5 files changed, 189 insertions(+), 23 deletions(-) create mode 100644 config_examples/config_freqai_futures.example.json rename config_examples/{config_freqai.example.json => config_freqai_spot.example.json} (89%) diff --git a/.gitignore b/.gitignore index dc87402b4..a066001db 100644 --- a/.gitignore +++ b/.gitignore @@ -109,4 +109,5 @@ target/ !config_examples/config_ftx.example.json !config_examples/config_full.example.json !config_examples/config_kraken.example.json -!config_examples/config_freqai.example.json +!config_examples/config_freqai_futures.example.json +!config_examples/config_freqai_spot.example.json diff --git a/config_examples/config_freqai_futures.example.json b/config_examples/config_freqai_futures.example.json new file mode 100644 index 000000000..cb545acdc --- /dev/null +++ b/config_examples/config_freqai_futures.example.json @@ -0,0 +1,93 @@ +{ + "trading_mode": "futures", + "margin_mode": "isolated", + "max_open_trades": 5, + "stake_currency": "USDT", + "stake_amount": 200, + "tradable_balance_ratio": 1, + "fiat_display_currency": "USD", + "dry_run": true, + "timeframe": "3m", + "dry_run_wallet": 1000, + "cancel_open_orders_on_exit": true, + "unfilledtimeout": { + "entry": 10, + "exit": 30 + }, + "exchange": { + "name": "okx", + "key": "", + "secret": "", + "ccxt_config": { + "enableRateLimit": true + }, + "ccxt_async_config": { + "enableRateLimit": true, + "rateLimit": 200 + }, + "pair_whitelist": [ + "AGLD/USDT:USDT", + "1INCH/USDT:USDT", + "AAVE/USDT:USDT", + "ALGO/USDT:USDT", + "ALPHA/USDT:USDT", + "API3/USDT:USDT", + "AVAX/USDT:USDT", + "AXS/USDT:USDT", + "BCH/USDT:USDT" + ], + "pair_blacklist": [] + }, + "entry_pricing": { + "price_side": "same", + "use_order_book": true, + "order_book_top": 1, + "price_last_balance": 0.0, + "check_depth_of_market": { + "enabled": false, + "bids_to_ask_delta": 1 + } + }, + "exit_pricing": { + "price_side": "other", + "use_order_book": true, + "order_book_top": 1 + }, + "pairlists": [ + { + "method": "StaticPairList" + } + ], + "freqai": { + "timeframes": [ + "3m", + "15m", + "1h" + ], + "train_period": 20, + "backtest_period": 2, + "identifier": "example", + "live_trained_timestamp": 0, + "corr_pairlist": [ + "BTC/USDT:USDT", + "ETH/USDT:USDT" + ], + "feature_parameters": { + "period": 20, + "shift": 2, + "DI_threshold": 0, + "weight_factor": 0.9, + "principal_component_analysis": false, + "use_SVM_to_remove_outliers": true, + "stratify": 0 + }, + "data_split_parameters": { + "test_size": 0.33, + "random_state": 1 + }, + "model_training_parameters": { + "n_estimators": 1000, + "task_type": "CPU" + } + } +} diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai_spot.example.json similarity index 89% rename from config_examples/config_freqai.example.json rename to config_examples/config_freqai_spot.example.json index b6c7ba7d8..e311fe280 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai_spot.example.json @@ -6,7 +6,7 @@ "fiat_display_currency": "USD", "dry_run": true, "timeframe": "5m", - "dry_run_wallet": 1000, + "dry_run_wallet": 4000, "cancel_open_orders_on_exit": true, "unfilledtimeout": { "entry": 10, @@ -51,7 +51,8 @@ "freqai": { "timeframes": [ "5m", - "15m" + "15m", + "4h" ], "train_period": 30, "backtest_period": 7, @@ -60,16 +61,18 @@ "corr_pairlist": [ "BTC/USDT", "ETH/USDT", - "DOT/USDT" + "DOT/USDT", + "MATIC/USDT", + "SOL/USDT" ], "feature_parameters": { - "period": 24, + "period": 20, "shift": 1, "DI_threshold": 0, "weight_factor": 0.9, "principal_component_analysis": false, - "use_SVM_to_remove_outliers": true, - "stratify": 3 + "use_SVM_to_remove_outliers": false, + "stratify": 0 }, "data_split_parameters": { "test_size": 0.33, @@ -77,8 +80,6 @@ }, "model_training_parameters": { "n_estimators": 1000, - "random_state": 1, - "learning_rate": 0.1, "task_type": "CPU" } }, diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index ac0ad9d5a..0d5b27385 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -56,6 +56,7 @@ class IFreqaiModel(ABC): self.set_full_path() self.data_drawer = FreqaiDataDrawer(Path(self.full_path), self.config['exchange']['pair_whitelist']) + self.lock = threading.Lock() def assert_config(self, config: Dict[str, Any]) -> None: diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index ed7c828cc..94fa0266d 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -7,8 +7,10 @@ import talib.abstract as ta from pandas import DataFrame from technical import qtpylib +from freqtrade.exchange import timeframe_to_prev_date from freqtrade.freqai.strategy_bridge import CustomModel -from freqtrade.strategy import merge_informative_pair +from freqtrade.persistence import Trade +from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy @@ -46,6 +48,11 @@ class FreqaiExampleStrategy(IStrategy): stoploss = -0.05 use_sell_signal = True startup_candle_count: int = 300 + can_short = False + + linear_roi_offset = DecimalParameter(0.00, 0.02, default=0.005, space='sell', + optimize=False, load=True) + max_roi_time_long = IntParameter(0, 800, default=400, space='sell', optimize=False, load=True) def informative_pairs(self): whitelist_pairs = self.dp.current_whitelist() @@ -182,25 +189,88 @@ class FreqaiExampleStrategy(IStrategy): dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] return dataframe - def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: - buy_conditions = [ - (dataframe["prediction"] > dataframe["target_roi"]) & (dataframe["do_predict"] == 1) + enter_long_conditions = [ + df['do_predict'] == 1, + df['prediction'] > df["target_roi"] ] - if buy_conditions: - dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1 + if enter_long_conditions: + df.loc[reduce(lambda x, y: x & y, + enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long') - return dataframe - - def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - sell_conditions = [ - (dataframe["do_predict"] <= 0) + enter_short_conditions = [ + df['do_predict'] == 1, + df['prediction'] < df["sell_roi"] ] - if sell_conditions: - dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1 - return dataframe + if enter_short_conditions: + df.loc[reduce(lambda x, y: x & y, + enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short') + + return df + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + exit_long_conditions = [ + df['do_predict'] == 1, + df['prediction'] < df['sell_roi'] * 0.25 + ] + if exit_long_conditions: + df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 + + exit_short_conditions = [ + df['do_predict'] == 1, + df['prediction'] > df['target_roi'] * 0.25 + ] + if exit_short_conditions: + df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 + + return df def get_ticker_indicator(self): return int(self.config["timeframe"][:-1]) + + def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, + current_profit, **kwargs): + + dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) + + trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc) + trade_candle = dataframe.loc[(dataframe['date'] == trade_date)] + + if trade_candle.empty: + return None + trade_candle = trade_candle.squeeze() + pair_dict = self.model.bridge.data_drawer.pair_dict + entry_tag = trade.enter_tag + + if 'prediction' + entry_tag not in pair_dict[pair]: + with self.model.bridge.lock: + self.model.bridge.data_drawer.pair_dict[pair][ + 'prediction' + entry_tag] = abs(trade_candle['prediction']) + self.model.bridge.data_drawer.save_drawer_to_disk() + else: + if pair_dict[pair]['prediction' + entry_tag] > 0: + roi_price = abs(trade_candle['prediction' + entry_tag]) + else: + with self.model.bridge.lock: + self.model.bridge.data_drawer.pair_dict[pair][ + 'prediction' + entry_tag] = abs(trade_candle['prediction']) + self.model.bridge.data_drawer.save_drawer_to_disk() + + roi_price = abs(trade_candle['prediction']) + roi_time = self.max_roi_time_long.value + + roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) / + (roi_time * 60)) + if roi_decay < 0: + roi_decay = self.linear_roi_offset.value + else: + roi_decay += self.linear_roi_offset.value + + if current_profit > roi_price: # roi_decay: + with self.model.bridge.lock: + self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0 + self.model.bridge.data_drawer.save_drawer_to_disk() + return 'roi_custom_win' From fe36b08fcec9e2f4b52349e1aa6dbd4db0f3eaf7 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 16:26:34 +0200 Subject: [PATCH 070/518] fix key error in example strat --- freqtrade/templates/FreqaiExampleStrategy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 94fa0266d..13df1f846 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -252,7 +252,7 @@ class FreqaiExampleStrategy(IStrategy): self.model.bridge.data_drawer.save_drawer_to_disk() else: if pair_dict[pair]['prediction' + entry_tag] > 0: - roi_price = abs(trade_candle['prediction' + entry_tag]) + roi_price = abs(trade_candle['prediction']) else: with self.model.bridge.lock: self.model.bridge.data_drawer.pair_dict[pair][ From 0aa71620553a8e72162939a2ccd7dc8eefe11fec Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 16:36:46 +0200 Subject: [PATCH 071/518] ensure the prediction is reset in the pair_dict after any trade exit, not just custom_exit --- freqtrade/templates/FreqaiExampleStrategy.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 13df1f846..ecda10919 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -269,8 +269,17 @@ class FreqaiExampleStrategy(IStrategy): else: roi_decay += self.linear_roi_offset.value - if current_profit > roi_price: # roi_decay: - with self.model.bridge.lock: - self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0 - self.model.bridge.data_drawer.save_drawer_to_disk() + if current_profit > roi_price: return 'roi_custom_win' + + def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, + rate: float, time_in_force: str, exit_reason: str, + current_time, **kwargs) -> bool: + + entry_tag = trade.enter_tag + + with self.model.bridge.lock: + self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0 + self.model.bridge.data_drawer.save_drawer_to_disk() + + return True From 4eb4753e20434ce8c4010f14f2e305dd44995a74 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 17:44:35 +0200 Subject: [PATCH 072/518] allow subdaily retraining for backtesting --- docs/freqai.md | 7 ++++--- freqtrade/freqai/data_kitchen.py | 22 +++++++++++++--------- freqtrade/freqai/freqai_interface.py | 3 ++- 3 files changed, 19 insertions(+), 13 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 57ff8f897..d6998a8b6 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -280,9 +280,10 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example. By default, Freqai will not find find any existing models and will start by training a new one given the user configuration settings. Following training, it will use that model to predict for the duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain -a new model, and begin making predictions with the updated model. FreqAI in live mode permits -the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent -retraining. +a new model, and begin making predictions with the updated model. FreqAI backtesting and live both +permit the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent +retraining. But the user should be careful that using a fractional `backtest_period` with a large +`--timerange` in backtesting will result in a huge amount of required trainings/models. If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse the same `identifier` parameter diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index a1bda34bf..08a00c68d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -60,11 +60,11 @@ class FreqaiDataKitchen: self.pair = pair self.svm_model: linear_model.SGDOneClassSVM = None if not self.live: - if config.get('freqai', {}).get('backtest_period') < 1: - raise OperationalException('backtest_period < 1,' - 'Can only backtest on full day increments' - 'backtest_period. Only live/dry mode' - 'allows fractions of days') + # if config.get('freqai', {}).get('backtest_period') < 1: + # raise OperationalException('backtest_period < 1,' + # 'Can only backtest on full day increments' + # 'backtest_period. Only live/dry mode' + # 'allows fractions of days') self.full_timerange = self.create_fulltimerange(self.config["timerange"], self.freqai_config.get("train_period") ) @@ -401,6 +401,8 @@ class FreqaiDataKitchen: tr_training_list = [] tr_backtesting_list = [] + tr_training_list_timerange = [] + tr_backtesting_list_timerange = [] first = True # within_config_timerange = True while True: @@ -412,6 +414,7 @@ class FreqaiDataKitchen: start = datetime.datetime.utcfromtimestamp(timerange_train.startts) stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts) tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) + tr_training_list_timerange.append(copy.deepcopy(timerange_train)) # associated backtest period @@ -425,16 +428,17 @@ class FreqaiDataKitchen: start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) + tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest)) # ensure we are predicting on exactly same amount of data as requested by user defined # --timerange if timerange_backtest.stopts == config_timerange.stopts: break - print(tr_training_list, tr_backtesting_list) - return tr_training_list, tr_backtesting_list + # print(tr_training_list, tr_backtesting_list) + return tr_training_list_timerange, tr_backtesting_list_timerange - def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: + def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame: """ Given a full dataframe, extract the user desired window :params: @@ -442,7 +446,7 @@ class FreqaiDataKitchen: :df: Dataframe containing all candles to run the entire backtest. Here it is sliced down to just the present training period. """ - timerange = TimeRange.parse_timerange(tr) + # timerange = TimeRange.parse_timerange(tr) start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc) stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc) df = df.loc[df["date"] >= start, :] diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 0d5b27385..db66ef033 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -142,10 +142,11 @@ class IFreqaiModel(ABC): gc.collect() dh.data = {} # clean the pair specific data between training window sliding self.training_timerange = tr_train + # self.training_timerange_timerange = tr_train dataframe_train = dh.slice_dataframe(tr_train, dataframe) dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe) logger.info("training %s for %s", metadata["pair"], tr_train) - trained_timestamp = TimeRange.parse_timerange(tr_train) + trained_timestamp = tr_train # TimeRange.parse_timerange(tr_train) dh.data_path = Path(dh.full_path / str("sub-train" + "-" + metadata['pair'].split("/")[0] + str(int(trained_timestamp.stopts)))) From cc6cae47ec53009922e941a6118cf555b0113ee6 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 19:49:43 +0200 Subject: [PATCH 073/518] allow pairs deeper in the queue to get trained if the higher priority pairs dont need training --- freqtrade/freqai/freqai_interface.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index db66ef033..90d96ad87 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -348,7 +348,8 @@ class IFreqaiModel(ABC): # f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}') dh.save_data(model, coin=metadata['pair']) - self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) + if self.data_drawer.pair_dict[metadata['pair']['priority']] == 1: + self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) self.training_on_separate_thread = False self.retrain = False return From 3f722632788f13e471b9ed5fe4f0301f38634d8d Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 20:02:43 +0200 Subject: [PATCH 074/518] allow pairs deeper in the queue to get trained if the higher priority pairs dont need training --- freqtrade/freqai/freqai_interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 90d96ad87..319240248 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -348,7 +348,7 @@ class IFreqaiModel(ABC): # f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}') dh.save_data(model, coin=metadata['pair']) - if self.data_drawer.pair_dict[metadata['pair']['priority']] == 1: + if self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1: self.data_drawer.pair_to_end_of_training_queue(metadata['pair']) self.training_on_separate_thread = False self.retrain = False From a79032bf75cf299c576ee7bf9f8cccbb29814c52 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 20:19:32 +0200 Subject: [PATCH 075/518] fixing bug in training queue --- freqtrade/freqai/freqai_interface.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 319240248..c09200a30 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -190,7 +190,8 @@ class IFreqaiModel(ABC): trained_timestamp, coin_first) = self.data_drawer.get_pair_dict_info(metadata) - if not self.training_on_separate_thread: + if (not self.training_on_separate_thread and + self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1): file_exists = False if trained_timestamp != 0: # historical model available From d59eac3321f83301079b5a7326096f04ecc66212 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 29 May 2022 21:33:38 +0200 Subject: [PATCH 076/518] revert a79032b --- freqtrade/freqai/freqai_interface.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index c09200a30..4d02afb69 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -190,8 +190,7 @@ class IFreqaiModel(ABC): trained_timestamp, coin_first) = self.data_drawer.get_pair_dict_info(metadata) - if (not self.training_on_separate_thread and - self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1): + if (not self.training_on_separate_thread): file_exists = False if trained_timestamp != 0: # historical model available From 2f1a2c1cd7651ab7f5e4bb16ffc4e8083824ca56 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 30 May 2022 02:12:31 +0200 Subject: [PATCH 077/518] allow users to store data in custom formats, update spot config to reflect better target horizon to training period ratio --- config_examples/config_freqai_spot.example.json | 10 ++++++---- freqtrade/freqai/data_kitchen.py | 6 +++++- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/config_examples/config_freqai_spot.example.json b/config_examples/config_freqai_spot.example.json index e311fe280..e730335e9 100644 --- a/config_examples/config_freqai_spot.example.json +++ b/config_examples/config_freqai_spot.example.json @@ -7,6 +7,7 @@ "dry_run": true, "timeframe": "5m", "dry_run_wallet": 4000, + "dataformat_ohlcv": "hdf5", "cancel_open_orders_on_exit": true, "unfilledtimeout": { "entry": 10, @@ -24,7 +25,8 @@ "rateLimit": 200 }, "pair_whitelist": [ - "BTC/USDT" + "BTC/USDT", + "ETH/USDT" ], "pair_blacklist": [] }, @@ -39,7 +41,7 @@ } }, "exit_pricing": { - "price_side": "same", + "price_side": "other", "use_order_book": true, "order_book_top": 1 }, @@ -54,7 +56,7 @@ "15m", "4h" ], - "train_period": 30, + "train_period": 60, "backtest_period": 7, "identifier": "example", "live_trained_timestamp": 0, @@ -66,7 +68,7 @@ "SOL/USDT" ], "feature_parameters": { - "period": 20, + "period": 500, "shift": 1, "DI_threshold": 0, "weight_factor": 0.9, diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 08a00c68d..5ab066bfd 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -735,7 +735,7 @@ class FreqaiDataKitchen: exchange, pairs=pairs, timeframes=self.freqai_config.get('timeframes'), datadir=self.config['datadir'], timerange=timerange, new_pairs_days=self.config['new_pairs_days'], - erase=False, data_format=self.config['dataformat_ohlcv'], + erase=False, data_format=self.config.get('dataformat_ohlcv', 'json'), trading_mode=self.config.get('trading_mode', 'spot'), prepend=self.config.get('prepend_data', False) ) @@ -751,6 +751,8 @@ class FreqaiDataKitchen: base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, pair=metadata['pair'], timerange=timerange, + data_format=self.config.get( + 'dataformat_ohlcv', 'json'), candle_type=self.config.get( 'trading_mode', 'spot')) if pairs: @@ -762,6 +764,8 @@ class FreqaiDataKitchen: corr_dataframes[p][tf] = load_pair_history(datadir=self.config['datadir'], timeframe=tf, pair=p, timerange=timerange, + data_format=self.config.get( + 'dataformat_ohlcv', 'json'), candle_type=self.config.get( 'trading_mode', 'spot')) From a20651efd8f99bef142e1219b4c80d3f2f4283e2 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 30 May 2022 11:37:05 +0200 Subject: [PATCH 078/518] Increase performance by only predicting on most recent candle instead of full strat provided dataframe. Collect predictions and store them so that we can feed true predictions back to strategy (so that frequi isnt updating historic predictions based on newly trained models). --- freqtrade/freqai/data_drawer.py | 26 ++++++++++++++++++++++++++ freqtrade/freqai/data_kitchen.py | 2 +- freqtrade/freqai/freqai_interface.py | 12 ++++++++++-- 3 files changed, 37 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 477f45d84..6a5393cb8 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -1,4 +1,5 @@ +import copy import json import logging from pathlib import Path @@ -24,6 +25,7 @@ class FreqaiDataDrawer: self.pair_dict: Dict[str, Any] = {} # dictionary holding all actively inferenced models in memory given a model filename self.model_dictionary: Dict[str, Any] = {} + self.model_return_values: Dict[str, Any] = {} self.pair_data_dict: Dict[str, Any] = {} self.full_path = full_path self.load_drawer_from_disk() @@ -83,3 +85,27 @@ class FreqaiDataDrawer: self.pair_dict[p]['priority'] -= 1 # send pair to end of queue self.pair_dict[pair]['priority'] = len(self.pair_dict) + + def set_initial_return_values(self, pair, dh): + self.model_return_values[pair] = {} + self.model_return_values[pair]['predictions'] = dh.full_predictions + self.model_return_values[pair]['do_preds'] = dh.full_do_predict + self.model_return_values[pair]['target_mean'] = dh.full_target_mean + self.model_return_values[pair]['target_std'] = dh.full_target_std + + def append_model_predictions(self, pair, predictions, do_preds, + target_mean, target_std, dh) -> None: + + pred_store = self.model_return_values[pair]['predictions'] + do_pred_store = self.model_return_values[pair]['do_preds'] + tm_store = self.model_return_values[pair]['target_mean'] + ts_store = self.model_return_values[pair]['target_std'] + pred_store = np.append(pred_store[1:], predictions[-1]) + do_pred_store = np.append(do_pred_store[1:], do_preds[-1]) + tm_store = np.append(tm_store[1:], target_mean) + ts_store = np.append(ts_store[1:], target_std) + + dh.full_predictions = copy.deepcopy(pred_store) + dh.full_do_predict = copy.deepcopy(do_pred_store) + dh.full_target_mean = copy.deepcopy(tm_store) + dh.full_target_std = copy.deepcopy(ts_store) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 5ab066bfd..2b8306b5c 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -623,7 +623,7 @@ class FreqaiDataKitchen: Append backtest prediction from current backtest period to all previous periods """ - ones = np.ones(len_dataframe) + ones = np.ones(len(predictions)) target_mean, target_std = ones * self.data["target_mean"], ones * self.data["target_std"] self.full_predictions = np.append(self.full_predictions, predictions) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4d02afb69..386fab9fc 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -219,8 +219,16 @@ class IFreqaiModel(ABC): self.check_if_feature_list_matches_strategy(dataframe, dh) - preds, do_preds = self.predict(dataframe, dh) - dh.append_predictions(preds, do_preds, len(dataframe)) + if metadata['pair'] not in self.data_drawer.model_return_values: + preds, do_preds = self.predict(dataframe, dh) + dh.append_predictions(preds, do_preds, len(dataframe)) + dh.fill_predictions(len(dataframe)) + self.data_drawer.set_initial_return_values(metadata['pair'], dh) + else: + preds, do_preds = self.predict(dataframe.iloc[-2:], dh) + self.data_drawer.append_model_predictions(metadata['pair'], preds, do_preds, + self.dh.data["target_mean"], + self.dh.data["target_std"], dh) return dh From e229902381ae21107cf379170ca92acb0cc841a2 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 30 May 2022 12:48:22 +0200 Subject: [PATCH 079/518] fix bug in previous commit --- freqtrade/freqai/data_drawer.py | 5 +++-- freqtrade/freqai/freqai_interface.py | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 6a5393cb8..5421db6cf 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -86,14 +86,15 @@ class FreqaiDataDrawer: # send pair to end of queue self.pair_dict[pair]['priority'] = len(self.pair_dict) - def set_initial_return_values(self, pair, dh): + def set_initial_return_values(self, pair: str, dh): + self.model_return_values[pair] = {} self.model_return_values[pair]['predictions'] = dh.full_predictions self.model_return_values[pair]['do_preds'] = dh.full_do_predict self.model_return_values[pair]['target_mean'] = dh.full_target_mean self.model_return_values[pair]['target_std'] = dh.full_target_std - def append_model_predictions(self, pair, predictions, do_preds, + def append_model_predictions(self, pair: str, predictions, do_preds, target_mean, target_std, dh) -> None: pred_store = self.model_return_values[pair]['predictions'] diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 386fab9fc..014b80208 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -227,8 +227,8 @@ class IFreqaiModel(ABC): else: preds, do_preds = self.predict(dataframe.iloc[-2:], dh) self.data_drawer.append_model_predictions(metadata['pair'], preds, do_preds, - self.dh.data["target_mean"], - self.dh.data["target_std"], dh) + dh.data["target_mean"], + dh.data["target_std"], dh) return dh From 5b4c649d434801af8485d36c68b89c31814db59f Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 30 May 2022 13:55:46 +0200 Subject: [PATCH 080/518] detect variable sized dataframes coming from strat, adjust our stored/returned data accordingly --- freqtrade/freqai/data_drawer.py | 48 ++++++++++++++++++++-------- freqtrade/freqai/freqai_interface.py | 3 +- 2 files changed, 37 insertions(+), 14 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 5421db6cf..fc816ea48 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -95,18 +95,40 @@ class FreqaiDataDrawer: self.model_return_values[pair]['target_std'] = dh.full_target_std def append_model_predictions(self, pair: str, predictions, do_preds, - target_mean, target_std, dh) -> None: + target_mean, target_std, dh, len_df) -> None: - pred_store = self.model_return_values[pair]['predictions'] - do_pred_store = self.model_return_values[pair]['do_preds'] - tm_store = self.model_return_values[pair]['target_mean'] - ts_store = self.model_return_values[pair]['target_std'] - pred_store = np.append(pred_store[1:], predictions[-1]) - do_pred_store = np.append(do_pred_store[1:], do_preds[-1]) - tm_store = np.append(tm_store[1:], target_mean) - ts_store = np.append(ts_store[1:], target_std) + # strat seems to feed us variable sized dataframes - and since we are trying to build our + # own return array in the same shape, we need to figure out how the size has changed + # and adapt our stored/returned info accordingly. + length_difference = len(self.model_return_values[pair]['predictions']) - len_df + i = 0 - dh.full_predictions = copy.deepcopy(pred_store) - dh.full_do_predict = copy.deepcopy(do_pred_store) - dh.full_target_mean = copy.deepcopy(tm_store) - dh.full_target_std = copy.deepcopy(ts_store) + if length_difference == 0: + i = 1 + elif length_difference > 0: + i = length_difference + 1 + + self.model_return_values[pair]['predictions'] = np.append( + self.model_return_values[pair]['predictions'][i:], predictions[-1]) + self.model_return_values[pair]['do_preds'] = np.append( + self.model_return_values[pair]['do_preds'][i:], do_preds[-1]) + self.model_return_values[pair]['target_mean'] = np.append( + self.model_return_values[pair]['target_mean'][i:], target_mean) + self.model_return_values[pair]['target_std'] = np.append( + self.model_return_values[pair]['target_std'][i:], target_std) + + if length_difference < 0: + prepend = np.zeros(abs(length_difference) - 1) + self.model_return_values[pair]['predictions'] = np.insert( + self.model_return_values[pair]['predictions'], 0, prepend) + self.model_return_values[pair]['do_preds'] = np.insert( + self.model_return_values[pair]['do_preds'], 0, prepend) + self.model_return_values[pair]['target_mean'] = np.insert( + self.model_return_values[pair]['target_mean'], 0, prepend) + self.model_return_values[pair]['target_std'] = np.insert( + self.model_return_values[pair]['target_std'], 0, prepend) + + dh.full_predictions = copy.deepcopy(self.model_return_values[pair]['predictions']) + dh.full_do_predict = copy.deepcopy(self.model_return_values[pair]['do_preds']) + dh.full_target_mean = copy.deepcopy(self.model_return_values[pair]['target_mean']) + dh.full_target_std = copy.deepcopy(self.model_return_values[pair]['target_std']) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 014b80208..2831475bb 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -228,7 +228,8 @@ class IFreqaiModel(ABC): preds, do_preds = self.predict(dataframe.iloc[-2:], dh) self.data_drawer.append_model_predictions(metadata['pair'], preds, do_preds, dh.data["target_mean"], - dh.data["target_std"], dh) + dh.data["target_std"], dh, + len(dataframe)) return dh From 606f18e5c1c9043ba0954e215652e33e2413dc88 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 30 May 2022 21:35:48 +0200 Subject: [PATCH 081/518] Add follow_mode feature so that secondary bots can be launched with the same identifier and load models trained by the leader --- .../config_freqai_futures.example.json | 10 +++- .../config_freqai_spot.example.json | 2 +- docs/freqai.md | 20 ++++++- freqtrade/freqai/data_drawer.py | 58 +++++++++++++++++-- freqtrade/freqai/freqai_interface.py | 25 ++++++-- 5 files changed, 99 insertions(+), 16 deletions(-) diff --git a/config_examples/config_freqai_futures.example.json b/config_examples/config_freqai_futures.example.json index cb545acdc..5cd867e53 100644 --- a/config_examples/config_freqai_futures.example.json +++ b/config_examples/config_freqai_futures.example.json @@ -66,7 +66,7 @@ ], "train_period": 20, "backtest_period": 2, - "identifier": "example", + "identifier": "example2", "live_trained_timestamp": 0, "corr_pairlist": [ "BTC/USDT:USDT", @@ -86,8 +86,14 @@ "random_state": 1 }, "model_training_parameters": { - "n_estimators": 1000, + "n_estimators": 200, "task_type": "CPU" } + }, + "bot_name": "", + "force_entry_enable": true, + "initial_state": "running", + "internals": { + "process_throttle_secs": 5 } } diff --git a/config_examples/config_freqai_spot.example.json b/config_examples/config_freqai_spot.example.json index e730335e9..0b4d4e7c5 100644 --- a/config_examples/config_freqai_spot.example.json +++ b/config_examples/config_freqai_spot.example.json @@ -56,7 +56,7 @@ "15m", "4h" ], - "train_period": 60, + "train_period": 30, "backtest_period": 7, "identifier": "example", "live_trained_timestamp": 0, diff --git a/docs/freqai.md b/docs/freqai.md index d6998a8b6..c4ac30415 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -391,7 +391,7 @@ Freqai will train an SVM on the training data (or components if the user activat `principal_component_analysis`) and remove any data point that it deems to be sit beyond the feature space. -## Stratifying the data +### Stratifying the data The user can stratify the training/testing data using: @@ -403,10 +403,26 @@ The user can stratify the training/testing data using: } ``` -which will split the data chronologically so that every X data points is a testing data point. In the +which will split the data chronologically so that every Xth data points is a testing data point. In the present example, the user is asking for every third data point in the dataframe to be used for testing, the other points are used for training. +### Setting up a follower + +The user can define: + +```json + "freqai": { + "follow_mode": true, + "identifier": "example" + } +``` + +to indicate to the bot that it should not train models, but instead should look for models trained +by a leader with the same `identifier`. In this example, the user has a leader bot with the +`identifier: "example"` already running or launching simultaneously as the present follower. +The follower will load models created by the leader and inference them to obtain predictions. + - - ## Additional information ### Common pitfalls FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). -This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, +This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends -new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due +new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`. ### Feature normalization -The feature set created by the user is automatically normalized to the training -data only. This includes all test data and unseen prediction data (dry/live/backtest). +The feature set created by the user is automatically normalized to the training data only. +This includes all test data and unseen prediction data (dry/live/backtest). ### File structure -`user_data_dir/models/` contains all the data associated with the trainings and -backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` -and should thus not be modified. +`user_data_dir/models/` contains all the data associated with the trainings and backtests. +This file structure is heavily controlled and read by the `FreqaiDataKitchen()` +and should therefore not be modified. From 3acc86957016f9727c52d9a5ac5460204c43840b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 12:42:24 +0200 Subject: [PATCH 177/518] improve the dataframe key description, update outdated parts of doc --- docs/freqai.md | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 1e597696a..04f442778 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -111,16 +111,17 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
**Datatype:** float. | `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
**Datatype:** float. -### Return values for use in strategy -Here are the values you can expect to receive inside the dataframe returned by FreqAI: +### Important FreqAI dataframe key patterns +Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`): -| Parameter | Description | +| DataFrame Key | Description | |------------|-------------| -| `&-s*` | user defined labels in the user made strategy. Anything prepended with `&` is treated as a training target inside FreqAI. These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `&-s_close`. FreqAI makes the predictions and gives them back to the user under the same key (`&-s_close`) to be used in `populate_entry/exit_trend()`.
**Datatype:** depends on the output of the model. -| `&-s*_std/mean` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
**Datatype:** float. -| `do_predict` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
**Datatype:** integer between -1 and 2. -| `DI_values` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
**Datatype:** float. +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** depends on the output of the model. +| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
**Datatype:** float. +| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
**Datatype:** integer between -1 and 2. +| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
**Datatype:** float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). Note: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. ### Example config file @@ -338,6 +339,9 @@ and adding this to the `train_period_days`. The units need to be in the base can !!! Note In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live. +!!! Note + Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. + ## Running Freqai ### Training and backtesting @@ -419,12 +423,7 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example. ``` By default, Freqai will not find find any existing models and will start by training a new one -given the user configuration settings. Following training, it will use that model to predict for the -duration of `backtest_period_days`. After a full `backtest_period_days` has elapsed, Freqai will auto retrain -a new model, and begin making predictions with the updated model. FreqAI backtesting and live both -permit the user to use fractional days (i.e. 0.1) in the `backtest_period_days`, which enables more frequent -retraining. But the user should be careful that using a fractional `backtest_period_days` with a large -`--timerange` in backtesting will result in a huge amount of required trainings/models. +given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse the same `identifier` parameter @@ -439,7 +438,6 @@ the same `identifier` parameter In this case, although Freqai will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained, and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. -It is common to want constant retraining, in which case, the user should set `live_retrain_hours` to 0. ## Data analysis techniques From c2d6a0e891744791a9ad27db216bd141fd8e45d3 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 13:04:06 +0200 Subject: [PATCH 178/518] add record of contribution to doc and source --- docs/freqai.md | 17 +++++++++++++++++ freqtrade/freqai/data_drawer.py | 20 +++++++++++++++++--- freqtrade/freqai/freqai_interface.py | 18 +++++++++++++++++- 3 files changed, 51 insertions(+), 4 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 04f442778..5072d3721 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -685,3 +685,20 @@ This includes all test data and unseen prediction data (dry/live/backtest). `user_data_dir/models/` contains all the data associated with the trainings and backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` and should therefore not be modified. + +## Credits +FreqAI was developed by a group of individuals who all contributed specific skillsets to the +project. + +Conception and software development: +Robert Caulk @robcaulk + +Theoretical brainstorming: +Elin Törnquist @thorntwig + +Code review, software architecture brainstorming: +@xmatthias + +Beta testing and bug reporting: +@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm +Juha Nykänen @suikula, Wagner Costa @wagnercosta diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index b0493e766..baaa55c7e 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -13,9 +13,6 @@ from joblib.externals import cloudpickle from pandas import DataFrame -# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen - - logger = logging.getLogger(__name__) @@ -25,6 +22,23 @@ class FreqaiDataDrawer: /loading to/from disk. This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is reinstantiated for each coin. + + Record of contribution: + FreqAI was developed by a group of individuals who all contributed specific skillsets to the + project. + + Conception and software development: + Robert Caulk @robcaulk + + Theoretical brainstorming: + Elin Törnquist @thorntwig + + Code review, software architecture brainstorming: + @xmatthias + + Beta testing and bug reporting: + @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm + Juha Nykänen @suikula, Wagner Costa @wagnercosta """ def __init__(self, full_path: Path, config: dict, follow_mode: bool = False): diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 55f5a03a0..5fa15ebf8 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -38,7 +38,23 @@ class IFreqaiModel(ABC): """ Class containing all tools for training and prediction in the strategy. Base*PredictionModels inherit from this class. - Author: Robert Caulk, rob.caulk@gmail.com + + Record of contribution: + FreqAI was developed by a group of individuals who all contributed specific skillsets to the + project. + + Conception and software development: + Robert Caulk @robcaulk + + Theoretical brainstorming: + Elin Törnquist @thorntwig + + Code review, software architecture brainstorming: + @xmatthias + + Beta testing and bug reporting: + @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm + Juha Nykänen @suikula, Wagner Costa @wagnercosta """ def __init__(self, config: Dict[str, Any]) -> None: From a1cff377ec7b32046fbe254a29e5ef115c01e4a8 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 13:32:04 +0200 Subject: [PATCH 179/518] add record of contribution to data_kitchen.py --- freqtrade/freqai/data_kitchen.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 4ba6badf9..9662f4f3a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -35,7 +35,23 @@ class FreqaiDataKitchen: """ Class designed to analyze data for a single pair. Employed by the IFreqaiModel class. Functionalities include holding, saving, loading, and analyzing the data. - author: Robert Caulk, rob.caulk@gmail.com + + Record of contribution: + FreqAI was developed by a group of individuals who all contributed specific skillsets to the + project. + + Conception and software development: + Robert Caulk @robcaulk + + Theoretical brainstorming: + Elin Törnquist @thorntwig + + Code review, software architecture brainstorming: + @xmatthias + + Beta testing and bug reporting: + @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm + Juha Nykänen @suikula, Wagner Costa @wagnercosta """ def __init__( From 50d630a1555a26251e162aab50102d491d5946d3 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 13:35:44 +0200 Subject: [PATCH 180/518] remove unnecessary comments from data_drawer.py --- freqtrade/freqai/data_drawer.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index baaa55c7e..f9736a498 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -76,7 +76,7 @@ class FreqaiDataDrawer: :returns: exists: bool = whether or not the drawer was located """ - exists = self.pair_dictionary_path.is_file() # resolve().exists() + exists = self.pair_dictionary_path.is_file() if exists: with open(self.pair_dictionary_path, "r") as fp: self.pair_dict = json.load(fp) @@ -96,7 +96,7 @@ class FreqaiDataDrawer: :returns: exists: bool = whether or not the drawer was located """ - exists = self.historic_predictions_path.is_file() # resolve().exists() + exists = self.historic_predictions_path.is_file() if exists: with open(self.historic_predictions_path, "rb") as fp: self.historic_predictions = cloudpickle.load(fp) @@ -143,11 +143,7 @@ class FreqaiDataDrawer: whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist") - exists = ( - self.follower_dict_path.is_file() - # .resolve() - # .exists() - ) + exists = self.follower_dict_path.is_file() if exists: logger.info("Found an existing follower dictionary") From c91e23dc504e97e9d55b0e2f0636099cce378be2 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 16:14:13 +0200 Subject: [PATCH 181/518] let user avoid normalizing labels --- freqtrade/freqai/data_kitchen.py | 37 ++++++++++++++++---------------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 9662f4f3a..1c54a6375 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -356,7 +356,7 @@ class FreqaiDataKitchen: return self.data_dictionary - def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: + def normalize_data(self, data_dictionary: Dict, do_labels: bool = True) -> Dict[Any, Any]: """ Normalize all data in the data_dictionary according to the training dataset :params: @@ -374,27 +374,28 @@ class FreqaiDataKitchen: 2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1 ) - train_labels_max = data_dictionary["train_labels"].max() - train_labels_min = data_dictionary["train_labels"].min() - data_dictionary["train_labels"] = ( - 2 - * (data_dictionary["train_labels"] - train_labels_min) - / (train_labels_max - train_labels_min) - - 1 - ) - data_dictionary["test_labels"] = ( - 2 - * (data_dictionary["test_labels"] - train_labels_min) - / (train_labels_max - train_labels_min) - - 1 - ) - for item in train_max.keys(): self.data[item + "_max"] = train_max[item] self.data[item + "_min"] = train_min[item] - self.data["labels_max"] = train_labels_max.to_dict() - self.data["labels_min"] = train_labels_min.to_dict() + if do_labels: + train_labels_max = data_dictionary["train_labels"].max() + train_labels_min = data_dictionary["train_labels"].min() + data_dictionary["train_labels"] = ( + 2 + * (data_dictionary["train_labels"] - train_labels_min) + / (train_labels_max - train_labels_min) + - 1 + ) + data_dictionary["test_labels"] = ( + 2 + * (data_dictionary["test_labels"] - train_labels_min) + / (train_labels_max - train_labels_min) + - 1 + ) + + self.data["labels_max"] = train_labels_max.to_dict() + self.data["labels_min"] = train_labels_min.to_dict() return data_dictionary From 8fa6e8b4ba9edc8db557bba04adf9b0c6aa87741 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 23 Jul 2022 15:58:31 +0200 Subject: [PATCH 182/518] Remove freqAI model bridge in favor of `self.freqai` --- freqtrade/freqai/strategy_bridge.py | 12 -------- freqtrade/strategy/interface.py | 9 ++++++ freqtrade/templates/FreqaiExampleStrategy.py | 30 +++++++++----------- tests/strategy/strats/freqai_test_strat.py | 10 ++----- 4 files changed, 25 insertions(+), 36 deletions(-) delete mode 100644 freqtrade/freqai/strategy_bridge.py diff --git a/freqtrade/freqai/strategy_bridge.py b/freqtrade/freqai/strategy_bridge.py deleted file mode 100644 index bb43084a0..000000000 --- a/freqtrade/freqai/strategy_bridge.py +++ /dev/null @@ -1,12 +0,0 @@ -from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver - - -class CustomModel: - """ - A bridge between the user defined IFreqaiModel class - and the strategy. - """ - - def __init__(self, config): - - self.bridge = FreqaiModelResolver.load_freqaimodel(config) diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index df73981bd..83d16b6f6 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -145,11 +145,20 @@ class IStrategy(ABC, HyperStrategyMixin): informative_data.candle_type = config['candle_type_def'] self._ft_informative.append((informative_data, cls_method)) + def load_freqAI_model(self) -> None: + if self.config.get('freqai', None): + # Import here to avoid importing this if freqAI is disabled + from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver + + self.freqai = FreqaiModelResolver.load_freqaimodel(self.config) + def ft_bot_start(self, **kwargs) -> None: """ Strategy init - runs after dataprovider has been added. Must call bot_start() """ + self.load_freqAI_model() + strategy_safe_wrapper(self.bot_start)() self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 402aa9d1c..86f141567 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -7,7 +7,6 @@ from pandas import DataFrame from technical import qtpylib from freqtrade.exchange import timeframe_to_prev_date -from freqtrade.freqai.strategy_bridge import CustomModel from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy @@ -21,7 +20,7 @@ class FreqaiExampleStrategy(IStrategy): Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) - self.model.bridge.start(dataframe, metadata) + self.freqai.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically generates the variety of features indicated by the user in the @@ -67,9 +66,6 @@ class FreqaiExampleStrategy(IStrategy): informative_pairs.append((pair, tf)) return informative_pairs - def bot_start(self): - self.model = CustomModel(self.config) - def populate_any_indicators( self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False ): @@ -88,7 +84,7 @@ class FreqaiExampleStrategy(IStrategy): :coin: the name of the coin which will modify the feature names. """ - with self.model.bridge.lock: + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -180,7 +176,7 @@ class FreqaiExampleStrategy(IStrategy): # the target mean/std values for each of the labels created by user in # `populate_any_indicators()` for each training period. - dataframe = self.model.bridge.start(dataframe, metadata, self) + dataframe = self.freqai.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 @@ -234,9 +230,9 @@ class FreqaiExampleStrategy(IStrategy): follow_mode = self.config.get("freqai", {}).get("follow_mode", False) if not follow_mode: - pair_dict = self.model.bridge.dd.pair_dict + pair_dict = self.freqai.dd.pair_dict else: - pair_dict = self.model.bridge.dd.follower_dict + pair_dict = self.freqai.dd.follower_dict entry_tag = trade.enter_tag @@ -244,12 +240,12 @@ class FreqaiExampleStrategy(IStrategy): "prediction" + entry_tag not in pair_dict[pair] or pair_dict[pair]["prediction" + entry_tag] > 0 ): - with self.model.bridge.lock: + with self.freqai.lock: pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) if not follow_mode: - self.model.bridge.dd.save_drawer_to_disk() + self.freqai.dd.save_drawer_to_disk() else: - self.model.bridge.dd.save_follower_dict_to_disk() + self.freqai.dd.save_follower_dict_to_disk() roi_price = pair_dict[pair]["prediction" + entry_tag] roi_time = self.max_roi_time_long.value @@ -284,16 +280,16 @@ class FreqaiExampleStrategy(IStrategy): entry_tag = trade.enter_tag follow_mode = self.config.get("freqai", {}).get("follow_mode", False) if not follow_mode: - pair_dict = self.model.bridge.dd.pair_dict + pair_dict = self.freqai.dd.pair_dict else: - pair_dict = self.model.bridge.dd.follower_dict + pair_dict = self.freqai.dd.follower_dict - with self.model.bridge.lock: + with self.freqai.lock: pair_dict[pair]["prediction" + entry_tag] = 0 if not follow_mode: - self.model.bridge.dd.save_drawer_to_disk() + self.freqai.dd.save_drawer_to_disk() else: - self.model.bridge.dd.save_follower_dict_to_disk() + self.freqai.dd.save_follower_dict_to_disk() return True diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py index 28e3dce54..221942bd3 100644 --- a/tests/strategy/strats/freqai_test_strat.py +++ b/tests/strategy/strats/freqai_test_strat.py @@ -5,7 +5,6 @@ import pandas as pd import talib.abstract as ta from pandas import DataFrame -from freqtrade.freqai.strategy_bridge import CustomModel from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy @@ -18,7 +17,7 @@ class freqai_test_strat(IStrategy): Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) - self.model.bridge.start(dataframe, metadata) + self.freqai.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically generates the variety of features indicated by the user in the @@ -64,9 +63,6 @@ class freqai_test_strat(IStrategy): informative_pairs.append((pair, tf)) return informative_pairs - def bot_start(self): - self.model = CustomModel(self.config) - def populate_any_indicators( self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False ): @@ -85,7 +81,7 @@ class freqai_test_strat(IStrategy): :coin: the name of the coin which will modify the feature names. """ - with self.model.bridge.lock: + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -146,7 +142,7 @@ class freqai_test_strat(IStrategy): # the model will return 4 values, its prediction, an indication of whether or not the # prediction should be accepted, the target mean/std values from the labels used during # each training period. - dataframe = self.model.bridge.start(dataframe, metadata, self) + dataframe = self.freqai.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 From 62f7606d2c025e793aa24bd041a9d50a07bd0748 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 23 Jul 2022 16:05:25 +0200 Subject: [PATCH 183/518] Update tests to new variant --- tests/freqai/conftest.py | 2 +- tests/freqai/test_freqai_datakitchen.py | 8 ++++---- tests/freqai/test_freqai_interface.py | 8 ++++---- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 549ba2663..1d98fd863 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -61,7 +61,7 @@ def get_patched_data_kitchen(mocker, freqaiconf): def get_patched_freqai_strategy(mocker, freqaiconf): strategy = StrategyResolver.load_strategy(freqaiconf) - strategy.bot_start() + strategy.ft_bot_start() return strategy diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 1964d1423..d0d82b489 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -69,7 +69,7 @@ def test_update_historic_data(mocker, default_conf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") @@ -105,7 +105,7 @@ def test_load_all_pairs_histories(mocker, default_conf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") @@ -125,7 +125,7 @@ def test_get_base_and_corr_dataframes(mocker, default_conf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") @@ -153,7 +153,7 @@ def test_use_strategy_to_populate_indicators(mocker, default_conf): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index 9219baee3..e812dd47e 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -21,7 +21,7 @@ def test_train_model_in_series_LightGBM(mocker, default_conf): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") @@ -113,7 +113,7 @@ def test_start_backtesting(mocker, default_conf): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") @@ -139,7 +139,7 @@ def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") @@ -162,7 +162,7 @@ def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") From 8a3cffcd1b80882d296c877d6b05198d3efacac5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 23 Jul 2022 16:06:46 +0200 Subject: [PATCH 184/518] Remove remaining CustomModel references --- docs/freqai.md | 12 ++++-------- freqtrade/templates/FreqaiExampleStrategy.py | 1 - tests/freqai/conftest.py | 6 +++--- tests/strategy/strats/freqai_test_strat.py | 1 - 4 files changed, 7 insertions(+), 13 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 5072d3721..48b8968a3 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -166,7 +166,7 @@ config setup includes: Features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%` and labels are added by prepending `&`. There are some important components/structures that the user *must* include when building their feature set. -As shown below, `with self.model.bridge.lock:` must be used to ensure thread safety - especially when using third +As shown below, `with self.freqai.lock:` must be used to ensure thread safety - especially when using third party libraries for indicator construction such as TA-lib. Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). This is where the user will add single features and labels to their feature set to avoid duplication from @@ -191,7 +191,7 @@ various configuration parameters which multiply the feature set such as `include :coin: the name of the coin which will modify the feature names. """ - with self.model.bridge.lock: + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -370,7 +370,6 @@ for each pair, for each backtesting window within the bigger `--timerange`. The Freqai strategy requires the user to include the following lines of code in the strategy: ```python - from freqtrade.freqai.strategy_bridge import CustomModel def informative_pairs(self): whitelist_pairs = self.dp.current_whitelist() @@ -385,9 +384,6 @@ The Freqai strategy requires the user to include the following lines of code in informative_pairs.append((pair, tf)) return informative_pairs - def bot_start(self): - self.model = CustomModel(self.config) - def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] @@ -400,7 +396,7 @@ The Freqai strategy requires the user to include the following lines of code in # the target mean/std values for each of the labels created by user in # `populate_any_indicators()` for each training period. - dataframe = self.model.bridge.start(dataframe, metadata, self) + dataframe = self.freqai.start(dataframe, metadata, self) return dataframe ``` @@ -648,7 +644,7 @@ below this value. An example usage in the strategy may look something like: dataframe["do_predict"], dataframe["target_upper_quantile"], dataframe["target_lower_quantile"], - ) = self.model.bridge.start(dataframe, metadata, self) + ) = self.freqai.start(dataframe, metadata, self) return dataframe diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 86f141567..b3c2cc8d7 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -19,7 +19,6 @@ class FreqaiExampleStrategy(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: - self.model = CustomModel(self.config) self.freqai.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 1d98fd863..1d0ea0e54 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -76,7 +76,7 @@ def get_freqai_live_analyzed_dataframe(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") @@ -91,7 +91,7 @@ def get_freqai_analyzed_dataframe(mocker, freqaiconf): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") @@ -107,7 +107,7 @@ def get_ready_to_train(mocker, freqaiconf): exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) - freqai = strategy.model.bridge + freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py index 221942bd3..49f00d6e2 100644 --- a/tests/strategy/strats/freqai_test_strat.py +++ b/tests/strategy/strats/freqai_test_strat.py @@ -16,7 +16,6 @@ class freqai_test_strat(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: - self.model = CustomModel(self.config) self.freqai.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically From 81c1aa3c13cdc674cb6e34f16287a2c83481a94b Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 23 Jul 2022 16:14:11 +0200 Subject: [PATCH 185/518] Update imports in freqAI sample strategies --- freqtrade/templates/FreqaiExampleStrategy.py | 3 +-- tests/strategy/strats/freqai_test_strat.py | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index b3c2cc8d7..7008008a3 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -8,8 +8,7 @@ from technical import qtpylib from freqtrade.exchange import timeframe_to_prev_date from freqtrade.persistence import Trade -from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair -from freqtrade.strategy.interface import IStrategy +from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair logger = logging.getLogger(__name__) diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py index 49f00d6e2..8679d4d74 100644 --- a/tests/strategy/strats/freqai_test_strat.py +++ b/tests/strategy/strats/freqai_test_strat.py @@ -5,8 +5,7 @@ import pandas as pd import talib.abstract as ta from pandas import DataFrame -from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair -from freqtrade.strategy.interface import IStrategy +from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair logger = logging.getLogger(__name__) From f3d46613ee501888defb30926cd1f1b3a610e93b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 23 Jul 2022 17:14:11 +0200 Subject: [PATCH 186/518] move prediction denormalization into datakitchen. remove duplicate associated code. avoid normalization/denormalization for string dtypes. --- freqtrade/freqai/data_kitchen.py | 37 +++++++++++++++---- freqtrade/freqai/freqai_interface.py | 8 +--- .../prediction_models/BaseRegressionModel.py | 7 +--- 3 files changed, 32 insertions(+), 20 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 1c54a6375..3899c82df 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -356,7 +356,7 @@ class FreqaiDataKitchen: return self.data_dictionary - def normalize_data(self, data_dictionary: Dict, do_labels: bool = True) -> Dict[Any, Any]: + def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: """ Normalize all data in the data_dictionary according to the training dataset :params: @@ -378,24 +378,26 @@ class FreqaiDataKitchen: self.data[item + "_max"] = train_max[item] self.data[item + "_min"] = train_min[item] - if do_labels: - train_labels_max = data_dictionary["train_labels"].max() - train_labels_min = data_dictionary["train_labels"].min() + for item in data_dictionary["train_labels"].keys(): + if data_dictionary["train_labels"][item].dtype == str: + continue + train_labels_max = data_dictionary["train_labels"][item].max() + train_labels_min = data_dictionary["train_labels"][item].min() data_dictionary["train_labels"] = ( 2 - * (data_dictionary["train_labels"] - train_labels_min) + * (data_dictionary["train_labels"][item] - train_labels_min) / (train_labels_max - train_labels_min) - 1 ) - data_dictionary["test_labels"] = ( + data_dictionary["test_labels"][item] = ( 2 * (data_dictionary["test_labels"] - train_labels_min) / (train_labels_max - train_labels_min) - 1 ) - self.data["labels_max"] = train_labels_max.to_dict() - self.data["labels_min"] = train_labels_min.to_dict() + self.data[f"{item}_max"] = train_labels_max # .to_dict() + self.data[f"{item}_min"] = train_labels_min # .to_dict() return data_dictionary @@ -417,6 +419,25 @@ class FreqaiDataKitchen: return df + def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame: + """ + Normalize a set of data using the mean and standard deviation from + the associated training data. + :params: + :df: Dataframe of predictions to be denormalized + """ + + for label in self.label_list: + if df[label].dtype == str: + continue + df[label] = ( + (df[label] + 1) + * (self.data[f"{label}_max"] - self.data[f"{label}_min"]) + / 2 + ) + self.data[f"{label}_min"] + + return df + def split_timerange( self, tr: str, train_split: int = 28, bt_split: int = 7 ) -> Tuple[list, list]: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 5fa15ebf8..ac8cf6e60 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -501,12 +501,8 @@ class IFreqaiModel(ABC): ) -> None: trained_predictions = model.predict(df) pred_df = DataFrame(trained_predictions, columns=dk.label_list) - for label in dk.label_list: - pred_df[label] = ( - (pred_df[label] + 1) - * (dk.data["labels_max"][label] - dk.data["labels_min"][label]) - / 2 - ) + dk.data["labels_min"][label] + + pred_df = dk.denormalize_labels_from_metadata(pred_df) self.dd.historic_predictions[pair] = pd.DataFrame() self.dd.historic_predictions[pair] = copy.deepcopy(pred_df) diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index ffe30ef2a..2654b3726 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -107,11 +107,6 @@ class BaseRegressionModel(IFreqaiModel): predictions = self.model.predict(dk.data_dictionary["prediction_features"]) pred_df = DataFrame(predictions, columns=dk.label_list) - for label in dk.label_list: - pred_df[label] = ( - (pred_df[label] + 1) - * (dk.data["labels_max"][label] - dk.data["labels_min"][label]) - / 2 - ) + dk.data["labels_min"][label] + pred_df = dk.denormalize_labels_from_metadata(pred_df) return (pred_df, dk.do_predict) From 95f5218ceb49540f2f3110c907db1335d5085f52 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 24 Jul 2022 07:32:13 +0200 Subject: [PATCH 187/518] Reenable Catboost test (#7118) * Reenable Catboost test * Simplify freqAI tests, ensure they use a tempdir for modelstorage --- tests/freqai/conftest.py | 7 +- tests/freqai/test_freqai_datakitchen.py | 79 +++++++------- tests/freqai/test_freqai_interface.py | 130 ++++++++++-------------- 3 files changed, 96 insertions(+), 120 deletions(-) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 1d0ea0e54..ede991240 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -2,6 +2,8 @@ from copy import deepcopy from pathlib import Path from unittest.mock import MagicMock +import pytest + from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_kitchen import FreqaiDataKitchen @@ -10,13 +12,14 @@ from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver from tests.conftest import get_patched_exchange -# @pytest.fixture(scope="function") -def freqai_conf(default_conf): +@pytest.fixture(scope="function") +def freqai_conf(default_conf, tmpdir): freqaiconf = deepcopy(default_conf) freqaiconf.update( { "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", + "user_data_dir": Path(tmpdir), "strategy-path": "freqtrade/tests/strategy/strats", "freqaimodel": "LightGBMPredictionModel", "freqaimodel_path": "freqai/prediction_models", diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index d0d82b489..ee1d83982 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -1,6 +1,3 @@ -# from unittest.mock import MagicMock -# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge -import copy import datetime import shutil from pathlib import Path @@ -13,7 +10,7 @@ from freqtrade.data.dataprovider import DataProvider from freqtrade.exceptions import OperationalException from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from tests.conftest import get_patched_exchange -from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_freqai_strategy +from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy @pytest.mark.parametrize( @@ -24,15 +21,15 @@ from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_pat ], ) def test_create_fulltimerange( - timerange, train_period_days, expected_result, default_conf, mocker, caplog + timerange, train_period_days, expected_result, freqai_conf, mocker, caplog ): - dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf))) + dk = get_patched_data_kitchen(mocker, freqai_conf) assert dk.create_fulltimerange(timerange, train_period_days) == expected_result shutil.rmtree(Path(dk.full_path)) -def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf): - dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf))) +def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf): + dk = get_patched_data_kitchen(mocker, freqai_conf) with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"): dk.create_fulltimerange("20220101-20220201", 0.5) with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"): @@ -49,11 +46,10 @@ def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf): ], ) def test_split_timerange( - mocker, default_conf, timerange, train_period_days, backtest_period_days, expected_result + mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result ): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - freqaiconf.update({"timerange": "20220101-20220401"}) - dk = get_patched_data_kitchen(mocker, freqaiconf) + freqai_conf.update({"timerange": "20220101-20220401"}) + dk = get_patched_data_kitchen(mocker, freqai_conf) tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days) assert len(tr_list) == len(bt_list) == expected_result @@ -64,14 +60,13 @@ def test_split_timerange( shutil.rmtree(Path(dk.full_path)) -def test_update_historic_data(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) +def test_update_historic_data(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) @@ -93,69 +88,65 @@ def test_update_historic_data(mocker, default_conf): (datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False), ], ) -def test_check_if_model_expired(mocker, default_conf, timestamp, expected): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - dk = get_patched_data_kitchen(mocker, freqaiconf) +def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected): + dk = get_patched_data_kitchen(mocker, freqai_conf) assert dk.check_if_model_expired(timestamp) == expected shutil.rmtree(Path(dk.full_path)) -def test_load_all_pairs_histories(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) +def test_load_all_pairs_histories(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) assert len(freqai.dd.historic_data.keys()) == len( - freqaiconf.get("exchange", {}).get("pair_whitelist") + freqai_conf.get("exchange", {}).get("pair_whitelist") ) assert len(freqai.dd.historic_data["ADA/BTC"]) == len( - freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") ) shutil.rmtree(Path(freqai.dk.full_path)) -def test_get_base_and_corr_dataframes(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) +def test_get_base_and_corr_dataframes(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180111-20180114") corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") num_tfs = len( - freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") ) assert len(base_df.keys()) == num_tfs assert len(corr_df.keys()) == len( - freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist") + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist") ) assert len(corr_df["ADA/BTC"].keys()) == num_tfs shutil.rmtree(Path(freqai.dk.full_path)) -def test_use_strategy_to_populate_indicators(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) - strategy.freqai_info = freqaiconf.get("freqai", {}) +def test_use_strategy_to_populate_indicators(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180111-20180114") diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index e812dd47e..0bb2dac79 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -1,29 +1,29 @@ # from unittest.mock import MagicMock # from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge -import copy -# import platform +import platform import shutil from pathlib import Path from unittest.mock import MagicMock +import pytest + from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from tests.conftest import get_patched_exchange, log_has_re -from tests.freqai.conftest import freqai_conf, get_patched_freqai_strategy +from tests.freqai.conftest import get_patched_freqai_strategy -def test_train_model_in_series_LightGBM(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - freqaiconf.update({"timerange": "20180110-20180130"}) +def test_train_model_in_series_LightGBM(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) - strategy.freqai_info = freqaiconf.get("freqai", {}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dk.load_all_pair_histories(timerange) @@ -58,64 +58,47 @@ def test_train_model_in_series_LightGBM(mocker, default_conf): shutil.rmtree(Path(freqai.dk.full_path)) -# FIXME: hits segfault -# @pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM..") -# def test_train_model_in_series_Catboost(mocker, default_conf): -# freqaiconf = freqai_conf(copy.deepcopy(default_conf)) -# freqaiconf.update({"timerange": "20180110-20180130"}) -# freqaiconf.update({"freqaimodel": "CatboostPredictionModel"}) -# strategy = get_patched_freqai_strategy(mocker, freqaiconf) -# exchange = get_patched_exchange(mocker, freqaiconf) -# strategy.dp = DataProvider(freqaiconf, exchange) -# strategy.freqai_info = freqaiconf.get("freqai", {}) -# freqai = strategy.model.bridge -# freqai.live = True -# freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) -# timerange = TimeRange.parse_timerange("20180110-20180130") -# freqai.dk.load_all_pair_histories(timerange) +@pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...") +def test_train_model_in_series_Catboost(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + freqai_conf.update({"freqaimodel": "CatboostPredictionModel"}) + del freqai_conf['freqai']['model_training_parameters']['verbosity'] + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) -# freqai.dd.pair_dict = MagicMock() + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dk.load_all_pair_histories(timerange) -# data_load_timerange = TimeRange.parse_timerange("20180110-20180130") -# new_timerange = TimeRange.parse_timerange("20180120-20180130") + freqai.dd.pair_dict = MagicMock() -# freqai.train_model_in_series(new_timerange, "ADA/BTC", -# strategy, freqai.dk, data_load_timerange) + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") -# assert ( -# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib")) -# .resolve() -# .exists() -# ) -# assert ( -# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json")) -# .resolve() -# .exists() -# ) -# assert ( -# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl")) -# .resolve() -# .exists() -# ) -# assert ( -# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib")) -# .resolve() -# .exists() -# ) + freqai.train_model_in_series(new_timerange, "ADA/BTC", + strategy, freqai.dk, data_load_timerange) -# shutil.rmtree(Path(freqai.dk.full_path)) + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists() + + shutil.rmtree(Path(freqai.dk.full_path)) -def test_start_backtesting(mocker, default_conf): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - freqaiconf.update({"timerange": "20180120-20180130"}) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) - strategy.freqai_info = freqaiconf.get("freqai", {}) +def test_start_backtesting(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180120-20180130"}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180110-20180130") @@ -132,16 +115,15 @@ def test_start_backtesting(mocker, default_conf): shutil.rmtree(Path(freqai.dk.full_path)) -def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog): - freqaiconf = freqai_conf(copy.deepcopy(default_conf)) - freqaiconf.update({"timerange": "20180120-20180130"}) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) - strategy.freqai_info = freqaiconf.get("freqai", {}) +def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): + freqai_conf.update({"timerange": "20180120-20180130"}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180110-20180130") @@ -157,14 +139,14 @@ def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog): # without deleting the exiting folder structure, re-run - freqaiconf.update({"timerange": "20180120-20180130"}) - strategy = get_patched_freqai_strategy(mocker, freqaiconf) - exchange = get_patched_exchange(mocker, freqaiconf) - strategy.dp = DataProvider(freqaiconf, exchange) - strategy.freqai_info = freqaiconf.get("freqai", {}) + freqai_conf.update({"timerange": "20180120-20180130"}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180110-20180130") From fff39eff9edf8a33a094e78a4f0f6b68259b5ec8 Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Sun, 24 Jul 2022 08:42:50 +0200 Subject: [PATCH 188/518] fix multitarget bug --- freqtrade/freqai/data_kitchen.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 3899c82df..569cc0c22 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -383,7 +383,7 @@ class FreqaiDataKitchen: continue train_labels_max = data_dictionary["train_labels"][item].max() train_labels_min = data_dictionary["train_labels"][item].min() - data_dictionary["train_labels"] = ( + data_dictionary["train_labels"][item] = ( 2 * (data_dictionary["train_labels"][item] - train_labels_min) / (train_labels_max - train_labels_min) @@ -391,14 +391,13 @@ class FreqaiDataKitchen: ) data_dictionary["test_labels"][item] = ( 2 - * (data_dictionary["test_labels"] - train_labels_min) + * (data_dictionary["test_labels"][item] - train_labels_min) / (train_labels_max - train_labels_min) - 1 ) self.data[f"{item}_max"] = train_labels_max # .to_dict() self.data[f"{item}_min"] = train_labels_min # .to_dict() - return data_dictionary def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame: @@ -412,8 +411,8 @@ class FreqaiDataKitchen: for item in df.keys(): df[item] = ( 2 - * (df[item] - self.data[item + "_min"]) - / (self.data[item + "_max"] - self.data[item + "_min"]) + * (df[item] - self.data[f"{item}_min"]) + / (self.data[f"{item}_max"] - self.data[f"{item}_min"]) - 1 ) From 88e10f73065c53f847aa17a932c98d9239e5f505 Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Sun, 24 Jul 2022 09:01:23 +0200 Subject: [PATCH 189/518] add exception for not passing timerange. Remove hard coded arguments for CatboostPredictionModels. Update docs --- docs/freqai.md | 2 +- freqtrade/freqai/data_kitchen.py | 3 +++ freqtrade/freqai/prediction_models/CatboostPredictionModel.py | 2 -- .../freqai/prediction_models/CatboostPredictionMultiModel.py | 3 --- 4 files changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 48b8968a3..b2ee2407a 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -83,7 +83,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. | `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. **Datatype:** Float > 0. | `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default.
**Datatype:** boolean. -| `live_trained_timestamp` | Useful if user wants to start from models trained during a *backtest*. The timestamp can be located in the `user_data/models` backtesting folder. This is not a commonly used parameter, leave undefined for most applications.
**Datatype:** positive integer. +| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
**Datatype:** positive integer. | `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics.
**Datatype:** positive integer. | `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`.
**Datatype:** boolean. | `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire.
**Datatype:** positive integer. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 569cc0c22..e732649ff 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -76,6 +76,9 @@ class FreqaiDataKitchen: self.keras = self.freqai_config.get("keras", False) self.set_all_pairs() if not self.live: + if not self.config["timerange"]: + raise OperationalException( + 'Please pass --timerange if you intend to use FreqAI for backtesting.') self.full_timerange = self.create_fulltimerange( self.config["timerange"], self.freqai_config.get("train_period_days") ) diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index fafb12abe..c69602025 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -38,8 +38,6 @@ class CatboostPredictionModel(BaseRegressionModel): model = CatBoostRegressor( allow_writing_files=False, - verbose=100, - early_stopping_rounds=400, **self.model_training_parameters, ) model.fit(X=train_data, eval_set=test_data) diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py index becfb43eb..1b91fe0c6 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py @@ -27,9 +27,6 @@ class CatboostPredictionMultiModel(BaseRegressionModel): cbr = CatBoostRegressor( allow_writing_files=False, - gpu_ram_part=0.5, - verbose=100, - early_stopping_rounds=400, **self.model_training_parameters, ) From 70b7a254afdd701a9dd2a2d35c65668abac6088e Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 24 Jul 2022 16:51:48 +0200 Subject: [PATCH 190/518] Update some areas to use default docstring formatting --- docs/freqai.md | 11 ++- freqtrade/freqai/data_drawer.py | 22 +++--- freqtrade/freqai/freqai_interface.py | 75 +++++++++----------- freqtrade/strategy/interface.py | 11 ++- freqtrade/templates/FreqaiExampleStrategy.py | 11 ++- 5 files changed, 59 insertions(+), 71 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index b2ee2407a..4060b5394 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -183,12 +183,11 @@ various configuration parameters which multiply the feature set such as `include (see convention below). I.e. user should not prepend any supporting metrics (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the model. - :params: - :pair: pair to be used as informative - :df: strategy dataframe which will receive merges from informatives - :tf: timeframe of the dataframe which will modify the feature names - :informative: the dataframe associated with the informative pair - :coin: the name of the coin which will modify the feature names. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. """ with self.freqai.lock: diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index f9736a498..e18ecdbed 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -163,13 +163,12 @@ class FreqaiDataDrawer: Locate and load existing model metadata from persistent storage. If not located, create a new one and append the current pair to it and prepare it for its first training - :params: - metadata: dict = strategy furnished pair metadata - :returns: - model_filename: str = unique filename used for loading persistent objects from disk - trained_timestamp: int = the last time the coin was trained - coin_first: bool = If the coin is fresh without metadata - return_null_array: bool = Follower could not find pair metadata + :param pair: str: pair to lookup + :return: + model_filename: str = unique filename used for loading persistent objects from disk + trained_timestamp: int = the last time the coin was trained + coin_first: bool = If the coin is fresh without metadata + return_null_array: bool = Follower could not find pair metadata """ pair_in_dict = self.pair_dict.get(pair) data_path_set = self.pair_dict.get(pair, {}).get("data_path", None) @@ -277,13 +276,12 @@ class FreqaiDataDrawer: ) df = pd.concat([prepend_df, df], axis=0) - def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame: + def attach_return_values_to_return_dataframe( + self, pair: str, dataframe: DataFrame) -> DataFrame: """ Attach the return values to the strat dataframe - :params: - dataframe: DataFrame = strat dataframe - :returns: - dataframe: DataFrame = strat dataframe with return values attached + :param dataframe: DataFrame = strategy dataframe + :return: DataFrame = strat dataframe with return values attached """ df = self.model_return_values[pair] to_keep = [col for col in dataframe.columns if not col.startswith("&")] diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index ac8cf6e60..b88923285 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -129,8 +129,7 @@ class IFreqaiModel(ABC): Function designed to constantly scan pairs for retraining on a separate thread (intracandle) to improve model youth. This function is agnostic to data preparation/collection/storage, it simply trains on what ever data is available in the self.dd. - :params: - strategy: IStrategy = The user defined strategy class + :param strategy: IStrategy = The user defined strategy class """ while 1: time.sleep(1) @@ -164,12 +163,11 @@ class IFreqaiModel(ABC): following the training window). FreqAI slides the window and sequentially builds the backtesting results before returning the concatenated results for the full backtesting period back to the strategy. - :params: - dataframe: DataFrame = strategy passed dataframe - metadata: Dict = pair metadata - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only - :returns: - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + :param dataframe: DataFrame = strategy passed dataframe + :param metadata: Dict = pair metadata + :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only + :return: + FreqaiDataKitchen = Data management/analysis tool associated to present pair only """ self.pair_it += 1 @@ -239,13 +237,12 @@ class IFreqaiModel(ABC): """ The main broad execution for dry/live. This function will check if a retraining should be performed, and if so, retrain and reset the model. - :params: - dataframe: DataFrame = strategy passed dataframe - metadata: Dict = pair metadata - strategy: IStrategy = currently employed strategy - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + :param dataframe: DataFrame = strategy passed dataframe + :param metadata: Dict = pair metadata + :param strategy: IStrategy = currently employed strategy + dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only :returns: - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only """ # update follower @@ -353,9 +350,9 @@ class IFreqaiModel(ABC): """ Ensure user is passing the proper feature set if they are reusing an `identifier` pointing to a folder holding existing models. - :params: - dataframe: DataFrame = strategy provided dataframe - dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop + :param dataframe: DataFrame = strategy provided dataframe + :param dk: FreqaiDataKitchen = non-persistent data container/analyzer for + current coin/bot loop """ dk.find_features(dataframe) if "training_features_list_raw" in dk.data: @@ -461,13 +458,14 @@ class IFreqaiModel(ABC): """ Retreive data and train model in single threaded mode (only used if model directory is empty upon startup for dry/live ) - :params: - new_trained_timerange: TimeRange = the timerange to train the model on - metadata: dict = strategy provided metadata - strategy: IStrategy = user defined strategy object - dk: FreqaiDataKitchen = non-persistent data container for current coin/loop - data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators - (larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs) + :param new_trained_timerange: TimeRange = the timerange to train the model on + :param metadata: dict = strategy provided metadata + :param strategy: IStrategy = user defined strategy object + :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop + :param data_load_timerange: TimeRange = the amount of data to be loaded + for populate_any_indicators + (larger than new_trained_timerange so that + new_trained_timerange does not contain any NaNs) """ corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes( @@ -515,11 +513,9 @@ class IFreqaiModel(ABC): """ Filter the training data and train a model to it. Train makes heavy use of the datahandler for storing, saving, loading, and analyzing the data. - :params: - :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. - :returns: - :model: Trained model which can be used to inference (self.predict) + :param unfiltered_dataframe: Full dataframe for the current training period + :param metadata: pair metadata from strategy. + :return: Trained model which can be used to inference (self.predict) """ @abstractmethod @@ -528,9 +524,8 @@ class IFreqaiModel(ABC): Most regressors use the same function names and arguments e.g. user can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. - :params: - data_dictionary: Dict = the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: Dict = the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ return @@ -541,9 +536,9 @@ class IFreqaiModel(ABC): ) -> Tuple[DataFrame, npt.ArrayLike]: """ Filter the prediction features data and predict with it. - :param: - unfiltered_dataframe: Full dataframe for the current backtest period. - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only + :param unfiltered_dataframe: Full dataframe for the current backtest period. + :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only + :param first: boolean = whether this is the first prediction or not. :return: :predictions: np.array of predictions :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove @@ -554,12 +549,10 @@ class IFreqaiModel(ABC): def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame: """ User defines the dataframe to be returned to strategy here. - :params: - dataframe: DataFrame = the full dataframe for the current prediction (live) - or --timerange (backtesting) - dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only - :returns: - dataframe: DataFrame = dataframe filled with user defined data + :param dataframe: DataFrame = the full dataframe for the current prediction (live) + or --timerange (backtesting) + :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only + :return: dataframe: DataFrame = dataframe filled with user defined data """ return diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 83d16b6f6..431e67a98 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -566,12 +566,11 @@ class IStrategy(ABC, HyperStrategyMixin): additional features here, but must follow the naming convention. This method is *only* used in FreqaiDataKitchen class and therefore it is only called if FreqAI is active. - :params: - :pair: pair to be used as informative - :df: strategy dataframe which will receive merges from informatives - :tf: timeframe of the dataframe which will modify the feature names - :informative: the dataframe associated with the informative pair - :coin: the name of the coin which will modify the feature names. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. """ return df diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 7008008a3..58eb47532 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -74,12 +74,11 @@ class FreqaiExampleStrategy(IStrategy): (see convention below). I.e. user should not prepend any supporting metrics (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the model. - :params: - :pair: pair to be used as informative - :df: strategy dataframe which will receive merges from informatives - :tf: timeframe of the dataframe which will modify the feature names - :informative: the dataframe associated with the informative pair - :coin: the name of the coin which will modify the feature names. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. """ with self.freqai.lock: From 1885deb632e52bcfb58c6e8bdb33178844477fd9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 24 Jul 2022 16:54:39 +0200 Subject: [PATCH 191/518] More docstring changes --- freqtrade/freqai/freqai_interface.py | 8 ++++---- freqtrade/freqai/prediction_models/BaseRegressionModel.py | 7 +++---- freqtrade/freqai/prediction_models/BaseTensorFlowModel.py | 5 ++--- .../freqai/prediction_models/CatboostPredictionModel.py | 5 ++--- .../prediction_models/CatboostPredictionMultiModel.py | 5 ++--- .../freqai/prediction_models/LightGBMPredictionModel.py | 5 ++--- .../prediction_models/LightGBMPredictionMultiModel.py | 5 ++--- 7 files changed, 17 insertions(+), 23 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index b88923285..f5a1d667c 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -92,11 +92,11 @@ class IFreqaiModel(ABC): Entry point to the FreqaiModel from a specific pair, it will train a new model if necessary before making the prediction. - :params: - :dataframe: Full dataframe coming from strategy - it contains entire - backtesting timerange + additional historical data necessary to train + :param dataframe: Full dataframe coming from strategy - it contains entire + backtesting timerange + additional historical data necessary to train the model. - :metadata: pair metadata coming from strategy. + :param metadata: pair metadata coming from strategy. + :param strategy: Strategy to train on """ self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 2654b3726..c2fd53d0f 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -32,10 +32,9 @@ class BaseRegressionModel(IFreqaiModel): """ Filter the training data and train a model to it. Train makes heavy use of the datakitchen for storing, saving, loading, and analyzing the data. - :params: - :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. - :returns: + :param unfiltered_dataframe: Full dataframe for the current training period + :param metadata: pair metadata from strategy. + :return: :model: Trained model which can be used to inference (self.predict) """ diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py index 098ff24dd..268bb00c9 100644 --- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py +++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py @@ -31,9 +31,8 @@ class BaseTensorFlowModel(IFreqaiModel): """ Filter the training data and train a model to it. Train makes heavy use of the datakitchen for storing, saving, loading, and analyzing the data. - :params: - :unfiltered_dataframe: Full dataframe for the current training period - :metadata: pair metadata from strategy. + :param unfiltered_dataframe: Full dataframe for the current training period + :param metadata: pair metadata from strategy. :returns: :model: Trained model which can be used to inference (self.predict) """ diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index c69602025..f41760472 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -19,9 +19,8 @@ class CatboostPredictionModel(BaseRegressionModel): def fit(self, data_dictionary: Dict) -> Any: """ User sets up the training and test data to fit their desired model here - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ train_data = Pool( diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py index 1b91fe0c6..17b5e6c68 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py @@ -20,9 +20,8 @@ class CatboostPredictionMultiModel(BaseRegressionModel): def fit(self, data_dictionary: Dict) -> Any: """ User sets up the training and test data to fit their desired model here - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ cbr = CatBoostRegressor( diff --git a/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py index 6a91837da..525566cf4 100644 --- a/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py +++ b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py @@ -21,9 +21,8 @@ class LightGBMPredictionModel(BaseRegressionModel): Most regressors use the same function names and arguments e.g. user can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) diff --git a/freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py b/freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py index 89aad4323..4c51c9008 100644 --- a/freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py +++ b/freqtrade/freqai/prediction_models/LightGBMPredictionMultiModel.py @@ -20,9 +20,8 @@ class LightGBMPredictionMultiModel(BaseRegressionModel): def fit(self, data_dictionary: Dict) -> Any: """ User sets up the training and test data to fit their desired model here - :params: - :data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. """ lgb = LGBMRegressor(**self.model_training_parameters) From 520ee3f7a1e8e741fd7ed23b08c5337520416351 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 24 Jul 2022 17:07:45 +0200 Subject: [PATCH 192/518] Convert freqAI into packages --- freqtrade/freqai/__init__.py | 0 freqtrade/freqai/prediction_models/__init__.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 freqtrade/freqai/__init__.py create mode 100644 freqtrade/freqai/prediction_models/__init__.py diff --git a/freqtrade/freqai/__init__.py b/freqtrade/freqai/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/freqtrade/freqai/prediction_models/__init__.py b/freqtrade/freqai/prediction_models/__init__.py new file mode 100644 index 000000000..e69de29bb From ab587747fb4f686f687306be40ad69918df0913c Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Sun, 24 Jul 2022 23:32:02 +0200 Subject: [PATCH 193/518] first fix for follower path bug --- freqtrade/freqai/data_drawer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index e18ecdbed..98fca339d 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -151,7 +151,7 @@ class FreqaiDataDrawer: for pair in whitelist_pairs: self.follower_dict[pair] = {} - with open(self.follow_path, "w") as fp: + with open(self.follow_path_dict, "w") as fp: json.dump(self.follower_dict, fp, default=self.np_encoder) def np_encoder(self, object): From c9d46a5237a251193390a4c511737741773f851c Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Mon, 25 Jul 2022 09:24:40 +0200 Subject: [PATCH 194/518] finish bringing follow_mode up to date --- freqtrade/freqai/data_drawer.py | 2 +- freqtrade/freqai/freqai_interface.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 98fca339d..8aca4b371 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -151,7 +151,7 @@ class FreqaiDataDrawer: for pair in whitelist_pairs: self.follower_dict[pair] = {} - with open(self.follow_path_dict, "w") as fp: + with open(self.follower_dict_path, "w") as fp: json.dump(self.follower_dict, fp, default=self.np_encoder) def np_encoder(self, object): diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index f5a1d667c..fc352ea8b 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -287,7 +287,7 @@ class IFreqaiModel(ABC): elif self.follow_mode: dk.set_paths(metadata["pair"], trained_timestamp) logger.info( - "FreqAI instance set to follow_mode, finding existing pair" + "FreqAI instance set to follow_mode, finding existing pair " f"using { self.identifier }" ) From 4abc26b582445d4c407327b77be9b8920812dbc6 Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Mon, 25 Jul 2022 10:48:04 +0200 Subject: [PATCH 195/518] add test for follow_mode --- tests/freqai/test_freqai_interface.py | 73 +++++++++++++++++++-------- 1 file changed, 53 insertions(+), 20 deletions(-) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index 0bb2dac79..ce1a52bbf 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -34,26 +34,10 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf): freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) - assert ( - Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib")) - .resolve() - .exists() - ) - assert ( - Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json")) - .resolve() - .exists() - ) - assert ( - Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl")) - .resolve() - .exists() - ) - assert ( - Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib")) - .resolve() - .exists() - ) + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() shutil.rmtree(Path(freqai.dk.full_path)) @@ -161,3 +145,52 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): ) shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_follow_mode(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dk.load_all_pair_histories(timerange) + + metadata = {"pair": "ADA/BTC"} + freqai.dd.set_pair_dict_info(metadata) + # freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) + + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() + + # start the follower and ask it to predict on existing files + + freqai_conf.get("freqai", {}).update({"follow_mode": "true"}) + + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd, freqai.live) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dk.load_all_pair_histories(timerange) + + df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m') + freqai.start_live(df, metadata, strategy, freqai.dk) + + assert len(freqai.dk.return_dataframe.index) == 5702 + + shutil.rmtree(Path(freqai.dk.full_path)) From 7b105532d13bd0edab34f5a1913a9cc22d3d08c2 Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Mon, 25 Jul 2022 11:46:59 +0200 Subject: [PATCH 196/518] fix mypy error and add test for principal component analysis --- freqtrade/freqai/freqai_interface.py | 5 ++-- .../prediction_models/BaseRegressionModel.py | 7 ++--- tests/freqai/test_freqai_interface.py | 27 +++++++++++++++++++ 3 files changed, 33 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index fc352ea8b..c393420b5 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -65,7 +65,6 @@ class IFreqaiModel(ABC): self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters") self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters") self.feature_parameters = config.get("freqai", {}).get("feature_parameters") - self.model = None self.retrain = False self.first = True self.set_full_path() @@ -372,8 +371,8 @@ class IFreqaiModel(ABC): """ Base data cleaning method for train Any function inside this method should drop training data points from the filtered_dataframe - based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example - of how outlier data points are dropped from the dataframe used for training. + based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an + example of how outlier data points are dropped from the dataframe used for training. """ if self.freqai_info.get("feature_parameters", {}).get( diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index c2fd53d0f..2db025fd6 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -1,6 +1,7 @@ import logging -from typing import Tuple +from typing import Any, Tuple +import numpy.typing as npt from pandas import DataFrame from freqtrade.freqai.data_kitchen import FreqaiDataKitchen @@ -28,7 +29,7 @@ class BaseRegressionModel(IFreqaiModel): def train( self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen - ) -> Tuple[DataFrame, DataFrame]: + ) -> Any: """ Filter the training data and train a model to it. Train makes heavy use of the datakitchen for storing, saving, loading, and analyzing the data. @@ -83,7 +84,7 @@ class BaseRegressionModel(IFreqaiModel): def predict( self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False - ) -> Tuple[DataFrame, DataFrame]: + ) -> Tuple[DataFrame, npt.ArrayLike]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index ce1a52bbf..6699ef563 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -194,3 +194,30 @@ def test_follow_mode(mocker, freqai_conf): assert len(freqai.dk.return_dataframe.index) == 5702 shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_principal_component_analysis(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( + {"princpial_component_analysis": "true"}) + + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dk.load_all_pair_histories(timerange) + + freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) + + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl") + + shutil.rmtree(Path(freqai.dk.full_path)) From 897f18a8c87c4d7d350757782be21f6b5caeb5de Mon Sep 17 00:00:00 2001 From: Robert Caulk Date: Mon, 25 Jul 2022 15:07:09 +0200 Subject: [PATCH 197/518] ensure proper integer type casting for timestamps. Add check test for backtesting subdaily time periods --- freqtrade/freqai/data_kitchen.py | 4 ++-- freqtrade/freqai/freqai_interface.py | 5 +++-- tests/freqai/test_freqai_interface.py | 28 ++++++++++++++++++++++++++- 3 files changed, 32 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 80c037a3d..5ca64e504 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -909,10 +909,10 @@ class FreqaiDataKitchen: coin, _ = pair.split("/") self.data_path = Path( self.full_path - / str("sub-train" + "-" + pair.split("/")[0] + "_" + str(int(trained_timerange.stopts))) + / f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}" ) - self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) + self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}" def download_all_data_for_training(self, timerange: TimeRange) -> None: """ diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index c393420b5..a8527f9e1 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -210,11 +210,12 @@ class IFreqaiModel(ABC): ) ) if not self.model_exists( - metadata["pair"], dk, trained_timestamp=trained_timestamp.stopts + metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts) ): dk.find_features(dataframe_train) self.model = self.train(dataframe_train, metadata["pair"], dk) - self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts + self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int( + trained_timestamp.stopts) dk.set_new_model_names(metadata["pair"], trained_timestamp) dk.save_data(self.model, metadata["pair"]) else: diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index 6699ef563..e4df8772c 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -90,7 +90,7 @@ def test_start_backtesting(mocker, freqai_conf): df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") - metadata = {"pair": "ADA/BTC"} + metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] @@ -99,6 +99,32 @@ def test_start_backtesting(mocker, freqai_conf): shutil.rmtree(Path(freqai.dk.full_path)) +def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180120-20180124"}) + freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = False + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dk.load_all_pair_histories(timerange) + sub_timerange = TimeRange.parse_timerange("20180110-20180130") + corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") + + df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + + metadata = {"pair": "LTC/BTC"} + freqai.start_backtesting(df, metadata, freqai.dk) + model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] + pytest.set_trace() + assert len(model_folders) == 8 + + shutil.rmtree(Path(freqai.dk.full_path)) + + def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): freqai_conf.update({"timerange": "20180120-20180130"}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) From 55cf378ec2cc69d3123febb0cb78781307d38ffc Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 25 Jul 2022 17:13:24 +0200 Subject: [PATCH 198/518] remove leftover breakpoint from test file --- tests/freqai/test_freqai_interface.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index e4df8772c..f368ebe1d 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -119,7 +119,6 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf): metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] - pytest.set_trace() assert len(model_folders) == 8 shutil.rmtree(Path(freqai.dk.full_path)) From 56b17e6f3cc8e8286a582be2bf57db1c59af3725 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 25 Jul 2022 19:40:13 +0200 Subject: [PATCH 199/518] allow user to pass test_size = 0 and avoid using eval sets in prediction models --- freqtrade/freqai/data_kitchen.py | 80 +++++++++++-------- .../CatboostPredictionModel.py | 17 ++-- .../CatboostPredictionMultiModel.py | 8 +- .../LightGBMPredictionModel.py | 6 +- 4 files changed, 67 insertions(+), 44 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 5ca64e504..1b0ef7f33 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -243,20 +243,28 @@ class FreqaiDataKitchen: else: stratification = None - ( - train_features, - test_features, - train_labels, - test_labels, - train_weights, - test_weights, - ) = train_test_split( - filtered_dataframe[: filtered_dataframe.shape[0]], - labels, - weights, - stratify=stratification, - **self.config["freqai"]["data_split_parameters"], - ) + if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + ( + train_features, + test_features, + train_labels, + test_labels, + train_weights, + test_weights, + ) = train_test_split( + filtered_dataframe[: filtered_dataframe.shape[0]], + labels, + weights, + stratify=stratification, + **self.config["freqai"]["data_split_parameters"], + ) + else: + test_labels = np.zeros(2) + test_features = pd.DataFrame() + test_weights = np.zeros(2) + train_features = filtered_dataframe + train_labels = labels + train_weights = weights return self.build_data_dictionary( train_features, test_features, train_labels, test_labels, train_weights, test_weights @@ -392,12 +400,13 @@ class FreqaiDataKitchen: / (train_labels_max - train_labels_min) - 1 ) - data_dictionary["test_labels"][item] = ( - 2 - * (data_dictionary["test_labels"][item] - train_labels_min) - / (train_labels_max - train_labels_min) - - 1 - ) + if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + data_dictionary["test_labels"][item] = ( + 2 + * (data_dictionary["test_labels"][item] - train_labels_min) + / (train_labels_max - train_labels_min) + - 1 + ) self.data[f"{item}_max"] = train_labels_max # .to_dict() self.data[f"{item}_min"] = train_labels_min # .to_dict() @@ -555,11 +564,12 @@ class FreqaiDataKitchen: self.data["training_features_list_raw"] = copy.deepcopy(self.training_features_list) self.training_features_list = self.data_dictionary["train_features"].columns - self.data_dictionary["test_features"] = pd.DataFrame( - data=test_components, - columns=["PC" + str(i) for i in range(0, n_keep_components)], - index=self.data_dictionary["test_features"].index, - ) + if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + self.data_dictionary["test_features"] = pd.DataFrame( + data=test_components, + columns=["PC" + str(i) for i in range(0, n_keep_components)], + index=self.data_dictionary["test_features"].index, + ) self.data["n_kept_components"] = n_keep_components self.pca = pca2 @@ -652,15 +662,17 @@ class FreqaiDataKitchen: ) # same for test data - y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) - dropped_points = np.where(y_pred == -1, 0, y_pred) - self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ - (y_pred == 1) - ] - self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][(y_pred == 1)] - self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ - (y_pred == 1) - ] + if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) + dropped_points = np.where(y_pred == -1, 0, y_pred) + self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ + (y_pred == 1) + ] + self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][( + y_pred == 1)] + self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ + (y_pred == 1) + ] logger.info( f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}" diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index f41760472..56b84c08d 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -28,17 +28,22 @@ class CatboostPredictionModel(BaseRegressionModel): label=data_dictionary["train_labels"], weight=data_dictionary["train_weights"], ) - - test_data = Pool( - data=data_dictionary["test_features"], - label=data_dictionary["test_labels"], - weight=data_dictionary["test_weights"], - ) + if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: + test_data = None + else: + test_data = Pool( + data=data_dictionary["test_features"], + label=data_dictionary["test_labels"], + weight=data_dictionary["test_weights"], + ) model = CatBoostRegressor( allow_writing_files=False, **self.model_training_parameters, ) + + if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: + test_data = None model.fit(X=train_data, eval_set=test_data) return model diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py index 17b5e6c68..35a93e808 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py @@ -36,7 +36,9 @@ class CatboostPredictionMultiModel(BaseRegressionModel): model = MultiOutputRegressor(estimator=cbr) model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set) - train_score = model.score(X, y) - test_score = model.score(*eval_set) - logger.info(f"Train score {train_score}, Test score {test_score}") + + if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + train_score = model.score(X, y) + test_score = model.score(*eval_set) + logger.info(f"Train score {train_score}, Test score {test_score}") return model diff --git a/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py index 525566cf4..c94bc5698 100644 --- a/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py +++ b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py @@ -25,11 +25,15 @@ class LightGBMPredictionModel(BaseRegressionModel): all the training and test data/labels. """ - eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) + if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: + eval_set = None + else: + eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) X = data_dictionary["train_features"] y = data_dictionary["train_labels"] model = LGBMRegressor(**self.model_training_parameters) + model.fit(X=X, y=y, eval_set=eval_set) return model From e213d0ad55db09d83a172019234398b64469de6f Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 26 Jul 2022 10:24:14 +0200 Subject: [PATCH 200/518] isolate data_drawer functions from data_kitchen, accommodate tests, add new test --- freqtrade/freqai/data_drawer.py | 222 ++++++++++++++++- freqtrade/freqai/data_kitchen.py | 234 +----------------- freqtrade/freqai/freqai_interface.py | 36 ++- .../prediction_models/BaseRegressionModel.py | 9 +- .../prediction_models/BaseTensorFlowModel.py | 10 +- tests/freqai/conftest.py | 12 +- tests/freqai/test_freqai_datadrawer.py | 95 +++++++ tests/freqai/test_freqai_datakitchen.py | 92 +------ tests/freqai/test_freqai_interface.py | 77 ++++-- tests/rpc/test_rpc_apiserver.py | 1 + .../strats/freqai_test_multimodel_strat.py | 188 ++++++++++++++ tests/strategy/test_strategy_loading.py | 6 +- 12 files changed, 606 insertions(+), 376 deletions(-) create mode 100644 tests/freqai/test_freqai_datadrawer.py create mode 100644 tests/strategy/strats/freqai_test_multimodel_strat.py diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 8aca4b371..e1fc28d92 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -7,11 +7,17 @@ import threading from pathlib import Path from typing import Any, Dict, Tuple +import numpy.typing as npt import numpy as np import pandas as pd from joblib.externals import cloudpickle from pandas import DataFrame - +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from joblib import dump, load +from freqtrade.strategy.interface import IStrategy +from freqtrade.exceptions import OperationalException +from freqtrade.data.history import load_pair_history +from freqtrade.configuration import TimeRange logger = logging.getLogger(__name__) @@ -214,7 +220,8 @@ class FreqaiDataDrawer: # send pair to end of queue self.pair_dict[pair]["priority"] = len(self.pair_dict) - def set_initial_return_values(self, pair: str, dk, pred_df, do_preds) -> None: + def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen, + pred_df: DataFrame, do_preds: npt.ArrayLike) -> None: """ Set the initial return values to a persistent dataframe. This avoids needing to repredict on historical candles, and also stores historical predictions despite retrainings (so stored @@ -351,6 +358,217 @@ class FreqaiDataDrawer: if self.config.get("freqai", {}).get("purge_old_models", False): self.purge_old_models() + # Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer + + def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None: + """ + Saves all data associated with a model for a single sub-train time range + :params: + :model: User trained model which can be reused for inferencing to generate + predictions + """ + + if not dk.data_path.is_dir(): + dk.data_path.mkdir(parents=True, exist_ok=True) + + save_path = Path(dk.data_path) + + # Save the trained model + if not dk.keras: + dump(model, save_path / f"{dk.model_filename}_model.joblib") + else: + model.save(save_path / f"{dk.model_filename}_model.h5") + + if dk.svm_model is not None: + dump(dk.svm_model, save_path / str(dk.model_filename + "_svm_model.joblib")) + + dk.data["data_path"] = str(dk.data_path) + dk.data["model_filename"] = str(dk.model_filename) + dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns) + dk.data["label_list"] = dk.label_list + # store the metadata + with open(save_path / str(dk.model_filename + "_metadata.json"), "w") as fp: + json.dump(dk.data, fp, default=dk.np_encoder) + + # save the train data to file so we can check preds for area of applicability later + dk.data_dictionary["train_features"].to_pickle( + save_path / str(dk.model_filename + "_trained_df.pkl") + ) + + if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"): + cloudpickle.dump( + dk.pca, open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "wb") + ) + + # if self.live: + self.model_dictionary[dk.model_filename] = model + self.pair_dict[coin]["model_filename"] = dk.model_filename + self.pair_dict[coin]["data_path"] = str(dk.data_path) + self.save_drawer_to_disk() + + return + + def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any: + """ + loads all data required to make a prediction on a sub-train time range + :returns: + :model: User trained model which can be inferenced for new predictions + """ + + if not self.pair_dict[coin]["model_filename"]: + return None + + if dk.live: + dk.model_filename = self.pair_dict[coin]["model_filename"] + dk.data_path = Path(self.pair_dict[coin]["data_path"]) + if self.freqai_info.get("follow_mode", False): + # follower can be on a different system which is rsynced to the leader: + dk.data_path = Path( + self.config["user_data_dir"] + / "models" + / dk.data_path.parts[-2] + / dk.data_path.parts[-1] + ) + + with open(dk.data_path / str(dk.model_filename + "_metadata.json"), "r") as fp: + dk.data = json.load(fp) + dk.training_features_list = dk.data["training_features_list"] + dk.label_list = dk.data["label_list"] + + dk.data_dictionary["train_features"] = pd.read_pickle( + dk.data_path / str(dk.model_filename + "_trained_df.pkl") + ) + + # try to access model in memory instead of loading object from disk to save time + if dk.live and dk.model_filename in self.model_dictionary: + model = self.model_dictionary[dk.model_filename] + elif not dk.keras: + model = load(dk.data_path / str(dk.model_filename + "_model.joblib")) + else: + from tensorflow import keras + + model = keras.models.load_model(dk.data_path / str(dk.model_filename + "_model.h5")) + + if Path(dk.data_path / str(dk.model_filename + "_svm_model.joblib")).resolve().exists(): + dk.svm_model = load(dk.data_path / str(dk.model_filename + "_svm_model.joblib")) + + if not model: + raise OperationalException( + f"Unable to load model, ensure model exists at " f"{dk.data_path} " + ) + + if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: + dk.pca = cloudpickle.load( + open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb") + ) + + return model + + def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None: + """ + Append new candles to our stores historic data (in memory) so that + we do not need to load candle history from disk and we dont need to + pinging exchange multiple times for the same candle. + :params: + dataframe: DataFrame = strategy provided dataframe + """ + feat_params = self.freqai_info.get("feature_parameters", {}) + with self.history_lock: + history_data = self.historic_data + + for pair in dk.all_pairs: + for tf in feat_params.get("include_timeframes"): + + # check if newest candle is already appended + df_dp = strategy.dp.get_pair_dataframe(pair, tf) + if len(df_dp.index) == 0: + continue + if str(history_data[pair][tf].iloc[-1]["date"]) == str( + df_dp.iloc[-1:]["date"].iloc[-1] + ): + continue + + try: + index = ( + df_dp.loc[ + df_dp["date"] == history_data[pair][tf].iloc[-1]["date"] + ].index[0] + + 1 + ) + except IndexError: + logger.warning( + f"Unable to update pair history for {pair}. " + "If this does not resolve itself after 1 additional candle, " + "please report the error to #freqai discord channel" + ) + return + + history_data[pair][tf] = pd.concat( + [ + history_data[pair][tf], + strategy.dp.get_pair_dataframe(pair, tf).iloc[index:], + ], + ignore_index=True, + axis=0, + ) + + def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None: + """ + Load pair histories for all whitelist and corr_pairlist pairs. + Only called once upon startup of bot. + :params: + timerange: TimeRange = full timerange required to populate all indicators + for training according to user defined train_period_days + """ + history_data = self.historic_data + + for pair in dk.all_pairs: + if pair not in history_data: + history_data[pair] = {} + for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"): + history_data[pair][tf] = load_pair_history( + datadir=self.config["datadir"], + timeframe=tf, + pair=pair, + timerange=timerange, + data_format=self.config.get("dataformat_ohlcv", "json"), + candle_type=self.config.get("trading_mode", "spot"), + ) + + def get_base_and_corr_dataframes( + self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen + ) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: + """ + Searches through our historic_data in memory and returns the dataframes relevant + to the present pair. + :params: + timerange: TimeRange = full timerange required to populate all indicators + for training according to user defined train_period_days + metadata: dict = strategy furnished pair metadata + """ + + with self.history_lock: + corr_dataframes: Dict[Any, Any] = {} + base_dataframes: Dict[Any, Any] = {} + historic_data = self.historic_data + pairs = self.freqai_info.get("feature_parameters", {}).get( + "include_corr_pairlist", [] + ) + + for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"): + base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf]) + if pairs: + for p in pairs: + if pair in p: + continue # dont repeat anything from whitelist + if p not in corr_dataframes: + corr_dataframes[p] = {} + corr_dataframes[p][tf] = dk.slice_dataframe( + timerange, historic_data[p][tf] + ) + + return corr_dataframes, base_dataframes + # to be used if we want to send predictions directly to the follower instead of forcing # follower to load models and inference # def save_model_return_values_to_disk(self) -> None: diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 1b0ef7f33..f16e169b9 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -1,6 +1,5 @@ import copy import datetime -import json import logging import shutil from pathlib import Path @@ -9,18 +8,14 @@ from typing import Any, Dict, List, Tuple import numpy as np import numpy.typing as npt import pandas as pd -from joblib import dump, load # , Parallel, delayed # used for auto distribution assignment -from joblib.externals import cloudpickle from pandas import DataFrame from sklearn import linear_model from sklearn.metrics.pairwise import pairwise_distances from sklearn.model_selection import train_test_split from freqtrade.configuration import TimeRange -from freqtrade.data.history import load_pair_history from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data from freqtrade.exceptions import OperationalException -from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.resolvers import ExchangeResolver from freqtrade.strategy.interface import IStrategy @@ -57,7 +52,6 @@ class FreqaiDataKitchen: def __init__( self, config: Dict[str, Any], - data_drawer: FreqaiDataDrawer, live: bool = False, pair: str = "", ): @@ -69,6 +63,7 @@ class FreqaiDataKitchen: self.append_df: DataFrame = DataFrame() self.data_path = Path() self.label_list: List = [] + self.training_features_list: List = [] self.model_filename: str = "" self.live = live self.pair = pair @@ -89,8 +84,6 @@ class FreqaiDataKitchen: config["freqai"]["backtest_period_days"], ) - self.dd = data_drawer - def set_paths( self, pair: str, @@ -113,110 +106,6 @@ class FreqaiDataKitchen: return - def save_data(self, model: Any, coin: str = "", label=None) -> None: - """ - Saves all data associated with a model for a single sub-train time range - :params: - :model: User trained model which can be reused for inferencing to generate - predictions - """ - - if not self.data_path.is_dir(): - self.data_path.mkdir(parents=True, exist_ok=True) - - save_path = Path(self.data_path) - - # Save the trained model - if not self.keras: - dump(model, save_path / f"{self.model_filename}_model.joblib") - else: - model.save(save_path / f"{self.model_filename}_model.h5") - - if self.svm_model is not None: - dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib")) - - self.data["data_path"] = str(self.data_path) - self.data["model_filename"] = str(self.model_filename) - self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns) - self.data["label_list"] = self.label_list - # store the metadata - with open(save_path / str(self.model_filename + "_metadata.json"), "w") as fp: - json.dump(self.data, fp, default=self.np_encoder) - - # save the train data to file so we can check preds for area of applicability later - self.data_dictionary["train_features"].to_pickle( - save_path / str(self.model_filename + "_trained_df.pkl") - ) - - if self.freqai_config.get("feature_parameters", {}).get("principal_component_analysis"): - cloudpickle.dump( - self.pca, open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "wb") - ) - - # if self.live: - self.dd.model_dictionary[self.model_filename] = model - self.dd.pair_dict[coin]["model_filename"] = self.model_filename - self.dd.pair_dict[coin]["data_path"] = str(self.data_path) - self.dd.save_drawer_to_disk() - - return - - def load_data(self, coin: str = "") -> Any: - """ - loads all data required to make a prediction on a sub-train time range - :returns: - :model: User trained model which can be inferenced for new predictions - """ - - if not self.dd.pair_dict[coin]["model_filename"]: - return None - - if self.live: - self.model_filename = self.dd.pair_dict[coin]["model_filename"] - self.data_path = Path(self.dd.pair_dict[coin]["data_path"]) - if self.freqai_config.get("follow_mode", False): - # follower can be on a different system which is rsynced to the leader: - self.data_path = Path( - self.config["user_data_dir"] - / "models" - / self.data_path.parts[-2] - / self.data_path.parts[-1] - ) - - with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp: - self.data = json.load(fp) - self.training_features_list = self.data["training_features_list"] - self.label_list = self.data["label_list"] - - self.data_dictionary["train_features"] = pd.read_pickle( - self.data_path / str(self.model_filename + "_trained_df.pkl") - ) - - # try to access model in memory instead of loading object from disk to save time - if self.live and self.model_filename in self.dd.model_dictionary: - model = self.dd.model_dictionary[self.model_filename] - elif not self.keras: - model = load(self.data_path / str(self.model_filename + "_model.joblib")) - else: - from tensorflow import keras - - model = keras.models.load_model(self.data_path / str(self.model_filename + "_model.h5")) - - if Path(self.data_path / str(self.model_filename + "_svm_model.joblib")).resolve().exists(): - self.svm_model = load(self.data_path / str(self.model_filename + "_svm_model.joblib")) - - if not model: - raise OperationalException( - f"Unable to load model, ensure model exists at " f"{self.data_path} " - ) - - if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: - self.pca = cloudpickle.load( - open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "rb") - ) - - return model - def make_train_test_datasets( self, filtered_dataframe: DataFrame, labels: DataFrame ) -> Dict[Any, Any]: @@ -953,56 +842,6 @@ class FreqaiDataKitchen: prepend=self.config.get("prepend_data", False), ) - def update_historic_data(self, strategy: IStrategy) -> None: - """ - Append new candles to our stores historic data (in memory) so that - we do not need to load candle history from disk and we dont need to - pinging exchange multiple times for the same candle. - :params: - dataframe: DataFrame = strategy provided dataframe - """ - feat_params = self.freqai_config.get("feature_parameters", {}) - with self.dd.history_lock: - history_data = self.dd.historic_data - - for pair in self.all_pairs: - for tf in feat_params.get("include_timeframes"): - - # check if newest candle is already appended - df_dp = strategy.dp.get_pair_dataframe(pair, tf) - if len(df_dp.index) == 0: - continue - if str(history_data[pair][tf].iloc[-1]["date"]) == str( - df_dp.iloc[-1:]["date"].iloc[-1] - ): - continue - - try: - index = ( - df_dp.loc[ - df_dp["date"] == history_data[pair][tf].iloc[-1]["date"] - ].index[0] - + 1 - ) - except IndexError: - logger.warning( - f"Unable to update pair history for {pair}. " - "If this does not resolve itself after 1 additional candle, " - "please report the error to #freqai discord channel" - ) - return - - history_data[pair][tf] = pd.concat( - [ - history_data[pair][tf], - strategy.dp.get_pair_dataframe(pair, tf).iloc[index:], - ], - ignore_index=True, - axis=0, - ) - - # logger.info(f'Length of history data {len(history_data[pair][tf])}') - def set_all_pairs(self) -> None: self.all_pairs = copy.deepcopy( @@ -1012,63 +851,6 @@ class FreqaiDataKitchen: if pair not in self.all_pairs: self.all_pairs.append(pair) - def load_all_pair_histories(self, timerange: TimeRange) -> None: - """ - Load pair histories for all whitelist and corr_pairlist pairs. - Only called once upon startup of bot. - :params: - timerange: TimeRange = full timerange required to populate all indicators - for training according to user defined train_period_days - """ - history_data = self.dd.historic_data - - for pair in self.all_pairs: - if pair not in history_data: - history_data[pair] = {} - for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"): - history_data[pair][tf] = load_pair_history( - datadir=self.config["datadir"], - timeframe=tf, - pair=pair, - timerange=timerange, - data_format=self.config.get("dataformat_ohlcv", "json"), - candle_type=self.config.get("trading_mode", "spot"), - ) - - def get_base_and_corr_dataframes( - self, timerange: TimeRange, pair: str - ) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: - """ - Searches through our historic_data in memory and returns the dataframes relevant - to the present pair. - :params: - timerange: TimeRange = full timerange required to populate all indicators - for training according to user defined train_period_days - metadata: dict = strategy furnished pair metadata - """ - - with self.dd.history_lock: - corr_dataframes: Dict[Any, Any] = {} - base_dataframes: Dict[Any, Any] = {} - historic_data = self.dd.historic_data - pairs = self.freqai_config.get("feature_parameters", {}).get( - "include_corr_pairlist", [] - ) - - for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"): - base_dataframes[tf] = self.slice_dataframe(timerange, historic_data[pair][tf]) - if pairs: - for p in pairs: - if pair in p: - continue # dont repeat anything from whitelist - if p not in corr_dataframes: - corr_dataframes[p] = {} - corr_dataframes[p][tf] = self.slice_dataframe( - timerange, historic_data[p][tf] - ) - - return corr_dataframes, base_dataframes - def use_strategy_to_populate_indicators( self, strategy: IStrategy, @@ -1134,20 +916,6 @@ class FreqaiDataKitchen: return dataframe - def fit_live_predictions(self) -> None: - """ - Fit the labels with a gaussian distribution - """ - import scipy as spy - - num_candles = self.freqai_config.get("fit_live_predictions_candles", 100) - self.data["labels_mean"], self.data["labels_std"] = {}, {} - for label in self.label_list: - f = spy.stats.norm.fit(self.dd.historic_predictions[self.pair][label].tail(num_candles)) - self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1] - - return - def fit_labels(self) -> None: """ Fit the labels with a gaussian distribution diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index a8527f9e1..6edb6b78f 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -102,7 +102,7 @@ class IFreqaiModel(ABC): self.dd.set_pair_dict_info(metadata) if self.live: - self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"]) + self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"]) dk = self.start_live(dataframe, metadata, strategy, self.dk) # For backtesting, each pair enters and then gets trained for each window along the @@ -111,7 +111,7 @@ class IFreqaiModel(ABC): # FreqAI slides the window and sequentially builds the backtesting results before returning # the concatenated results for the full backtesting period back to the strategy. elif not self.follow_mode: - self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"]) + self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"]) logger.info(f"Training {len(self.dk.training_timeranges)} timeranges") dataframe = self.dk.use_strategy_to_populate_indicators( @@ -138,7 +138,7 @@ class IFreqaiModel(ABC): if self.dd.pair_dict[pair]["priority"] != 1: continue - dk = FreqaiDataKitchen(self.config, self.dd, self.live, pair) + dk = FreqaiDataKitchen(self.config, self.live, pair) dk.set_paths(pair, trained_timestamp) ( retrain, @@ -217,9 +217,9 @@ class IFreqaiModel(ABC): self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int( trained_timestamp.stopts) dk.set_new_model_names(metadata["pair"], trained_timestamp) - dk.save_data(self.model, metadata["pair"]) + self.dd.save_data(self.model, metadata["pair"], dk) else: - self.model = dk.load_data(metadata["pair"]) + self.model = self.dd.load_data(metadata["pair"], dk) self.check_if_feature_list_matches_strategy(dataframe_train, dk) @@ -260,7 +260,7 @@ class IFreqaiModel(ABC): # append the historic data once per round if self.dd.historic_data: - dk.update_historic_data(strategy) + self.dd.update_historic_data(strategy, dk) logger.debug(f'Updating historic data on pair {metadata["pair"]}') if not self.follow_mode: @@ -278,7 +278,7 @@ class IFreqaiModel(ABC): "data saved" ) dk.download_all_data_for_training(data_load_timerange) - dk.load_all_pair_histories(data_load_timerange) + self.dd.load_all_pair_histories(data_load_timerange, dk) if not self.scanning: self.scanning = True @@ -292,7 +292,7 @@ class IFreqaiModel(ABC): ) # load the model and associated data into the data kitchen - self.model = dk.load_data(coin=metadata["pair"]) + self.model = self.dd.load_data(metadata["pair"], dk) dataframe = self.dk.use_strategy_to_populate_indicators( strategy, prediction_dataframe=dataframe, pair=metadata["pair"] @@ -468,8 +468,8 @@ class IFreqaiModel(ABC): new_trained_timerange does not contain any NaNs) """ - corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes( - data_load_timerange, pair + corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes( + data_load_timerange, pair, dk ) unfiltered_dataframe = dk.use_strategy_to_populate_indicators( @@ -489,7 +489,7 @@ class IFreqaiModel(ABC): if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning: with self.lock: self.dd.pair_to_end_of_training_queue(pair) - dk.save_data(model, coin=pair) + self.dd.save_data(model, pair, dk) if self.freqai_info.get("purge_old_models", False): self.dd.purge_old_models() @@ -505,6 +505,20 @@ class IFreqaiModel(ABC): self.dd.historic_predictions[pair] = pd.DataFrame() self.dd.historic_predictions[pair] = copy.deepcopy(pred_df) + def fit_live_predictions(self, dk: FreqaiDataKitchen) -> None: + """ + Fit the labels with a gaussian distribution + """ + import scipy as spy + + num_candles = self.freqai_info.get("fit_live_predictions_candles", 100) + dk.data["labels_mean"], dk.data["labels_std"] = {}, {} + for label in dk.label_list: + f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles)) + dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1] + + return + # Following methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModlel.py for an example. diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 2db025fd6..611b5fb04 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -55,8 +55,6 @@ class BaseRegressionModel(IFreqaiModel): f"{end_date}--------------------") # split data into train/test data. data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) - if not self.freqai_info.get('fit_live_predictions', 0): - dk.fit_labels() # normalize all data based on train_dataset only data_dictionary = dk.normalize_data(data_dictionary) @@ -73,8 +71,11 @@ class BaseRegressionModel(IFreqaiModel): if pair not in self.dd.historic_predictions: self.set_initial_historic_predictions( data_dictionary['train_features'], model, dk, pair) - elif self.freqai_info.get('fit_live_predictions_candles', 0): - dk.fit_live_predictions() + + if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: + self.fit_live_predictions(dk) + else: + dk.fit_labels() self.dd.save_historic_predictions_to_disk() diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py index 268bb00c9..d14ac0eee 100644 --- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py +++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py @@ -49,8 +49,7 @@ class BaseTensorFlowModel(IFreqaiModel): # split data into train/test data. data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) - if not self.freqai_info.get('fit_live_predictions', 0): - dk.fit_labels() + # normalize all data based on train_dataset only data_dictionary = dk.normalize_data(data_dictionary) @@ -67,8 +66,11 @@ class BaseTensorFlowModel(IFreqaiModel): if pair not in self.dd.historic_predictions: self.set_initial_historic_predictions( data_dictionary['train_features'], model, dk, pair) - elif self.freqai_info.get('fit_live_predictions_candles', 0): - dk.fit_live_predictions() + + if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: + self.fit_live_predictions(dk) + else: + dk.fit_labels() self.dd.save_historic_predictions_to_disk() diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index ede991240..c039222c0 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -1,12 +1,12 @@ from copy import deepcopy from pathlib import Path -from unittest.mock import MagicMock import pytest from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.resolvers import StrategyResolver from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver from tests.conftest import get_patched_exchange @@ -57,11 +57,17 @@ def freqai_conf(default_conf, tmpdir): def get_patched_data_kitchen(mocker, freqaiconf): - dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) - dk = FreqaiDataKitchen(freqaiconf, dd) + # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) + dk = FreqaiDataKitchen(freqaiconf) return dk +def get_patched_data_drawer(mocker, freqaiconf): + # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) + dd = FreqaiDataDrawer(freqaiconf) + return dd + + def get_patched_freqai_strategy(mocker, freqaiconf): strategy = StrategyResolver.load_strategy(freqaiconf) strategy.ft_bot_start() diff --git a/tests/freqai/test_freqai_datadrawer.py b/tests/freqai/test_freqai_datadrawer.py new file mode 100644 index 000000000..81503cf15 --- /dev/null +++ b/tests/freqai/test_freqai_datadrawer.py @@ -0,0 +1,95 @@ + +import shutil +from pathlib import Path + +from freqtrade.configuration import TimeRange +from freqtrade.data.dataprovider import DataProvider +# from freqtrade.freqai.data_drawer import FreqaiDataDrawer +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from tests.conftest import get_patched_exchange +from tests.freqai.conftest import get_patched_freqai_strategy + + +def test_update_historic_data(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + timerange = TimeRange.parse_timerange("20180110-20180114") + + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) + dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m")) + candle_difference = dp_candles - historic_candles + freqai.dd.update_historic_data(strategy, freqai.dk) + + updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) + + assert updated_historic_candles - historic_candles == candle_difference + shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_load_all_pairs_histories(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + timerange = TimeRange.parse_timerange("20180110-20180114") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + + assert len(freqai.dd.historic_data.keys()) == len( + freqai_conf.get("exchange", {}).get("pair_whitelist") + ) + assert len(freqai.dd.historic_data["ADA/BTC"]) == len( + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") + ) + shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_get_base_and_corr_dataframes(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + timerange = TimeRange.parse_timerange("20180110-20180114") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + sub_timerange = TimeRange.parse_timerange("20180111-20180114") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) + + num_tfs = len( + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") + ) + + assert len(base_df.keys()) == num_tfs + + assert len(corr_df.keys()) == len( + freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist") + ) + + assert len(corr_df["ADA/BTC"].keys()) == num_tfs + shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_use_strategy_to_populate_indicators(mocker, freqai_conf): + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + timerange = TimeRange.parse_timerange("20180110-20180114") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + sub_timerange = TimeRange.parse_timerange("20180111-20180114") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) + + df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC') + + assert len(df.columns) == 45 + shutil.rmtree(Path(freqai.dk.full_path)) diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index ee1d83982..9f2a2f71e 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -4,13 +4,8 @@ from pathlib import Path import pytest -from freqtrade.configuration import TimeRange -from freqtrade.data.dataprovider import DataProvider -# from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.exceptions import OperationalException -from freqtrade.freqai.data_kitchen import FreqaiDataKitchen -from tests.conftest import get_patched_exchange -from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy +from tests.freqai.conftest import get_patched_data_kitchen @pytest.mark.parametrize( @@ -60,27 +55,6 @@ def test_split_timerange( shutil.rmtree(Path(dk.full_path)) -def test_update_historic_data(mocker, freqai_conf): - strategy = get_patched_freqai_strategy(mocker, freqai_conf) - exchange = get_patched_exchange(mocker, freqai_conf) - strategy.dp = DataProvider(freqai_conf, exchange) - freqai = strategy.freqai - freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) - timerange = TimeRange.parse_timerange("20180110-20180114") - - freqai.dk.load_all_pair_histories(timerange) - historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) - dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m")) - candle_difference = dp_candles - historic_candles - freqai.dk.update_historic_data(strategy) - - updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) - - assert updated_historic_candles - historic_candles == candle_difference - shutil.rmtree(Path(freqai.dk.full_path)) - - @pytest.mark.parametrize( "timestamp, expected", [ @@ -92,67 +66,3 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected): dk = get_patched_data_kitchen(mocker, freqai_conf) assert dk.check_if_model_expired(timestamp) == expected shutil.rmtree(Path(dk.full_path)) - - -def test_load_all_pairs_histories(mocker, freqai_conf): - strategy = get_patched_freqai_strategy(mocker, freqai_conf) - exchange = get_patched_exchange(mocker, freqai_conf) - strategy.dp = DataProvider(freqai_conf, exchange) - freqai = strategy.freqai - freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) - timerange = TimeRange.parse_timerange("20180110-20180114") - freqai.dk.load_all_pair_histories(timerange) - - assert len(freqai.dd.historic_data.keys()) == len( - freqai_conf.get("exchange", {}).get("pair_whitelist") - ) - assert len(freqai.dd.historic_data["ADA/BTC"]) == len( - freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") - ) - shutil.rmtree(Path(freqai.dk.full_path)) - - -def test_get_base_and_corr_dataframes(mocker, freqai_conf): - strategy = get_patched_freqai_strategy(mocker, freqai_conf) - exchange = get_patched_exchange(mocker, freqai_conf) - strategy.dp = DataProvider(freqai_conf, exchange) - freqai = strategy.freqai - freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) - timerange = TimeRange.parse_timerange("20180110-20180114") - freqai.dk.load_all_pair_histories(timerange) - sub_timerange = TimeRange.parse_timerange("20180111-20180114") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") - - num_tfs = len( - freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes") - ) - - assert len(base_df.keys()) == num_tfs - - assert len(corr_df.keys()) == len( - freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist") - ) - - assert len(corr_df["ADA/BTC"].keys()) == num_tfs - shutil.rmtree(Path(freqai.dk.full_path)) - - -def test_use_strategy_to_populate_indicators(mocker, freqai_conf): - strategy = get_patched_freqai_strategy(mocker, freqai_conf) - exchange = get_patched_exchange(mocker, freqai_conf) - strategy.dp = DataProvider(freqai_conf, exchange) - strategy.freqai_info = freqai_conf.get("freqai", {}) - freqai = strategy.freqai - freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) - timerange = TimeRange.parse_timerange("20180110-20180114") - freqai.dk.load_all_pair_histories(timerange) - sub_timerange = TimeRange.parse_timerange("20180111-20180114") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") - - df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC') - - assert len(df.columns) == 45 - shutil.rmtree(Path(freqai.dk.full_path)) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index f368ebe1d..c4302e756 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -1,5 +1,3 @@ -# from unittest.mock import MagicMock -# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge import platform import shutil from pathlib import Path @@ -23,9 +21,9 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() @@ -42,6 +40,36 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf): shutil.rmtree(Path(freqai.dk.full_path)) +def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + freqai_conf.update({"strategy": "freqai_test_multimodel_strat"}) + freqai_conf.update({"freqaimodel": "LightGBMPredictionMultiModel"}) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + + freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) + + assert len(freqai.dk.label_list) == 2 + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() + + shutil.rmtree(Path(freqai.dk.full_path)) + + @pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...") def test_train_model_in_series_Catboost(mocker, freqai_conf): freqai_conf.update({"timerange": "20180110-20180130"}) @@ -54,9 +82,9 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() @@ -82,11 +110,11 @@ def test_start_backtesting(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") @@ -108,11 +136,11 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") @@ -132,11 +160,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") @@ -155,11 +183,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") - corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") + corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") freqai.start_backtesting(df, metadata, freqai.dk) @@ -181,13 +209,12 @@ def test_follow_mode(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) metadata = {"pair": "ADA/BTC"} freqai.dd.set_pair_dict_info(metadata) - # freqai.dd.pair_dict = MagicMock() data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") @@ -209,9 +236,9 @@ def test_follow_mode(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd, freqai.live) + freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m') freqai.start_live(df, metadata, strategy, freqai.dk) @@ -232,9 +259,9 @@ def test_principal_component_analysis(mocker, freqai_conf): strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True - freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd) + freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") - freqai.dk.load_all_pair_histories(timerange) + freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 86fe0bf03..3afdd5245 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -1404,6 +1404,7 @@ def test_api_strategies(botclient): 'StrategyTestV3', 'StrategyTestV3Analysis', 'StrategyTestV3Futures', + 'freqai_test_multimodel_strat', 'freqai_test_strat' ]} diff --git a/tests/strategy/strats/freqai_test_multimodel_strat.py b/tests/strategy/strats/freqai_test_multimodel_strat.py new file mode 100644 index 000000000..9652e816b --- /dev/null +++ b/tests/strategy/strats/freqai_test_multimodel_strat.py @@ -0,0 +1,188 @@ +import logging +from functools import reduce + +import pandas as pd +import talib.abstract as ta +from pandas import DataFrame + +from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair + + +logger = logging.getLogger(__name__) + + +class freqai_test_multimodel_strat(IStrategy): + """ + Example strategy showing how the user connects their own + IFreqaiModel to the strategy. Namely, the user uses: + self.freqai.start(dataframe, metadata) + + to make predictions on their data. populate_any_indicators() automatically + generates the variety of features indicated by the user in the + canonical freqtrade configuration file under config['freqai']. + """ + + minimal_roi = {"0": 0.1, "240": -1} + + plot_config = { + "main_plot": {}, + "subplots": { + "prediction": {"prediction": {"color": "blue"}}, + "target_roi": { + "target_roi": {"color": "brown"}, + }, + "do_predict": { + "do_predict": {"color": "brown"}, + }, + }, + } + + process_only_new_candles = True + stoploss = -0.05 + use_exit_signal = True + startup_candle_count: int = 300 + can_short = False + + linear_roi_offset = DecimalParameter( + 0.00, 0.02, default=0.005, space="sell", optimize=False, load=True + ) + max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True) + + def informative_pairs(self): + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] + informative_pairs = [] + for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication + informative_pairs.append((pair, tf)) + return informative_pairs + + def populate_any_indicators( + self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name and merge features + from user indicated timeframes in the configuration file. User controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e. user should not prepend any supporting metrics + (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :params: + :pair: pair to be used as informative + :df: strategy dataframe which will receive merges from informatives + :tf: timeframe of the dataframe which will modify the feature names + :informative: the dataframe associated with the informative pair + :coin: the name of the coin which will modify the feature names. + """ + + with self.freqai.lock: + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + informative[f"%-{coin}raw_price"] = informative["close"] + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + df["&-s_range"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .max() + - + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .min() + ) + + return df + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + self.freqai_info = self.config["freqai"] + + # All indicators must be populated by populate_any_indicators() for live functionality + # to work correctly. + # the model will return 4 values, its prediction, an indication of whether or not the + # prediction should be accepted, the target mean/std values from the labels used during + # each training period. + dataframe = self.freqai.start(dataframe, metadata, self) + + dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 + dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 + return dataframe + + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + + enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]] + + if enter_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] + ] = (1, "long") + + enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]] + + if enter_short_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] + ] = (1, "short") + + return df + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25] + if exit_long_conditions: + df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 + + exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25] + if exit_short_conditions: + df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 + + return df diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py index aaad26e5b..5b6f15d11 100644 --- a/tests/strategy/test_strategy_loading.py +++ b/tests/strategy/test_strategy_loading.py @@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed(): directory = Path(__file__).parent / "strats" strategies = StrategyResolver.search_all_objects(directory, enum_failed=False) assert isinstance(strategies, list) - assert len(strategies) == 8 + assert len(strategies) == 9 assert isinstance(strategies[0], dict) @@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed(): directory = Path(__file__).parent / "strats" strategies = StrategyResolver.search_all_objects(directory, enum_failed=True) assert isinstance(strategies, list) - assert len(strategies) == 9 + assert len(strategies) == 10 # with enum_failed=True search_all_objects() shall find 2 good strategies # and 1 which fails to load - assert len([x for x in strategies if x['class'] is not None]) == 8 + assert len([x for x in strategies if x['class'] is not None]) == 9 assert len([x for x in strategies if x['class'] is None]) == 1 From fb4e8430cd5677864b83b855517dd5bf546397c5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 26 Jul 2022 10:51:39 +0200 Subject: [PATCH 201/518] isort auto import sorting --- freqtrade/freqai/data_drawer.py | 14 ++++++++------ tests/freqai/conftest.py | 2 +- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index e1fc28d92..89708fd3e 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -7,17 +7,19 @@ import threading from pathlib import Path from typing import Any, Dict, Tuple -import numpy.typing as npt import numpy as np +import numpy.typing as npt import pandas as pd +from joblib import dump, load from joblib.externals import cloudpickle from pandas import DataFrame -from freqtrade.freqai.data_kitchen import FreqaiDataKitchen -from joblib import dump, load -from freqtrade.strategy.interface import IStrategy -from freqtrade.exceptions import OperationalException -from freqtrade.data.history import load_pair_history + from freqtrade.configuration import TimeRange +from freqtrade.data.history import load_pair_history +from freqtrade.exceptions import OperationalException +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.strategy.interface import IStrategy + logger = logging.getLogger(__name__) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index c039222c0..ff70a40a4 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -5,8 +5,8 @@ import pytest from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider -from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.data_drawer import FreqaiDataDrawer +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.resolvers import StrategyResolver from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver from tests.conftest import get_patched_exchange From ad25a4cb5642b44225c40ec6f7feec277dc6da7f Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 26 Jul 2022 15:58:40 +0200 Subject: [PATCH 202/518] reduce number of pair_dict lookups, remove coin_first --- freqtrade/freqai/data_drawer.py | 25 ++++++++++++------------- freqtrade/freqai/freqai_interface.py | 6 +++--- tests/freqai/test_freqai_datadrawer.py | 1 - 3 files changed, 15 insertions(+), 17 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 89708fd3e..8c26d01b0 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -166,7 +166,7 @@ class FreqaiDataDrawer: if isinstance(object, np.generic): return object.item() - def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool, bool]: + def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]: """ Locate and load existing model metadata from persistent storage. If not located, create a new one and append the current pair to it and prepare it for its first @@ -175,23 +175,20 @@ class FreqaiDataDrawer: :return: model_filename: str = unique filename used for loading persistent objects from disk trained_timestamp: int = the last time the coin was trained - coin_first: bool = If the coin is fresh without metadata return_null_array: bool = Follower could not find pair metadata """ - pair_in_dict = self.pair_dict.get(pair) + pair_dict = self.pair_dict.get(pair) data_path_set = self.pair_dict.get(pair, {}).get("data_path", None) return_null_array = False - if pair_in_dict: - model_filename = self.pair_dict[pair]["model_filename"] - trained_timestamp = self.pair_dict[pair]["trained_timestamp"] - coin_first = self.pair_dict[pair]["first"] + if pair_dict: + model_filename = pair_dict["model_filename"] + trained_timestamp = pair_dict["trained_timestamp"] elif not self.follow_mode: - self.pair_dict[pair] = {} - model_filename = self.pair_dict[pair]["model_filename"] = "" - coin_first = self.pair_dict[pair]["first"] = True - trained_timestamp = self.pair_dict[pair]["trained_timestamp"] = 0 - self.pair_dict[pair]["priority"] = len(self.pair_dict) + pair_dict = self.pair_dict[pair] = {} + model_filename = pair_dict["model_filename"] = "" + trained_timestamp = pair_dict["trained_timestamp"] = 0 + pair_dict["priority"] = len(self.pair_dict) if not data_path_set and self.follow_mode: logger.warning( @@ -199,9 +196,11 @@ class FreqaiDataDrawer: f"pair_dictionary at path {self.full_path}, sending null values " "back to strategy." ) + trained_timestamp = 0 + model_filename = '' return_null_array = True - return model_filename, trained_timestamp, coin_first, return_null_array + return model_filename, trained_timestamp, return_null_array def set_pair_dict_info(self, metadata: dict) -> None: pair_in_dict = self.pair_dict.get(metadata["pair"]) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 6edb6b78f..5eecffd9c 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -134,7 +134,7 @@ class IFreqaiModel(ABC): time.sleep(1) for pair in self.config.get("exchange", {}).get("pair_whitelist"): - (_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair) + (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair) if self.dd.pair_dict[pair]["priority"] != 1: continue @@ -177,7 +177,7 @@ class IFreqaiModel(ABC): # following tr_train. Both of these windows slide through the # entire backtest for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges): - (_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"]) + (_, _, _) = self.dd.get_pair_dict_info(metadata["pair"]) train_it += 1 total_trains = len(dk.backtesting_timeranges) gc.collect() @@ -250,7 +250,7 @@ class IFreqaiModel(ABC): self.dd.update_follower_metadata() # get the model metadata associated with the current pair - (_, trained_timestamp, _, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"]) + (_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"]) # if the metadata doesnt exist, the follower returns null arrays to strategy if self.follow_mode and return_null_array: diff --git a/tests/freqai/test_freqai_datadrawer.py b/tests/freqai/test_freqai_datadrawer.py index 81503cf15..a6df60e61 100644 --- a/tests/freqai/test_freqai_datadrawer.py +++ b/tests/freqai/test_freqai_datadrawer.py @@ -4,7 +4,6 @@ from pathlib import Path from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider -# from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from tests.conftest import get_patched_exchange from tests.freqai.conftest import get_patched_freqai_strategy From 3f149c40673871c6056b97f02c89c97f7eaf7f8b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 26 Jul 2022 16:01:54 +0200 Subject: [PATCH 203/518] fix return type in BaseTensorFlowModel --- freqtrade/freqai/prediction_models/BaseTensorFlowModel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py index d14ac0eee..bfbd5aa46 100644 --- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py +++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py @@ -1,5 +1,5 @@ import logging -from typing import Tuple +from typing import Any from pandas import DataFrame @@ -27,7 +27,7 @@ class BaseTensorFlowModel(IFreqaiModel): def train( self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen - ) -> Tuple[DataFrame, DataFrame]: + ) -> Any: """ Filter the training data and train a model to it. Train makes heavy use of the datakitchen for storing, saving, loading, and analyzing the data. From 324e54c0159aa823167d90eb639ba8ba0b9aaa4d Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 26 Jul 2022 17:29:29 +0200 Subject: [PATCH 204/518] fix possible memory leak associated with Catboost Pool object --- freqtrade/freqai/freqai_interface.py | 6 +++--- .../freqai/prediction_models/BaseRegressionModel.py | 2 +- .../freqai/prediction_models/BaseTensorFlowModel.py | 2 +- .../freqai/prediction_models/CatboostPredictionModel.py | 9 ++++++--- 4 files changed, 11 insertions(+), 8 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 5eecffd9c..4bee3fefd 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -120,7 +120,8 @@ class IFreqaiModel(ABC): dk = self.start_backtesting(dataframe, metadata, self.dk) dataframe = dk.remove_features_from_df(dk.return_dataframe) - return self.return_values(dataframe, dk) + del dk + return self.return_values(dataframe) @threaded def start_scanning(self, strategy: IStrategy) -> None: @@ -560,12 +561,11 @@ class IFreqaiModel(ABC): """ @abstractmethod - def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame: + def return_values(self, dataframe: DataFrame) -> DataFrame: """ User defines the dataframe to be returned to strategy here. :param dataframe: DataFrame = the full dataframe for the current prediction (live) or --timerange (backtesting) - :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only :return: dataframe: DataFrame = dataframe filled with user defined data """ diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 611b5fb04..2baec9fc3 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -18,7 +18,7 @@ class BaseRegressionModel(IFreqaiModel): such as prediction_models/CatboostPredictionModel.py for guidance. """ - def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame: + def return_values(self, dataframe: DataFrame) -> DataFrame: """ User uses this function to add any additional return values to the dataframe. e.g. diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py index bfbd5aa46..d94378494 100644 --- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py +++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py @@ -16,7 +16,7 @@ class BaseTensorFlowModel(IFreqaiModel): User *must* inherit from this class and set fit() and predict(). """ - def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame: + def return_values(self, dataframe: DataFrame) -> DataFrame: """ User uses this function to add any additional return values to the dataframe. e.g. diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 56b84c08d..884933803 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -1,6 +1,6 @@ import logging from typing import Any, Dict - +import gc from catboost import CatBoostRegressor, Pool from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel @@ -42,8 +42,11 @@ class CatboostPredictionModel(BaseRegressionModel): **self.model_training_parameters, ) - if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: - test_data = None model.fit(X=train_data, eval_set=test_data) + # some evidence that catboost pools have memory leaks: + # https://github.com/catboost/catboost/issues/1835 + del train_data, test_data + gc.collect() + return model From bc760b7eb236cc2fbec745c2f628755f33c2899d Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 26 Jul 2022 19:41:49 +0200 Subject: [PATCH 205/518] Simplify small segment in datadrawer --- freqtrade/freqai/data_drawer.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 8c26d01b0..5562ffba0 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -63,7 +63,7 @@ class FreqaiDataDrawer: self.historic_predictions: Dict[str, Any] = {} self.follower_dict: Dict[str, Any] = {} self.full_path = full_path - self.follower_name = self.config.get("bot_name", "follower1") + self.follower_name: str = self.config.get("bot_name", "follower1") self.follower_dict_path = Path( self.full_path / f"follower_dictionary-{self.follower_name}.json" ) @@ -159,8 +159,7 @@ class FreqaiDataDrawer: for pair in whitelist_pairs: self.follower_dict[pair] = {} - with open(self.follower_dict_path, "w") as fp: - json.dump(self.follower_dict, fp, default=self.np_encoder) + self.save_follower_dict_to_disk() def np_encoder(self, object): if isinstance(object, np.generic): From 229e8864bbeb33bbd0b4c3bc525131939e276ad2 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 26 Jul 2022 20:15:49 +0200 Subject: [PATCH 206/518] Add send_msg capability to dataprovider --- freqtrade/data/dataprovider.py | 21 +++++++++++++++++++++ freqtrade/enums/rpcmessagetype.py | 2 ++ freqtrade/freqtradebot.py | 1 + freqtrade/rpc/rpc_manager.py | 12 ++++++++++++ freqtrade/rpc/telegram.py | 3 ++- 5 files changed, 38 insertions(+), 1 deletion(-) diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py index b9b118c00..800254533 100644 --- a/freqtrade/data/dataprovider.py +++ b/freqtrade/data/dataprovider.py @@ -5,12 +5,14 @@ including ticker and orderbook data, live and historical candle (OHLCV) data Common Interface for bot and strategy to access data. """ import logging +from collections import deque from datetime import datetime, timezone from typing import Any, Dict, List, Optional, Tuple from pandas import DataFrame from freqtrade.configuration import TimeRange +from freqtrade.configuration.PeriodicCache import PeriodicCache from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe from freqtrade.data.history import load_pair_history from freqtrade.enums import CandleType, RunMode @@ -33,6 +35,9 @@ class DataProvider: self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {} self.__slice_index: Optional[int] = None self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {} + self._msg_queue: deque = deque() + self.__msg_cache = PeriodicCache( + maxsize=1000, ttl=timeframe_to_seconds(self._config['timeframe'])) def _set_dataframe_max_index(self, limit_index: int): """ @@ -265,3 +270,19 @@ class DataProvider: if self._exchange is None: raise OperationalException(NO_EXCHANGE_EXCEPTION) return self._exchange.fetch_l2_order_book(pair, maximum) + + def send_msg(self, message: str, always_send: bool = False) -> None: + """ + TODO: Document me + :param message: Message to be sent. Must be below 4096. + :param always_send: If False, will send the message only once per candle, and surpress + identical messages. + Careful as this can end up spaming your chat. + Defaults to False + """ + if self.runmode not in (RunMode.DRY_RUN, RunMode.LIVE): + return + + if always_send or message not in self.__msg_cache: + self._msg_queue.append(message) + self.__msg_cache[message] = True diff --git a/freqtrade/enums/rpcmessagetype.py b/freqtrade/enums/rpcmessagetype.py index 584a011c2..415d8f18c 100644 --- a/freqtrade/enums/rpcmessagetype.py +++ b/freqtrade/enums/rpcmessagetype.py @@ -17,6 +17,8 @@ class RPCMessageType(Enum): PROTECTION_TRIGGER = 'protection_trigger' PROTECTION_TRIGGER_GLOBAL = 'protection_trigger_global' + STRATEGY_MSG = 'strategy_msg' + def __repr__(self): return self.value diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 43608cae7..9ea195c45 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -214,6 +214,7 @@ class FreqtradeBot(LoggingMixin): if self.trading_mode == TradingMode.FUTURES: self._schedule.run_pending() Trade.commit() + self.rpc.process_msg_queue(self.dataprovider._msg_queue) self.last_process = datetime.now(timezone.utc) def process_stopped(self) -> None: diff --git a/freqtrade/rpc/rpc_manager.py b/freqtrade/rpc/rpc_manager.py index 66e84029f..3ccf23228 100644 --- a/freqtrade/rpc/rpc_manager.py +++ b/freqtrade/rpc/rpc_manager.py @@ -2,6 +2,7 @@ This module contains class to manage RPC communications (Telegram, API, ...) """ import logging +from collections import deque from typing import Any, Dict, List from freqtrade.enums import RPCMessageType @@ -77,6 +78,17 @@ class RPCManager: except NotImplementedError: logger.error(f"Message type '{msg['type']}' not implemented by handler {mod.name}.") + def process_msg_queue(self, queue: deque) -> None: + """ + Process all messages in the queue. + """ + while queue: + msg = queue.popleft() + self.send_msg({ + 'type': RPCMessageType.STRATEGY_MSG, + 'msg': msg, + }) + def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None: if config['dry_run']: self.send_msg({ diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 2aff1d210..121324d90 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -376,7 +376,8 @@ class Telegram(RPCHandler): elif msg_type == RPCMessageType.STARTUP: message = f"{msg['status']}" - + elif msg_type == RPCMessageType.STRATEGY_MSG: + message = f"{msg['msg']}" else: raise NotImplementedError(f"Unknown message type: {msg_type}") return message From 7bac0546681e770d3156db987620a666e4e1a489 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 26 Jul 2022 20:24:52 +0200 Subject: [PATCH 207/518] Add documentation and clarity for send_msg --- docs/strategy-customization.md | 17 +++++++++++++++++ docs/telegram-usage.md | 4 +++- freqtrade/constants.py | 4 ++++ freqtrade/data/dataprovider.py | 8 +++++--- 4 files changed, 29 insertions(+), 4 deletions(-) diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md index 6947380dd..38d34d51b 100644 --- a/docs/strategy-customization.md +++ b/docs/strategy-customization.md @@ -731,6 +731,23 @@ if self.dp: !!! Warning "Warning about backtesting" This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results. +### Send Notification + +The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy. +Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True. + +``` python + self.dp.send_msg(f"{metadata['pair']} just got hot!") + + # Force send this notification, avoid caching (Please read warning below!) + self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True) +``` + +Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting. + +!!! Warning "Spamming" + You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds. + ### Complete Data-provider sample ```python diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md index 9853e15c6..a690e18b9 100644 --- a/docs/telegram-usage.md +++ b/docs/telegram-usage.md @@ -98,6 +98,7 @@ Example configuration showing the different settings: "exit_fill": "off", "protection_trigger": "off", "protection_trigger_global": "on", + "strategy_msg": "off", "show_candle": "off" }, "reload": true, @@ -109,7 +110,8 @@ Example configuration showing the different settings: `exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange. `*_fill` notifications are off by default and must be explicitly enabled. `protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered. -`show_candle` - show candle values as part of entry/exit messages. Only possible value is "ohlc". +`strategy_msg` - Receive notifications from the strategy, sent via `self.dp.send_msg()` from the strategy [more details](strategy-customization.md#send-notification). +`show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`. `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown. `reload` allows you to disable reload-buttons on selected messages. diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 6d74ceafd..1d83d21a0 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -317,6 +317,10 @@ CONF_SCHEMA = { 'type': 'string', 'enum': ['off', 'ohlc'], }, + 'strategy_msg': { + 'type': 'string', + 'enum': TELEGRAM_SETTING_OPTIONS, + }, } }, 'reload': {'type': 'boolean'}, diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py index 800254533..e21f10193 100644 --- a/freqtrade/data/dataprovider.py +++ b/freqtrade/data/dataprovider.py @@ -36,8 +36,9 @@ class DataProvider: self.__slice_index: Optional[int] = None self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {} self._msg_queue: deque = deque() + self.__msg_cache = PeriodicCache( - maxsize=1000, ttl=timeframe_to_seconds(self._config['timeframe'])) + maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h'))) def _set_dataframe_max_index(self, limit_index: int): """ @@ -271,9 +272,10 @@ class DataProvider: raise OperationalException(NO_EXCHANGE_EXCEPTION) return self._exchange.fetch_l2_order_book(pair, maximum) - def send_msg(self, message: str, always_send: bool = False) -> None: + def send_msg(self, message: str, *, always_send: bool = False) -> None: """ - TODO: Document me + Send custom RPC Notifications from your bot. + Will not send any bot in modes other than Dry-run or Live. :param message: Message to be sent. Must be below 4096. :param always_send: If False, will send the message only once per candle, and surpress identical messages. From 0adfa4d9efe26d9423d769ca8829e723569c0545 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 27 Jul 2022 06:32:23 +0200 Subject: [PATCH 208/518] Add tests for dataprovider send-message methods --- tests/data/test_dataprovider.py | 19 +++++++++++++++++++ tests/rpc/test_rpc_manager.py | 21 +++++++++++++++++++-- tests/rpc/test_rpc_telegram.py | 10 ++++++++++ tests/test_freqtradebot.py | 6 ++++++ 4 files changed, 54 insertions(+), 2 deletions(-) diff --git a/tests/data/test_dataprovider.py b/tests/data/test_dataprovider.py index 93f82de5d..843d60786 100644 --- a/tests/data/test_dataprovider.py +++ b/tests/data/test_dataprovider.py @@ -311,3 +311,22 @@ def test_no_exchange_mode(default_conf): with pytest.raises(OperationalException, match=message): dp.available_pairs() + + +def test_dp_send_msg(default_conf): + + default_conf["runmode"] = RunMode.DRY_RUN + + default_conf["timeframe"] = '1h' + dp = DataProvider(default_conf, None) + msg = 'Test message' + dp.send_msg(msg) + + assert msg in dp._msg_queue + dp._msg_queue.pop() + assert msg not in dp._msg_queue + # Message is not resent due to caching + dp.send_msg(msg) + assert msg not in dp._msg_queue + dp.send_msg(msg, always_send=True) + assert msg in dp._msg_queue diff --git a/tests/rpc/test_rpc_manager.py b/tests/rpc/test_rpc_manager.py index 596b5ae20..b9ae16a20 100644 --- a/tests/rpc/test_rpc_manager.py +++ b/tests/rpc/test_rpc_manager.py @@ -1,6 +1,7 @@ # pragma pylint: disable=missing-docstring, C0103 import logging import time +from collections import deque from unittest.mock import MagicMock from freqtrade.enums import RPCMessageType @@ -81,9 +82,25 @@ def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None: assert telegram_mock.call_count == 0 +def test_process_msg_queue(mocker, default_conf, caplog) -> None: + telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg') + mocker.patch('freqtrade.rpc.telegram.Telegram._init') + + freqtradebot = get_patched_freqtradebot(mocker, default_conf) + rpc_manager = RPCManager(freqtradebot) + queue = deque() + queue.append('Test message') + queue.append('Test message 2') + rpc_manager.process_msg_queue(queue) + + assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message'}", caplog) + assert log_has("Sending rpc message: {'type': strategy_msg, 'msg': 'Test message 2'}", caplog) + assert telegram_mock.call_count == 2 + + def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None: - telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock()) - mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) + telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg') + mocker.patch('freqtrade.rpc.telegram.Telegram._init') freqtradebot = get_patched_freqtradebot(mocker, default_conf) rpc_manager = RPCManager(freqtradebot) diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index f69b7e878..8d244f3fd 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -1994,6 +1994,16 @@ def test_startup_notification(default_conf, mocker) -> None: assert msg_mock.call_args[0][0] == '*Custom:* `Hello World`' +def test_send_msg_strategy_msg_notification(default_conf, mocker) -> None: + + telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf) + telegram.send_msg({ + 'type': RPCMessageType.STRATEGY_MSG, + 'msg': 'hello world, Test msg' + }) + assert msg_mock.call_args[0][0] == 'hello world, Test msg' + + def test_send_msg_unknown_type(default_conf, mocker) -> None: telegram, _, _ = get_telegram_testobject(mocker, default_conf) with pytest.raises(NotImplementedError, match=r'Unknown message type: None'): diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index 66cbd7d9b..438a2704c 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -68,6 +68,12 @@ def test_process_stopped(mocker, default_conf_usdt) -> None: assert coo_mock.call_count == 1 +def test_process_calls_sendmsg(mocker, default_conf_usdt) -> None: + freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) + freqtrade.process() + assert freqtrade.rpc.process_msg_queue.call_count == 1 + + def test_bot_cleanup(mocker, default_conf_usdt, caplog) -> None: mock_cleanup = mocker.patch('freqtrade.freqtradebot.cleanup_db') coo_mock = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.cancel_all_open_orders') From 31ddec834816d980d2802810868b62f44df787d1 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 27 Jul 2022 06:51:56 +0200 Subject: [PATCH 209/518] Add missing test to confirm backtesting won't send messages --- tests/data/test_dataprovider.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/data/test_dataprovider.py b/tests/data/test_dataprovider.py index 843d60786..49603feac 100644 --- a/tests/data/test_dataprovider.py +++ b/tests/data/test_dataprovider.py @@ -330,3 +330,8 @@ def test_dp_send_msg(default_conf): assert msg not in dp._msg_queue dp.send_msg(msg, always_send=True) assert msg in dp._msg_queue + + default_conf["runmode"] = RunMode.BACKTEST + dp = DataProvider(default_conf, None) + dp.send_msg(msg, always_send=True) + assert msg not in dp._msg_queue From f31106dc6196107f9eaad6b841ab1f5d698c2eeb Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 27 Jul 2022 07:27:24 +0200 Subject: [PATCH 210/518] Minor comment fixes --- freqtrade/freqai/data_drawer.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 5562ffba0..4f3c0062f 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -81,8 +81,7 @@ class FreqaiDataDrawer: """ Locate and load a previously saved data drawer full of all pair model metadata in present model folder. - :returns: - exists: bool = whether or not the drawer was located + :return: bool - whether or not the drawer was located """ exists = self.pair_dictionary_path.is_file() if exists: @@ -101,8 +100,7 @@ class FreqaiDataDrawer: def load_historic_predictions_from_disk(self): """ Locate and load a previously saved historic predictions. - :returns: - exists: bool = whether or not the drawer was located + :return: bool - whether or not the drawer was located """ exists = self.historic_predictions_path.is_file() if exists: @@ -422,7 +420,7 @@ class FreqaiDataDrawer: dk.model_filename = self.pair_dict[coin]["model_filename"] dk.data_path = Path(self.pair_dict[coin]["data_path"]) if self.freqai_info.get("follow_mode", False): - # follower can be on a different system which is rsynced to the leader: + # follower can be on a different system which is rsynced from the leader: dk.data_path = Path( self.config["user_data_dir"] / "models" From a2a0d35a24ebc55fa6be095d72dd8e59f1aea796 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 28 Jul 2022 07:07:40 +0200 Subject: [PATCH 211/518] Update missing typehints --- freqtrade/freqai/data_drawer.py | 9 +++++---- freqtrade/freqai/freqai_interface.py | 4 ++-- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 4f3c0062f..4d37ef8c1 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -8,10 +8,10 @@ from pathlib import Path from typing import Any, Dict, Tuple import numpy as np -import numpy.typing as npt import pandas as pd from joblib import dump, load from joblib.externals import cloudpickle +from numpy.typing import ArrayLike from pandas import DataFrame from freqtrade.configuration import TimeRange @@ -219,7 +219,7 @@ class FreqaiDataDrawer: self.pair_dict[pair]["priority"] = len(self.pair_dict) def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen, - pred_df: DataFrame, do_preds: npt.ArrayLike) -> None: + pred_df: DataFrame, do_preds: ArrayLike) -> None: """ Set the initial return values to a persistent dataframe. This avoids needing to repredict on historical candles, and also stores historical predictions despite retrainings (so stored @@ -238,7 +238,8 @@ class FreqaiDataDrawer: mrv_df["do_predict"] = do_preds - def append_model_predictions(self, pair: str, predictions, do_preds, dk, len_df) -> None: + def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: ArrayLike, + dk: FreqaiDataKitchen, len_df: int) -> None: # strat seems to feed us variable sized dataframes - and since we are trying to build our # own return array in the same shape, we need to figure out how the size has changed @@ -293,7 +294,7 @@ class FreqaiDataDrawer: dataframe = pd.concat([dataframe[to_keep], df], axis=1) return dataframe - def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None: + def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None: """ Build 0 filled dataframe to return to strategy """ diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4bee3fefd..4c74be7ab 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -11,8 +11,8 @@ from pathlib import Path from typing import Any, Dict, Tuple import numpy as np -import numpy.typing as npt import pandas as pd +from numpy.typing import ArrayLike from pandas import DataFrame from freqtrade.configuration import TimeRange @@ -548,7 +548,7 @@ class IFreqaiModel(ABC): @abstractmethod def predict( self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True - ) -> Tuple[DataFrame, npt.ArrayLike]: + ) -> Tuple[DataFrame, ArrayLike]: """ Filter the prediction features data and predict with it. :param unfiltered_dataframe: Full dataframe for the current backtest period. From efbd83c56db75a1d904cd115549a385c474ea374 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 28 Jul 2022 07:24:30 +0200 Subject: [PATCH 212/518] Small type and typo fixes in freqai_interface --- freqtrade/freqai/freqai_interface.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4c74be7ab..ec69a78c4 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -61,19 +61,21 @@ class IFreqaiModel(ABC): self.config = config self.assert_config(self.config) - self.freqai_info = config["freqai"] - self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters") - self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters") + self.freqai_info: Dict[str, Any] = config["freqai"] + self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get( + "data_split_parameters", {}) + self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get( + "model_training_parameters", {}) self.feature_parameters = config.get("freqai", {}).get("feature_parameters") self.retrain = False self.first = True self.set_full_path() - self.follow_mode = self.freqai_info.get("follow_mode", False) + self.follow_mode: bool = self.freqai_info.get("follow_mode", False) self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode) self.lock = threading.Lock() - self.identifier = self.freqai_info.get("identifier", "no_id_provided") + self.identifier: str = self.freqai_info.get("identifier", "no_id_provided") self.scanning = False - self.keras = self.freqai_info.get("keras", False) + self.keras: bool = self.freqai_info.get("keras", False) if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0): self.freqai_info["feature_parameters"]["DI_threshold"] = 0 logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") @@ -253,7 +255,7 @@ class IFreqaiModel(ABC): # get the model metadata associated with the current pair (_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"]) - # if the metadata doesnt exist, the follower returns null arrays to strategy + # if the metadata doesn't exist, the follower returns null arrays to strategy if self.follow_mode and return_null_array: logger.info("Returning null array from follower to strategy") self.dd.return_null_values_to_strategy(dataframe, dk) @@ -364,7 +366,7 @@ class IFreqaiModel(ABC): raise OperationalException( "Trying to access pretrained model with `identifier` " "but found different features furnished by current strategy." - "Change `identifer` to train from scratch, or ensure the" + "Change `identifier` to train from scratch, or ensure the" "strategy is furnishing the same features as the pretrained" "model" ) @@ -457,7 +459,7 @@ class IFreqaiModel(ABC): data_load_timerange: TimeRange, ): """ - Retreive data and train model in single threaded mode (only used if model directory is empty + Retrieve data and train model in single threaded mode (only used if model directory is empty upon startup for dry/live ) :param new_trained_timerange: TimeRange = the timerange to train the model on :param metadata: dict = strategy provided metadata From c84d54b35ec455e02818bca592ce80c98a89d756 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 29 Jul 2022 08:12:50 +0200 Subject: [PATCH 213/518] Fix typing issue, avoid using .get() when unnecessary, convert to fstrings --- freqtrade/freqai/data_drawer.py | 45 ++++++++++--------- freqtrade/freqai/data_kitchen.py | 28 ++++++------ freqtrade/freqai/freqai_interface.py | 34 +++++++------- .../prediction_models/BaseRegressionModel.py | 4 +- .../CatboostPredictionModel.py | 3 +- 5 files changed, 57 insertions(+), 57 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 4d37ef8c1..97cf7607a 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -11,7 +11,7 @@ import numpy as np import pandas as pd from joblib import dump, load from joblib.externals import cloudpickle -from numpy.typing import ArrayLike +from numpy.typing import ArrayLike, NDArray from pandas import DataFrame from freqtrade.configuration import TimeRange @@ -233,12 +233,13 @@ class FreqaiDataDrawer: mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label] mrv_df[f"{label}_std"] = dk.data["labels_std"][label] - if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0: + if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: mrv_df["DI_values"] = dk.DI_values mrv_df["do_predict"] = do_preds - def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: ArrayLike, + def append_model_predictions(self, pair: str, predictions: DataFrame, + do_preds: NDArray[np.int_], dk: FreqaiDataKitchen, len_df: int) -> None: # strat seems to feed us variable sized dataframes - and since we are trying to build our @@ -266,10 +267,10 @@ class FreqaiDataDrawer: df[label].iloc[-1] = predictions[label].iloc[-1] df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label] df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label] - # df['prediction'].iloc[-1] = predictions[-1] + df["do_predict"].iloc[-1] = do_preds[-1] - if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0: + if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: df["DI_values"].iloc[-1] = dk.DI_values[-1] # append the new predictions to persistent storage @@ -309,7 +310,7 @@ class FreqaiDataDrawer: # dataframe['prediction'] = 0 dataframe["do_predict"] = 0 - if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0: + if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: dataframe["DI_value"] = 0 dk.return_dataframe = dataframe @@ -379,24 +380,24 @@ class FreqaiDataDrawer: model.save(save_path / f"{dk.model_filename}_model.h5") if dk.svm_model is not None: - dump(dk.svm_model, save_path / str(dk.model_filename + "_svm_model.joblib")) + dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib") dk.data["data_path"] = str(dk.data_path) dk.data["model_filename"] = str(dk.model_filename) dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns) dk.data["label_list"] = dk.label_list # store the metadata - with open(save_path / str(dk.model_filename + "_metadata.json"), "w") as fp: + with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp: json.dump(dk.data, fp, default=dk.np_encoder) # save the train data to file so we can check preds for area of applicability later dk.data_dictionary["train_features"].to_pickle( - save_path / str(dk.model_filename + "_trained_df.pkl") + save_path / f"{dk.model_filename}_trained_df.pkl" ) - if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"): + if self.freqai_info["feature_parameters"].get("principal_component_analysis"): cloudpickle.dump( - dk.pca, open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "wb") + dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb") ) # if self.live: @@ -429,27 +430,27 @@ class FreqaiDataDrawer: / dk.data_path.parts[-1] ) - with open(dk.data_path / str(dk.model_filename + "_metadata.json"), "r") as fp: + with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp: dk.data = json.load(fp) dk.training_features_list = dk.data["training_features_list"] dk.label_list = dk.data["label_list"] dk.data_dictionary["train_features"] = pd.read_pickle( - dk.data_path / str(dk.model_filename + "_trained_df.pkl") + dk.data_path / f"{dk.model_filename}_trained_df.pkl" ) # try to access model in memory instead of loading object from disk to save time if dk.live and dk.model_filename in self.model_dictionary: model = self.model_dictionary[dk.model_filename] elif not dk.keras: - model = load(dk.data_path / str(dk.model_filename + "_model.joblib")) + model = load(dk.data_path / f"{dk.model_filename}_model.joblib") else: from tensorflow import keras - model = keras.models.load_model(dk.data_path / str(dk.model_filename + "_model.h5")) + model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5") - if Path(dk.data_path / str(dk.model_filename + "_svm_model.joblib")).resolve().exists(): - dk.svm_model = load(dk.data_path / str(dk.model_filename + "_svm_model.joblib")) + if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file(): + dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib") if not model: raise OperationalException( @@ -458,7 +459,7 @@ class FreqaiDataDrawer: if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]: dk.pca = cloudpickle.load( - open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb") + open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb") ) return model @@ -471,7 +472,7 @@ class FreqaiDataDrawer: :params: dataframe: DataFrame = strategy provided dataframe """ - feat_params = self.freqai_info.get("feature_parameters", {}) + feat_params = self.freqai_info["feature_parameters"] with self.history_lock: history_data = self.historic_data @@ -524,7 +525,7 @@ class FreqaiDataDrawer: for pair in dk.all_pairs: if pair not in history_data: history_data[pair] = {} - for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"): + for tf in self.freqai_info["feature_parameters"].get("include_timeframes"): history_data[pair][tf] = load_pair_history( datadir=self.config["datadir"], timeframe=tf, @@ -550,11 +551,11 @@ class FreqaiDataDrawer: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} historic_data = self.historic_data - pairs = self.freqai_info.get("feature_parameters", {}).get( + pairs = self.freqai_info["feature_parameters"].get( "include_corr_pairlist", [] ) - for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"): + for tf in self.freqai_info["feature_parameters"].get("include_timeframes"): base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf]) if pairs: for p in pairs: diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index f16e169b9..b5a3295b5 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -116,7 +116,7 @@ class FreqaiDataKitchen: :filtered_dataframe: cleaned dataframe ready to be split. :labels: cleaned labels ready to be split. """ - feat_dict = self.freqai_config.get("feature_parameters", {}) + feat_dict = self.freqai_config["feature_parameters"] weights: npt.ArrayLike if feat_dict.get("weight_factor", 0) > 0: @@ -515,7 +515,9 @@ class FreqaiDataKitchen: return if predict: - assert self.svm_model, "No svm model available for outlier removal" + if not self.svm_model: + logger.warning("No svm model available for outlier removal") + return y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"]) do_predict = np.where(y_pred == -1, 0, y_pred) @@ -528,7 +530,7 @@ class FreqaiDataKitchen: else: # use SGDOneClassSVM to increase speed? - nu = self.freqai_config.get("feature_parameters", {}).get("svm_nu", 0.2) + nu = self.freqai_config["feature_parameters"].get("svm_nu", 0.2) self.svm_model = linear_model.SGDOneClassSVM(nu=nu).fit( self.data_dictionary["train_features"] ) @@ -551,7 +553,7 @@ class FreqaiDataKitchen: ) # same for test data - if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0: y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) dropped_points = np.where(y_pred == -1, 0, y_pred) self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ @@ -605,7 +607,7 @@ class FreqaiDataKitchen: self.DI_values = distance.min(axis=0) / self.data["avg_mean_dist"] do_predict = np.where( - self.DI_values < self.freqai_config.get("feature_parameters", {}).get("DI_threshold"), + self.DI_values < self.freqai_config["feature_parameters"]["DI_threshold"], 1, 0, ) @@ -640,7 +642,7 @@ class FreqaiDataKitchen: self.append_df[f"{label}_std"] = self.data["labels_std"][label] self.append_df["do_predict"] = do_predict - if self.freqai_config.get("feature_parameters", {}).get("DI_threshold", 0) > 0: + if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0: self.append_df["DI_values"] = self.DI_values if self.full_df.empty: @@ -701,7 +703,7 @@ class FreqaiDataKitchen: full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d") self.full_path = Path( - self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier")) + self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}" ) config_path = Path(self.config["config_files"][0]) @@ -741,10 +743,10 @@ class FreqaiDataKitchen: data_load_timerange = TimeRange() # find the max indicator length required - max_timeframe_chars = self.freqai_config.get("feature_parameters", {}).get( + max_timeframe_chars = self.freqai_config["feature_parameters"].get( "include_timeframes" )[-1] - max_period = self.freqai_config.get("feature_parameters", {}).get( + max_period = self.freqai_config["feature_parameters"].get( "indicator_max_period_candles", 50 ) additional_seconds = 0 @@ -832,7 +834,7 @@ class FreqaiDataKitchen: refresh_backtest_ohlcv_data( exchange, pairs=self.all_pairs, - timeframes=self.freqai_config.get("feature_parameters", {}).get("include_timeframes"), + timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"), datadir=self.config["datadir"], timerange=timerange, new_pairs_days=new_pairs_days, @@ -845,7 +847,7 @@ class FreqaiDataKitchen: def set_all_pairs(self) -> None: self.all_pairs = copy.deepcopy( - self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", []) + self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) ) for pair in self.config.get("exchange", "").get("pair_whitelist"): if pair not in self.all_pairs: @@ -876,8 +878,8 @@ class FreqaiDataKitchen: # for prediction dataframe creation, we let dataprovider handle everything in the strategy # so we create empty dictionaries, which allows us to pass None to # `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe. - tfs = self.freqai_config.get("feature_parameters", {}).get("include_timeframes") - pairs = self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", []) + tfs = self.freqai_config["feature_parameters"].get("include_timeframes") + pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) if not prediction_dataframe.empty: dataframe = prediction_dataframe.copy() for tf in tfs: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index ec69a78c4..47aeb32e4 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -12,7 +12,7 @@ from typing import Any, Dict, Tuple import numpy as np import pandas as pd -from numpy.typing import ArrayLike +from numpy.typing import NDArray from pandas import DataFrame from freqtrade.configuration import TimeRange @@ -204,14 +204,9 @@ class IFreqaiModel(ABC): dk.data_path = Path( dk.full_path - / str( - "sub-train" - + "-" - + metadata["pair"].split("/")[0] - + "_" - + str(int(trained_timestamp.stopts)) + / + f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}" ) - ) if not self.model_exists( metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts) ): @@ -331,7 +326,8 @@ class IFreqaiModel(ABC): return elif self.dk.check_if_model_expired(trained_timestamp): pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list) - do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2) + do_preds = np.ones(2, dtype=np.int_) * 2 + dk.DI_values = np.zeros(2) logger.warning( f"Model expired for {pair}, returning null values to strategy. Strategy " "construction should take care to consider this event with " @@ -379,15 +375,15 @@ class IFreqaiModel(ABC): example of how outlier data points are dropped from the dataframe used for training. """ - if self.freqai_info.get("feature_parameters", {}).get( + if self.freqai_info["feature_parameters"].get( "principal_component_analysis", False ): dk.principal_component_analysis() - if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False): + if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False): dk.use_SVM_to_remove_outliers(predict=False) - if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0): + if self.freqai_info["feature_parameters"].get("DI_threshold", 0): dk.data["avg_mean_dist"] = dk.compute_distances() def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None: @@ -401,15 +397,15 @@ class IFreqaiModel(ABC): of how the do_predict vector is modified. do_predict is ultimately passed back to strategy for buy signals. """ - if self.freqai_info.get("feature_parameters", {}).get( + if self.freqai_info["feature_parameters"].get( "principal_component_analysis", False ): dk.pca_transform(dataframe) - if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False): + if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False): dk.use_SVM_to_remove_outliers(predict=True) - if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0): + if self.freqai_info["feature_parameters"].get("DI_threshold", 0): dk.check_if_pred_in_training_spaces() def model_exists( @@ -430,9 +426,9 @@ class IFreqaiModel(ABC): coin, _ = pair.split("/") if not self.live: - dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp) + dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}" - path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib")) + path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib") file_exists = path_to_modelfile.is_file() if file_exists and not scanning: logger.info("Found model at %s", dk.data_path / dk.model_filename) @@ -442,7 +438,7 @@ class IFreqaiModel(ABC): def set_full_path(self) -> None: self.full_path = Path( - self.config["user_data_dir"] / "models" / str(self.freqai_info.get("identifier")) + self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}" ) self.full_path.mkdir(parents=True, exist_ok=True) shutil.copy( @@ -550,7 +546,7 @@ class IFreqaiModel(ABC): @abstractmethod def predict( self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True - ) -> Tuple[DataFrame, ArrayLike]: + ) -> Tuple[DataFrame, NDArray[np.int_]]: """ Filter the prediction features data and predict with it. :param unfiltered_dataframe: Full dataframe for the current backtest period. diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 2baec9fc3..85d7ae1ee 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -3,7 +3,7 @@ from typing import Any, Tuple import numpy.typing as npt from pandas import DataFrame - +import numpy as np from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel @@ -85,7 +85,7 @@ class BaseRegressionModel(IFreqaiModel): def predict( self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False - ) -> Tuple[DataFrame, npt.ArrayLike]: + ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. diff --git a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py index 884933803..9731e0c01 100644 --- a/freqtrade/freqai/prediction_models/CatboostPredictionModel.py +++ b/freqtrade/freqai/prediction_models/CatboostPredictionModel.py @@ -1,6 +1,7 @@ +import gc import logging from typing import Any, Dict -import gc + from catboost import CatBoostRegressor, Pool from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel From 59624181bd1005feb93e822e0fffddf0644fec44 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 29 Jul 2022 08:23:44 +0200 Subject: [PATCH 214/518] isort BaseRegressionModel imports --- freqtrade/freqai/prediction_models/BaseRegressionModel.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 85d7ae1ee..1901c18fe 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -1,9 +1,10 @@ import logging from typing import Any, Tuple +import numpy as np import numpy.typing as npt from pandas import DataFrame -import numpy as np + from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel From 08d3ac7ef821e42e428d8250c279fb00c8da553e Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 29 Jul 2022 08:49:35 +0200 Subject: [PATCH 215/518] add keras and conv_width to schema and documentation --- docs/freqai.md | 3 +++ freqtrade/constants.py | 2 ++ 2 files changed, 5 insertions(+) diff --git a/docs/freqai.md b/docs/freqai.md index 4060b5394..3c04d5b31 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -110,6 +110,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
**Datatype:** integer. | `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
**Datatype:** float. | `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
**Datatype:** float. +| | **Extraneous parameters** +| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
**Datatype:** boolean. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
**Datatype:** integer. ### Important FreqAI dataframe key patterns diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 90c87809b..0134dafc0 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -481,6 +481,8 @@ CONF_SCHEMA = { "freqai": { "type": "object", "properties": { + "keras": {"type": "boolean", "default": False}, + "conv_width": {"type": "integer", "default": 2}, "train_period_days": {"type": "integer", "default": 0}, "backtest_period_days": {"type": "float", "default": 7}, "identifier": {"type": "str", "default": "example"}, From f22b14078259aea39ba6731091538dc307fccc30 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 29 Jul 2022 17:27:35 +0200 Subject: [PATCH 216/518] fix backtesting bug, undo move of label stat calc, fix example strat exit logic --- freqtrade/freqai/data_kitchen.py | 18 +++++++++--------- freqtrade/freqai/freqai_interface.py | 3 --- .../prediction_models/BaseRegressionModel.py | 4 ++-- freqtrade/templates/FreqaiExampleStrategy.py | 2 +- 4 files changed, 12 insertions(+), 15 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b5a3295b5..788572ba6 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -635,20 +635,20 @@ class FreqaiDataKitchen: Append backtest prediction from current backtest period to all previous periods """ - self.append_df = DataFrame() + append_df = DataFrame() for label in self.label_list: - self.append_df[label] = predictions[label] - self.append_df[f"{label}_mean"] = self.data["labels_mean"][label] - self.append_df[f"{label}_std"] = self.data["labels_std"][label] + append_df[label] = predictions[label] + append_df[f"{label}_mean"] = self.data["labels_mean"][label] + append_df[f"{label}_std"] = self.data["labels_std"][label] - self.append_df["do_predict"] = do_predict + append_df["do_predict"] = do_predict if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0: - self.append_df["DI_values"] = self.DI_values + append_df["DI_values"] = self.DI_values if self.full_df.empty: - self.full_df = self.append_df + self.full_df = append_df else: - self.full_df = pd.concat([self.full_df, self.append_df], axis=0) + self.full_df = pd.concat([self.full_df, append_df], axis=0) return @@ -668,7 +668,7 @@ class FreqaiDataKitchen: to_keep = [col for col in dataframe.columns if not col.startswith("&")] self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1) - self.append_df = DataFrame() + # self.append_df = DataFrame() self.full_df = DataFrame() return diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 47aeb32e4..3b97637a8 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -1,7 +1,6 @@ # import contextlib import copy import datetime -import gc import logging import shutil import threading @@ -183,8 +182,6 @@ class IFreqaiModel(ABC): (_, _, _) = self.dd.get_pair_dict_info(metadata["pair"]) train_it += 1 total_trains = len(dk.backtesting_timeranges) - gc.collect() - dk.data = {} # clean the pair specific data between training window sliding self.training_timerange = tr_train dataframe_train = dk.slice_dataframe(tr_train, dataframe) dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe) diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index 1901c18fe..112e48183 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -56,6 +56,8 @@ class BaseRegressionModel(IFreqaiModel): f"{end_date}--------------------") # split data into train/test data. data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) + if not self.freqai_info.get('fit_live_predictions', 0) or not self.live: + dk.fit_labels() # normalize all data based on train_dataset only data_dictionary = dk.normalize_data(data_dictionary) @@ -75,8 +77,6 @@ class BaseRegressionModel(IFreqaiModel): if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: self.fit_live_predictions(dk) - else: - dk.fit_labels() self.dd.save_historic_predictions_to_disk() diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 58eb47532..1196405ab 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -235,7 +235,7 @@ class FreqaiExampleStrategy(IStrategy): if ( "prediction" + entry_tag not in pair_dict[pair] - or pair_dict[pair]["prediction" + entry_tag] > 0 + or pair_dict[pair]["prediction" + entry_tag] == 0 ): with self.freqai.lock: pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) From dd8288c090a7abb0c6e503e80bdfd7c2220decca Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 30 Jul 2022 13:40:05 +0200 Subject: [PATCH 217/518] expose full parameter set for SVM outlier detection. Set default shuffle to false to improve reproducibility --- docs/freqai.md | 2 +- freqtrade/freqai/data_kitchen.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 3c04d5b31..c4d0ad0f8 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -97,7 +97,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](##controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). | `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. | `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. -| `svm_nu` | The `nu` parameter for the support vector machine. *Very* broadly, this is the percentage of data points that should be considered outliers.
**Datatype:** float between 0 and 1. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reprodicibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. | `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
**Datatype:** positive integer. | `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
**Datatype:** positive integer. | `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
**Datatype:** list of positive integers. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 788572ba6..4a936475a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -530,8 +530,9 @@ class FreqaiDataKitchen: else: # use SGDOneClassSVM to increase speed? - nu = self.freqai_config["feature_parameters"].get("svm_nu", 0.2) - self.svm_model = linear_model.SGDOneClassSVM(nu=nu).fit( + svm_params = self.freqai_config["feature_parameters"].get( + "svm_params", {"shuffle": False, "nu": 0.1}) + self.svm_model = linear_model.SGDOneClassSVM(**svm_params).fit( self.data_dictionary["train_features"] ) y_pred = self.svm_model.predict(self.data_dictionary["train_features"]) From 995be90f91a21c351a5c22264135694f3e11d62b Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 28 Jul 2022 20:35:23 +0200 Subject: [PATCH 218/518] Liquidation should be a separate exit type --- freqtrade/enums/exittype.py | 1 + freqtrade/optimize/backtesting.py | 14 ++++++++++---- freqtrade/persistence/trade_model.py | 12 ++---------- freqtrade/plugins/protections/stoploss_guard.py | 2 +- freqtrade/strategy/interface.py | 13 ++++++++++++- 5 files changed, 26 insertions(+), 16 deletions(-) diff --git a/freqtrade/enums/exittype.py b/freqtrade/enums/exittype.py index b2c5b62ea..1e15e70cd 100644 --- a/freqtrade/enums/exittype.py +++ b/freqtrade/enums/exittype.py @@ -9,6 +9,7 @@ class ExitType(Enum): STOP_LOSS = "stop_loss" STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange" TRAILING_STOP_LOSS = "trailing_stop_loss" + LIQUIDATION = "liquidation" EXIT_SIGNAL = "exit_signal" FORCE_EXIT = "force_exit" EMERGENCY_EXIT = "emergency_exit" diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 4d16dc0f1..598ce6710 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -381,7 +381,8 @@ class Backtesting: Get close rate for backtesting result """ # Special handling if high or low hit STOP_LOSS or ROI - if exit.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS): + if exit.exit_type in ( + ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION): return self._get_close_rate_for_stoploss(row, trade, exit, trade_dur) elif exit.exit_type == (ExitType.ROI): return self._get_close_rate_for_roi(row, trade, exit, trade_dur) @@ -396,11 +397,16 @@ class Backtesting: is_short = trade.is_short or False leverage = trade.leverage or 1.0 side_1 = -1 if is_short else 1 + if exit.exit_type == ExitType.LIQUIDATION and trade.liquidation_price: + stoploss_value = trade.liquidation_price + else: + stoploss_value = trade.stop_loss + if is_short: - if trade.stop_loss < row[LOW_IDX]: + if stoploss_value < row[LOW_IDX]: return row[OPEN_IDX] else: - if trade.stop_loss > row[HIGH_IDX]: + if stoploss_value > row[HIGH_IDX]: return row[OPEN_IDX] # Special case: trailing triggers within same candle as trade opened. Assume most @@ -433,7 +439,7 @@ class Backtesting: return max(row[LOW_IDX], stop_rate) # Set close_rate to stoploss - return trade.stop_loss + return stoploss_value def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple, trade_dur: int) -> float: diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 5f302de71..2ff65d9d0 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -511,17 +511,9 @@ class LocalTrade(): Method you should use to set self.stop_loss. Assures stop_loss is not passed the liquidation price """ - if self.liquidation_price is not None: - if self.is_short: - sl = min(stop_loss, self.liquidation_price) - else: - sl = max(stop_loss, self.liquidation_price) - else: - sl = stop_loss - if not self.stop_loss: - self.initial_stop_loss = sl - self.stop_loss = sl + self.initial_stop_loss = stop_loss + self.stop_loss = stop_loss self.stop_loss_pct = -1 * abs(percent) self.stoploss_last_update = datetime.utcnow() diff --git a/freqtrade/plugins/protections/stoploss_guard.py b/freqtrade/plugins/protections/stoploss_guard.py index abc90a685..e80d13e9d 100644 --- a/freqtrade/plugins/protections/stoploss_guard.py +++ b/freqtrade/plugins/protections/stoploss_guard.py @@ -49,7 +49,7 @@ class StoplossGuard(IProtection): trades1 = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until) trades = [trade for trade in trades1 if (str(trade.exit_reason) in ( ExitType.TRAILING_STOP_LOSS.value, ExitType.STOP_LOSS.value, - ExitType.STOPLOSS_ON_EXCHANGE.value) + ExitType.STOPLOSS_ON_EXCHANGE.value, ExitType.LIQUIDATION.value) and trade.close_profit and trade.close_profit < self._profit_limit)] if self._only_per_side: diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index c60817c99..f50721583 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -963,7 +963,7 @@ class IStrategy(ABC, HyperStrategyMixin): # ROI # Trailing stoploss - if stoplossflag.exit_type == ExitType.STOP_LOSS: + if stoplossflag.exit_type in (ExitType.STOP_LOSS, ExitType.LIQUIDATION): logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}") exits.append(stoplossflag) @@ -1035,6 +1035,17 @@ class IStrategy(ABC, HyperStrategyMixin): sl_higher_long = (trade.stop_loss >= (low or current_rate) and not trade.is_short) sl_lower_short = (trade.stop_loss <= (high or current_rate) and trade.is_short) + liq_higher_long = (trade.liquidation_price + and trade.liquidation_price >= (low or current_rate) + and not trade.is_short) + liq_lower_short = (trade.liquidation_price + and trade.liquidation_price <= (high or current_rate) + and trade.is_short) + + if (liq_higher_long or liq_lower_short): + logger.debug(f"{trade.pair} - Liquidation price hit. exit_type=ExitType.LIQUIDATION") + return ExitCheckTuple(exit_type=ExitType.LIQUIDATION) + # evaluate if the stoploss was hit if stoploss is not on exchange # in Dry-Run, this handles stoploss logic as well, as the logic will not be different to # regular stoploss handling. From 8711b7d99f224b3d0ee26553e32774dd247d605a Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 28 Jul 2022 20:37:10 +0200 Subject: [PATCH 219/518] Liquidations cannot be rejected. --- freqtrade/freqtradebot.py | 5 +++-- freqtrade/optimize/backtesting.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 43608cae7..d58c05d7f 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1463,11 +1463,12 @@ class FreqtradeBot(LoggingMixin): amount = self._safe_exit_amount(trade.pair, trade.amount) time_in_force = self.strategy.order_time_in_force['exit'] - if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)( + if (exit_check.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper( + self.strategy.confirm_trade_exit, default_retval=True)( pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit, time_in_force=time_in_force, exit_reason=exit_reason, sell_reason=exit_reason, # sellreason -> compatibility - current_time=datetime.now(timezone.utc)): + current_time=datetime.now(timezone.utc))): logger.info(f"User denied exit for {trade.pair}.") return False diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 598ce6710..6bbace185 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -598,7 +598,8 @@ class Backtesting: # Confirm trade exit: time_in_force = self.strategy.order_time_in_force['exit'] - if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)( + if (exit_.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper( + self.strategy.confirm_trade_exit, default_retval=True)( pair=trade.pair, trade=trade, # type: ignore[arg-type] order_type='limit', @@ -607,7 +608,7 @@ class Backtesting: time_in_force=time_in_force, sell_reason=exit_reason, # deprecated exit_reason=exit_reason, - current_time=exit_candle_time): + current_time=exit_candle_time)): return None trade.exit_reason = exit_reason From f57ecb18615dc2e75fc73d0da78540ea0ebd6804 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 29 Jul 2022 06:55:42 +0200 Subject: [PATCH 220/518] Simplify adjust_stop test --- tests/test_persistence.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 838c4c22a..4703075eb 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1537,26 +1537,26 @@ def test_adjust_stop_loss(fee): # Get percent of profit with a custom rate (Higher than open rate) trade.adjust_stop_loss(1.3, -0.1) - assert round(trade.stop_loss, 8) == 1.17 + assert pytest.approx(trade.stop_loss) == 1.17 assert trade.stop_loss_pct == -0.1 assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 # current rate lower again ... should not change trade.adjust_stop_loss(1.2, 0.1) - assert round(trade.stop_loss, 8) == 1.17 + assert pytest.approx(trade.stop_loss) == 1.17 assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 # current rate higher... should raise stoploss trade.adjust_stop_loss(1.4, 0.1) - assert round(trade.stop_loss, 8) == 1.26 + assert pytest.approx(trade.stop_loss) == 1.26 assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 # Initial is true but stop_loss set - so doesn't do anything trade.adjust_stop_loss(1.7, 0.1, True) - assert round(trade.stop_loss, 8) == 1.26 + assert pytest.approx(trade.stop_loss) == 1.26 assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 assert trade.stop_loss_pct == -0.1 From 9852733ef7f7284765b8092eb6cd27edd2b7c0e8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 29 Jul 2022 06:58:59 +0200 Subject: [PATCH 221/518] Improve tests to align with modified logic --- freqtrade/persistence/trade_model.py | 6 ------ tests/test_persistence.py | 17 ++++++++++------- 2 files changed, 10 insertions(+), 13 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 2ff65d9d0..919750886 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -535,14 +535,8 @@ class LocalTrade(): leverage = self.leverage or 1.0 if self.is_short: new_loss = float(current_price * (1 + abs(stoploss / leverage))) - # If trading with leverage, don't set the stoploss below the liquidation price - if self.liquidation_price: - new_loss = min(self.liquidation_price, new_loss) else: new_loss = float(current_price * (1 - abs(stoploss / leverage))) - # If trading with leverage, don't set the stoploss below the liquidation price - if self.liquidation_price: - new_loss = max(self.liquidation_price, new_loss) # no stop loss assigned yet if self.initial_stop_loss_pct is None or refresh: diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 4703075eb..5476e1d50 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -133,13 +133,14 @@ def test_set_stop_loss_isolated_liq(fee): trade.set_isolated_liq(0.11) trade._set_stop_loss(0.1, 0) assert trade.liquidation_price == 0.11 - assert trade.stop_loss == 0.11 + # Stoploss does not change from liquidation price + assert trade.stop_loss == 0.1 assert trade.initial_stop_loss == 0.1 # lower stop doesn't move stoploss trade._set_stop_loss(0.1, 0) assert trade.liquidation_price == 0.11 - assert trade.stop_loss == 0.11 + assert trade.stop_loss == 0.1 assert trade.initial_stop_loss == 0.1 trade.stop_loss = None @@ -174,13 +175,14 @@ def test_set_stop_loss_isolated_liq(fee): trade.set_isolated_liq(0.07) trade._set_stop_loss(0.1, (1.0 / 8.0)) assert trade.liquidation_price == 0.07 - assert trade.stop_loss == 0.07 + # Stoploss does not change from liquidation price + assert trade.stop_loss == 0.1 assert trade.initial_stop_loss == 0.08 # Stop doesn't move stop higher trade._set_stop_loss(0.1, (1.0 / 9.0)) assert trade.liquidation_price == 0.07 - assert trade.stop_loss == 0.07 + assert trade.stop_loss == 0.1 assert trade.initial_stop_loss == 0.08 @@ -1609,9 +1611,10 @@ def test_adjust_stop_loss_short(fee): assert trade.initial_stop_loss == 1.05 assert trade.initial_stop_loss_pct == -0.05 assert trade.stop_loss_pct == -0.1 + # Liquidation price is lower than stoploss - so liquidation would trigger first. trade.set_isolated_liq(0.63) trade.adjust_stop_loss(0.59, -0.1) - assert trade.stop_loss == 0.63 + assert trade.stop_loss == 0.649 assert trade.liquidation_price == 0.63 @@ -2011,8 +2014,8 @@ def test_stoploss_reinitialization_short(default_conf, fee): # Stoploss can't go above liquidation price trade_adj.set_isolated_liq(0.985) trade.adjust_stop_loss(0.9799, -0.05) - assert trade_adj.stop_loss == 0.985 - assert trade_adj.stop_loss == 0.985 + assert trade_adj.stop_loss == 0.989699 + assert trade_adj.liquidation_price == 0.985 def test_update_fee(fee): From ff4cc5d3165b53058c1b80f8f94bb16df2345269 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 29 Jul 2022 07:14:25 +0200 Subject: [PATCH 222/518] Revamp liquidation test to actually make sense --- tests/test_persistence.py | 79 +++++++++++++++++++++++---------------- 1 file changed, 46 insertions(+), 33 deletions(-) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 5476e1d50..5dbd6b86b 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -120,70 +120,83 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.stop_loss is None assert trade.initial_stop_loss is None - trade._set_stop_loss(0.1, (1.0 / 9.0)) + trade.adjust_stop_loss(2.0, 0.2, True) assert trade.liquidation_price == 0.09 - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.1 + assert trade.stop_loss == 1.8 + assert trade.initial_stop_loss == 1.8 trade.set_isolated_liq(0.08) assert trade.liquidation_price == 0.08 - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.1 + assert trade.stop_loss == 1.8 + assert trade.initial_stop_loss == 1.8 trade.set_isolated_liq(0.11) - trade._set_stop_loss(0.1, 0) + trade.adjust_stop_loss(2.0, 0.2) assert trade.liquidation_price == 0.11 # Stoploss does not change from liquidation price - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.1 + assert trade.stop_loss == 1.8 + assert trade.initial_stop_loss == 1.8 # lower stop doesn't move stoploss - trade._set_stop_loss(0.1, 0) + trade.adjust_stop_loss(1.8, 0.2) assert trade.liquidation_price == 0.11 - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.1 + assert trade.stop_loss == 1.8 + assert trade.initial_stop_loss == 1.8 + + # higher stop does move stoploss + trade.adjust_stop_loss(2.1, 0.1) + assert trade.liquidation_price == 0.11 + assert pytest.approx(trade.stop_loss) == 1.994999 + assert trade.initial_stop_loss == 1.8 trade.stop_loss = None trade.liquidation_price = None trade.initial_stop_loss = None + trade.initial_stop_loss_pct = None - trade._set_stop_loss(0.07, 0) + trade.adjust_stop_loss(2.0, 0.1, True) assert trade.liquidation_price is None - assert trade.stop_loss == 0.07 - assert trade.initial_stop_loss == 0.07 + assert trade.stop_loss == 1.9 + assert trade.initial_stop_loss == 1.9 trade.is_short = True trade.recalc_open_trade_value() trade.stop_loss = None trade.initial_stop_loss = None + trade.initial_stop_loss_pct = None - trade.set_isolated_liq(0.09) - assert trade.liquidation_price == 0.09 + trade.set_isolated_liq(3.09) + assert trade.liquidation_price == 3.09 assert trade.stop_loss is None assert trade.initial_stop_loss is None - trade._set_stop_loss(0.08, (1.0 / 9.0)) - assert trade.liquidation_price == 0.09 - assert trade.stop_loss == 0.08 - assert trade.initial_stop_loss == 0.08 + trade.adjust_stop_loss(2.0, 0.2) + assert trade.liquidation_price == 3.09 + assert trade.stop_loss == 2.2 + assert trade.initial_stop_loss == 2.2 - trade.set_isolated_liq(0.1) - assert trade.liquidation_price == 0.1 - assert trade.stop_loss == 0.08 - assert trade.initial_stop_loss == 0.08 + trade.set_isolated_liq(3.1) + assert trade.liquidation_price == 3.1 + assert trade.stop_loss == 2.2 + assert trade.initial_stop_loss == 2.2 - trade.set_isolated_liq(0.07) - trade._set_stop_loss(0.1, (1.0 / 8.0)) - assert trade.liquidation_price == 0.07 + trade.set_isolated_liq(3.8) + assert trade.liquidation_price == 3.8 # Stoploss does not change from liquidation price - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.08 + assert trade.stop_loss == 2.2 + assert trade.initial_stop_loss == 2.2 # Stop doesn't move stop higher - trade._set_stop_loss(0.1, (1.0 / 9.0)) - assert trade.liquidation_price == 0.07 - assert trade.stop_loss == 0.1 - assert trade.initial_stop_loss == 0.08 + trade.adjust_stop_loss(2.0, 0.3) + assert trade.liquidation_price == 3.8 + assert trade.stop_loss == 2.2 + assert trade.initial_stop_loss == 2.2 + + # Stoploss does move lower + trade.adjust_stop_loss(1.8, 0.1) + assert trade.liquidation_price == 3.8 + assert pytest.approx(trade.stop_loss) == 1.89 + assert trade.initial_stop_loss == 2.2 @pytest.mark.parametrize('exchange,is_short,lev,minutes,rate,interest,trading_mode', [ From 15752ce3c2d4b2131e9141c3aa496dda25c5d9d7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 29 Jul 2022 07:15:01 +0200 Subject: [PATCH 223/518] Rename set_stoploss method to be fully private --- freqtrade/persistence/trade_model.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 919750886..1b8bcc42f 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -506,10 +506,9 @@ class LocalTrade(): return self.liquidation_price = liquidation_price - def _set_stop_loss(self, stop_loss: float, percent: float): + def __set_stop_loss(self, stop_loss: float, percent: float): """ - Method you should use to set self.stop_loss. - Assures stop_loss is not passed the liquidation price + Method used internally to set self.stop_loss. """ if not self.stop_loss: self.initial_stop_loss = stop_loss @@ -540,7 +539,7 @@ class LocalTrade(): # no stop loss assigned yet if self.initial_stop_loss_pct is None or refresh: - self._set_stop_loss(new_loss, stoploss) + self.__set_stop_loss(new_loss, stoploss) self.initial_stop_loss = new_loss self.initial_stop_loss_pct = -1 * abs(stoploss) @@ -555,7 +554,7 @@ class LocalTrade(): # ? decreasing the minimum stoploss if (higher_stop and not self.is_short) or (lower_stop and self.is_short): logger.debug(f"{self.pair} - Adjusting stoploss...") - self._set_stop_loss(new_loss, stoploss) + self.__set_stop_loss(new_loss, stoploss) else: logger.debug(f"{self.pair} - Keeping current stoploss...") From 4da96bc511c1a01ee717f304d60696141797d200 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 07:27:24 +0200 Subject: [PATCH 224/518] Update docs --- docs/strategy-callbacks.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index f584bd1bb..59d221bfc 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -623,6 +623,7 @@ class AwesomeStrategy(IStrategy): !!! Warning `confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits. + `confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected. ## Adjust trade position From 845cecd38fe0acfed72d99eddccc5aea37234cfc Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 08:12:48 +0200 Subject: [PATCH 225/518] Add stoploss or liquidation property --- freqtrade/persistence/trade_model.py | 10 ++++++++++ freqtrade/strategy/interface.py | 7 ------- tests/test_persistence.py | 10 +++++++++- 3 files changed, 19 insertions(+), 8 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 1b8bcc42f..244ca79cd 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -302,6 +302,16 @@ class LocalTrade(): # Futures properties funding_fees: Optional[float] = None + @property + def stoploss_or_liquidation(self) -> float: + if self.liquidation_price: + if self.is_short: + return min(self.stop_loss, self.liquidation_price) + else: + return max(self.stop_loss, self.liquidation_price) + + return self.stop_loss + @property def buy_tag(self) -> Optional[str]: """ diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index f50721583..824f31258 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -1063,13 +1063,6 @@ class IStrategy(ABC, HyperStrategyMixin): f"stoploss is {trade.stop_loss:.6f}, " f"initial stoploss was at {trade.initial_stop_loss:.6f}, " f"trade opened at {trade.open_rate:.6f}") - new_stoploss = ( - trade.stop_loss + trade.initial_stop_loss - if trade.is_short else - trade.stop_loss - trade.initial_stop_loss - ) - logger.debug(f"{trade.pair} - Trailing stop saved " - f"{new_stoploss:.6f}") return ExitCheckTuple(exit_type=exit_type) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 5dbd6b86b..3eca035c9 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -148,6 +148,7 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.liquidation_price == 0.11 assert pytest.approx(trade.stop_loss) == 1.994999 assert trade.initial_stop_loss == 1.8 + assert trade.stoploss_or_liquidation == trade.stop_loss trade.stop_loss = None trade.liquidation_price = None @@ -158,6 +159,7 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.liquidation_price is None assert trade.stop_loss == 1.9 assert trade.initial_stop_loss == 1.9 + assert trade.stoploss_or_liquidation == 1.9 trade.is_short = True trade.recalc_open_trade_value() @@ -174,11 +176,13 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.liquidation_price == 3.09 assert trade.stop_loss == 2.2 assert trade.initial_stop_loss == 2.2 + assert trade.stoploss_or_liquidation == 2.2 trade.set_isolated_liq(3.1) assert trade.liquidation_price == 3.1 assert trade.stop_loss == 2.2 assert trade.initial_stop_loss == 2.2 + assert trade.stoploss_or_liquidation == 2.2 trade.set_isolated_liq(3.8) assert trade.liquidation_price == 3.8 @@ -193,10 +197,14 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.initial_stop_loss == 2.2 # Stoploss does move lower + trade.set_isolated_liq(1.5) trade.adjust_stop_loss(1.8, 0.1) - assert trade.liquidation_price == 3.8 + assert trade.liquidation_price == 1.5 assert pytest.approx(trade.stop_loss) == 1.89 assert trade.initial_stop_loss == 2.2 + assert trade.stoploss_or_liquidation == 1.5 + + @pytest.mark.parametrize('exchange,is_short,lev,minutes,rate,interest,trading_mode', [ From dba7a7257dec1371c1bea14c099dbca06474e3ff Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 09:18:15 +0200 Subject: [PATCH 226/518] Use stop_or_liquidation instead of stop_loss --- freqtrade/freqtradebot.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index d58c05d7f..7440212c3 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1015,7 +1015,7 @@ class FreqtradeBot(LoggingMixin): trade.stoploss_order_id = None logger.error(f'Unable to place a stoploss order on exchange. {e}') logger.warning('Exiting the trade forcefully') - self.execute_trade_exit(trade, trade.stop_loss, exit_check=ExitCheckTuple( + self.execute_trade_exit(trade, stop_price, exit_check=ExitCheckTuple( exit_type=ExitType.EMERGENCY_EXIT)) except ExchangeError: @@ -1114,7 +1114,7 @@ class FreqtradeBot(LoggingMixin): :param order: Current on exchange stoploss order :return: None """ - stoploss_norm = self.exchange.price_to_precision(trade.pair, trade.stop_loss) + stoploss_norm = self.exchange.price_to_precision(trade.pair, trade.stoploss_or_liquidation) if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side): # we check if the update is necessary @@ -1132,7 +1132,7 @@ class FreqtradeBot(LoggingMixin): f"for pair {trade.pair}") # Create new stoploss order - if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss): + if not self.create_stoploss_order(trade=trade, stop_price=stoploss_norm): logger.warning(f"Could not create trailing stoploss order " f"for pair {trade.pair}.") @@ -1431,14 +1431,15 @@ class FreqtradeBot(LoggingMixin): ) exit_type = 'exit' exit_reason = exit_tag or exit_check.exit_reason - if exit_check.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS): + if exit_check.exit_type in ( + ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION): exit_type = 'stoploss' # if stoploss is on exchange and we are on dry_run mode, # we consider the sell price stop price if (self.config['dry_run'] and exit_type == 'stoploss' and self.strategy.order_types['stoploss_on_exchange']): - limit = trade.stop_loss + limit = trade.stoploss_or_liquidation # set custom_exit_price if available proposed_limit_rate = limit From d046f0cc5e766ec24f5c88fd967b44ea88698481 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 09:19:48 +0200 Subject: [PATCH 227/518] Improve method wording for liquidation price setter --- freqtrade/freqtradebot.py | 4 ++-- freqtrade/optimize/backtesting.py | 2 +- freqtrade/persistence/trade_model.py | 2 +- tests/test_persistence.py | 22 ++++++++++------------ 4 files changed, 14 insertions(+), 16 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 7440212c3..3490b58d6 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1085,7 +1085,7 @@ class FreqtradeBot(LoggingMixin): if (trade.is_open and stoploss_order and stoploss_order['status'] in ('canceled', 'cancelled')): - if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss): + if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation): return False else: trade.stoploss_order_id = None @@ -1662,7 +1662,7 @@ class FreqtradeBot(LoggingMixin): trade = self.cancel_stoploss_on_exchange(trade) # TODO: Margin will need to use interest_rate as well. # interest_rate = self.exchange.get_interest_rate() - trade.set_isolated_liq(self.exchange.get_liquidation_price( + trade.set_liquidation_price(self.exchange.get_liquidation_price( leverage=trade.leverage, pair=trade.pair, amount=trade.amount, diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 6bbace185..2c6cfb0e9 100755 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -814,7 +814,7 @@ class Backtesting: trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True) - trade.set_isolated_liq(self.exchange.get_liquidation_price( + trade.set_liquidation_price(self.exchange.get_liquidation_price( pair=pair, open_rate=propose_rate, amount=amount, diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 244ca79cd..44e148a0c 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -507,7 +507,7 @@ class LocalTrade(): self.max_rate = max(current_price, self.max_rate or self.open_rate) self.min_rate = min(current_price_low, self.min_rate or self.open_rate) - def set_isolated_liq(self, liquidation_price: Optional[float]): + def set_liquidation_price(self, liquidation_price: Optional[float]): """ Method you should use to set self.liquidation price. Assures stop_loss is not passed the liquidation price diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 3eca035c9..0c1fc01a5 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -99,7 +99,7 @@ def test_enter_exit_side(fee, is_short): @pytest.mark.usefixtures("init_persistence") -def test_set_stop_loss_isolated_liq(fee): +def test_set_stop_loss_liquidation(fee): trade = Trade( id=2, pair='ADA/USDT', @@ -115,7 +115,7 @@ def test_set_stop_loss_isolated_liq(fee): leverage=2.0, trading_mode=margin ) - trade.set_isolated_liq(0.09) + trade.set_liquidation_price(0.09) assert trade.liquidation_price == 0.09 assert trade.stop_loss is None assert trade.initial_stop_loss is None @@ -125,12 +125,12 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.stop_loss == 1.8 assert trade.initial_stop_loss == 1.8 - trade.set_isolated_liq(0.08) + trade.set_liquidation_price(0.08) assert trade.liquidation_price == 0.08 assert trade.stop_loss == 1.8 assert trade.initial_stop_loss == 1.8 - trade.set_isolated_liq(0.11) + trade.set_liquidation_price(0.11) trade.adjust_stop_loss(2.0, 0.2) assert trade.liquidation_price == 0.11 # Stoploss does not change from liquidation price @@ -167,7 +167,7 @@ def test_set_stop_loss_isolated_liq(fee): trade.initial_stop_loss = None trade.initial_stop_loss_pct = None - trade.set_isolated_liq(3.09) + trade.set_liquidation_price(3.09) assert trade.liquidation_price == 3.09 assert trade.stop_loss is None assert trade.initial_stop_loss is None @@ -178,13 +178,13 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.initial_stop_loss == 2.2 assert trade.stoploss_or_liquidation == 2.2 - trade.set_isolated_liq(3.1) + trade.set_liquidation_price(3.1) assert trade.liquidation_price == 3.1 assert trade.stop_loss == 2.2 assert trade.initial_stop_loss == 2.2 assert trade.stoploss_or_liquidation == 2.2 - trade.set_isolated_liq(3.8) + trade.set_liquidation_price(3.8) assert trade.liquidation_price == 3.8 # Stoploss does not change from liquidation price assert trade.stop_loss == 2.2 @@ -197,7 +197,7 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.initial_stop_loss == 2.2 # Stoploss does move lower - trade.set_isolated_liq(1.5) + trade.set_liquidation_price(1.5) trade.adjust_stop_loss(1.8, 0.1) assert trade.liquidation_price == 1.5 assert pytest.approx(trade.stop_loss) == 1.89 @@ -205,8 +205,6 @@ def test_set_stop_loss_isolated_liq(fee): assert trade.stoploss_or_liquidation == 1.5 - - @pytest.mark.parametrize('exchange,is_short,lev,minutes,rate,interest,trading_mode', [ ("binance", False, 3, 10, 0.0005, round(0.0008333333333333334, 8), margin), ("binance", True, 3, 10, 0.0005, 0.000625, margin), @@ -1633,7 +1631,7 @@ def test_adjust_stop_loss_short(fee): assert trade.initial_stop_loss_pct == -0.05 assert trade.stop_loss_pct == -0.1 # Liquidation price is lower than stoploss - so liquidation would trigger first. - trade.set_isolated_liq(0.63) + trade.set_liquidation_price(0.63) trade.adjust_stop_loss(0.59, -0.1) assert trade.stop_loss == 0.649 assert trade.liquidation_price == 0.63 @@ -2033,7 +2031,7 @@ def test_stoploss_reinitialization_short(default_conf, fee): assert trade_adj.initial_stop_loss == 1.01 assert trade_adj.initial_stop_loss_pct == -0.05 # Stoploss can't go above liquidation price - trade_adj.set_isolated_liq(0.985) + trade_adj.set_liquidation_price(0.985) trade.adjust_stop_loss(0.9799, -0.05) assert trade_adj.stop_loss == 0.989699 assert trade_adj.liquidation_price == 0.985 From dc82675f00e5ed1b006eeb1490a3f14147cde979 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 17:28:16 +0200 Subject: [PATCH 228/518] Add Test for liquidation in stop-loss-reached --- tests/strategy/test_interface.py | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index f6996a7a2..4257b2cf9 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -408,28 +408,31 @@ def test_min_roi_reached3(default_conf, fee) -> None: @pytest.mark.parametrize( - 'profit,adjusted,expected,trailing,custom,profit2,adjusted2,expected2,custom_stop', [ + 'profit,adjusted,expected,liq,trailing,custom,profit2,adjusted2,expected2,custom_stop', [ # Profit, adjusted stoploss(absolute), profit for 2nd call, enable trailing, # enable custom stoploss, expected after 1st call, expected after 2nd call - (0.2, 0.9, ExitType.NONE, False, False, 0.3, 0.9, ExitType.NONE, None), - (0.2, 0.9, ExitType.NONE, False, False, -0.2, 0.9, ExitType.STOP_LOSS, None), - (0.2, 1.14, ExitType.NONE, True, False, 0.05, 1.14, ExitType.TRAILING_STOP_LOSS, None), - (0.01, 0.96, ExitType.NONE, True, False, 0.05, 1, ExitType.NONE, None), - (0.05, 1, ExitType.NONE, True, False, -0.01, 1, ExitType.TRAILING_STOP_LOSS, None), + (0.2, 0.9, ExitType.NONE, None, False, False, 0.3, 0.9, ExitType.NONE, None), + (0.2, 0.9, ExitType.NONE, None, False, False, -0.2, 0.9, ExitType.STOP_LOSS, None), + (0.2, 0.9, ExitType.NONE, 0.8, False, False, -0.2, 0.9, ExitType.LIQUIDATION, None), + (0.2, 1.14, ExitType.NONE, None, True, False, 0.05, 1.14, ExitType.TRAILING_STOP_LOSS, + None), + (0.01, 0.96, ExitType.NONE, None, True, False, 0.05, 1, ExitType.NONE, None), + (0.05, 1, ExitType.NONE, None, True, False, -0.01, 1, ExitType.TRAILING_STOP_LOSS, None), # Default custom case - trails with 10% - (0.05, 0.95, ExitType.NONE, False, True, -0.02, 0.95, ExitType.NONE, None), - (0.05, 0.95, ExitType.NONE, False, True, -0.06, 0.95, ExitType.TRAILING_STOP_LOSS, None), - (0.05, 1, ExitType.NONE, False, True, -0.06, 1, ExitType.TRAILING_STOP_LOSS, + (0.05, 0.95, ExitType.NONE, None, False, True, -0.02, 0.95, ExitType.NONE, None), + (0.05, 0.95, ExitType.NONE, None, False, True, -0.06, 0.95, ExitType.TRAILING_STOP_LOSS, + None), + (0.05, 1, ExitType.NONE, None, False, True, -0.06, 1, ExitType.TRAILING_STOP_LOSS, lambda **kwargs: -0.05), - (0.05, 1, ExitType.NONE, False, True, 0.09, 1.04, ExitType.NONE, + (0.05, 1, ExitType.NONE, None, False, True, 0.09, 1.04, ExitType.NONE, lambda **kwargs: -0.05), - (0.05, 0.95, ExitType.NONE, False, True, 0.09, 0.98, ExitType.NONE, + (0.05, 0.95, ExitType.NONE, None, False, True, 0.09, 0.98, ExitType.NONE, lambda current_profit, **kwargs: -0.1 if current_profit < 0.6 else -(current_profit * 2)), # Error case - static stoploss in place - (0.05, 0.9, ExitType.NONE, False, True, 0.09, 0.9, ExitType.NONE, + (0.05, 0.9, ExitType.NONE, None, False, True, 0.09, 0.9, ExitType.NONE, lambda **kwargs: None), ]) -def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, trailing, custom, +def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, liq, trailing, custom, profit2, adjusted2, expected2, custom_stop) -> None: strategy = StrategyResolver.load_strategy(default_conf) @@ -442,6 +445,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili fee_close=fee.return_value, exchange='binance', open_rate=1, + liquidation_price=liq, ) trade.adjust_min_max_rates(trade.open_rate, trade.open_rate) strategy.trailing_stop = trailing From bad15f077c66710314b7dea75d0e5f7abee767a3 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 30 Jul 2022 17:49:06 +0200 Subject: [PATCH 229/518] Simplify fetch_positions by using already existing method --- freqtrade/exchange/exchange.py | 33 ++++++++++++++++----------------- tests/exchange/test_exchange.py | 14 -------------- 2 files changed, 16 insertions(+), 31 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 79bc769e6..e180c90b2 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -1332,11 +1332,19 @@ class Exchange: raise OperationalException(e) from e @retrier - def fetch_positions(self) -> List[Dict]: + def fetch_positions(self, pair: str = None) -> List[Dict]: + """ + Fetch positions from the exchange. + If no pair is given, all positions are returned. + :param pair: Pair for the query + """ if self._config['dry_run'] or self.trading_mode != TradingMode.FUTURES: return [] try: - positions: List[Dict] = self._api.fetch_positions() + symbols = [] + if pair: + symbols.append(pair) + positions: List[Dict] = self._api.fetch_positions(symbols) self._log_exchange_response('fetch_positions', positions) return positions except ccxt.DDoSProtection as e: @@ -2539,7 +2547,6 @@ class Exchange: else: return 0.0 - @retrier def get_or_calculate_liquidation_price( self, pair: str, @@ -2573,20 +2580,12 @@ class Exchange: upnl_ex_1=upnl_ex_1 ) else: - try: - positions = self._api.fetch_positions([pair]) - if len(positions) > 0: - pos = positions[0] - isolated_liq = pos['liquidationPrice'] - else: - return None - except ccxt.DDoSProtection as e: - raise DDosProtection(e) from e - except (ccxt.NetworkError, ccxt.ExchangeError) as e: - raise TemporaryError( - f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e - except ccxt.BaseError as e: - raise OperationalException(e) from e + positions = self.fetch_positions(pair) + if len(positions) > 0: + pos = positions[0] + isolated_liq = pos['liquidationPrice'] + else: + return None if isolated_liq: buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 9252040ea..e968b12c2 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -4099,20 +4099,6 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf): ) assert liq_price == 17.540699999999998 - ccxt_exceptionhandlers( - mocker, - default_conf, - api_mock, - "binance", - "get_or_calculate_liquidation_price", - "fetch_positions", - pair="XRP/USDT", - open_rate=0.0, - is_short=False, - position=0.0, - wallet_balance=0.0, - ) - @pytest.mark.parametrize('exchange,rate_start,rate_end,d1,d2,amount,expected_fees', [ ('binance', 0, 2, 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z$tm4T`rj-)$^y{2W{8zT1P2N55l2v1Rc2dD)zH@o3!2ov7-tNeOjTs#PFR?qLnf7U~NO*Q|X(C$G=iHqd%I@Psm0x{vHvu7RqIpfa-G8tMYe<)gC$ zxUn}N>{T)Qj6evbO2^?TX)wfo;9^o)s?8GARXJ ze)}AF)FSY@G%c7&BbR*Q_gIw$mYs6O@7;&p{NfY#!Bh{P%(o6+UJAdT-2+6yPP4(38C{n0zYMcpZ@?JH@=C(gB z$B(Lci&)>~U2^JlHFV6H*dWxd|)L1 z8!D1*FG2P^18i=;eIB9EKop#4RvxfjesBcl|NTxa)N#V8ZJ%%2y#4)2ki&lF+y2zG z%X6*yZ(9wCaED}z%xx@ve@a>|LEqsnDc~F`4|p#RHAZRvZ-Dl40<>B@1~Lq3Ii=jP zy2{JV$w}eAH2{uM%F<-Q{pEmH{=a+56!vdTr4wKqp^Vs{5|@iGcC)+uej*G{Jnj50 zKOpackNf1_$j;fpdR=&CHyKy<{~daU#u7Qy{(D#vJukU@^M8NY`2TnKe?PY(`gfy| z-whtBN&h>s%TEazL;LTF{(JWT **Datatype:** positive float (typically below 1). | `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. | `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reprodicibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. +| `svm_nu` | The `nu` parameter for the support vector machine. *Very* broadly, this is the percentage of data points that should be considered outliers.
**Datatype:** float between 0 and 1. | `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
**Datatype:** positive integer. | `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
**Datatype:** positive integer. | `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
**Datatype:** list of positive integers. @@ -110,9 +111,6 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
**Datatype:** integer. | `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
**Datatype:** float. | `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
**Datatype:** float. -| | **Extraneous parameters** -| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
**Datatype:** boolean. -| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
**Datatype:** integer. ### Important FreqAI dataframe key patterns From 09e5fb2f55cf368b8a3126fb1f00be2f8eb1f613 Mon Sep 17 00:00:00 2001 From: rzrymiak <106121613+rzrymiak@users.noreply.github.com> Date: Sat, 30 Jul 2022 22:37:46 +0000 Subject: [PATCH 231/518] Removed description header --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 828a3d1f9..aa446ad54 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,6 @@ [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io) [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability) -## Description - Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning. ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png) From e6ebc0443ee3d738b29f78ec41f2c8d1c9e82127 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 13:08:43 +0200 Subject: [PATCH 232/518] make single generalized config for freqai. update docs to reflect that. --- .gitignore | 3 +- ...xample.json => config_freqai.example.json} | 6 +- .../config_freqai_spot.example.json | 96 ------------------- docs/freqai.md | 9 +- 4 files changed, 9 insertions(+), 105 deletions(-) rename config_examples/{config_freqai_futures.example.json => config_freqai.example.json} (94%) delete mode 100644 config_examples/config_freqai_spot.example.json diff --git a/.gitignore b/.gitignore index 4498e42ac..73a11b47c 100644 --- a/.gitignore +++ b/.gitignore @@ -111,5 +111,4 @@ target/ !config_examples/config_ftx.example.json !config_examples/config_full.example.json !config_examples/config_kraken.example.json -!config_examples/config_freqai_futures.example.json -!config_examples/config_freqai_spot.example.json +!config_examples/config_freqai.example.json diff --git a/config_examples/config_freqai_futures.example.json b/config_examples/config_freqai.example.json similarity index 94% rename from config_examples/config_freqai_futures.example.json rename to config_examples/config_freqai.example.json index 60e1cce82..e9fc50a4a 100644 --- a/config_examples/config_freqai_futures.example.json +++ b/config_examples/config_freqai.example.json @@ -57,8 +57,7 @@ "train_period_days": 15, "backtest_period_days": 7, "live_retrain_hours": 0, - "identifier": "uniqe-id6", - "live_trained_timestamp": 0, + "identifier": "uniqe-id", "feature_parameters": { "include_timeframes": [ "3m", @@ -84,8 +83,7 @@ "random_state": 1 }, "model_training_parameters": { - "n_estimators": 1000, - "task_type": "CPU" + "n_estimators": 1000 } }, "bot_name": "", diff --git a/config_examples/config_freqai_spot.example.json b/config_examples/config_freqai_spot.example.json deleted file mode 100644 index 1ea6ddecb..000000000 --- a/config_examples/config_freqai_spot.example.json +++ /dev/null @@ -1,96 +0,0 @@ -{ - "max_open_trades": 1, - "stake_currency": "USDT", - "stake_amount": 900, - "tradable_balance_ratio": 1, - "fiat_display_currency": "USD", - "dry_run": true, - "timeframe": "5m", - "dry_run_wallet": 4000, - "dataformat_ohlcv": "json", - "cancel_open_orders_on_exit": true, - "unfilledtimeout": { - "entry": 10, - "exit": 30 - }, - "exchange": { - "name": "binance", - "key": "", - "secret": "", - "ccxt_config": { - "enableRateLimit": true - }, - "ccxt_async_config": { - "enableRateLimit": true, - "rateLimit": 200 - }, - "pair_whitelist": [ - "BTC/USDT", - "ETH/USDT" - ], - "pair_blacklist": [] - }, - "entry_pricing": { - "price_side": "same", - "use_order_book": true, - "order_book_top": 1, - "price_last_balance": 0.0, - "check_depth_of_market": { - "enabled": false, - "bids_to_ask_delta": 1 - } - }, - "exit_pricing": { - "price_side": "other", - "use_order_book": true, - "order_book_top": 1 - }, - "pairlists": [ - { - "method": "StaticPairList" - } - ], - "freqai": { - "startup_candles": 10000, - - "train_period_days": 30, - "backtest_period_days": 7, - "live_retrain_hours": 1, - "identifier": "example", - "live_trained_timestamp": 0, - "feature_parameters": { - "include_timeframes": [ - "5m", - "15m", - "4h" - ], - "include_corr_pairlist": [ - "BTC/USDT", - "ETH/USDT" - ], - "label_period_candles": 500, - "include_shifted_candles": 1, - "DI_threshold": 0, - "weight_factor": 0.9, - "principal_component_analysis": false, - "use_SVM_to_remove_outliers": false, - "stratify_training_data": 0, - "indicator_max_period_candles": 50, - "indicator_periods_candles": [10, 20] - }, - "data_split_parameters": { - "test_size": 0.33, - "random_state": 1 - }, - "model_training_parameters": { - "n_estimators": 1000, - "task_type": "CPU" - } - }, - "bot_name": "", - "initial_state": "running", - "forcebuy_enable": false, - "internals": { - "process_throttle_secs": 5 - } -} diff --git a/docs/freqai.md b/docs/freqai.md index bffa12e5d..de321b787 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -60,7 +60,7 @@ pip install -r requirements-freqai.txt An example strategy, an example prediction model, and example config can all be found in `freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMPredictionModel.py`, -`config_examples/config_freqai_futures.example.json`, respectively. +`config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with: @@ -98,7 +98,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](##controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). | `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. | `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. -| `svm_nu` | The `nu` parameter for the support vector machine. *Very* broadly, this is the percentage of data points that should be considered outliers.
**Datatype:** float between 0 and 1. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reprodicibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. | `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
**Datatype:** positive integer. | `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
**Datatype:** positive integer. | `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
**Datatype:** list of positive integers. @@ -111,6 +111,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
**Datatype:** integer. | `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
**Datatype:** float. | `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
**Datatype:** float. +| | **Extraneous parameters** +| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
**Datatype:** boolean. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
**Datatype:** integer. ### Important FreqAI dataframe key patterns @@ -349,7 +352,7 @@ and adding this to the `train_period_days`. The units need to be in the base can The freqai training/backtesting module can be executed with the following command: ```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai_futures.example.json --freqaimodel LightGBMPredictionModel --timerange 20210501-20210701 +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMPredictionModel --timerange 20210501-20210701 ``` If this command has never been executed with the existing config file, then it will train a new model From 61693f6c8bfc5c3e7eb4dc345fea4534ea5edb0a Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 13:20:11 +0200 Subject: [PATCH 233/518] fix tests after changing config_example file --- tests/freqai/conftest.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index ff70a40a4..ecd8b2d57 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -49,7 +49,7 @@ def freqai_conf(default_conf, tmpdir): "data_split_parameters": {"test_size": 0.33, "random_state": 1}, "model_training_parameters": {"n_estimators": 100, "verbosity": 0}, }, - "config_files": [Path('config_examples', 'config_freqai_futures.example.json')] + "config_files": [Path('config_examples', 'config_freqai.example.json')] } ) freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']}) @@ -57,7 +57,6 @@ def freqai_conf(default_conf, tmpdir): def get_patched_data_kitchen(mocker, freqaiconf): - # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) dk = FreqaiDataKitchen(freqaiconf) return dk From a4bada3ebe5ff927503b067545260ef5fe608c87 Mon Sep 17 00:00:00 2001 From: Kavinkumar <33546454+mkavinkumar1@users.noreply.github.com> Date: Sun, 31 Jul 2022 17:49:04 +0530 Subject: [PATCH 234/518] Partial exit using average price (#6545) Introduce Partial exits --- docs/strategy-callbacks.md | 67 ++- freqtrade/enums/exittype.py | 1 + freqtrade/exchange/exchange.py | 44 +- freqtrade/freqtradebot.py | 194 +++++--- freqtrade/optimize/backtesting.py | 115 +++-- freqtrade/persistence/migrations.py | 10 +- freqtrade/persistence/trade_model.py | 160 +++++-- freqtrade/rpc/rpc.py | 2 +- freqtrade/rpc/telegram.py | 101 +++-- freqtrade/strategy/interface.py | 19 +- .../subtemplates/strategy_methods_advanced.j2 | 26 +- tests/conftest.py | 3 +- tests/exchange/test_exchange.py | 199 ++++++--- .../test_backtesting_adjust_position.py | 86 ++++ tests/rpc/test_rpc.py | 5 +- tests/rpc/test_rpc_telegram.py | 102 ++++- tests/strategy/strats/strategy_test_v3.py | 9 +- tests/test_freqtradebot.py | 414 ++++++++++++++++-- tests/test_integration.py | 59 ++- tests/test_persistence.py | 193 +++++++- 20 files changed, 1462 insertions(+), 347 deletions(-) mode change 100755 => 100644 freqtrade/optimize/backtesting.py diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index 59d221bfc..18de3513b 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -629,7 +629,7 @@ class AwesomeStrategy(IStrategy): The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy. For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled. -`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging). +`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions. `max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys. @@ -637,10 +637,13 @@ The strategy is expected to return a stake_amount (in stake currency) between `m If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored. Additional orders also result in additional fees and those orders don't count towards `max_open_trades`. -This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`. +This callback is **not** called when there is an open order (either buy or sell) waiting for execution. + `adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible. -Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position, no matter if it's a long or short trade. Modifications to leverage are not possible. +Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits. + +Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible. !!! Note "About stake size" Using fixed stake size means it will be the amount used for the first order, just like without position adjustment. @@ -649,12 +652,12 @@ Position adjustments will always be applied in the direction of the trade, so a !!! Warning Stoploss is still calculated from the initial opening price, not averaged price. + Regular stoploss rules still apply (cannot move down). -!!! Warning "/stopbuy" While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades. !!! Warning "Backtesting" - During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected. + During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected. ``` python from freqtrade.persistence import Trade @@ -675,7 +678,7 @@ class DigDeeperStrategy(IStrategy): max_dca_multiplier = 5.5 # This is called when placing the initial order (opening trade) -def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, + def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: @@ -685,22 +688,41 @@ def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: f return proposed_stake / self.max_dca_multiplier def adjust_trade_position(self, trade: Trade, current_time: datetime, - current_rate: float, current_profit: float, min_stake: Optional[float], - max_stake: float, **kwargs): + current_rate: float, current_profit: float, + min_stake: Optional[float], max_stake: float, + current_entry_rate: float, current_exit_rate: float, + current_entry_profit: float, current_exit_profit: float, + **kwargs) -> Optional[float]: """ - Custom trade adjustment logic, returning the stake amount that a trade should be increased. - This means extra buy orders with additional fees. + Custom trade adjustment logic, returning the stake amount that a trade should be + increased or decreased. + This means extra buy or sell orders with additional fees. + Only called when `position_adjustment_enable` is set to True. + + For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ + + When not implemented by a strategy, returns None :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. - :param min_stake: Minimal stake size allowed by exchange. - :param max_stake: Balance available for trading. + :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) + :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). + :param current_entry_rate: Current rate using entry pricing. + :param current_exit_rate: Current rate using exit pricing. + :param current_entry_profit: Current profit using entry pricing. + :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. - :return float: Stake amount to adjust your trade + :return float: Stake amount to adjust your trade, + Positive values to increase position, Negative values to decrease position. + Return None for no action. """ + if current_profit > 0.05 and trade.nr_of_successful_exits == 0: + # Take half of the profit at +5% + return -(trade.amount / 2) + if current_profit > -0.05: return None @@ -735,6 +757,25 @@ def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: f ``` +### Position adjust calculations + +* Entry rates are calculated using weighted averages. +* Exits will not influence the average entry rate. +* Partial exit relative profit is relative to the average entry price at this point. +* Final exit relative profit is calculated based on the total invested capital. (See example below) + +??? example "Calculation example" + *This example assumes 0 fees for simplicity, and a long position on an imaginary coin.* + + * Buy 100@8\$ + * Buy 100@9\$ -> Avg price: 8.5\$ + * Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65% + * Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65% + * Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20% + * Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40% + + The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`). + ## Adjust Entry Price The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles. diff --git a/freqtrade/enums/exittype.py b/freqtrade/enums/exittype.py index 1e15e70cd..b025230ba 100644 --- a/freqtrade/enums/exittype.py +++ b/freqtrade/enums/exittype.py @@ -14,6 +14,7 @@ class ExitType(Enum): FORCE_EXIT = "force_exit" EMERGENCY_EXIT = "emergency_exit" CUSTOM_EXIT = "custom_exit" + PARTIAL_EXIT = "partial_exit" NONE = "" def __str__(self): diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index e180c90b2..b6996211f 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -1507,7 +1507,8 @@ class Exchange: return price_side def get_rate(self, pair: str, refresh: bool, - side: EntryExit, is_short: bool) -> float: + side: EntryExit, is_short: bool, + order_book: Optional[dict] = None, ticker: Optional[dict] = None) -> float: """ Calculates bid/ask target bid rate - between current ask price and last price @@ -1539,22 +1540,24 @@ class Exchange: if conf_strategy.get('use_order_book', False): order_book_top = conf_strategy.get('order_book_top', 1) - order_book = self.fetch_l2_order_book(pair, order_book_top) + if order_book is None: + order_book = self.fetch_l2_order_book(pair, order_book_top) logger.debug('order_book %s', order_book) # top 1 = index 0 try: rate = order_book[f"{price_side}s"][order_book_top - 1][0] except (IndexError, KeyError) as e: logger.warning( - f"{name} Price at location {order_book_top} from orderbook could not be " - f"determined. Orderbook: {order_book}" + f"{pair} - {name} Price at location {order_book_top} from orderbook " + f"could not be determined. Orderbook: {order_book}" ) raise PricingError from e - logger.debug(f"{name} price from orderbook {price_side_word}" + logger.debug(f"{pair} - {name} price from orderbook {price_side_word}" f"side - top {order_book_top} order book {side} rate {rate:.8f}") else: logger.debug(f"Using Last {price_side_word} / Last Price") - ticker = self.fetch_ticker(pair) + if ticker is None: + ticker = self.fetch_ticker(pair) ticker_rate = ticker[price_side] if ticker['last'] and ticker_rate: if side == 'entry' and ticker_rate > ticker['last']: @@ -1571,6 +1574,33 @@ class Exchange: return rate + def get_rates(self, pair: str, refresh: bool, is_short: bool) -> Tuple[float, float]: + entry_rate = None + exit_rate = None + if not refresh: + entry_rate = self._entry_rate_cache.get(pair) + exit_rate = self._exit_rate_cache.get(pair) + if entry_rate: + logger.debug(f"Using cached buy rate for {pair}.") + if exit_rate: + logger.debug(f"Using cached sell rate for {pair}.") + + entry_pricing = self._config.get('entry_pricing', {}) + exit_pricing = self._config.get('exit_pricing', {}) + order_book = ticker = None + if not entry_rate and entry_pricing.get('use_order_book', False): + order_book_top = max(entry_pricing.get('order_book_top', 1), + exit_pricing.get('order_book_top', 1)) + order_book = self.fetch_l2_order_book(pair, order_book_top) + entry_rate = self.get_rate(pair, refresh, 'entry', is_short, order_book=order_book) + elif not entry_rate: + ticker = self.fetch_ticker(pair) + entry_rate = self.get_rate(pair, refresh, 'entry', is_short, ticker=ticker) + if not exit_rate: + exit_rate = self.get_rate(pair, refresh, 'exit', + is_short, order_book=order_book, ticker=ticker) + return entry_rate, exit_rate + # Fee handling @retrier @@ -1989,7 +2019,7 @@ class Exchange: else: logger.debug( "Fetching trades for pair %s, since %s %s...", - pair, since, + pair, since, '(' + arrow.get(since // 1000).isoformat() + ') ' if since is not None else '' ) trades = await self._api_async.fetch_trades(pair, since=since, limit=1000) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 50cfb9d7b..757449c8c 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -5,6 +5,7 @@ import copy import logging import traceback from datetime import datetime, time, timedelta, timezone +from decimal import Decimal from math import isclose from threading import Lock from typing import Any, Dict, List, Optional, Tuple @@ -525,39 +526,61 @@ class FreqtradeBot(LoggingMixin): If the strategy triggers the adjustment, a new order gets issued. Once that completes, the existing trade is modified to match new data. """ - if self.strategy.max_entry_position_adjustment > -1: - count_of_buys = trade.nr_of_successful_entries - if count_of_buys > self.strategy.max_entry_position_adjustment: - logger.debug(f"Max adjustment entries for {trade.pair} has been reached.") - return - else: - logger.debug("Max adjustment entries is set to unlimited.") - current_rate = self.exchange.get_rate( - trade.pair, side='entry', is_short=trade.is_short, refresh=True) - current_profit = trade.calc_profit_ratio(current_rate) + current_entry_rate, current_exit_rate = self.exchange.get_rates( + trade.pair, True, trade.is_short) - min_stake_amount = self.exchange.get_min_pair_stake_amount(trade.pair, - current_rate, - self.strategy.stoploss) - max_stake_amount = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate) + current_entry_profit = trade.calc_profit_ratio(current_entry_rate) + current_exit_profit = trade.calc_profit_ratio(current_exit_rate) + + min_entry_stake = self.exchange.get_min_pair_stake_amount(trade.pair, + current_entry_rate, + self.strategy.stoploss) + min_exit_stake = self.exchange.get_min_pair_stake_amount(trade.pair, + current_exit_rate, + self.strategy.stoploss) + max_entry_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_entry_rate) stake_available = self.wallets.get_available_stake_amount() logger.debug(f"Calling adjust_trade_position for pair {trade.pair}") stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position, default_retval=None)( - trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_rate, - current_profit=current_profit, min_stake=min_stake_amount, - max_stake=min(max_stake_amount, stake_available)) + trade=trade, + current_time=datetime.now(timezone.utc), current_rate=current_entry_rate, + current_profit=current_entry_profit, min_stake=min_entry_stake, + max_stake=min(max_entry_stake, stake_available), + current_entry_rate=current_entry_rate, current_exit_rate=current_exit_rate, + current_entry_profit=current_entry_profit, current_exit_profit=current_exit_profit + ) if stake_amount is not None and stake_amount > 0.0: # We should increase our position - self.execute_entry(trade.pair, stake_amount, price=current_rate, + if self.strategy.max_entry_position_adjustment > -1: + count_of_entries = trade.nr_of_successful_entries + if count_of_entries > self.strategy.max_entry_position_adjustment: + logger.debug(f"Max adjustment entries for {trade.pair} has been reached.") + return + else: + logger.debug("Max adjustment entries is set to unlimited.") + self.execute_entry(trade.pair, stake_amount, price=current_entry_rate, trade=trade, is_short=trade.is_short) if stake_amount is not None and stake_amount < 0.0: # We should decrease our position - # TODO: Selling part of the trade not implemented yet. - logger.error(f"Unable to decrease trade position / sell partially" - f" for pair {trade.pair}, feature not implemented.") + amount = abs(float(Decimal(stake_amount) / Decimal(current_exit_rate))) + if amount > trade.amount: + # This is currently ineffective as remaining would become < min tradable + # Fixing this would require checking for 0.0 there - + # if we decide that this callback is allowed to "fully exit" + logger.info( + f"Adjusting amount to trade.amount as it is higher. {amount} > {trade.amount}") + amount = trade.amount + + remaining = (trade.amount - amount) * current_exit_rate + if remaining < min_exit_stake: + logger.info(f'Remaining amount of {remaining} would be too small.') + return + + self.execute_trade_exit(trade, current_exit_rate, exit_check=ExitCheckTuple( + exit_type=ExitType.PARTIAL_EXIT), sub_trade_amt=amount) def _check_depth_of_market(self, pair: str, conf: Dict, side: SignalDirection) -> bool: """ @@ -731,7 +754,7 @@ class FreqtradeBot(LoggingMixin): # Updating wallets self.wallets.update() - self._notify_enter(trade, order, order_type) + self._notify_enter(trade, order_obj, order_type, sub_trade=pos_adjust) if pos_adjust: if order_status == 'closed': @@ -740,8 +763,8 @@ class FreqtradeBot(LoggingMixin): else: logger.info(f"DCA order {order_status}, will wait for resolution: {trade}") - # Update fees if order is closed - if order_status == 'closed': + # Update fees if order is non-opened + if order_status in constants.NON_OPEN_EXCHANGE_STATES: self.update_trade_state(trade, order_id, order) return True @@ -830,13 +853,14 @@ class FreqtradeBot(LoggingMixin): return enter_limit_requested, stake_amount, leverage - def _notify_enter(self, trade: Trade, order: Dict, order_type: Optional[str] = None, - fill: bool = False) -> None: + def _notify_enter(self, trade: Trade, order: Order, order_type: Optional[str] = None, + fill: bool = False, sub_trade: bool = False) -> None: """ Sends rpc notification when a entry order occurred. """ msg_type = RPCMessageType.ENTRY_FILL if fill else RPCMessageType.ENTRY - open_rate = safe_value_fallback(order, 'average', 'price') + open_rate = order.safe_price + if open_rate is None: open_rate = trade.open_rate @@ -860,15 +884,17 @@ class FreqtradeBot(LoggingMixin): 'stake_amount': trade.stake_amount, 'stake_currency': self.config['stake_currency'], 'fiat_currency': self.config.get('fiat_display_currency', None), - 'amount': safe_value_fallback(order, 'filled', 'amount') or trade.amount, + 'amount': order.safe_amount_after_fee, 'open_date': trade.open_date or datetime.utcnow(), 'current_rate': current_rate, + 'sub_trade': sub_trade, } # Send the message self.rpc.send_msg(msg) - def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str) -> None: + def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str, + sub_trade: bool = False) -> None: """ Sends rpc notification when a entry order cancel occurred. """ @@ -893,6 +919,7 @@ class FreqtradeBot(LoggingMixin): 'open_date': trade.open_date, 'current_rate': current_rate, 'reason': reason, + 'sub_trade': sub_trade, } # Send the message @@ -1366,16 +1393,22 @@ class FreqtradeBot(LoggingMixin): trade.open_order_id = None trade.exit_reason = None cancelled = True + self.wallets.update() else: # TODO: figure out how to handle partially complete sell orders reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN'] cancelled = False - self.wallets.update() + order_obj = trade.select_order_by_order_id(order['id']) + if not order_obj: + raise DependencyException( + f"Order_obj not found for {order['id']}. This should not have happened.") + + sub_trade = order_obj.amount != trade.amount self._notify_exit_cancel( trade, order_type=self.strategy.order_types['exit'], - reason=reason + reason=reason, order=order_obj, sub_trade=sub_trade ) return cancelled @@ -1416,6 +1449,7 @@ class FreqtradeBot(LoggingMixin): *, exit_tag: Optional[str] = None, ordertype: Optional[str] = None, + sub_trade_amt: float = None, ) -> bool: """ Executes a trade exit for the given trade and limit @@ -1439,7 +1473,7 @@ class FreqtradeBot(LoggingMixin): # if stoploss is on exchange and we are on dry_run mode, # we consider the sell price stop price if (self.config['dry_run'] and exit_type == 'stoploss' - and self.strategy.order_types['stoploss_on_exchange']): + and self.strategy.order_types['stoploss_on_exchange']): limit = trade.stoploss_or_liquidation # set custom_exit_price if available @@ -1462,15 +1496,17 @@ class FreqtradeBot(LoggingMixin): # Emergency sells (default to market!) order_type = self.strategy.order_types.get("emergency_exit", "market") - amount = self._safe_exit_amount(trade.pair, trade.amount) + amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount) time_in_force = self.strategy.order_time_in_force['exit'] - if (exit_check.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper( - self.strategy.confirm_trade_exit, default_retval=True)( - pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit, - time_in_force=time_in_force, exit_reason=exit_reason, - sell_reason=exit_reason, # sellreason -> compatibility - current_time=datetime.now(timezone.utc))): + if (exit_check.exit_type != ExitType.LIQUIDATION + and not sub_trade_amt + and not strategy_safe_wrapper( + self.strategy.confirm_trade_exit, default_retval=True)( + pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit, + time_in_force=time_in_force, exit_reason=exit_reason, + sell_reason=exit_reason, # sellreason -> compatibility + current_time=datetime.now(timezone.utc))): logger.info(f"User denied exit for {trade.pair}.") return False @@ -1504,7 +1540,7 @@ class FreqtradeBot(LoggingMixin): self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), reason='Auto lock') - self._notify_exit(trade, order_type) + self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj) # In case of market sell orders the order can be closed immediately if order.get('status', 'unknown') in ('closed', 'expired'): self.update_trade_state(trade, trade.open_order_id, order) @@ -1512,16 +1548,27 @@ class FreqtradeBot(LoggingMixin): return True - def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False) -> None: + def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False, + sub_trade: bool = False, order: Order = None) -> None: """ Sends rpc notification when a sell occurred. """ - profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested - profit_trade = trade.calc_profit(rate=profit_rate) # Use cached rates here - it was updated seconds ago. current_rate = self.exchange.get_rate( trade.pair, side='exit', is_short=trade.is_short, refresh=False) if not fill else None - profit_ratio = trade.calc_profit_ratio(profit_rate) + + # second condition is for mypy only; order will always be passed during sub trade + if sub_trade and order is not None: + amount = order.safe_filled if fill else order.amount + profit_rate = order.safe_price + + profit = trade.calc_profit(rate=profit_rate, amount=amount, open_rate=trade.open_rate) + profit_ratio = trade.calc_profit_ratio(profit_rate, amount, trade.open_rate) + else: + profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested + profit = trade.calc_profit(rate=profit_rate) + trade.realized_profit + profit_ratio = trade.calc_profit_ratio(profit_rate) + amount = trade.amount gain = "profit" if profit_ratio > 0 else "loss" msg = { @@ -1535,11 +1582,11 @@ class FreqtradeBot(LoggingMixin): 'gain': gain, 'limit': profit_rate, 'order_type': order_type, - 'amount': trade.amount, + 'amount': amount, 'open_rate': trade.open_rate, - 'close_rate': trade.close_rate, + 'close_rate': profit_rate, 'current_rate': current_rate, - 'profit_amount': profit_trade, + 'profit_amount': profit, 'profit_ratio': profit_ratio, 'buy_tag': trade.enter_tag, 'enter_tag': trade.enter_tag, @@ -1547,19 +1594,18 @@ class FreqtradeBot(LoggingMixin): 'exit_reason': trade.exit_reason, 'open_date': trade.open_date, 'close_date': trade.close_date or datetime.utcnow(), + 'stake_amount': trade.stake_amount, 'stake_currency': self.config['stake_currency'], 'fiat_currency': self.config.get('fiat_display_currency'), + 'sub_trade': sub_trade, + 'cumulative_profit': trade.realized_profit, } - if 'fiat_display_currency' in self.config: - msg.update({ - 'fiat_currency': self.config['fiat_display_currency'], - }) - # Send the message self.rpc.send_msg(msg) - def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str) -> None: + def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str, + order: Order, sub_trade: bool = False) -> None: """ Sends rpc notification when a sell cancel occurred. """ @@ -1585,7 +1631,7 @@ class FreqtradeBot(LoggingMixin): 'gain': gain, 'limit': profit_rate or 0, 'order_type': order_type, - 'amount': trade.amount, + 'amount': order.safe_amount_after_fee, 'open_rate': trade.open_rate, 'current_rate': current_rate, 'profit_amount': profit_trade, @@ -1599,6 +1645,8 @@ class FreqtradeBot(LoggingMixin): 'stake_currency': self.config['stake_currency'], 'fiat_currency': self.config.get('fiat_display_currency', None), 'reason': reason, + 'sub_trade': sub_trade, + 'stake_amount': trade.stake_amount, } if 'fiat_display_currency' in self.config: @@ -1653,14 +1701,18 @@ class FreqtradeBot(LoggingMixin): self.handle_order_fee(trade, order_obj, order) trade.update_trade(order_obj) - # TODO: is the below necessary? it's already done in update_trade for filled buys - trade.recalc_trade_from_orders() Trade.commit() - if order['status'] in constants.NON_OPEN_EXCHANGE_STATES: + if order.get('status') in constants.NON_OPEN_EXCHANGE_STATES: # If a entry order was closed, force update on stoploss on exchange if order.get('side') == trade.entry_side: trade = self.cancel_stoploss_on_exchange(trade) + if not self.edge: + # TODO: should shorting/leverage be supported by Edge, + # then this will need to be fixed. + trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True) + if order.get('side') == trade.entry_side or trade.amount > 0: + # Must also run for partial exits # TODO: Margin will need to use interest_rate as well. # interest_rate = self.exchange.get_interest_rate() trade.set_liquidation_price(self.exchange.get_liquidation_price( @@ -1670,24 +1722,30 @@ class FreqtradeBot(LoggingMixin): open_rate=trade.open_rate, is_short=trade.is_short )) - if not self.edge: - # TODO: should shorting/leverage be supported by Edge, - # then this will need to be fixed. - trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True) # Updating wallets when order is closed self.wallets.update() - if not trade.is_open: - if send_msg and not stoploss_order and not trade.open_order_id: - self._notify_exit(trade, '', True) - self.handle_protections(trade.pair, trade.trade_direction) - elif send_msg and not trade.open_order_id and not stoploss_order: - # Enter fill - self._notify_enter(trade, order, fill=True) + self.order_close_notify(trade, order_obj, stoploss_order, send_msg) return False + def order_close_notify( + self, trade: Trade, order: Order, stoploss_order: bool, send_msg: bool): + """send "fill" notifications""" + + sub_trade = not isclose(order.safe_amount_after_fee, + trade.amount, abs_tol=constants.MATH_CLOSE_PREC) + if order.ft_order_side == trade.exit_side: + # Exit notification + if send_msg and not stoploss_order and not trade.open_order_id: + self._notify_exit(trade, '', fill=True, sub_trade=sub_trade, order=order) + if not trade.is_open: + self.handle_protections(trade.pair, trade.trade_direction) + elif send_msg and not trade.open_order_id and not stoploss_order: + # Enter fill + self._notify_enter(trade, order, fill=True, sub_trade=sub_trade) + def handle_protections(self, pair: str, side: LongShort) -> None: prot_trig = self.protections.stop_per_pair(pair, side=side) if prot_trig: diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py old mode 100755 new mode 100644 index 2c6cfb0e9..46774e8a5 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -287,8 +287,8 @@ class Backtesting: if unavailable_pairs: raise OperationalException( - f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. " - "It is therefore impossible to backtest with this pair at the moment.") + f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. " + "It is therefore impossible to backtest with this pair at the moment.") else: self.futures_data = {} @@ -503,16 +503,20 @@ class Backtesting: def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple ) -> LocalTrade: - current_profit = trade.calc_profit_ratio(row[OPEN_IDX]) - min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1) - max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, row[OPEN_IDX]) + current_rate = row[OPEN_IDX] + current_date = row[DATE_IDX].to_pydatetime() + current_profit = trade.calc_profit_ratio(current_rate) + min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, current_rate, -0.1) + max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate) stake_available = self.wallets.get_available_stake_amount() stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position, default_retval=None)( trade=trade, # type: ignore[arg-type] - current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX], + current_time=current_date, current_rate=current_rate, current_profit=current_profit, min_stake=min_stake, - max_stake=min(max_stake, stake_available)) + max_stake=min(max_stake, stake_available), + current_entry_rate=current_rate, current_exit_rate=current_rate, + current_entry_profit=current_profit, current_exit_profit=current_profit) # Check if we should increase our position if stake_amount is not None and stake_amount > 0.0: @@ -523,6 +527,24 @@ class Backtesting: self.wallets.update() return pos_trade + if stake_amount is not None and stake_amount < 0.0: + amount = abs(stake_amount) / current_rate + if amount > trade.amount: + # This is currently ineffective as remaining would become < min tradable + amount = trade.amount + remaining = (trade.amount - amount) * current_rate + if remaining < min_stake: + # Remaining stake is too low to be sold. + return trade + pos_trade = self._exit_trade(trade, row, current_rate, amount) + if pos_trade is not None: + order = pos_trade.orders[-1] + if self._get_order_filled(order.price, row): + order.close_bt_order(current_date, trade) + trade.recalc_trade_from_orders() + self.wallets.update() + return pos_trade + return trade def _get_order_filled(self, rate: float, row: Tuple) -> bool: @@ -602,7 +624,7 @@ class Backtesting: self.strategy.confirm_trade_exit, default_retval=True)( pair=trade.pair, trade=trade, # type: ignore[arg-type] - order_type='limit', + order_type=order_type, amount=trade.amount, rate=close_rate, time_in_force=time_in_force, @@ -613,32 +635,38 @@ class Backtesting: trade.exit_reason = exit_reason - self.order_id_counter += 1 - order = Order( - id=self.order_id_counter, - ft_trade_id=trade.id, - order_date=exit_candle_time, - order_update_date=exit_candle_time, - ft_is_open=True, - ft_pair=trade.pair, - order_id=str(self.order_id_counter), - symbol=trade.pair, - ft_order_side=trade.exit_side, - side=trade.exit_side, - order_type=order_type, - status="open", - price=close_rate, - average=close_rate, - amount=trade.amount, - filled=0, - remaining=trade.amount, - cost=trade.amount * close_rate, - ) - trade.orders.append(order) - return trade - + return self._exit_trade(trade, row, close_rate, trade.amount) return None + def _exit_trade(self, trade: LocalTrade, sell_row: Tuple, + close_rate: float, amount: float = None) -> Optional[LocalTrade]: + self.order_id_counter += 1 + exit_candle_time = sell_row[DATE_IDX].to_pydatetime() + order_type = self.strategy.order_types['exit'] + amount = amount or trade.amount + order = Order( + id=self.order_id_counter, + ft_trade_id=trade.id, + order_date=exit_candle_time, + order_update_date=exit_candle_time, + ft_is_open=True, + ft_pair=trade.pair, + order_id=str(self.order_id_counter), + symbol=trade.pair, + ft_order_side=trade.exit_side, + side=trade.exit_side, + order_type=order_type, + status="open", + price=close_rate, + average=close_rate, + amount=amount, + filled=0, + remaining=amount, + cost=amount * close_rate, + ) + trade.orders.append(order) + return trade + def _get_exit_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]: exit_candle_time: datetime = row[DATE_IDX].to_pydatetime() @@ -865,6 +893,8 @@ class Backtesting: # Ignore trade if entry-order did not fill yet continue exit_row = data[pair][-1] + self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount) + trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade) trade.close_date = exit_row[DATE_IDX].to_pydatetime() trade.exit_reason = ExitType.FORCE_EXIT.value @@ -1006,7 +1036,7 @@ class Backtesting: return None return row - def backtest(self, processed: Dict, + def backtest(self, processed: Dict, # noqa: max-complexity: 13 start_date: datetime, end_date: datetime, max_open_trades: int = 0, position_stacking: bool = False, enable_protections: bool = False) -> Dict[str, Any]: @@ -1108,14 +1138,19 @@ class Backtesting: if order and self._get_order_filled(order.price, row): order.close_bt_order(current_time, trade) trade.open_order_id = None - trade.close_date = current_time - trade.close(order.price, show_msg=False) + sub_trade = order.safe_amount_after_fee != trade.amount + if sub_trade: + order.close_bt_order(current_time, trade) + trade.recalc_trade_from_orders() + else: + trade.close_date = current_time + trade.close(order.price, show_msg=False) - # logger.debug(f"{pair} - Backtesting exit {trade}") - open_trade_count -= 1 - open_trades[pair].remove(trade) - LocalTrade.close_bt_trade(trade) - trades.append(trade) + # logger.debug(f"{pair} - Backtesting exit {trade}") + open_trade_count -= 1 + open_trades[pair].remove(trade) + LocalTrade.close_bt_trade(trade) + trades.append(trade) self.wallets.update() self.run_protections( enable_protections, pair, current_time, trade.trade_direction) diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py index 2a8e34cdf..81757a7de 100644 --- a/freqtrade/persistence/migrations.py +++ b/freqtrade/persistence/migrations.py @@ -95,6 +95,7 @@ def migrate_trades_and_orders_table( exit_reason = get_column_def(cols, 'sell_reason', get_column_def(cols, 'exit_reason', 'null')) strategy = get_column_def(cols, 'strategy', 'null') enter_tag = get_column_def(cols, 'buy_tag', get_column_def(cols, 'enter_tag', 'null')) + realized_profit = get_column_def(cols, 'realized_profit', '0.0') trading_mode = get_column_def(cols, 'trading_mode', 'null') @@ -155,7 +156,7 @@ def migrate_trades_and_orders_table( max_rate, min_rate, exit_reason, exit_order_status, strategy, enter_tag, timeframe, open_trade_value, close_profit_abs, trading_mode, leverage, liquidation_price, is_short, - interest_rate, funding_fees + interest_rate, funding_fees, realized_profit ) select id, lower(exchange), pair, {base_currency} base_currency, {stake_currency} stake_currency, @@ -181,7 +182,7 @@ def migrate_trades_and_orders_table( {open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs, {trading_mode} trading_mode, {leverage} leverage, {liquidation_price} liquidation_price, {is_short} is_short, {interest_rate} interest_rate, - {funding_fees} funding_fees + {funding_fees} funding_fees, {realized_profit} realized_profit from {trade_back_name} """)) @@ -297,8 +298,9 @@ def check_migrate(engine, decl_base, previous_tables) -> None: # Check if migration necessary # Migrates both trades and orders table! - if not has_column(cols_orders, 'stop_price'): - # if not has_column(cols_trades, 'base_currency'): + # if ('orders' not in previous_tables + # or not has_column(cols_orders, 'stop_price')): + if not has_column(cols_trades, 'realized_profit'): logger.info(f"Running database migration for trades - " f"backup: {table_back_name}, {order_table_bak_name}") migrate_trades_and_orders_table( diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 44e148a0c..fcb84a59a 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -4,13 +4,15 @@ This module contains the class to persist trades into SQLite import logging from datetime import datetime, timedelta, timezone from decimal import Decimal +from math import isclose from typing import Any, Dict, List, Optional from sqlalchemy import (Boolean, Column, DateTime, Enum, Float, ForeignKey, Integer, String, UniqueConstraint, desc, func) from sqlalchemy.orm import Query, lazyload, relationship -from freqtrade.constants import DATETIME_PRINT_FORMAT, NON_OPEN_EXCHANGE_STATES, BuySell, LongShort +from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPEN_EXCHANGE_STATES, + BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.leverage import interest @@ -176,10 +178,9 @@ class Order(_DECL_BASE): self.remaining = 0 self.status = 'closed' self.ft_is_open = False - if (self.ft_order_side == trade.entry_side - and len(trade.select_filled_orders(trade.entry_side)) == 1): + if (self.ft_order_side == trade.entry_side): trade.open_rate = self.price - trade.recalc_open_trade_value() + trade.recalc_trade_from_orders() trade.adjust_stop_loss(trade.open_rate, trade.stop_loss_pct, refresh=True) @staticmethod @@ -237,6 +238,7 @@ class LocalTrade(): trades: List['LocalTrade'] = [] trades_open: List['LocalTrade'] = [] total_profit: float = 0 + realized_profit: float = 0 id: int = 0 @@ -447,6 +449,7 @@ class LocalTrade(): if self.close_date else None), 'close_timestamp': int(self.close_date.replace( tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None, + 'realized_profit': self.realized_profit or 0.0, 'close_rate': self.close_rate, 'close_rate_requested': self.close_rate_requested, 'close_profit': self.close_profit, # Deprecated @@ -596,14 +599,28 @@ class LocalTrade(): if self.is_open: payment = "SELL" if self.is_short else "BUY" logger.info(f'{order.order_type.upper()}_{payment} has been fulfilled for {self}.') - self.open_order_id = None + # condition to avoid reset value when updating fees + if self.open_order_id == order.order_id: + self.open_order_id = None + else: + logger.warning( + f'Got different open_order_id {self.open_order_id} != {order.order_id}') self.recalc_trade_from_orders() elif order.ft_order_side == self.exit_side: if self.is_open: payment = "BUY" if self.is_short else "SELL" # * On margin shorts, you buy a little bit more than the amount (amount + interest) logger.info(f'{order.order_type.upper()}_{payment} has been fulfilled for {self}.') - self.close(order.safe_price) + # condition to avoid reset value when updating fees + if self.open_order_id == order.order_id: + self.open_order_id = None + else: + logger.warning( + f'Got different open_order_id {self.open_order_id} != {order.order_id}') + if isclose(order.safe_amount_after_fee, self.amount, abs_tol=MATH_CLOSE_PREC): + self.close(order.safe_price) + else: + self.recalc_trade_from_orders() elif order.ft_order_side == 'stoploss': self.stoploss_order_id = None self.close_rate_requested = self.stop_loss @@ -622,11 +639,11 @@ class LocalTrade(): """ self.close_rate = rate self.close_date = self.close_date or datetime.utcnow() - self.close_profit = self.calc_profit_ratio(rate) - self.close_profit_abs = self.calc_profit(rate) + self.close_profit_abs = self.calc_profit(rate) + self.realized_profit self.is_open = False self.exit_order_status = 'closed' self.open_order_id = None + self.recalc_trade_from_orders(is_closing=True) if show_msg: logger.info( 'Marking %s as closed as the trade is fulfilled and found no open orders for it.', @@ -672,12 +689,12 @@ class LocalTrade(): """ return len([o for o in self.orders if o.ft_order_side == self.exit_side]) - def _calc_open_trade_value(self) -> float: + def _calc_open_trade_value(self, amount: float, open_rate: float) -> float: """ Calculate the open_rate including open_fee. :return: Price in of the open trade incl. Fees """ - open_trade = Decimal(self.amount) * Decimal(self.open_rate) + open_trade = Decimal(amount) * Decimal(open_rate) fees = open_trade * Decimal(self.fee_open) if self.is_short: return float(open_trade - fees) @@ -689,7 +706,7 @@ class LocalTrade(): Recalculate open_trade_value. Must be called whenever open_rate, fee_open is changed. """ - self.open_trade_value = self._calc_open_trade_value() + self.open_trade_value = self._calc_open_trade_value(self.amount, self.open_rate) def calculate_interest(self) -> Decimal: """ @@ -721,7 +738,7 @@ class LocalTrade(): else: return close_trade - fees - def calc_close_trade_value(self, rate: float) -> float: + def calc_close_trade_value(self, rate: float, amount: float = None) -> float: """ Calculate the Trade's close value including fees :param rate: rate to compare with. @@ -730,96 +747,143 @@ class LocalTrade(): if rate is None and not self.close_rate: return 0.0 - amount = Decimal(self.amount) + amount1 = Decimal(amount or self.amount) trading_mode = self.trading_mode or TradingMode.SPOT if trading_mode == TradingMode.SPOT: - return float(self._calc_base_close(amount, rate, self.fee_close)) + return float(self._calc_base_close(amount1, rate, self.fee_close)) elif (trading_mode == TradingMode.MARGIN): total_interest = self.calculate_interest() if self.is_short: - amount = amount + total_interest - return float(self._calc_base_close(amount, rate, self.fee_close)) + amount1 = amount1 + total_interest + return float(self._calc_base_close(amount1, rate, self.fee_close)) else: # Currency already owned for longs, no need to purchase - return float(self._calc_base_close(amount, rate, self.fee_close) - total_interest) + return float(self._calc_base_close(amount1, rate, self.fee_close) - total_interest) elif (trading_mode == TradingMode.FUTURES): funding_fees = self.funding_fees or 0.0 # Positive funding_fees -> Trade has gained from fees. # Negative funding_fees -> Trade had to pay the fees. if self.is_short: - return float(self._calc_base_close(amount, rate, self.fee_close)) - funding_fees + return float(self._calc_base_close(amount1, rate, self.fee_close)) - funding_fees else: - return float(self._calc_base_close(amount, rate, self.fee_close)) + funding_fees + return float(self._calc_base_close(amount1, rate, self.fee_close)) + funding_fees else: raise OperationalException( f"{self.trading_mode.value} trading is not yet available using freqtrade") - def calc_profit(self, rate: float) -> float: + def calc_profit(self, rate: float, amount: float = None, open_rate: float = None) -> float: """ Calculate the absolute profit in stake currency between Close and Open trade :param rate: close rate to compare with. + :param amount: Amount to use for the calculation. Falls back to trade.amount if not set. + :param open_rate: open_rate to use. Defaults to self.open_rate if not provided. :return: profit in stake currency as float """ - close_trade_value = self.calc_close_trade_value(rate) + close_trade_value = self.calc_close_trade_value(rate, amount) + if amount is None or open_rate is None: + open_trade_value = self.open_trade_value + else: + open_trade_value = self._calc_open_trade_value(amount, open_rate) if self.is_short: - profit = self.open_trade_value - close_trade_value + profit = open_trade_value - close_trade_value else: - profit = close_trade_value - self.open_trade_value + profit = close_trade_value - open_trade_value return float(f"{profit:.8f}") - def calc_profit_ratio(self, rate: float) -> float: + def calc_profit_ratio( + self, rate: float, amount: float = None, open_rate: float = None) -> float: """ Calculates the profit as ratio (including fee). :param rate: rate to compare with. + :param amount: Amount to use for the calculation. Falls back to trade.amount if not set. + :param open_rate: open_rate to use. Defaults to self.open_rate if not provided. :return: profit ratio as float """ - close_trade_value = self.calc_close_trade_value(rate) + close_trade_value = self.calc_close_trade_value(rate, amount) + + if amount is None or open_rate is None: + open_trade_value = self.open_trade_value + else: + open_trade_value = self._calc_open_trade_value(amount, open_rate) short_close_zero = (self.is_short and close_trade_value == 0.0) - long_close_zero = (not self.is_short and self.open_trade_value == 0.0) + long_close_zero = (not self.is_short and open_trade_value == 0.0) leverage = self.leverage or 1.0 if (short_close_zero or long_close_zero): return 0.0 else: if self.is_short: - profit_ratio = (1 - (close_trade_value / self.open_trade_value)) * leverage + profit_ratio = (1 - (close_trade_value / open_trade_value)) * leverage else: - profit_ratio = ((close_trade_value / self.open_trade_value) - 1) * leverage + profit_ratio = ((close_trade_value / open_trade_value) - 1) * leverage return float(f"{profit_ratio:.8f}") - def recalc_trade_from_orders(self): + def recalc_trade_from_orders(self, is_closing: bool = False): + + current_amount = 0.0 + current_stake = 0.0 + total_stake = 0.0 # Total stake after all buy orders (does not subtract!) + avg_price = 0.0 + close_profit = 0.0 + close_profit_abs = 0.0 - total_amount = 0.0 - total_stake = 0.0 for o in self.orders: - if (o.ft_is_open or - (o.ft_order_side != self.entry_side) or - (o.status not in NON_OPEN_EXCHANGE_STATES)): + if o.ft_is_open or not o.filled: continue tmp_amount = o.safe_amount_after_fee - tmp_price = o.average or o.price - if tmp_amount > 0.0 and tmp_price is not None: - total_amount += tmp_amount - total_stake += tmp_price * tmp_amount + tmp_price = o.safe_price - if total_amount > 0: + is_exit = o.ft_order_side != self.entry_side + side = -1 if is_exit else 1 + if tmp_amount > 0.0 and tmp_price is not None: + current_amount += tmp_amount * side + price = avg_price if is_exit else tmp_price + current_stake += price * tmp_amount * side + + if current_amount > 0: + avg_price = current_stake / current_amount + + if is_exit: + # Process partial exits + exit_rate = o.safe_price + exit_amount = o.safe_amount_after_fee + profit = self.calc_profit(rate=exit_rate, amount=exit_amount, open_rate=avg_price) + close_profit_abs += profit + close_profit = self.calc_profit_ratio( + exit_rate, amount=exit_amount, open_rate=avg_price) + if current_amount <= 0: + profit = close_profit_abs + else: + total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price) + + if close_profit: + self.close_profit = close_profit + self.realized_profit = close_profit_abs + self.close_profit_abs = profit + + if current_amount > 0: + # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. - self.open_rate = total_stake / total_amount - self.stake_amount = total_stake / (self.leverage or 1.0) - self.amount = total_amount - self.fee_open_cost = self.fee_open * total_stake + self.open_rate = current_stake / current_amount + self.stake_amount = current_stake / (self.leverage or 1.0) + self.amount = current_amount + self.fee_open_cost = self.fee_open * current_stake self.recalc_open_trade_value() if self.stop_loss_pct is not None and self.open_rate is not None: self.adjust_stop_loss(self.open_rate, self.stop_loss_pct) + elif is_closing and total_stake > 0: + # Close profit abs / maximum owned + # Fees are considered as they are part of close_profit_abs + self.close_profit = (close_profit_abs / total_stake) * self.leverage def select_order_by_order_id(self, order_id: str) -> Optional[Order]: """ @@ -841,7 +905,7 @@ class LocalTrade(): """ orders = self.orders if order_side: - orders = [o for o in self.orders if o.ft_order_side == order_side] + orders = [o for o in orders if o.ft_order_side == order_side] if is_open is not None: orders = [o for o in orders if o.ft_is_open == is_open] if len(orders) > 0: @@ -856,9 +920,9 @@ class LocalTrade(): :return: array of Order objects """ return [o for o in self.orders if ((o.ft_order_side == order_side) or (order_side is None)) - and o.ft_is_open is False and - (o.filled or 0) > 0 and - o.status in NON_OPEN_EXCHANGE_STATES] + and o.ft_is_open is False + and o.filled + and o.status in NON_OPEN_EXCHANGE_STATES] def select_filled_or_open_orders(self) -> List['Order']: """ @@ -1023,6 +1087,7 @@ class Trade(_DECL_BASE, LocalTrade): open_trade_value = Column(Float) close_rate: Optional[float] = Column(Float) close_rate_requested = Column(Float) + realized_profit = Column(Float, default=0.0) close_profit = Column(Float) close_profit_abs = Column(Float) stake_amount = Column(Float, nullable=False) @@ -1068,6 +1133,7 @@ class Trade(_DECL_BASE, LocalTrade): def __init__(self, **kwargs): super().__init__(**kwargs) + self.realized_profit = 0 self.recalc_open_trade_value() def delete(self) -> None: diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index e6948c9e2..9d6696803 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -201,7 +201,7 @@ class RPC: trade_dict = trade.to_json() trade_dict.update(dict( - close_profit=trade.close_profit if trade.close_profit is not None else None, + close_profit=trade.close_profit if not trade.is_open else None, current_rate=current_rate, current_profit=current_profit, # Deprecated current_profit_pct=round(current_profit * 100, 2), # Deprecated diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 121324d90..66192fb16 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -274,7 +274,7 @@ class Telegram(RPCHandler): f"{emoji} *{self._exchange_from_msg(msg)}:*" f" {entry_side['entered'] if is_fill else entry_side['enter']} {msg['pair']}" f" (#{msg['trade_id']})\n" - ) + ) message += self._add_analyzed_candle(msg['pair']) message += f"*Enter Tag:* `{msg['enter_tag']}`\n" if msg.get('enter_tag') else "" message += f"*Amount:* `{msg['amount']:.8f}`\n" @@ -315,20 +315,36 @@ class Telegram(RPCHandler): msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount( msg['profit_amount'], msg['stake_currency'], msg['fiat_currency']) msg['profit_extra'] = ( - f" ({msg['gain']}: {msg['profit_amount']:.8f} {msg['stake_currency']}" - f" / {msg['profit_fiat']:.3f} {msg['fiat_currency']})") + f" / {msg['profit_fiat']:.3f} {msg['fiat_currency']}") else: msg['profit_extra'] = '' + msg['profit_extra'] = ( + f" ({msg['gain']}: {msg['profit_amount']:.8f} {msg['stake_currency']}" + f"{msg['profit_extra']})") is_fill = msg['type'] == RPCMessageType.EXIT_FILL + is_sub_trade = msg.get('sub_trade') + is_sub_profit = msg['profit_amount'] != msg.get('cumulative_profit') + profit_prefix = ('Sub ' if is_sub_profit + else 'Cumulative ') if is_sub_trade else '' + cp_extra = '' + if is_sub_profit and is_sub_trade: + if self._rpc._fiat_converter: + cp_fiat = self._rpc._fiat_converter.convert_amount( + msg['cumulative_profit'], msg['stake_currency'], msg['fiat_currency']) + cp_extra = f" / {cp_fiat:.3f} {msg['fiat_currency']}" + else: + cp_extra = '' + cp_extra = f"*Cumulative Profit:* (`{msg['cumulative_profit']:.8f} " \ + f"{msg['stake_currency']}{cp_extra}`)\n" message = ( f"{msg['emoji']} *{self._exchange_from_msg(msg)}:* " f"{'Exited' if is_fill else 'Exiting'} {msg['pair']} (#{msg['trade_id']})\n" f"{self._add_analyzed_candle(msg['pair'])}" - f"*{'Profit' if is_fill else 'Unrealized Profit'}:* " + f"*{f'{profit_prefix}Profit' if is_fill else f'Unrealized {profit_prefix}Profit'}:* " f"`{msg['profit_ratio']:.2%}{msg['profit_extra']}`\n" + f"{cp_extra}" f"*Enter Tag:* `{msg['enter_tag']}`\n" f"*Exit Reason:* `{msg['exit_reason']}`\n" - f"*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`\n" f"*Direction:* `{msg['direction']}`\n" f"{msg['leverage_text']}" f"*Amount:* `{msg['amount']:.8f}`\n" @@ -336,11 +352,25 @@ class Telegram(RPCHandler): ) if msg['type'] == RPCMessageType.EXIT: message += (f"*Current Rate:* `{msg['current_rate']:.8f}`\n" - f"*Close Rate:* `{msg['limit']:.8f}`") + f"*Exit Rate:* `{msg['limit']:.8f}`") elif msg['type'] == RPCMessageType.EXIT_FILL: - message += f"*Close Rate:* `{msg['close_rate']:.8f}`" + message += f"*Exit Rate:* `{msg['close_rate']:.8f}`" + if msg.get('sub_trade'): + if self._rpc._fiat_converter: + msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount( + msg['stake_amount'], msg['stake_currency'], msg['fiat_currency']) + else: + msg['stake_amount_fiat'] = 0 + rem = round_coin_value(msg['stake_amount'], msg['stake_currency']) + message += f"\n*Remaining:* `({rem}" + if msg.get('fiat_currency', None): + message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}" + + message += ")`" + else: + message += f"\n*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`" return message def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str: @@ -353,7 +383,8 @@ class Telegram(RPCHandler): elif msg_type in (RPCMessageType.ENTRY_CANCEL, RPCMessageType.EXIT_CANCEL): msg['message_side'] = 'enter' if msg_type in [RPCMessageType.ENTRY_CANCEL] else 'exit' message = (f"\N{WARNING SIGN} *{self._exchange_from_msg(msg)}:* " - f"Cancelling {msg['message_side']} Order for {msg['pair']} " + f"Cancelling {'partial ' if msg.get('sub_trade') else ''}" + f"{msg['message_side']} Order for {msg['pair']} " f"(#{msg['trade_id']}). Reason: {msg['reason']}.") elif msg_type == RPCMessageType.PROTECTION_TRIGGER: @@ -424,7 +455,7 @@ class Telegram(RPCHandler): else: return "\N{CROSS MARK}" - def _prepare_entry_details(self, filled_orders: List, quote_currency: str, is_open: bool): + def _prepare_order_details(self, filled_orders: List, quote_currency: str, is_open: bool): """ Prepare details of trade with entry adjustment enabled """ @@ -433,44 +464,51 @@ class Telegram(RPCHandler): first_avg = filled_orders[0]["safe_price"] for x, order in enumerate(filled_orders): - if not order['ft_is_entry'] or order['is_open'] is True: + if order['is_open'] is True: continue + wording = 'Entry' if order['ft_is_entry'] else 'Exit' + cur_entry_datetime = arrow.get(order["order_filled_date"]) - cur_entry_amount = order["amount"] + cur_entry_amount = order["filled"] or order["amount"] cur_entry_average = order["safe_price"] lines.append(" ") if x == 0: - lines.append(f"*Entry #{x+1}:*") + lines.append(f"*{wording} #{x+1}:*") lines.append( - f"*Entry Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})") - lines.append(f"*Average Entry Price:* {cur_entry_average}") + f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})") + lines.append(f"*Average Price:* {cur_entry_average}") else: sumA = 0 sumB = 0 for y in range(x): - sumA += (filled_orders[y]["amount"] * filled_orders[y]["safe_price"]) - sumB += filled_orders[y]["amount"] + amount = filled_orders[y]["filled"] or filled_orders[y]["amount"] + sumA += amount * filled_orders[y]["safe_price"] + sumB += amount prev_avg_price = sumA / sumB + # TODO: This calculation ignores fees. price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg) minus_on_entry = 0 if prev_avg_price: minus_on_entry = (cur_entry_average - prev_avg_price) / prev_avg_price - dur_entry = cur_entry_datetime - arrow.get( - filled_orders[x - 1]["order_filled_date"]) - days = dur_entry.days - hours, remainder = divmod(dur_entry.seconds, 3600) - minutes, seconds = divmod(remainder, 60) - lines.append(f"*Entry #{x+1}:* at {minus_on_entry:.2%} avg profit") + lines.append(f"*{wording} #{x+1}:* at {minus_on_entry:.2%} avg profit") if is_open: lines.append("({})".format(cur_entry_datetime .humanize(granularity=["day", "hour", "minute"]))) lines.append( - f"*Entry Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})") - lines.append(f"*Average Entry Price:* {cur_entry_average} " + f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})") + lines.append(f"*Average {wording} Price:* {cur_entry_average} " f"({price_to_1st_entry:.2%} from 1st entry rate)") - lines.append(f"*Order filled at:* {order['order_filled_date']}") - lines.append(f"({days}d {hours}h {minutes}m {seconds}s from previous entry)") + lines.append(f"*Order filled:* {order['order_filled_date']}") + + # TODO: is this really useful? + # dur_entry = cur_entry_datetime - arrow.get( + # filled_orders[x - 1]["order_filled_date"]) + # days = dur_entry.days + # hours, remainder = divmod(dur_entry.seconds, 3600) + # minutes, seconds = divmod(remainder, 60) + # lines.append( + # f"({days}d {hours}h {minutes}m {seconds}s from previous {wording.lower()})") return lines @authorized_only @@ -486,7 +524,14 @@ class Telegram(RPCHandler): if context.args and 'table' in context.args: self._status_table(update, context) return + else: + self._status_msg(update, context) + def _status_msg(self, update: Update, context: CallbackContext) -> None: + """ + handler for `/status` and `/status `. + + """ try: # Check if there's at least one numerical ID provided. @@ -529,6 +574,8 @@ class Telegram(RPCHandler): ]) if r['is_open']: + if r.get('realized_profit'): + lines.append("*Realized Profit:* `{realized_profit:.8f}`") if (r['stop_loss_abs'] != r['initial_stop_loss_abs'] and r['initial_stop_loss_ratio'] is not None): # Adding initial stoploss only if it is different from stoploss @@ -546,7 +593,7 @@ class Telegram(RPCHandler): else: lines.append("*Open Order:* `{open_order}`") - lines_detail = self._prepare_entry_details( + lines_detail = self._prepare_order_details( r['orders'], r['quote_currency'], r['is_open']) lines.extend(lines_detail if lines_detail else "") diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 824f31258..5e0aba2fe 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -463,10 +463,13 @@ class IStrategy(ABC, HyperStrategyMixin): def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, + current_entry_rate: float, current_exit_rate: float, + current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ - Custom trade adjustment logic, returning the stake amount that a trade should be increased. - This means extra buy orders with additional fees. + Custom trade adjustment logic, returning the stake amount that a trade should be + increased or decreased. + This means extra buy or sell orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ @@ -477,10 +480,16 @@ class IStrategy(ABC, HyperStrategyMixin): :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. - :param min_stake: Minimal stake size allowed by exchange. - :param max_stake: Balance available for trading. + :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) + :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). + :param current_entry_rate: Current rate using entry pricing. + :param current_exit_rate: Current rate using exit pricing. + :param current_entry_profit: Current profit using entry pricing. + :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. - :return float: Stake amount to adjust your trade + :return float: Stake amount to adjust your trade, + Positive values to increase position, Negative values to decrease position. + Return None for no action. """ return None diff --git a/freqtrade/templates/subtemplates/strategy_methods_advanced.j2 b/freqtrade/templates/subtemplates/strategy_methods_advanced.j2 index 989f1d37a..488ca2fd7 100644 --- a/freqtrade/templates/subtemplates/strategy_methods_advanced.j2 +++ b/freqtrade/templates/subtemplates/strategy_methods_advanced.j2 @@ -247,12 +247,16 @@ def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order', """ return False -def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime', - current_rate: float, current_profit: float, min_stake: Optional[float], - max_stake: float, **kwargs) -> 'Optional[float]': +def adjust_trade_position(self, trade: 'Trade', current_time: datetime, + current_rate: float, current_profit: float, + min_stake: Optional[float], max_stake: float, + current_entry_rate: float, current_exit_rate: float, + current_entry_profit: float, current_exit_profit: float, + **kwargs) -> Optional[float]: """ - Custom trade adjustment logic, returning the stake amount that a trade should be increased. - This means extra buy orders with additional fees. + Custom trade adjustment logic, returning the stake amount that a trade should be + increased or decreased. + This means extra buy or sell orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ @@ -263,10 +267,16 @@ def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime', :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. - :param min_stake: Minimal stake size allowed by exchange. - :param max_stake: Balance available for trading. + :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) + :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). + :param current_entry_rate: Current rate using entry pricing. + :param current_exit_rate: Current rate using exit pricing. + :param current_entry_profit: Current profit using entry pricing. + :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. - :return float: Stake amount to adjust your trade + :return float: Stake amount to adjust your trade, + Positive values to increase position, Negative values to decrease position. + Return None for no action. """ return None diff --git a/tests/conftest.py b/tests/conftest.py index ff3e1007f..a02fc4566 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1627,8 +1627,8 @@ def limit_buy_order_open(): 'timestamp': arrow.utcnow().int_timestamp * 1000, 'datetime': arrow.utcnow().isoformat(), 'price': 0.00001099, + 'average': 0.00001099, 'amount': 90.99181073, - 'average': None, 'filled': 0.0, 'cost': 0.0009999, 'remaining': 90.99181073, @@ -2817,6 +2817,7 @@ def limit_buy_order_usdt_open(): 'datetime': arrow.utcnow().isoformat(), 'timestamp': arrow.utcnow().int_timestamp * 1000, 'price': 2.00, + 'average': 2.00, 'amount': 30.0, 'filled': 0.0, 'cost': 60.0, diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index e968b12c2..d73e26683 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -27,6 +27,57 @@ from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has # Make sure to always keep one exchange here which is NOT subclassed!! EXCHANGES = ['bittrex', 'binance', 'kraken', 'ftx', 'gateio'] +get_entry_rate_data = [ + ('other', 20, 19, 10, 0.0, 20), # Full ask side + ('ask', 20, 19, 10, 0.0, 20), # Full ask side + ('ask', 20, 19, 10, 1.0, 10), # Full last side + ('ask', 20, 19, 10, 0.5, 15), # Between ask and last + ('ask', 20, 19, 10, 0.7, 13), # Between ask and last + ('ask', 20, 19, 10, 0.3, 17), # Between ask and last + ('ask', 5, 6, 10, 1.0, 5), # last bigger than ask + ('ask', 5, 6, 10, 0.5, 5), # last bigger than ask + ('ask', 20, 19, 10, None, 20), # price_last_balance missing + ('ask', 10, 20, None, 0.5, 10), # last not available - uses ask + ('ask', 4, 5, None, 0.5, 4), # last not available - uses ask + ('ask', 4, 5, None, 1, 4), # last not available - uses ask + ('ask', 4, 5, None, 0, 4), # last not available - uses ask + ('same', 21, 20, 10, 0.0, 20), # Full bid side + ('bid', 21, 20, 10, 0.0, 20), # Full bid side + ('bid', 21, 20, 10, 1.0, 10), # Full last side + ('bid', 21, 20, 10, 0.5, 15), # Between bid and last + ('bid', 21, 20, 10, 0.7, 13), # Between bid and last + ('bid', 21, 20, 10, 0.3, 17), # Between bid and last + ('bid', 6, 5, 10, 1.0, 5), # last bigger than bid + ('bid', 21, 20, 10, None, 20), # price_last_balance missing + ('bid', 6, 5, 10, 0.5, 5), # last bigger than bid + ('bid', 21, 20, None, 0.5, 20), # last not available - uses bid + ('bid', 6, 5, None, 0.5, 5), # last not available - uses bid + ('bid', 6, 5, None, 1, 5), # last not available - uses bid + ('bid', 6, 5, None, 0, 5), # last not available - uses bid +] + +get_sell_rate_data = [ + ('bid', 12.0, 11.0, 11.5, 0.0, 11.0), # full bid side + ('bid', 12.0, 11.0, 11.5, 1.0, 11.5), # full last side + ('bid', 12.0, 11.0, 11.5, 0.5, 11.25), # between bid and lat + ('bid', 12.0, 11.2, 10.5, 0.0, 11.2), # Last smaller than bid + ('bid', 12.0, 11.2, 10.5, 1.0, 11.2), # Last smaller than bid - uses bid + ('bid', 12.0, 11.2, 10.5, 0.5, 11.2), # Last smaller than bid - uses bid + ('bid', 0.003, 0.002, 0.005, 0.0, 0.002), + ('bid', 0.003, 0.002, 0.005, None, 0.002), + ('ask', 12.0, 11.0, 12.5, 0.0, 12.0), # full ask side + ('ask', 12.0, 11.0, 12.5, 1.0, 12.5), # full last side + ('ask', 12.0, 11.0, 12.5, 0.5, 12.25), # between bid and lat + ('ask', 12.2, 11.2, 10.5, 0.0, 12.2), # Last smaller than ask + ('ask', 12.0, 11.0, 10.5, 1.0, 12.0), # Last smaller than ask - uses ask + ('ask', 12.0, 11.2, 10.5, 0.5, 12.0), # Last smaller than ask - uses ask + ('ask', 10.0, 11.0, 11.0, 0.0, 10.0), + ('ask', 10.11, 11.2, 11.0, 0.0, 10.11), + ('ask', 0.001, 0.002, 11.0, 0.0, 0.001), + ('ask', 0.006, 1.0, 11.0, 0.0, 0.006), + ('ask', 0.006, 1.0, 11.0, None, 0.006), +] + def ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name, fun, mock_ccxt_fun, retries=API_RETRY_COUNT + 1, **kwargs): @@ -2360,34 +2411,7 @@ def test_fetch_l2_order_book_exception(default_conf, mocker, exchange_name): exchange.fetch_l2_order_book(pair='ETH/BTC', limit=50) -@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", [ - ('other', 20, 19, 10, 0.0, 20), # Full ask side - ('ask', 20, 19, 10, 0.0, 20), # Full ask side - ('ask', 20, 19, 10, 1.0, 10), # Full last side - ('ask', 20, 19, 10, 0.5, 15), # Between ask and last - ('ask', 20, 19, 10, 0.7, 13), # Between ask and last - ('ask', 20, 19, 10, 0.3, 17), # Between ask and last - ('ask', 5, 6, 10, 1.0, 5), # last bigger than ask - ('ask', 5, 6, 10, 0.5, 5), # last bigger than ask - ('ask', 20, 19, 10, None, 20), # price_last_balance missing - ('ask', 10, 20, None, 0.5, 10), # last not available - uses ask - ('ask', 4, 5, None, 0.5, 4), # last not available - uses ask - ('ask', 4, 5, None, 1, 4), # last not available - uses ask - ('ask', 4, 5, None, 0, 4), # last not available - uses ask - ('same', 21, 20, 10, 0.0, 20), # Full bid side - ('bid', 21, 20, 10, 0.0, 20), # Full bid side - ('bid', 21, 20, 10, 1.0, 10), # Full last side - ('bid', 21, 20, 10, 0.5, 15), # Between bid and last - ('bid', 21, 20, 10, 0.7, 13), # Between bid and last - ('bid', 21, 20, 10, 0.3, 17), # Between bid and last - ('bid', 6, 5, 10, 1.0, 5), # last bigger than bid - ('bid', 21, 20, 10, None, 20), # price_last_balance missing - ('bid', 6, 5, 10, 0.5, 5), # last bigger than bid - ('bid', 21, 20, None, 0.5, 20), # last not available - uses bid - ('bid', 6, 5, None, 0.5, 5), # last not available - uses bid - ('bid', 6, 5, None, 1, 5), # last not available - uses bid - ('bid', 6, 5, None, 0, 5), # last not available - uses bid -]) +@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data) def test_get_entry_rate(mocker, default_conf, caplog, side, ask, bid, last, last_ab, expected) -> None: caplog.set_level(logging.DEBUG) @@ -2411,27 +2435,7 @@ def test_get_entry_rate(mocker, default_conf, caplog, side, ask, bid, assert not log_has("Using cached entry rate for ETH/BTC.", caplog) -@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', [ - ('bid', 12.0, 11.0, 11.5, 0.0, 11.0), # full bid side - ('bid', 12.0, 11.0, 11.5, 1.0, 11.5), # full last side - ('bid', 12.0, 11.0, 11.5, 0.5, 11.25), # between bid and lat - ('bid', 12.0, 11.2, 10.5, 0.0, 11.2), # Last smaller than bid - ('bid', 12.0, 11.2, 10.5, 1.0, 11.2), # Last smaller than bid - uses bid - ('bid', 12.0, 11.2, 10.5, 0.5, 11.2), # Last smaller than bid - uses bid - ('bid', 0.003, 0.002, 0.005, 0.0, 0.002), - ('bid', 0.003, 0.002, 0.005, None, 0.002), - ('ask', 12.0, 11.0, 12.5, 0.0, 12.0), # full ask side - ('ask', 12.0, 11.0, 12.5, 1.0, 12.5), # full last side - ('ask', 12.0, 11.0, 12.5, 0.5, 12.25), # between bid and lat - ('ask', 12.2, 11.2, 10.5, 0.0, 12.2), # Last smaller than ask - ('ask', 12.0, 11.0, 10.5, 1.0, 12.0), # Last smaller than ask - uses ask - ('ask', 12.0, 11.2, 10.5, 0.5, 12.0), # Last smaller than ask - uses ask - ('ask', 10.0, 11.0, 11.0, 0.0, 10.0), - ('ask', 10.11, 11.2, 11.0, 0.0, 10.11), - ('ask', 0.001, 0.002, 11.0, 0.0, 0.001), - ('ask', 0.006, 1.0, 11.0, 0.0, 0.006), - ('ask', 0.006, 1.0, 11.0, None, 0.006), -]) +@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', get_sell_rate_data) def test_get_exit_rate(default_conf, mocker, caplog, side, bid, ask, last, last_ab, expected) -> None: caplog.set_level(logging.DEBUG) @@ -2481,14 +2485,14 @@ def test_get_ticker_rate_error(mocker, entry, default_conf, caplog, side, is_sho @pytest.mark.parametrize('is_short,side,expected', [ - (False, 'bid', 0.043936), # Value from order_book_l2 fitxure - bids side - (False, 'ask', 0.043949), # Value from order_book_l2 fitxure - asks side - (False, 'other', 0.043936), # Value from order_book_l2 fitxure - bids side - (False, 'same', 0.043949), # Value from order_book_l2 fitxure - asks side - (True, 'bid', 0.043936), # Value from order_book_l2 fitxure - bids side - (True, 'ask', 0.043949), # Value from order_book_l2 fitxure - asks side - (True, 'other', 0.043949), # Value from order_book_l2 fitxure - asks side - (True, 'same', 0.043936), # Value from order_book_l2 fitxure - bids side + (False, 'bid', 0.043936), # Value from order_book_l2 fixture - bids side + (False, 'ask', 0.043949), # Value from order_book_l2 fixture - asks side + (False, 'other', 0.043936), # Value from order_book_l2 fixture - bids side + (False, 'same', 0.043949), # Value from order_book_l2 fixture - asks side + (True, 'bid', 0.043936), # Value from order_book_l2 fixture - bids side + (True, 'ask', 0.043949), # Value from order_book_l2 fixture - asks side + (True, 'other', 0.043949), # Value from order_book_l2 fixture - asks side + (True, 'same', 0.043936), # Value from order_book_l2 fixture - bids side ]) def test_get_exit_rate_orderbook( default_conf, mocker, caplog, is_short, side, expected, order_book_l2): @@ -2521,7 +2525,8 @@ def test_get_exit_rate_orderbook_exception(default_conf, mocker, caplog): exchange = get_patched_exchange(mocker, default_conf) with pytest.raises(PricingError): exchange.get_rate(pair, refresh=True, side="exit", is_short=False) - assert log_has_re(r"Exit Price at location 1 from orderbook could not be determined\..*", + assert log_has_re(rf"{pair} - Exit Price at location 1 from orderbook " + rf"could not be determined\..*", caplog) @@ -2548,6 +2553,84 @@ def test_get_exit_rate_exception(default_conf, mocker, is_short): assert exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short) == 0.13 +@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data) +@pytest.mark.parametrize("side2", ['bid', 'ask']) +@pytest.mark.parametrize("use_order_book", [True, False]) +def test_get_rates_testing_buy(mocker, default_conf, caplog, side, ask, bid, + last, last_ab, expected, + side2, use_order_book, order_book_l2) -> None: + caplog.set_level(logging.DEBUG) + if last_ab is None: + del default_conf['entry_pricing']['price_last_balance'] + else: + default_conf['entry_pricing']['price_last_balance'] = last_ab + default_conf['entry_pricing']['price_side'] = side + default_conf['exit_pricing']['price_side'] = side2 + default_conf['exit_pricing']['use_order_book'] = use_order_book + api_mock = MagicMock() + api_mock.fetch_l2_order_book = order_book_l2 + api_mock.fetch_ticker = MagicMock( + return_value={'ask': ask, 'last': last, 'bid': bid}) + exchange = get_patched_exchange(mocker, default_conf, api_mock) + + assert exchange.get_rates('ETH/BTC', refresh=True, is_short=False)[0] == expected + assert not log_has("Using cached buy rate for ETH/BTC.", caplog) + + api_mock.fetch_l2_order_book.reset_mock() + api_mock.fetch_ticker.reset_mock() + assert exchange.get_rates('ETH/BTC', refresh=False, is_short=False)[0] == expected + assert log_has("Using cached buy rate for ETH/BTC.", caplog) + assert api_mock.fetch_l2_order_book.call_count == 0 + assert api_mock.fetch_ticker.call_count == 0 + # Running a 2nd time with Refresh on! + caplog.clear() + + assert exchange.get_rates('ETH/BTC', refresh=True, is_short=False)[0] == expected + assert not log_has("Using cached buy rate for ETH/BTC.", caplog) + + assert api_mock.fetch_l2_order_book.call_count == int(use_order_book) + assert api_mock.fetch_ticker.call_count == 1 + + +@pytest.mark.parametrize('side,ask,bid,last,last_ab,expected', get_sell_rate_data) +@pytest.mark.parametrize("side2", ['bid', 'ask']) +@pytest.mark.parametrize("use_order_book", [True, False]) +def test_get_rates_testing_sell(default_conf, mocker, caplog, side, bid, ask, + last, last_ab, expected, + side2, use_order_book, order_book_l2) -> None: + caplog.set_level(logging.DEBUG) + + default_conf['exit_pricing']['price_side'] = side + if last_ab is not None: + default_conf['exit_pricing']['price_last_balance'] = last_ab + + default_conf['entry_pricing']['price_side'] = side2 + default_conf['entry_pricing']['use_order_book'] = use_order_book + api_mock = MagicMock() + api_mock.fetch_l2_order_book = order_book_l2 + api_mock.fetch_ticker = MagicMock( + return_value={'ask': ask, 'last': last, 'bid': bid}) + exchange = get_patched_exchange(mocker, default_conf, api_mock) + + pair = "ETH/BTC" + + # Test regular mode + rate = exchange.get_rates(pair, refresh=True, is_short=False)[1] + assert not log_has("Using cached sell rate for ETH/BTC.", caplog) + assert isinstance(rate, float) + assert rate == expected + # Use caching + api_mock.fetch_l2_order_book.reset_mock() + api_mock.fetch_ticker.reset_mock() + + rate = exchange.get_rates(pair, refresh=False, is_short=False)[1] + assert rate == expected + assert log_has("Using cached sell rate for ETH/BTC.", caplog) + + assert api_mock.fetch_l2_order_book.call_count == 0 + assert api_mock.fetch_ticker.call_count == 0 + + @pytest.mark.parametrize("exchange_name", EXCHANGES) @pytest.mark.asyncio async def test___async_get_candle_history_sort(default_conf, mocker, exchange_name): diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index fca9c01b2..2bb7de574 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -1,8 +1,10 @@ # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument from copy import deepcopy +from unittest.mock import MagicMock import pandas as pd +import pytest from arrow import Arrow from freqtrade.configuration import TimeRange @@ -87,3 +89,87 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or round(ln.iloc[0]["low"], 6) < round( t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)) + + +def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> None: + default_conf['use_exit_signal'] = False + mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) + mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=10) + mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) + patch_exchange(mocker) + default_conf.update({ + "stake_amount": 100.0, + "dry_run_wallet": 1000.0, + "strategy": "StrategyTestV3" + }) + backtesting = Backtesting(default_conf) + backtesting._set_strategy(backtesting.strategylist[0]) + pair = 'XRP/USDT' + row = [ + pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0), + 2.1, # Open + 2.2, # High + 1.9, # Low + 2.1, # Close + 1, # enter_long + 0, # exit_long + 0, # enter_short + 0, # exit_short + '', # enter_tag + '', # exit_tag + ] + trade = backtesting._enter_trade(pair, row=row, direction='long') + trade.orders[0].close_bt_order(row[0], trade) + assert trade + assert pytest.approx(trade.stake_amount) == 100.0 + assert pytest.approx(trade.amount) == 47.61904762 + assert len(trade.orders) == 1 + backtesting.strategy.adjust_trade_position = MagicMock(return_value=None) + + trade = backtesting._get_adjust_trade_entry_for_candle(trade, row) + assert trade + assert pytest.approx(trade.stake_amount) == 100.0 + assert pytest.approx(trade.amount) == 47.61904762 + assert len(trade.orders) == 1 + # Increase position by 100 + backtesting.strategy.adjust_trade_position = MagicMock(return_value=100) + + trade = backtesting._get_adjust_trade_entry_for_candle(trade, row) + + assert trade + assert pytest.approx(trade.stake_amount) == 200.0 + assert pytest.approx(trade.amount) == 95.23809524 + assert len(trade.orders) == 2 + + # Reduce by more than amount - no change to trade. + backtesting.strategy.adjust_trade_position = MagicMock(return_value=-500) + + trade = backtesting._get_adjust_trade_entry_for_candle(trade, row) + + assert trade + assert pytest.approx(trade.stake_amount) == 200.0 + assert pytest.approx(trade.amount) == 95.23809524 + assert len(trade.orders) == 2 + assert trade.nr_of_successful_entries == 2 + + # Reduce position by 50 + backtesting.strategy.adjust_trade_position = MagicMock(return_value=-100) + trade = backtesting._get_adjust_trade_entry_for_candle(trade, row) + + assert trade + assert pytest.approx(trade.stake_amount) == 100.0 + assert pytest.approx(trade.amount) == 47.61904762 + assert len(trade.orders) == 3 + assert trade.nr_of_successful_entries == 2 + assert trade.nr_of_successful_exits == 1 + + # Adjust below minimum + backtesting.strategy.adjust_trade_position = MagicMock(return_value=-99) + trade = backtesting._get_adjust_trade_entry_for_candle(trade, row) + + assert trade + assert pytest.approx(trade.stake_amount) == 100.0 + assert pytest.approx(trade.amount) == 47.61904762 + assert len(trade.orders) == 3 + assert trade.nr_of_successful_entries == 2 + assert trade.nr_of_successful_exits == 1 diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 6e19fcaf3..02c62e337 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -111,6 +111,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'stoploss_entry_dist': -0.00010475, 'stoploss_entry_dist_ratio': -0.10448878, 'open_order': None, + 'realized_profit': 0.0, 'exchange': 'binance', 'leverage': 1.0, 'interest_rate': 0.0, @@ -196,6 +197,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'stoploss_entry_dist_ratio': -0.10448878, 'open_order': None, 'exchange': 'binance', + 'realized_profit': 0.0, 'leverage': 1.0, 'interest_rate': 0.0, 'liquidation_price': None, @@ -841,7 +843,8 @@ def test_rpc_force_exit(default_conf, ticker, fee, mocker) -> None: 'side': 'sell', 'amount': amount, 'remaining': amount, - 'filled': 0.0 + 'filled': 0.0, + 'id': trade.orders[0].order_id, } ) msg = rpc._rpc_force_exit('3') diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index 8d244f3fd..98c06c8e9 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -272,7 +272,7 @@ def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None: msg = msg_mock.call_args_list[0][0][0] assert re.search(r'Number of Entries.*2', msg) assert re.search(r'Average Entry Price', msg) - assert re.search(r'Order filled at', msg) + assert re.search(r'Order filled', msg) assert re.search(r'Close Date:', msg) is None assert re.search(r'Close Profit:', msg) is None @@ -959,6 +959,9 @@ def test_telegram_forceexit_handle(default_conf, update, ticker, fee, 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'stake_amount': 0.0009999999999054, + 'sub_trade': False, + 'cumulative_profit': 0.0, } == last_msg @@ -1028,6 +1031,9 @@ def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee, 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'stake_amount': 0.0009999999999054, + 'sub_trade': False, + 'cumulative_profit': 0.0, } == last_msg @@ -1087,6 +1093,9 @@ def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'stake_amount': 0.0009999999999054, + 'sub_trade': False, + 'cumulative_profit': 0.0, } == msg @@ -1437,7 +1446,7 @@ def test_whitelist_static(default_conf, update, mocker) -> None: def test_whitelist_dynamic(default_conf, update, mocker) -> None: mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True)) default_conf['pairlists'] = [{'method': 'VolumePairList', - 'number_assets': 4 + 'number_assets': 4 }] telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf) @@ -1789,7 +1798,6 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en 'leverage': leverage, 'stake_amount': 0.01465333, 'direction': entered, - # 'stake_amount_fiat': 0.0, 'stake_currency': 'BTC', 'fiat_currency': 'USD', 'open_rate': 1.099e-05, @@ -1806,6 +1814,33 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en '*Total:* `(0.01465333 BTC, 180.895 USD)`' ) + msg_mock.reset_mock() + telegram.send_msg({ + 'type': message_type, + 'trade_id': 1, + 'enter_tag': enter_signal, + 'exchange': 'Binance', + 'pair': 'ETH/BTC', + 'leverage': leverage, + 'stake_amount': 0.01465333, + 'sub_trade': True, + 'direction': entered, + 'stake_currency': 'BTC', + 'fiat_currency': 'USD', + 'open_rate': 1.099e-05, + 'amount': 1333.3333333333335, + 'open_date': arrow.utcnow().shift(hours=-1) + }) + + assert msg_mock.call_args[0][0] == ( + f'\N{CHECK MARK} *Binance (dry):* {entered}ed ETH/BTC (#1)\n' + f'*Enter Tag:* `{enter_signal}`\n' + '*Amount:* `1333.33333333`\n' + f"{leverage_text}" + '*Open Rate:* `0.00001099`\n' + '*Total:* `(0.01465333 BTC, 180.895 USD)`' + ) + def test_send_msg_sell_notification(default_conf, mocker) -> None: @@ -1840,14 +1875,53 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None: '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n' '*Enter Tag:* `buy_signal1`\n' '*Exit Reason:* `stop_loss`\n' - '*Duration:* `1:00:00 (60.0 min)`\n' '*Direction:* `Long`\n' '*Amount:* `1333.33333333`\n' '*Open Rate:* `0.00007500`\n' '*Current Rate:* `0.00003201`\n' - '*Close Rate:* `0.00003201`' + '*Exit Rate:* `0.00003201`\n' + '*Duration:* `1:00:00 (60.0 min)`' ) + msg_mock.reset_mock() + telegram.send_msg({ + 'type': RPCMessageType.EXIT, + 'trade_id': 1, + 'exchange': 'Binance', + 'pair': 'KEY/ETH', + 'direction': 'Long', + 'gain': 'loss', + 'limit': 3.201e-05, + 'amount': 1333.3333333333335, + 'order_type': 'market', + 'open_rate': 7.5e-05, + 'current_rate': 3.201e-05, + 'cumulative_profit': -0.15746268, + 'profit_amount': -0.05746268, + 'profit_ratio': -0.57405275, + 'stake_currency': 'ETH', + 'fiat_currency': 'USD', + 'enter_tag': 'buy_signal1', + 'exit_reason': ExitType.STOP_LOSS.value, + 'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30), + 'close_date': arrow.utcnow(), + 'stake_amount': 0.01, + 'sub_trade': True, + }) + assert msg_mock.call_args[0][0] == ( + '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n' + '*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n' + '*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n' + '*Enter Tag:* `buy_signal1`\n' + '*Exit Reason:* `stop_loss`\n' + '*Direction:* `Long`\n' + '*Amount:* `1333.33333333`\n' + '*Open Rate:* `0.00007500`\n' + '*Current Rate:* `0.00003201`\n' + '*Exit Rate:* `0.00003201`\n' + '*Remaining:* `(0.01 ETH, -24.812 USD)`' + ) + msg_mock.reset_mock() telegram.send_msg({ 'type': RPCMessageType.EXIT, @@ -1871,15 +1945,15 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None: }) assert msg_mock.call_args[0][0] == ( '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n' - '*Unrealized Profit:* `-57.41%`\n' + '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n' '*Enter Tag:* `buy_signal1`\n' '*Exit Reason:* `stop_loss`\n' - '*Duration:* `1 day, 2:30:00 (1590.0 min)`\n' '*Direction:* `Long`\n' '*Amount:* `1333.33333333`\n' '*Open Rate:* `0.00007500`\n' '*Current Rate:* `0.00003201`\n' - '*Close Rate:* `0.00003201`' + '*Exit Rate:* `0.00003201`\n' + '*Duration:* `1 day, 2:30:00 (1590.0 min)`' ) # Reset singleton function to avoid random breaks telegram._rpc._fiat_converter.convert_amount = old_convamount @@ -1954,15 +2028,15 @@ def test_send_msg_sell_fill_notification(default_conf, mocker, direction, leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else '' assert msg_mock.call_args[0][0] == ( '\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n' - '*Profit:* `-57.41%`\n' + '*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n' f'*Enter Tag:* `{enter_signal}`\n' '*Exit Reason:* `stop_loss`\n' - '*Duration:* `1 day, 2:30:00 (1590.0 min)`\n' f"*Direction:* `{direction}`\n" f"{leverage_text}" '*Amount:* `1333.33333333`\n' '*Open Rate:* `0.00007500`\n' - '*Close Rate:* `0.00003201`' + '*Exit Rate:* `0.00003201`\n' + '*Duration:* `1 day, 2:30:00 (1590.0 min)`' ) @@ -2090,16 +2164,16 @@ def test_send_msg_sell_notification_no_fiat( leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else '' assert msg_mock.call_args[0][0] == ( '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n' - '*Unrealized Profit:* `-57.41%`\n' + '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n' f'*Enter Tag:* `{enter_signal}`\n' '*Exit Reason:* `stop_loss`\n' - '*Duration:* `2:35:03 (155.1 min)`\n' f'*Direction:* `{direction}`\n' f'{leverage_text}' '*Amount:* `1333.33333333`\n' '*Open Rate:* `0.00007500`\n' '*Current Rate:* `0.00003201`\n' - '*Close Rate:* `0.00003201`' + '*Exit Rate:* `0.00003201`\n' + '*Duration:* `2:35:03 (155.1 min)`' ) diff --git a/tests/strategy/strats/strategy_test_v3.py b/tests/strategy/strats/strategy_test_v3.py index 2c7ccbdf2..088ab21d4 100644 --- a/tests/strategy/strats/strategy_test_v3.py +++ b/tests/strategy/strats/strategy_test_v3.py @@ -185,9 +185,12 @@ class StrategyTestV3(IStrategy): return 3.0 - def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, - current_profit: float, - min_stake: Optional[float], max_stake: float, **kwargs): + def adjust_trade_position(self, trade: Trade, current_time: datetime, + current_rate: float, current_profit: float, + min_stake: Optional[float], max_stake: float, + current_entry_rate: float, current_exit_rate: float, + current_entry_profit: float, current_exit_profit: float, + **kwargs) -> Optional[float]: if current_profit < -0.0075: orders = trade.select_filled_orders(trade.entry_side) diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index 438a2704c..0b073a062 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -843,8 +843,8 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order, # In case of closed order order['status'] = 'closed' - order['price'] = 10 - order['cost'] = 100 + order['average'] = 10 + order['cost'] = 300 order['id'] = '444' mocker.patch('freqtrade.exchange.Exchange.create_order', @@ -855,7 +855,7 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order, assert trade assert trade.open_order_id is None assert trade.open_rate == 10 - assert trade.stake_amount == round(order['price'] * order['filled'] / leverage, 8) + assert trade.stake_amount == round(order['average'] * order['filled'] / leverage, 8) assert pytest.approx(trade.liquidation_price) == liq_price # In case of rejected or expired order and partially filled @@ -863,8 +863,8 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order, order['amount'] = 30.0 order['filled'] = 20.0 order['remaining'] = 10.00 - order['price'] = 0.5 - order['cost'] = 15.0 + order['average'] = 0.5 + order['cost'] = 10.0 order['id'] = '555' mocker.patch('freqtrade.exchange.Exchange.create_order', MagicMock(return_value=order)) @@ -872,9 +872,9 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order, trade = Trade.query.all()[3] trade.is_short = is_short assert trade - assert trade.open_order_id == '555' + assert trade.open_order_id is None assert trade.open_rate == 0.5 - assert trade.stake_amount == round(order['price'] * order['filled'] / leverage, 8) + assert trade.stake_amount == round(order['average'] * order['filled'] / leverage, 8) # Test with custom stake order['status'] = 'open' @@ -901,7 +901,7 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order, order['amount'] = 30.0 * leverage order['filled'] = 0.0 order['remaining'] = 30.0 - order['price'] = 0.5 + order['average'] = 0.5 order['cost'] = 0.0 order['id'] = '66' mocker.patch('freqtrade.exchange.Exchange.create_order', @@ -1083,7 +1083,7 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_ 'last': 1.9 }), create_order=MagicMock(side_effect=[ - {'id': enter_order['id']}, + enter_order, exit_order, ]), get_fee=fee, @@ -1109,20 +1109,20 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_ # should do nothing and return false trade.is_open = True trade.open_order_id = None - trade.stoploss_order_id = 100 + trade.stoploss_order_id = "100" hanging_stoploss_order = MagicMock(return_value={'status': 'open'}) mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', hanging_stoploss_order) assert freqtrade.handle_stoploss_on_exchange(trade) is False - assert trade.stoploss_order_id == 100 + assert trade.stoploss_order_id == "100" # Third case: when stoploss was set but it was canceled for some reason # should set a stoploss immediately and return False caplog.clear() trade.is_open = True trade.open_order_id = None - trade.stoploss_order_id = 100 + trade.stoploss_order_id = "100" canceled_stoploss_order = MagicMock(return_value={'status': 'canceled'}) mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', canceled_stoploss_order) @@ -2039,6 +2039,7 @@ def test_update_trade_state_exception(mocker, default_conf_usdt, is_short, limit trade = MagicMock() trade.open_order_id = '123' + trade.amount = 123 # Test raise of OperationalException exception mocker.patch( @@ -2352,9 +2353,9 @@ def test_close_trade( trade.is_short = is_short assert trade - oobj = Order.parse_from_ccxt_object(enter_order, enter_order['symbol'], 'buy') + oobj = Order.parse_from_ccxt_object(enter_order, enter_order['symbol'], trade.enter_side) trade.update_trade(oobj) - oobj = Order.parse_from_ccxt_object(exit_order, exit_order['symbol'], 'sell') + oobj = Order.parse_from_ccxt_object(exit_order, exit_order['symbol'], trade.exit_side) trade.update_trade(oobj) assert trade.is_open is False @@ -2397,8 +2398,8 @@ def test_manage_open_orders_entry_usercustom( 'freqtrade.exchange.Exchange', fetch_ticker=ticker_usdt, fetch_order=MagicMock(return_value=old_order), - cancel_order_with_result=cancel_order_wr_mock, cancel_order=cancel_order_mock, + cancel_order_with_result=cancel_order_wr_mock, get_fee=fee ) freqtrade = FreqtradeBot(default_conf_usdt) @@ -2446,7 +2447,9 @@ def test_manage_open_orders_entry( ) -> None: old_order = limit_sell_order_old if is_short else limit_buy_order_old rpc_mock = patch_RPCManager(mocker) - old_order['id'] = open_trade.open_order_id + open_trade.open_order_id = old_order['id'] + order = Order.parse_from_ccxt_object(old_order, 'mocked', 'buy') + open_trade.orders[0] = order limit_buy_cancel = deepcopy(old_order) limit_buy_cancel['status'] = 'canceled' cancel_order_mock = MagicMock(return_value=limit_buy_cancel) @@ -2637,7 +2640,9 @@ def test_manage_open_orders_exit_usercustom( is_short, open_trade_usdt, caplog ) -> None: default_conf_usdt["unfilledtimeout"] = {"entry": 1440, "exit": 1440, "exit_timeout_count": 1} - limit_sell_order_old['id'] = open_trade_usdt.open_order_id + open_trade_usdt.open_order_id = limit_sell_order_old['id'] + order = Order.parse_from_ccxt_object(limit_sell_order_old, 'mocked', 'sell') + open_trade_usdt.orders[0] = order if is_short: limit_sell_order_old['side'] = 'buy' open_trade_usdt.is_short = is_short @@ -3250,6 +3255,9 @@ def test_execute_trade_exit_up(default_conf_usdt, ticker_usdt, fee, ticker_usdt_ 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'sub_trade': False, + 'cumulative_profit': 0.0, + 'stake_amount': pytest.approx(60), } == last_msg @@ -3310,6 +3318,9 @@ def test_execute_trade_exit_down(default_conf_usdt, ticker_usdt, fee, ticker_usd 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'sub_trade': False, + 'cumulative_profit': 0.0, + 'stake_amount': pytest.approx(60), } == last_msg @@ -3391,6 +3402,9 @@ def test_execute_trade_exit_custom_exit_price( 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'sub_trade': False, + 'cumulative_profit': 0.0, + 'stake_amount': pytest.approx(60), } == last_msg @@ -3459,6 +3473,9 @@ def test_execute_trade_exit_down_stoploss_on_exchange_dry_run( 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'sub_trade': False, + 'cumulative_profit': 0.0, + 'stake_amount': pytest.approx(60), } == last_msg @@ -3690,7 +3707,7 @@ def test_execute_trade_exit_market_order( ) assert not trade.is_open - assert trade.close_profit == profit_ratio + assert pytest.approx(trade.close_profit) == profit_ratio assert rpc_mock.call_count == 4 last_msg = rpc_mock.call_args_list[-2][0][0] @@ -3718,6 +3735,9 @@ def test_execute_trade_exit_market_order( 'open_date': ANY, 'close_date': ANY, 'close_rate': ANY, + 'sub_trade': False, + 'cumulative_profit': 0.0, + 'stake_amount': pytest.approx(60), } == last_msg @@ -3789,7 +3809,7 @@ def test_exit_profit_only( 'last': bid }), create_order=MagicMock(side_effect=[ - limit_order_open[eside], + limit_order[eside], {'id': 1234553382}, ]), get_fee=fee, @@ -4081,7 +4101,7 @@ def test_trailing_stop_loss_positive( 'last': enter_price - (-0.01 if is_short else 0.01), }), create_order=MagicMock(side_effect=[ - limit_order_open[eside], + limit_order[eside], {'id': 1234553382}, ]), get_fee=fee, @@ -4632,7 +4652,7 @@ def test_order_book_entry_pricing1(mocker, default_conf_usdt, order_book_l2, exc with pytest.raises(PricingError): freqtrade.exchange.get_rate('ETH/USDT', side="entry", is_short=False, refresh=True) assert log_has_re( - r'Entry Price at location 1 from orderbook could not be determined.', caplog) + r'ETH/USDT - Entry Price at location 1 from orderbook could not be determined.', caplog) else: assert freqtrade.exchange.get_rate( 'ETH/USDT', side="entry", is_short=False, refresh=True) == 0.043935 @@ -4711,8 +4731,9 @@ def test_order_book_exit_pricing( return_value={'bids': [[]], 'asks': [[]]}) with pytest.raises(PricingError): freqtrade.handle_trade(trade) - assert log_has_re(r'Exit Price at location 1 from orderbook could not be determined\..*', - caplog) + assert log_has_re( + r"ETH/USDT - Exit Price at location 1 from orderbook could not be determined\..*", + caplog) def test_startup_state(default_conf_usdt, mocker): @@ -5385,7 +5406,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None: 'status': None, 'price': 9, 'amount': 12, - 'cost': 100, + 'cost': 108, 'ft_is_open': True, 'id': '651', 'order_id': '651' @@ -5480,7 +5501,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None: assert trade.open_order_id is None assert pytest.approx(trade.open_rate) == 9.90909090909 assert trade.amount == 22 - assert trade.stake_amount == 218 + assert pytest.approx(trade.stake_amount) == 218 orders = Order.query.all() assert orders @@ -5533,6 +5554,329 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None: # Make sure the closed order is found as the second order. order = trade.select_order('buy', False) assert order.order_id == '652' + closed_sell_dca_order_1 = { + 'ft_pair': pair, + 'status': 'closed', + 'ft_order_side': 'sell', + 'side': 'sell', + 'type': 'limit', + 'price': 8, + 'average': 8, + 'amount': 15, + 'filled': 15, + 'cost': 120, + 'ft_is_open': False, + 'id': '653', + 'order_id': '653' + } + mocker.patch('freqtrade.exchange.Exchange.create_order', + MagicMock(return_value=closed_sell_dca_order_1)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order', + MagicMock(return_value=closed_sell_dca_order_1)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', + MagicMock(return_value=closed_sell_dca_order_1)) + assert freqtrade.execute_trade_exit(trade=trade, limit=8, + exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT), + sub_trade_amt=15) + + # Assert trade is as expected (averaged dca) + trade = Trade.query.first() + assert trade + assert trade.open_order_id is None + assert trade.is_open + assert trade.amount == 22 + assert trade.stake_amount == 192.05405405405406 + assert pytest.approx(trade.open_rate) == 8.729729729729 + + orders = Order.query.all() + assert orders + assert len(orders) == 4 + + # Make sure the closed order is found as the second order. + order = trade.select_order('sell', False) + assert order.order_id == '653' + + +def test_position_adjust2(mocker, default_conf_usdt, fee) -> None: + """ + TODO: Should be adjusted to test both long and short + buy 100 @ 11 + sell 50 @ 8 + sell 50 @ 16 + """ + patch_RPCManager(mocker) + patch_exchange(mocker) + patch_wallet(mocker, free=10000) + default_conf_usdt.update({ + "position_adjustment_enable": True, + "dry_run": False, + "stake_amount": 200.0, + "dry_run_wallet": 1000.0, + }) + freqtrade = FreqtradeBot(default_conf_usdt) + freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True) + bid = 11 + amount = 100 + buy_rate_mock = MagicMock(return_value=bid) + mocker.patch.multiple( + 'freqtrade.exchange.Exchange', + get_rate=buy_rate_mock, + fetch_ticker=MagicMock(return_value={ + 'bid': 10, + 'ask': 12, + 'last': 11 + }), + get_min_pair_stake_amount=MagicMock(return_value=1), + get_fee=fee, + ) + pair = 'ETH/USDT' + # Initial buy + closed_successful_buy_order = { + 'pair': pair, + 'ft_pair': pair, + 'ft_order_side': 'buy', + 'side': 'buy', + 'type': 'limit', + 'status': 'closed', + 'price': bid, + 'average': bid, + 'cost': bid * amount, + 'amount': amount, + 'filled': amount, + 'ft_is_open': False, + 'id': '600', + 'order_id': '600' + } + mocker.patch('freqtrade.exchange.Exchange.create_order', + MagicMock(return_value=closed_successful_buy_order)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', + MagicMock(return_value=closed_successful_buy_order)) + assert freqtrade.execute_entry(pair, amount) + # Should create an closed trade with an no open order id + # Order is filled and trade is open + orders = Order.query.all() + assert orders + assert len(orders) == 1 + trade = Trade.query.first() + assert trade + assert trade.is_open is True + assert trade.open_order_id is None + assert trade.open_rate == bid + assert trade.stake_amount == bid * amount + + # Assume it does nothing since order is closed and trade is open + freqtrade.update_closed_trades_without_assigned_fees() + + trade = Trade.query.first() + assert trade + assert trade.is_open is True + assert trade.open_order_id is None + assert trade.open_rate == bid + assert trade.stake_amount == bid * amount + assert not trade.fee_updated(trade.entry_side) + + freqtrade.manage_open_orders() + + trade = Trade.query.first() + assert trade + assert trade.is_open is True + assert trade.open_order_id is None + assert trade.open_rate == bid + assert trade.stake_amount == bid * amount + assert not trade.fee_updated(trade.entry_side) + + amount = 50 + ask = 8 + closed_sell_dca_order_1 = { + 'ft_pair': pair, + 'status': 'closed', + 'ft_order_side': 'sell', + 'side': 'sell', + 'type': 'limit', + 'price': ask, + 'average': ask, + 'amount': amount, + 'filled': amount, + 'cost': amount * ask, + 'ft_is_open': False, + 'id': '601', + 'order_id': '601' + } + mocker.patch('freqtrade.exchange.Exchange.create_order', + MagicMock(return_value=closed_sell_dca_order_1)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order', + MagicMock(return_value=closed_sell_dca_order_1)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', + MagicMock(return_value=closed_sell_dca_order_1)) + assert freqtrade.execute_trade_exit(trade=trade, limit=ask, + exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT), + sub_trade_amt=amount) + trades: List[Trade] = trade.get_open_trades_without_assigned_fees() + assert len(trades) == 1 + # Assert trade is as expected (averaged dca) + + trade = Trade.query.first() + assert trade + assert trade.open_order_id is None + assert trade.amount == 50 + assert trade.open_rate == 11 + assert trade.stake_amount == 550 + assert pytest.approx(trade.realized_profit) == -152.375 + assert pytest.approx(trade.close_profit_abs) == -152.375 + + orders = Order.query.all() + assert orders + assert len(orders) == 2 + # Make sure the closed order is found as the second order. + order = trade.select_order('sell', False) + assert order.order_id == '601' + + amount = 50 + ask = 16 + closed_sell_dca_order_2 = { + 'ft_pair': pair, + 'status': 'closed', + 'ft_order_side': 'sell', + 'side': 'sell', + 'type': 'limit', + 'price': ask, + 'average': ask, + 'amount': amount, + 'filled': amount, + 'cost': amount * ask, + 'ft_is_open': False, + 'id': '602', + 'order_id': '602' + } + mocker.patch('freqtrade.exchange.Exchange.create_order', + MagicMock(return_value=closed_sell_dca_order_2)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order', + MagicMock(return_value=closed_sell_dca_order_2)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', + MagicMock(return_value=closed_sell_dca_order_2)) + assert freqtrade.execute_trade_exit(trade=trade, limit=ask, + exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT), + sub_trade_amt=amount) + # Assert trade is as expected (averaged dca) + + trade = Trade.query.first() + assert trade + assert trade.open_order_id is None + assert trade.amount == 50 + assert trade.open_rate == 11 + assert trade.stake_amount == 550 + # Trade fully realized + assert pytest.approx(trade.realized_profit) == 94.25 + assert pytest.approx(trade.close_profit_abs) == 94.25 + orders = Order.query.all() + assert orders + assert len(orders) == 3 + + # Make sure the closed order is found as the second order. + order = trade.select_order('sell', False) + assert order.order_id == '602' + assert trade.is_open is False + + +@pytest.mark.parametrize('data', [ + ( + # tuple 1 - side amount, price + # tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit + (('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)), + (('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)), + (('sell', 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.044788)), + (('sell', 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.59201995)), + (('sell', 50, 5), (50.0, 12.5, 625.0, 336.625, 336.625, 0.1343142)), # final profit (sum) + ), + ( + (('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)), + (('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)), + (('sell', 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 1.189027)), + (('buy', 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 1.189027)), + (('sell', 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.7186579)), + (('sell', 150, 23), (150.0, 11.0, 1650.0, 3175.75, 3175.75, 0.9747170)), # final profit + ) +]) +def test_position_adjust3(mocker, default_conf_usdt, fee, data) -> None: + default_conf_usdt.update({ + "position_adjustment_enable": True, + "dry_run": False, + "stake_amount": 200.0, + "dry_run_wallet": 1000.0, + }) + patch_RPCManager(mocker) + patch_exchange(mocker) + patch_wallet(mocker, free=10000) + freqtrade = FreqtradeBot(default_conf_usdt) + trade = None + freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True) + for idx, (order, result) in enumerate(data): + amount = order[1] + price = order[2] + price_mock = MagicMock(return_value=price) + mocker.patch.multiple( + 'freqtrade.exchange.Exchange', + get_rate=price_mock, + fetch_ticker=MagicMock(return_value={ + 'bid': 10, + 'ask': 12, + 'last': 11 + }), + get_min_pair_stake_amount=MagicMock(return_value=1), + get_fee=fee, + ) + pair = 'ETH/USDT' + closed_successful_order = { + 'pair': pair, + 'ft_pair': pair, + 'ft_order_side': order[0], + 'side': order[0], + 'type': 'limit', + 'status': 'closed', + 'price': price, + 'average': price, + 'cost': price * amount, + 'amount': amount, + 'filled': amount, + 'ft_is_open': False, + 'id': f'60{idx}', + 'order_id': f'60{idx}' + } + mocker.patch('freqtrade.exchange.Exchange.create_order', + MagicMock(return_value=closed_successful_order)) + mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', + MagicMock(return_value=closed_successful_order)) + if order[0] == 'buy': + assert freqtrade.execute_entry(pair, amount, trade=trade) + else: + assert freqtrade.execute_trade_exit( + trade=trade, limit=price, + exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT), + sub_trade_amt=amount) + + orders1 = Order.query.all() + assert orders1 + assert len(orders1) == idx + 1 + + trade = Trade.query.first() + assert trade + if idx < len(data) - 1: + assert trade.is_open is True + assert trade.open_order_id is None + assert trade.amount == result[0] + assert trade.open_rate == result[1] + assert trade.stake_amount == result[2] + assert pytest.approx(trade.realized_profit) == result[3] + assert pytest.approx(trade.close_profit_abs) == result[4] + assert pytest.approx(trade.close_profit) == result[5] + + order_obj = trade.select_order(order[0], False) + assert order_obj.order_id == f'60{idx}' + + trade = Trade.query.first() + assert trade + assert trade.open_order_id is None + assert trade.is_open is False def test_process_open_trade_positions_exception(mocker, default_conf_usdt, fee, caplog) -> None: @@ -5556,9 +5900,25 @@ def test_check_and_call_adjust_trade_position(mocker, default_conf_usdt, fee, ca "max_entry_position_adjustment": 0, }) freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) - + buy_rate_mock = MagicMock(return_value=10) + mocker.patch.multiple( + 'freqtrade.exchange.Exchange', + get_rate=buy_rate_mock, + fetch_ticker=MagicMock(return_value={ + 'bid': 10, + 'ask': 12, + 'last': 11 + }), + get_min_pair_stake_amount=MagicMock(return_value=1), + get_fee=fee, + ) create_mock_trades(fee) caplog.set_level(logging.DEBUG) - + freqtrade.strategy.adjust_trade_position = MagicMock(return_value=10) freqtrade.process_open_trade_positions() assert log_has_re(r"Max adjustment entries for .* has been reached\.", caplog) + + caplog.clear() + freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-10) + freqtrade.process_open_trade_positions() + assert log_has_re(r"LIMIT_SELL has been fulfilled.*", caplog) diff --git a/tests/test_integration.py b/tests/test_integration.py index 83f54becb..40fdb4277 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -6,7 +6,7 @@ from freqtrade.enums import ExitCheckTuple, ExitType from freqtrade.persistence import Trade from freqtrade.persistence.models import Order from freqtrade.rpc.rpc import RPC -from tests.conftest import get_patched_freqtradebot, patch_get_signal +from tests.conftest import get_patched_freqtradebot, log_has_re, patch_get_signal def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee, @@ -455,3 +455,60 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None: # Check the 2 filled orders equal the above amount assert pytest.approx(trade.orders[1].amount) == 30.150753768 assert pytest.approx(trade.orders[-1].amount) == 61.538461232 + + +def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> None: + default_conf_usdt['position_adjustment_enable'] = True + + freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) + mocker.patch.multiple( + 'freqtrade.exchange.Exchange', + fetch_ticker=ticker_usdt, + get_fee=fee, + amount_to_precision=lambda s, x, y: y, + price_to_precision=lambda s, x, y: y, + get_min_pair_stake_amount=MagicMock(return_value=10), + ) + + patch_get_signal(freqtrade) + freqtrade.enter_positions() + + assert len(Trade.get_trades().all()) == 1 + trade = Trade.get_trades().first() + assert len(trade.orders) == 1 + assert pytest.approx(trade.stake_amount) == 60 + assert pytest.approx(trade.amount) == 30.0 + assert trade.open_rate == 2.0 + + # Too small size + freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-59) + freqtrade.process() + trade = Trade.get_trades().first() + assert len(trade.orders) == 1 + assert pytest.approx(trade.stake_amount) == 60 + assert pytest.approx(trade.amount) == 30.0 + assert log_has_re("Remaining amount of 1.6.* would be too small.", caplog) + + freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-20) + + freqtrade.process() + trade = Trade.get_trades().first() + assert len(trade.orders) == 2 + assert trade.orders[-1].ft_order_side == 'sell' + assert pytest.approx(trade.stake_amount) == 40.198 + assert pytest.approx(trade.amount) == 20.099 + assert trade.open_rate == 2.0 + assert trade.is_open + caplog.clear() + + # Sell more than what we got (we got ~20 coins left) + # First adjusts the amount to 20 - then rejects. + freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-50) + freqtrade.process() + assert log_has_re("Adjusting amount to trade.amount as it is higher.*", caplog) + assert log_has_re("Remaining amount of 0.0 would be too small.", caplog) + trade = Trade.get_trades().first() + assert len(trade.orders) == 2 + assert trade.orders[-1].ft_order_side == 'sell' + assert pytest.approx(trade.stake_amount) == 40.198 + assert trade.is_open diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 0c1fc01a5..42fcc7413 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -500,7 +500,7 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_ assert trade.close_profit is None assert trade.close_date is None - trade.open_order_id = 'something' + trade.open_order_id = enter_order['id'] oobj = Order.parse_from_ccxt_object(enter_order, 'ADA/USDT', entry_side) trade.orders.append(oobj) trade.update_trade(oobj) @@ -515,7 +515,7 @@ def test_update_limit_order(fee, caplog, limit_buy_order_usdt, limit_sell_order_ caplog) caplog.clear() - trade.open_order_id = 'something' + trade.open_order_id = enter_order['id'] time_machine.move_to("2022-03-31 21:45:05 +00:00") oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', exit_side) trade.orders.append(oobj) @@ -550,7 +550,7 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee, leverage=1.0, ) - trade.open_order_id = 'something' + trade.open_order_id = 'mocked_market_buy' oobj = Order.parse_from_ccxt_object(market_buy_order_usdt, 'ADA/USDT', 'buy') trade.orders.append(oobj) trade.update_trade(oobj) @@ -565,7 +565,7 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee, caplog.clear() trade.is_open = True - trade.open_order_id = 'something' + trade.open_order_id = 'mocked_market_sell' oobj = Order.parse_from_ccxt_object(market_sell_order_usdt, 'ADA/USDT', 'sell') trade.orders.append(oobj) trade.update_trade(oobj) @@ -630,14 +630,14 @@ def test_calc_open_close_trade_price( trade.open_rate = 2.0 trade.close_rate = 2.2 trade.recalc_open_trade_value() - assert isclose(trade._calc_open_trade_value(), open_value) + assert isclose(trade._calc_open_trade_value(trade.amount, trade.open_rate), open_value) assert isclose(trade.calc_close_trade_value(trade.close_rate), close_value) assert isclose(trade.calc_profit(trade.close_rate), round(profit, 8)) assert pytest.approx(trade.calc_profit_ratio(trade.close_rate)) == profit_ratio @pytest.mark.usefixtures("init_persistence") -def test_trade_close(limit_buy_order_usdt, limit_sell_order_usdt, fee): +def test_trade_close(fee): trade = Trade( pair='ADA/USDT', stake_amount=60.0, @@ -815,7 +815,7 @@ def test_calc_open_trade_value( trade.update_trade(oobj) # Buy @ 2.0 # Get the open rate price with the standard fee rate - assert trade._calc_open_trade_value() == result + assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == result @pytest.mark.parametrize( @@ -905,7 +905,7 @@ def test_calc_close_trade_price( ('binance', False, 1, 1.9, 0.003, -3.3209999, -0.055211970, spot, 0), ('binance', False, 1, 2.2, 0.003, 5.6520000, 0.093965087, spot, 0), - # # FUTURES, funding_fee=1 + # FUTURES, funding_fee=1 ('binance', False, 1, 2.1, 0.0025, 3.6925, 0.06138819, futures, 1), ('binance', False, 3, 2.1, 0.0025, 3.6925, 0.18416458, futures, 1), ('binance', True, 1, 2.1, 0.0025, -2.3074999, -0.03855472, futures, 1), @@ -1191,6 +1191,11 @@ def test_calc_profit( assert pytest.approx(trade.calc_profit(rate=close_rate)) == round(profit, 8) assert pytest.approx(trade.calc_profit_ratio(rate=close_rate)) == round(profit_ratio, 8) + assert pytest.approx(trade.calc_profit(close_rate, trade.amount, + trade.open_rate)) == round(profit, 8) + assert pytest.approx(trade.calc_profit_ratio(close_rate, trade.amount, + trade.open_rate)) == round(profit_ratio, 8) + def test_migrate_new(mocker, default_conf, fee, caplog): """ @@ -1382,7 +1387,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog): assert log_has("trying trades_bak2", caplog) assert log_has("Running database migration for trades - backup: trades_bak2, orders_bak0", caplog) - assert trade.open_trade_value == trade._calc_open_trade_value() + assert trade.open_trade_value == trade._calc_open_trade_value(trade.amount, trade.open_rate) assert trade.close_profit_abs is None orders = trade.orders @@ -1744,6 +1749,7 @@ def test_to_json(fee): 'stake_amount': 0.001, 'trade_duration': None, 'trade_duration_s': None, + 'realized_profit': 0.0, 'close_profit': None, 'close_profit_pct': None, 'close_profit_abs': None, @@ -1820,6 +1826,7 @@ def test_to_json(fee): 'initial_stop_loss_abs': None, 'initial_stop_loss_pct': None, 'initial_stop_loss_ratio': None, + 'realized_profit': 0.0, 'close_profit': None, 'close_profit_pct': None, 'close_profit_abs': None, @@ -2262,7 +2269,7 @@ def test_update_order_from_ccxt(caplog): 'symbol': 'ADA/USDT', 'type': 'limit', 'price': 1234.5, - 'amount': 20.0, + 'amount': 20.0, 'filled': 9, 'remaining': 11, 'status': 'open', @@ -2421,7 +2428,7 @@ def test_recalc_trade_from_orders(fee): ) assert fee.return_value == 0.0025 - assert trade._calc_open_trade_value() == o1_trade_val + assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == o1_trade_val assert trade.amount == o1_amount assert trade.stake_amount == o1_cost assert trade.open_rate == o1_rate @@ -2533,7 +2540,8 @@ def test_recalc_trade_from_orders(fee): assert pytest.approx(trade.fee_open_cost) == o1_fee_cost + o2_fee_cost + o3_fee_cost assert pytest.approx(trade.open_trade_value) == o1_trade_val + o2_trade_val + o3_trade_val - # Just to make sure sell orders are ignored, let's calculate one more time. + # Just to make sure full sell orders are ignored, let's calculate one more time. + sell1 = Order( ft_order_side='sell', ft_pair=trade.pair, @@ -2695,7 +2703,7 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short): assert trade.open_trade_value == 2 * o1_trade_val assert trade.nr_of_successful_entries == 2 - # Just to make sure exit orders are ignored, let's calculate one more time. + # Reduce position - this will reduce amount again. sell1 = Order( ft_order_side=exit_side, ft_pair=trade.pair, @@ -2706,7 +2714,7 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short): side=exit_side, price=4, average=3, - filled=2, + filled=o1_amount, remaining=1, cost=5, order_date=trade.open_date, @@ -2715,11 +2723,11 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short): trade.orders.append(sell1) trade.recalc_trade_from_orders() - assert trade.amount == 2 * o1_amount - assert trade.stake_amount == 2 * o1_amount + assert trade.amount == o1_amount + assert trade.stake_amount == o1_amount assert trade.open_rate == o1_rate - assert trade.fee_open_cost == 2 * o1_fee_cost - assert trade.open_trade_value == 2 * o1_trade_val + assert trade.fee_open_cost == o1_fee_cost + assert trade.open_trade_value == o1_trade_val assert trade.nr_of_successful_entries == 2 # Check with 1 order @@ -2743,11 +2751,11 @@ def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short): trade.recalc_trade_from_orders() # Calling recalc with single initial order should not change anything - assert trade.amount == 3 * o1_amount - assert trade.stake_amount == 3 * o1_amount + assert trade.amount == 2 * o1_amount + assert trade.stake_amount == 2 * o1_amount assert trade.open_rate == o1_rate - assert trade.fee_open_cost == 3 * o1_fee_cost - assert trade.open_trade_value == 3 * o1_trade_val + assert trade.fee_open_cost == 2 * o1_fee_cost + assert trade.open_trade_value == 2 * o1_trade_val assert trade.nr_of_successful_entries == 3 @@ -2815,3 +2823,144 @@ def test_order_to_ccxt(limit_buy_order_open): del raw_order['stopPrice'] del limit_buy_order_open['datetime'] assert raw_order == limit_buy_order_open + + +@pytest.mark.usefixtures("init_persistence") +@pytest.mark.parametrize('data', [ + { + # tuple 1 - side, amount, price + # tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit + 'orders': [ + (('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)), + (('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)), + (('sell', 50, 12), (150.0, 12.5, 1875.0, -25.0, -25.0, -0.04)), + (('sell', 100, 20), (50.0, 12.5, 625.0, 725.0, 750.0, 0.60)), + (('sell', 50, 5), (50.0, 12.5, 625.0, 350.0, -375.0, -0.60)), + ], + 'end_profit': 350.0, + 'end_profit_ratio': 0.14, + 'fee': 0.0, + }, + { + 'orders': [ + (('buy', 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)), + (('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)), + (('sell', 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.044788)), + (('sell', 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.59201995)), + (('sell', 50, 5), (50.0, 12.5, 625.0, 336.625, -377.1875, -0.60199501)), + ], + 'end_profit': 336.625, + 'end_profit_ratio': 0.1343142, + 'fee': 0.0025, + }, + { + 'orders': [ + (('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)), + (('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)), + (('sell', 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 1.189027)), + (('buy', 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 1.189027)), + (('sell', 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.7186579)), + (('sell', 150, 23), (150.0, 11.0, 1650.0, 3175.75, 1787.25, 1.08048062)), + ], + 'end_profit': 3175.75, + 'end_profit_ratio': 0.9747170, + 'fee': 0.0025, + }, + { + # Test above without fees + 'orders': [ + (('buy', 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)), + (('buy', 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)), + (('sell', 100, 11), (100.0, 5.0, 500.0, 600.0, 600.0, 1.2)), + (('buy', 150, 15), (250.0, 11.0, 2750.0, 600.0, 600.0, 1.2)), + (('sell', 100, 19), (150.0, 11.0, 1650.0, 1400.0, 800.0, 0.72727273)), + (('sell', 150, 23), (150.0, 11.0, 1650.0, 3200.0, 1800.0, 1.09090909)), + ], + 'end_profit': 3200.0, + 'end_profit_ratio': 0.98461538, + 'fee': 0.0, + }, + { + 'orders': [ + (('buy', 100, 8), (100.0, 8.0, 800.0, 0.0, None, None)), + (('buy', 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)), + (('sell', 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.17647059)), + (('buy', 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.17647059)), + (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 350.0, 0.2)), + (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 950.0, 0.40)), + ], + 'end_profit': 950.0, + 'end_profit_ratio': 0.283582, + 'fee': 0.0, + }, +]) +def test_recalc_trade_from_orders_dca(data) -> None: + + pair = 'ETH/USDT' + trade = Trade( + id=2, + pair=pair, + stake_amount=1000, + open_rate=data['orders'][0][0][2], + amount=data['orders'][0][0][1], + is_open=True, + open_date=arrow.utcnow().datetime, + fee_open=data['fee'], + fee_close=data['fee'], + exchange='binance', + is_short=False, + leverage=1.0, + trading_mode=TradingMode.SPOT + ) + Trade.query.session.add(trade) + + for idx, (order, result) in enumerate(data['orders']): + amount = order[1] + price = order[2] + + order_obj = Order( + ft_order_side=order[0], + ft_pair=trade.pair, + order_id=f"order_{order[0]}_{idx}", + ft_is_open=False, + status="closed", + symbol=trade.pair, + order_type="market", + side=order[0], + price=price, + average=price, + filled=amount, + remaining=0, + cost=amount * price, + order_date=arrow.utcnow().shift(hours=-10 + idx).datetime, + order_filled_date=arrow.utcnow().shift(hours=-10 + idx).datetime, + ) + trade.orders.append(order_obj) + trade.recalc_trade_from_orders() + Trade.commit() + + orders1 = Order.query.all() + assert orders1 + assert len(orders1) == idx + 1 + + trade = Trade.query.first() + assert trade + assert len(trade.orders) == idx + 1 + if idx < len(data) - 1: + assert trade.is_open is True + assert trade.open_order_id is None + assert trade.amount == result[0] + assert trade.open_rate == result[1] + assert trade.stake_amount == result[2] + # TODO: enable the below. + assert pytest.approx(trade.realized_profit) == result[3] + # assert pytest.approx(trade.close_profit_abs) == result[4] + assert pytest.approx(trade.close_profit) == result[5] + + trade.close(price) + assert pytest.approx(trade.close_profit_abs) == data['end_profit'] + assert pytest.approx(trade.close_profit) == data['end_profit_ratio'] + assert not trade.is_open + trade = Trade.query.first() + assert trade + assert trade.open_order_id is None From cbb05354a8968c10bfcb5944db2c5fa13eb6fd5b Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 31 Jul 2022 15:10:01 +0200 Subject: [PATCH 235/518] Add install variant for freqai --- requirements-freqai.txt | 1 - setup.py | 10 +++++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/requirements-freqai.txt b/requirements-freqai.txt index c3aa2e4db..060a5219d 100644 --- a/requirements-freqai.txt +++ b/requirements-freqai.txt @@ -3,7 +3,6 @@ # Required for freqai scikit-learn==1.1.1 -scikit-optimize==0.9.0 joblib==1.1.0 catboost==1.0.4 lightgbm==3.3.2 diff --git a/setup.py b/setup.py index 7aa56bf81..33e35c5ab 100644 --- a/setup.py +++ b/setup.py @@ -12,6 +12,13 @@ hyperopt = [ 'progressbar2', ] +freqai = [ + 'scikit-learn', + 'joblib', + 'catboost', + 'lightgbm', +] + develop = [ 'coveralls', 'flake8', @@ -31,7 +38,7 @@ jupyter = [ 'nbconvert', ] -all_extra = plot + develop + jupyter + hyperopt +all_extra = plot + develop + jupyter + hyperopt + freqai setup( tests_require=[ @@ -79,6 +86,7 @@ setup( 'plot': plot, 'jupyter': jupyter, 'hyperopt': hyperopt, + 'freqai': freqai, 'all': all_extra, }, ) From 659870312df456036372a3afa7825220292a3229 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 31 Jul 2022 15:23:27 +0200 Subject: [PATCH 236/518] Use JSON Schema validation for freaAI schema validation --- freqtrade/configuration/config_validation.py | 17 ------------ freqtrade/constants.py | 29 +++++++++----------- 2 files changed, 13 insertions(+), 33 deletions(-) diff --git a/freqtrade/configuration/config_validation.py b/freqtrade/configuration/config_validation.py index 5d9667196..ee846e7e6 100644 --- a/freqtrade/configuration/config_validation.py +++ b/freqtrade/configuration/config_validation.py @@ -85,7 +85,6 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False) _validate_unlimited_amount(conf) _validate_ask_orderbook(conf) validate_migrated_strategy_settings(conf) - _validate_freqai(conf) # validate configuration before returning logger.info('Validating configuration ...') @@ -164,22 +163,6 @@ def _validate_edge(conf: Dict[str, Any]) -> None: ) -def _validate_freqai(conf: Dict[str, Any]) -> None: - """ - Freqai param validator - """ - - if not conf.get('freqai', {}): - return - - for param in constants.SCHEMA_FREQAI_REQUIRED: - if param not in conf.get('freqai', {}): - if param not in conf.get('freqai', {}).get('feature_parameters', {}): - raise OperationalException( - f'{param} not found in Freqai config' - ) - - def _validate_whitelist(conf: Dict[str, Any]) -> None: """ Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does. diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 0134dafc0..af2e4748a 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -241,6 +241,7 @@ CONF_SCHEMA = { }, 'exchange': {'$ref': '#/definitions/exchange'}, 'edge': {'$ref': '#/definitions/edge'}, + 'freqai': {'$ref': '#/definitions/freqai'}, 'experimental': { 'type': 'object', 'properties': { @@ -484,20 +485,21 @@ CONF_SCHEMA = { "keras": {"type": "boolean", "default": False}, "conv_width": {"type": "integer", "default": 2}, "train_period_days": {"type": "integer", "default": 0}, - "backtest_period_days": {"type": "float", "default": 7}, - "identifier": {"type": "str", "default": "example"}, + "backtest_period_days": {"type": "number", "default": 7}, + "identifier": {"type": "string", "default": "example"}, "feature_parameters": { "type": "object", "properties": { - "include_corr_pairlist": {"type": "list"}, - "include_timeframes": {"type": "list"}, + "include_corr_pairlist": {"type": "array"}, + "include_timeframes": {"type": "array"}, "label_period_candles": {"type": "integer"}, "include_shifted_candles": {"type": "integer", "default": 0}, - "DI_threshold": {"type": "float", "default": 0}, + "DI_threshold": {"type": "number", "default": 0}, "weight_factor": {"type": "number", "default": 0}, "principal_component_analysis": {"type": "boolean", "default": False}, "use_SVM_to_remove_outliers": {"type": "boolean", "default": False}, }, + "required": ["include_timeframes", "include_corr_pairlist", ] }, "data_split_parameters": { "type": "object", @@ -516,6 +518,12 @@ CONF_SCHEMA = { }, }, }, + "required": ["train_period_days", + "backtest_period_days", + "identifier", + "feature_parameters", + "data_split_parameters", + "model_training_parameters"] }, }, } @@ -560,17 +568,6 @@ SCHEMA_MINIMAL_REQUIRED = [ 'dataformat_trades', ] -SCHEMA_FREQAI_REQUIRED = [ - 'include_timeframes', - 'train_period_days', - 'backtest_period_days', - 'identifier', - 'include_corr_pairlist', - 'feature_parameters', - 'data_split_parameters', - 'model_training_parameters' -] - CANCEL_REASON = { "TIMEOUT": "cancelled due to timeout", "PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open", From 153336d4241b79c8c9195ca19124fe6be56cfb29 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 15:45:15 +0200 Subject: [PATCH 237/518] move corr_pairlist expansion to after expand_pairlist() --- freqtrade/plugins/pairlist/pairlist_helpers.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/freqtrade/plugins/pairlist/pairlist_helpers.py b/freqtrade/plugins/pairlist/pairlist_helpers.py index ee17c1be9..a07a0f783 100644 --- a/freqtrade/plugins/pairlist/pairlist_helpers.py +++ b/freqtrade/plugins/pairlist/pairlist_helpers.py @@ -43,12 +43,10 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str], def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]: + expanded_pairs = expand_pairlist(config['pairs'], markets) if config.get('freqai', {}): corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist'] - full_pairs = config['pairs'] + [pair for pair in corr_pairlist - if pair not in config['pairs']] - expanded_pairs = expand_pairlist(full_pairs, markets) - else: - expanded_pairs = expand_pairlist(config['pairs'], markets) + expanded_pairs += [pair for pair in corr_pairlist + if pair not in config['pairs']] return expanded_pairs From d830105605863c2c88839d7df864c35b6db7de4f Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 17:05:29 +0200 Subject: [PATCH 238/518] *BREAKING CHANGE* remove unnecessary arguments from populate_any_indicators(), accommodate tests --- freqtrade/freqai/data_kitchen.py | 13 +++----- freqtrade/strategy/interface.py | 5 ++- freqtrade/templates/FreqaiExampleStrategy.py | 31 +++++++++++++------ .../strats/freqai_test_multimodel_strat.py | 4 ++- tests/strategy/strats/freqai_test_strat.py | 4 ++- 5 files changed, 35 insertions(+), 22 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 4a936475a..aedf01d11 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -892,29 +892,26 @@ class FreqaiDataKitchen: else: dataframe = base_dataframes[self.config["timeframe"]].copy() - sgi = True + sgi = False for tf in tfs: + if tf == tfs[-1]: + sgi = True # doing this last allows user to use all tf raw prices in labels dataframe = strategy.populate_any_indicators( - pair, pair, dataframe.copy(), tf, informative=base_dataframes[tf], - coin=pair.split("/")[0] + "-", - set_generalized_indicators=sgi, + set_generalized_indicators=sgi ) - sgi = False if pairs: for i in pairs: if pair in i: continue # dont repeat anything from whitelist dataframe = strategy.populate_any_indicators( - pair, i, dataframe.copy(), tf, - informative=corr_dataframes[i][tf], - coin=i.split("/")[0] + "-", + informative=corr_dataframes[i][tf] ) return dataframe diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 431e67a98..be6447811 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -557,8 +557,8 @@ class IStrategy(ABC, HyperStrategyMixin): """ return None - def populate_any_indicators(self, basepair: str, pair: str, df: DataFrame, tf: str, - informative: DataFrame = None, coin: str = "", + def populate_any_indicators(self, pair: str, df: DataFrame, tf: str, + informative: DataFrame = None, set_generalized_indicators: bool = False) -> DataFrame: """ Function designed to automatically generate, name and merge features @@ -570,7 +570,6 @@ class IStrategy(ABC, HyperStrategyMixin): :param df: strategy dataframe which will receive merges from informatives :param tf: timeframe of the dataframe which will modify the feature names :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. """ return df diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 1196405ab..90343cc80 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -65,7 +65,7 @@ class FreqaiExampleStrategy(IStrategy): return informative_pairs def populate_any_indicators( - self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False + self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ Function designed to automatically generate, name and merge features @@ -78,9 +78,10 @@ class FreqaiExampleStrategy(IStrategy): :param df: strategy dataframe which will receive merges from informatives :param tf: timeframe of the dataframe which will modify the feature names :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. """ + coin = pair.split('/')[0] + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -92,11 +93,8 @@ class FreqaiExampleStrategy(IStrategy): informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t) - informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t) - informative[f"%-{coin}close_over_20sma-period_{t}"] = ( - informative["close"] / informative[f"{coin}20sma-period_{t}"] - ) + informative[f"{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) + informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) @@ -148,8 +146,6 @@ class FreqaiExampleStrategy(IStrategy): df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py df["&-s_close"] = ( df["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) @@ -159,6 +155,23 @@ class FreqaiExampleStrategy(IStrategy): - 1 ) + # If user wishes to use multiple targets, they can add more by + # appending more columns with '&'. User should keep in mind that multi targets + # requires a multioutput prediction model such as + # templates/CatboostPredictionMultiModel.py, + + # df["&-s_range"] = ( + # df["close"] + # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + # .max() + # - + # df["close"] + # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + # .min() + # ) + return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: diff --git a/tests/strategy/strats/freqai_test_multimodel_strat.py b/tests/strategy/strats/freqai_test_multimodel_strat.py index 9652e816b..e58086757 100644 --- a/tests/strategy/strats/freqai_test_multimodel_strat.py +++ b/tests/strategy/strats/freqai_test_multimodel_strat.py @@ -62,7 +62,7 @@ class freqai_test_multimodel_strat(IStrategy): return informative_pairs def populate_any_indicators( - self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False + self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ Function designed to automatically generate, name and merge features @@ -79,6 +79,8 @@ class freqai_test_multimodel_strat(IStrategy): :coin: the name of the coin which will modify the feature names. """ + coin = pair.split('/')[0] + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py index 8679d4d74..8288228d1 100644 --- a/tests/strategy/strats/freqai_test_strat.py +++ b/tests/strategy/strats/freqai_test_strat.py @@ -62,7 +62,7 @@ class freqai_test_strat(IStrategy): return informative_pairs def populate_any_indicators( - self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False + self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ Function designed to automatically generate, name and merge features @@ -79,6 +79,8 @@ class freqai_test_strat(IStrategy): :coin: the name of the coin which will modify the feature names. """ + coin = pair.split('/')[0] + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) From 4e68626bcbced4ac2d7e6a41b6e330802b6bf097 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 17:51:19 +0200 Subject: [PATCH 239/518] ensure convolutional window is prepended for frequi consistency --- freqtrade/freqai/data_drawer.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 97cf7607a..6844af0ec 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -238,6 +238,15 @@ class FreqaiDataDrawer: mrv_df["do_predict"] = do_preds + # for keras type models, the conv_window needs to be prepended so + # viewing is correct in frequi + if self.freqai_info.get('keras', False): + n_lost_points = self.freqai_info.get('conv_width', 2) + zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))), + columns=mrv_df.columns) + self.model_return_values[pair] = pd.concat( + [zeros_df, mrv_df], axis=0, ignore_index=True) + def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: NDArray[np.int_], dk: FreqaiDataKitchen, len_df: int) -> None: From 946d4c7cfcc645f3061b714d6b3579bdb68c820d Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 31 Jul 2022 18:39:46 +0200 Subject: [PATCH 240/518] fix trailing whitespace for flake8 --- freqtrade/freqai/data_drawer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 6844af0ec..f14277bed 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -238,7 +238,7 @@ class FreqaiDataDrawer: mrv_df["do_predict"] = do_preds - # for keras type models, the conv_window needs to be prepended so + # for keras type models, the conv_window needs to be prepended so # viewing is correct in frequi if self.freqai_info.get('keras', False): n_lost_points = self.freqai_info.get('conv_width', 2) From 7a696f58f95839c917d3a95225e86c166fb21afd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:01:30 +0000 Subject: [PATCH 241/518] Bump ccxt from 1.91.29 to 1.91.52 Bumps [ccxt](https://github.com/ccxt/ccxt) from 1.91.29 to 1.91.52. - [Release notes](https://github.com/ccxt/ccxt/releases) - [Changelog](https://github.com/ccxt/ccxt/blob/master/exchanges.cfg) - [Commits](https://github.com/ccxt/ccxt/compare/1.91.29...1.91.52) --- updated-dependencies: - dependency-name: ccxt dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b9e87749d..9aec6af63 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.1 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.91.29 +ccxt==1.91.52 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 From a75fa26caf915d3235b9e0e7bda794a43441a8b6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:01:38 +0000 Subject: [PATCH 242/518] Bump scipy from 1.8.1 to 1.9.0 Bumps [scipy](https://github.com/scipy/scipy) from 1.8.1 to 1.9.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.8.1...v1.9.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements-hyperopt.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt index 94e59ec15..cc659fc50 100644 --- a/requirements-hyperopt.txt +++ b/requirements-hyperopt.txt @@ -2,7 +2,7 @@ -r requirements.txt # Required for hyperopt -scipy==1.8.1 +scipy==1.9.0 scikit-learn==1.1.1 scikit-optimize==0.9.0 filelock==3.7.1 From b4ded59c63f6c2fa92e6e1722f0bb6cf758589e2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:01:43 +0000 Subject: [PATCH 243/518] Bump flake8 from 4.0.1 to 5.0.1 Bumps [flake8](https://github.com/pycqa/flake8) from 4.0.1 to 5.0.1. - [Release notes](https://github.com/pycqa/flake8/releases) - [Commits](https://github.com/pycqa/flake8/compare/4.0.1...5.0.1) --- updated-dependencies: - dependency-name: flake8 dependency-type: direct:development update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index 3b98e20db..ee7899eeb 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -5,7 +5,7 @@ -r docs/requirements-docs.txt coveralls==3.3.1 -flake8==4.0.1 +flake8==5.0.1 flake8-tidy-imports==4.8.0 mypy==0.971 pre-commit==2.20.0 From 372be542528457a22ce16f5d408551abe170377e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:01:46 +0000 Subject: [PATCH 244/518] Bump types-requests from 2.28.3 to 2.28.6 Bumps [types-requests](https://github.com/python/typeshed) from 2.28.3 to 2.28.6. - [Release notes](https://github.com/python/typeshed/releases) - [Commits](https://github.com/python/typeshed/commits) --- updated-dependencies: - dependency-name: types-requests dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index 3b98e20db..d34e551d2 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -24,6 +24,6 @@ nbconvert==6.5.0 # mypy types types-cachetools==5.2.1 types-filelock==3.2.7 -types-requests==2.28.3 +types-requests==2.28.6 types-tabulate==0.8.11 types-python-dateutil==2.8.19 From ed230dd750e48ecc2148f91ea72bb39cf95dac09 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:01:52 +0000 Subject: [PATCH 245/518] Bump orjson from 3.7.8 to 3.7.11 Bumps [orjson](https://github.com/ijl/orjson) from 3.7.8 to 3.7.11. - [Release notes](https://github.com/ijl/orjson/releases) - [Changelog](https://github.com/ijl/orjson/blob/master/CHANGELOG.md) - [Commits](https://github.com/ijl/orjson/compare/3.7.8...3.7.11) --- updated-dependencies: - dependency-name: orjson dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b9e87749d..7aee44d85 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,7 +28,7 @@ py_find_1st==1.1.5 # Load ticker files 30% faster python-rapidjson==1.8 # Properly format api responses -orjson==3.7.8 +orjson==3.7.11 # Notify systemd sdnotify==0.3.2 From 79b650258ee719b5438b37036ef255379aa0fafe Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:02:02 +0000 Subject: [PATCH 246/518] Bump urllib3 from 1.26.10 to 1.26.11 Bumps [urllib3](https://github.com/urllib3/urllib3) from 1.26.10 to 1.26.11. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/1.26.11/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/1.26.10...1.26.11) --- updated-dependencies: - dependency-name: urllib3 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b9e87749d..36989119c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,7 +11,7 @@ python-telegram-bot==13.13 arrow==1.2.2 cachetools==4.2.2 requests==2.28.1 -urllib3==1.26.10 +urllib3==1.26.11 jsonschema==4.7.2 TA-Lib==0.4.24 technical==1.3.0 From 97064a9ce3bbc25350966a47d1e549f985b076ab Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 03:13:38 +0000 Subject: [PATCH 247/518] Bump pypa/gh-action-pypi-publish from 1.5.0 to 1.5.1 Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.5.0 to 1.5.1. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.5.0...v1.5.1) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7b077be04..bb5bc209e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -351,7 +351,7 @@ jobs: python setup.py sdist bdist_wheel - name: Publish to PyPI (Test) - uses: pypa/gh-action-pypi-publish@v1.5.0 + uses: pypa/gh-action-pypi-publish@v1.5.1 if: (github.event_name == 'release') with: user: __token__ @@ -359,7 +359,7 @@ jobs: repository_url: https://test.pypi.org/legacy/ - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@v1.5.0 + uses: pypa/gh-action-pypi-publish@v1.5.1 if: (github.event_name == 'release') with: user: __token__ From 3013282dbf83ee06985478cfcf8866b633c5af53 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 1 Aug 2022 05:39:38 +0200 Subject: [PATCH 248/518] remove non-catboost stuff from schema --- freqtrade/constants.py | 11 +++++++---- freqtrade/templates/FreqaiExampleStrategy.py | 4 ++-- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/freqtrade/constants.py b/freqtrade/constants.py index af2e4748a..d1a009ece 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -498,6 +498,12 @@ CONF_SCHEMA = { "weight_factor": {"type": "number", "default": 0}, "principal_component_analysis": {"type": "boolean", "default": False}, "use_SVM_to_remove_outliers": {"type": "boolean", "default": False}, + "svm_params": {"type": "object", + "properties": { + "shuffle": {"type": "boolean", "default": False}, + "nu": {"type": "number", "default": 0.1} + }, + } }, "required": ["include_timeframes", "include_corr_pairlist", ] }, @@ -511,10 +517,7 @@ CONF_SCHEMA = { "model_training_parameters": { "type": "object", "properties": { - "n_estimators": {"type": "integer", "default": 2000}, - "random_state": {"type": "integer", "default": 1}, - "learning_rate": {"type": "number", "default": 0.02}, - "task_type": {"type": "string", "default": "CPU"}, + "n_estimators": {"type": "integer", "default": 1000} }, }, }, diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 90343cc80..4f632f907 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -93,8 +93,8 @@ class FreqaiExampleStrategy(IStrategy): informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) - informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) + informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) + informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) From f3154a4313cb03ea09f3517272b554ccb9b6664a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Aug 2022 04:35:30 +0000 Subject: [PATCH 249/518] Bump jsonschema from 4.7.2 to 4.9.0 Bumps [jsonschema](https://github.com/python-jsonschema/jsonschema) from 4.7.2 to 4.9.0. - [Release notes](https://github.com/python-jsonschema/jsonschema/releases) - [Changelog](https://github.com/python-jsonschema/jsonschema/blob/main/CHANGELOG.rst) - [Commits](https://github.com/python-jsonschema/jsonschema/compare/v4.7.2...v4.9.0) --- updated-dependencies: - dependency-name: jsonschema dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 69f6d1d24..f6ba2a444 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ arrow==1.2.2 cachetools==4.2.2 requests==2.28.1 urllib3==1.26.11 -jsonschema==4.7.2 +jsonschema==4.9.0 TA-Lib==0.4.24 technical==1.3.0 tabulate==0.8.10 From 707a4e7c9eee26ec29749465dda9aa12de69c9bd Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 1 Aug 2022 06:41:08 +0200 Subject: [PATCH 250/518] types-requests bump pre-commit --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 759ac0a6a..398d09875 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: additional_dependencies: - types-cachetools==5.2.1 - types-filelock==3.2.7 - - types-requests==2.28.3 + - types-requests==2.28.6 - types-tabulate==0.8.11 - types-python-dateutil==2.8.19 # stages: [push] From d75e0a982091bbe99e5f7795ca83fb56c210617d Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 1 Aug 2022 06:43:59 +0200 Subject: [PATCH 251/518] Fix Flake8 errors after flake update --- freqtrade/optimize/backtesting.py | 2 +- freqtrade/optimize/optimize_reports.py | 2 +- freqtrade/persistence/trade_model.py | 8 ++++---- tests/edge/test_edge.py | 2 +- tests/exchange/test_exchange.py | 4 ++-- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 46774e8a5..029946cfb 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -595,7 +595,7 @@ class Backtesting: if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT): # Checks and adds an exit tag, after checking that the length of the # row has the length for an exit tag column - if( + if ( len(row) > EXIT_TAG_IDX and row[EXIT_TAG_IDX] is not None and len(row[EXIT_TAG_IDX]) > 0 diff --git a/freqtrade/optimize/optimize_reports.py b/freqtrade/optimize/optimize_reports.py index 44ac4a5b3..519022db2 100644 --- a/freqtrade/optimize/optimize_reports.py +++ b/freqtrade/optimize/optimize_reports.py @@ -639,7 +639,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr :param stake_currency: stake-currency - used to correctly name headers :return: pretty printed table with tabulate as string """ - if(tag_type == "enter_tag"): + if (tag_type == "enter_tag"): headers = _get_line_header("TAG", stake_currency) else: headers = _get_line_header("TAG", stake_currency, 'Sells') diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index fcb84a59a..19d9361b6 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -1300,7 +1300,7 @@ class Trade(_DECL_BASE, LocalTrade): """ filters = [Trade.is_open.is_(False)] - if(pair is not None): + if (pair is not None): filters.append(Trade.pair == pair) enter_tag_perf = Trade.query.with_entities( @@ -1333,7 +1333,7 @@ class Trade(_DECL_BASE, LocalTrade): """ filters = [Trade.is_open.is_(False)] - if(pair is not None): + if (pair is not None): filters.append(Trade.pair == pair) sell_tag_perf = Trade.query.with_entities( @@ -1366,7 +1366,7 @@ class Trade(_DECL_BASE, LocalTrade): """ filters = [Trade.is_open.is_(False)] - if(pair is not None): + if (pair is not None): filters.append(Trade.pair == pair) mix_tag_perf = Trade.query.with_entities( @@ -1386,7 +1386,7 @@ class Trade(_DECL_BASE, LocalTrade): enter_tag = enter_tag if enter_tag is not None else "Other" exit_reason = exit_reason if exit_reason is not None else "Other" - if(exit_reason is not None and enter_tag is not None): + if (exit_reason is not None and enter_tag is not None): mix_tag = enter_tag + " " + exit_reason i = 0 if not any(item["mix_tag"] == mix_tag for item in return_list): diff --git a/tests/edge/test_edge.py b/tests/edge/test_edge.py index b30d6f998..1b0191fda 100644 --- a/tests/edge/test_edge.py +++ b/tests/edge/test_edge.py @@ -136,7 +136,7 @@ def test_adjust(mocker, edge_conf): )) pairs = ['A/B', 'C/D', 'E/F', 'G/H'] - assert(edge.adjust(pairs) == ['E/F', 'C/D']) + assert (edge.adjust(pairs) == ['E/F', 'C/D']) def test_stoploss(mocker, edge_conf): diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index d73e26683..bbe424430 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -3810,8 +3810,8 @@ def test__get_funding_fees_from_exchange(default_conf, mocker, exchange_name): since=unix_time ) - assert(isclose(expected_fees, fees_from_datetime)) - assert(isclose(expected_fees, fees_from_unix_time)) + assert (isclose(expected_fees, fees_from_datetime)) + assert (isclose(expected_fees, fees_from_unix_time)) ccxt_exceptionhandlers( mocker, From 895ebbfd1836b3d9964b70422ba221aa084df2f3 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 1 Aug 2022 07:32:25 +0000 Subject: [PATCH 252/518] Exclude aarch64 from catboost requirements --- docs/freqai.md | 3 +++ requirements-freqai.txt | 2 +- setup.py | 2 +- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index de321b787..adf64dd07 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -56,6 +56,9 @@ Use `pip` to install the prerequisites with: pip install -r requirements-freqai.txt ``` +!!! Note + Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform. + ## Running from the example files An example strategy, an example prediction model, and example config can all be found in diff --git a/requirements-freqai.txt b/requirements-freqai.txt index 060a5219d..e4f4a7c17 100644 --- a/requirements-freqai.txt +++ b/requirements-freqai.txt @@ -4,5 +4,5 @@ # Required for freqai scikit-learn==1.1.1 joblib==1.1.0 -catboost==1.0.4 +catboost==1.0.4; platform_machine != 'aarch64' lightgbm==3.3.2 diff --git a/setup.py b/setup.py index 33e35c5ab..dfadbdefa 100644 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ hyperopt = [ freqai = [ 'scikit-learn', 'joblib', - 'catboost', + 'catboost; platform_machine != "aarch64"', 'lightgbm', ] From ae0d6f63fa458cc2435dcfd8704a2258b7a82358 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 1 Aug 2022 19:43:13 +0200 Subject: [PATCH 253/518] Version bump ccxt to 1.91.55 closes #7151 --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f6ba2a444..4cb6519b3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.1 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.91.52 +ccxt==1.91.55 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 From 95d3009a9531c99802cfa5ce3c9c46ff8010d633 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 2 Aug 2022 20:14:02 +0200 Subject: [PATCH 254/518] give user ability to analyze live trade dataframe inside custom prediction model. Add documentation to explain new functionality --- docs/freqai.md | 40 ++++++++++++++++ freqtrade/freqai/data_drawer.py | 22 +++++++-- freqtrade/freqai/data_kitchen.py | 30 ++++++++++-- freqtrade/freqai/freqai_interface.py | 68 ++++++++++++++++++++++++++-- 4 files changed, 147 insertions(+), 13 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index adf64dd07..0a07f45ef 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -619,6 +619,46 @@ If the user sets this value, FreqAI will initially use the predictions from the and then subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if the user stops and restarts with the same `identifier`. +## Extra returns per train + +Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain. +Users can include these metrics by assigining them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction +model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. +The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is +the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training. +Another example is shown below if the user wants to use live metrics from the trade databse. + +The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes: + +```json + "freqai": { + "extra_returns_per_train": {"total_profit": 4} + } +``` + +These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs +a default initial value - this is the value that will be returned. + +## Analyzing the trade live database + +Users can analyze the live trade database by calling `analyze_trade_database()` in their custom prediction model. FreqAI already has the +database setup in a pandas dataframe and ready to be analyzed. Here is an example usecase: + +```python + def analyze_trade_database(self, dk: FreqaiDataKitchen, pair: str) -> None: + """ + User analyzes the trade database here and returns summary stats which will be passed back + to the strategy for reinforcement learning or for additional adaptive metrics for use + in entry/exit signals. Store these metrics in dk.data['extra_returns_per_train'] and + they will format themselves into the dataframe as an additional column in the user + strategy. User has access to the current trade database in dk.trade_database_df. + """ + total_profit = dk.trade_database_df['close_profit_abs'].sum() + dk.data['extra_returns_per_train']['total_profit'] = total_profit + + return +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + FreqAI + + + + + + + + + + + + + + + diff --git a/docs/assets/freqai_logo_no_md.svg b/docs/assets/freqai_logo_no_md.svg deleted file mode 100644 index 62c32217f..000000000 --- a/docs/assets/freqai_logo_no_md.svg +++ /dev/null @@ -1,198 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - FreqAI - - - - - - - - - - - - - diff --git a/docs/freqai.md b/docs/freqai.md index b3682127c..9b2377557 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -1,21 +1,37 @@ -![freqai-logo](assets/freqai_logo_no_md.svg) +![freqai-logo](assets/freqai_doc_logo.svg) # FreqAI -FreqAI is a module designed to automate a variety of tasks associated with -training a predictive model to provide signals based on input features. +FreqAI is a module designed to automate a variety of tasks associated with training a predictive model to generate market forecasts given a set of input features. Among the the features included: -* Create large rich feature sets (10k+ features) based on simple user created strategies. -* Sweep model training and backtesting to simulate consistent model retraining through time. -* Remove outliers automatically from training and prediction sets using a Dissimilarity Index and Support Vector Machines. -* Reduce the dimensionality of the data with Principal Component Analysis. -* Store models to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs). -* Normalize the data automatically in a smart and statistically safe way. -* Automated data download and data handling. -* Clean the incoming data of NaNs in a safe way before training and prediction. -* Retrain live automatically so that the model self-adapts to the market in an unsupervised manner. +* **Self-adaptive retraining**: automatically retrain models during live deployments to self-adapt to the market in an unsupervised manner. +* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies. +* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. +* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining. +* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available. +* **Smart outlier removal**: remove outliers automatically from training and prediction sets using a variety of outlier detection techniques. +* **Crash resilience**: automatic model storage to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs). +* **Automated data normalization**: automatically normalize the data automatically in a smart and statistically safe way. +* **Automatic data download**: automatically compute the data download timerange and downloads data accordingly (in live deployments). +* **Clean the incoming data of NaNs in a safe way before training and prediction. +* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis. +* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades. + +## Quick start + +The easiest way to quickly test FreqAI is to run it in dry run with the following command + +```bash +freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates +``` + +where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading. + +The example strategy, example prediction model, and example config can all be found in +`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, +`config_examples/config_freqai.example.json`, respectively. ## General approach @@ -30,7 +46,7 @@ An overview of the algorithm is shown here to help users understand the data pro ## Background and vocabulary **Features** are the quantities with which a model is trained. $X_i$ represents the -vector of all features for a single candle. In Freqai, the user +vector of all features for a single candle. In FreqAI, the user builds the features from anything they can construct in the strategy. **Labels** are the target values with which the weights inside a model are trained @@ -50,7 +66,7 @@ directly influence nodal weights within the model. ## Install prerequisites -Use `pip` to install the prerequisites with: +The normal Freqtrade install process will ask the user if they wish to install `FreqAI` dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with: ``` bash pip install -r requirements-freqai.txt @@ -59,18 +75,6 @@ pip install -r requirements-freqai.txt !!! Note Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform. -## Running from the example files - -An example strategy, an example prediction model, and example config can all be found in -`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, -`config_examples/config_freqai.example.json`, respectively. - -Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with: - -```bash -freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --timerange 20220101-20220201 -``` - ## Configuring the bot ### Parameter table @@ -92,13 +96,13 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`.
**Datatype:** boolean. | `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire.
**Datatype:** positive integer. | | **Feature Parameters** -| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#building-the-feature-set)
**Datatype:** dictionary. +| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering)
**Datatype:** dictionary. | `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** list of assets (strings). | `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set.
**Datatype:** list of timeframes (strings). | `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not.
**Datatype:** positive integer. | `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user.
**Datatype:** positive integer. | `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained more [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** positive float (typically below 1). -| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](##controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). +| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). | `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. | `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reprodicibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. @@ -133,8 +137,8 @@ Here are the values the user can expect to include/use inside the typical strate ### Example config file -The user interface is isolated to the typical config file. A typical Freqai -config setup includes: +The user interface is isolated to the typical config file. A typical FreqAI +config setup could include: ```json "freqai": { @@ -169,7 +173,7 @@ config setup includes: } ``` -### Building the feature set +### Feature engineering Features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%` and labels are added by prepending `&`. @@ -182,7 +186,7 @@ various configuration parameters which multiply the feature set such as `include ```python def populate_any_indicators( - self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False + self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ Function designed to automatically generate, name and merge features @@ -198,6 +202,8 @@ various configuration parameters which multiply the feature set such as `include :param coin: the name of the coin which will modify the feature names. """ + coint = pair.split('/')[0] + with self.freqai.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -265,7 +271,7 @@ various configuration parameters which multiply the feature set such as `include return df ``` -The user of the present example does not want to pass the `bb_lowerband` as a feature to the model, +The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model, and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the model for training/prediction and has therefore prepended it with `%`. @@ -313,7 +319,7 @@ set will include all the features from `populate_any_indicators` on all the `inc `include_shifted_candles` is another user controlled parameter which indicates the number of previous candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells -Freqai to include the the past 2 candles for each of the features included in the dataset. +FreqAI to include the the past 2 candles for each of the features included in the dataset. In total, the number of features the present user has created is: @@ -326,12 +332,12 @@ Users define the backtesting timerange with the typical `--timerange` parameter configuration file. `train_period_days` is the duration of the sliding training window, while `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be a float to indicate sub daily retraining in live/dry mode). In the present example, -the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days. +the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days. This means that if the user sets `--timerange 20210501-20210701`, -Freqai will train 8 separate models (because the full range comprises 8 weeks), +FreqAI will train 8 separate models (because the full range comprises 8 weeks), and then backtest the subsequent week associated with each of the 8 training data set timerange months. Users can think of this as a "sliding window" which -emulates Freqai retraining itself once per week in live using the previous +emulates FreqAI retraining itself once per week in live using the previous month of data. In live, the required training data is automatically computed and downloaded. However, in backtesting @@ -349,16 +355,18 @@ and adding this to the `train_period_days`. The units need to be in the base can !!! Note Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. -## Running Freqai +## Running FreqAI -### Training and backtesting +### Backtesting -The freqai training/backtesting module can be executed with the following command: +The FreqAI backtesting module can be executed with the following command: ```bash freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 ``` +Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). + If this command has never been executed with the existing config file, then it will train a new model for each pair, for each backtesting window within the bigger `--timerange`. @@ -374,7 +382,7 @@ for each pair, for each backtesting window within the bigger `--timerange`. ### Building a freqai strategy -The Freqai strategy requires the user to include the following lines of code in the strategy: +The FreqAI strategy requires the user to include the following lines of code in the strategy: ```python @@ -419,21 +427,16 @@ FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostC df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') ``` -### Building an IFreqaiModel - -FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. -However, users can customize and create their own prediction models using the `IFreqaiModel` class. -Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. ### Running the model live -Freqai can be run dry/live using the following command +FreqAI can be run dry/live using the following command ```bash -freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel +freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` -By default, Freqai will not find find any existing models and will start by training a new one +By default, FreqAI will not find find any existing models and will start by training a new one given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse @@ -446,7 +449,7 @@ the same `identifier` parameter } ``` -In this case, although Freqai will initiate with a +In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained, and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. @@ -473,7 +476,7 @@ the user is asking for `labels` that are 24 candles in the future. ### Removing outliers with the Dissimilarity Index The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each -prediction by the model. To do so, Freqai measures the distance between each training +prediction by the model. To do so, FreqAI measures the distance between each training data point and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ @@ -679,6 +682,11 @@ database setup in a pandas dataframe and ready to be analyzed. Here is an exampl return ``` +## Building an IFreqaiModel + +FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. +However, users can customize and create their own prediction models using the `IFreqaiModel` class. +Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. - ## Additional information ### Common pitfalls diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index adfd68c84..5e64d165d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -2,9 +2,8 @@ import copy import datetime import logging import shutil -import sqlite3 from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Tuple import numpy as np import numpy.typing as npt @@ -88,20 +87,6 @@ class FreqaiDataKitchen: config["freqai"]["backtest_period_days"], ) - self.database_path: Optional[Path] = None - - if self.live: - db_url = self.config.get('db_url', None) - self.database_path = Path(db_url) - if 'sqlite' not in self.database_path.parts[0]: - self.database_path = None - logger.warning('FreqAI database analyzer only available for sqlite dbs. ' - ' FreqAI will still run, but user cannot use database analyzer.') - else: - self.database_name = Path(*self.database_path.parts[1:]) - - self.trade_database_df: DataFrame = pd.DataFrame() - self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {}) self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1) self.train_dates: DataFrame = pd.DataFrame() @@ -1007,13 +992,6 @@ class FreqaiDataKitchen: f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label]) self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1] - # KEEPME incase we want to let user start to grab quantiles. - # upper_q = spy.stats.norm.ppf(self.freqai_config['feature_parameters'][ - # 'target_quantile'], *f) - # lower_q = spy.stats.norm.ppf(1 - self.freqai_config['feature_parameters'][ - # 'target_quantile'], *f) - # self.data["upper_quantile"] = upper_q - # self.data["lower_quantile"] = lower_q return def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame: @@ -1025,181 +1003,3 @@ class FreqaiDataKitchen: col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%") ] return dataframe[to_keep] - - def get_current_trade_database(self) -> None: - - if self.database_path is None: - logger.warning('No trade database found. Skipping analysis.') - return - - data = sqlite3.connect(self.database_name) - query = data.execute("SELECT * From trades") - cols = [column[0] for column in query.description] - df = pd.DataFrame.from_records(data=query.fetchall(), columns=cols) - self.trade_database_df = df.dropna(subset='close_date') - data.close() - - def np_encoder(self, object): - if isinstance(object, np.generic): - return object.item() - - # Functions containing useful data manipulation examples. but not actively in use. - - # Possibly phasing these outlier removal methods below out in favor of - # use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance). - # But these have good data manipulation examples, so keep them commented here for now. - - # def determine_statistical_distributions(self) -> None: - # from fitter import Fitter - - # logger.info('Determining best model for all features, may take some time') - - # def compute_quantiles(ft): - # f = Fitter(self.data_dictionary["train_features"][ft], - # distributions=['gamma', 'cauchy', 'laplace', - # 'beta', 'uniform', 'lognorm']) - # f.fit() - # # f.summary() - # dist = list(f.get_best().items())[0][0] - # params = f.get_best()[dist] - # upper_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.999, **params) - # lower_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.001, **params) - - # return ft, upper_q, lower_q, dist - - # quantiles_tuple = Parallel(n_jobs=-1)( - # delayed(compute_quantiles)(ft) for ft in self.data_dictionary[ - # 'train_features'].columns) - - # df = pd.DataFrame(quantiles_tuple, columns=['features', 'upper_quantiles', - # 'lower_quantiles', 'dist']) - # self.data_dictionary['upper_quantiles'] = df['upper_quantiles'] - # self.data_dictionary['lower_quantiles'] = df['lower_quantiles'] - - # return - - # def remove_outliers(self, predict: bool) -> None: - # """ - # Remove data that looks like an outlier based on the distribution of each - # variable. - # :params: - # :predict: boolean which tells the function if this is prediction data or - # training data coming in. - # """ - - # lower_quantile = self.data_dictionary["lower_quantiles"].to_numpy() - # upper_quantile = self.data_dictionary["upper_quantiles"].to_numpy() - - # if predict: - - # df = self.data_dictionary["prediction_features"][ - # (self.data_dictionary["prediction_features"] < upper_quantile) - # & (self.data_dictionary["prediction_features"] > lower_quantile) - # ] - # drop_index = pd.isnull(df).any(1) - # self.data_dictionary["prediction_features"].fillna(0, inplace=True) - # drop_index = ~drop_index - # do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) - - # logger.info( - # "remove_outliers() tossed %s predictions", - # len(do_predict) - do_predict.sum(), - # ) - # self.do_predict += do_predict - # self.do_predict -= 1 - - # else: - - # filter_train_df = self.data_dictionary["train_features"][ - # (self.data_dictionary["train_features"] < upper_quantile) - # & (self.data_dictionary["train_features"] > lower_quantile) - # ] - # drop_index = pd.isnull(filter_train_df).any(1) - # drop_index = drop_index.replace(True, 1).replace(False, 0) - # self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ - # (drop_index == 0) - # ] - # self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][ - # (drop_index == 0) - # ] - # self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][ - # (drop_index == 0) - # ] - - # logger.info( - # f'remove_outliers() tossed {drop_index.sum()}' - # f' training points from {len(filter_train_df)}' - # ) - - # # do the same for the test data - # filter_test_df = self.data_dictionary["test_features"][ - # (self.data_dictionary["test_features"] < upper_quantile) - # & (self.data_dictionary["test_features"] > lower_quantile) - # ] - # drop_index = pd.isnull(filter_test_df).any(1) - # drop_index = drop_index.replace(True, 1).replace(False, 0) - # self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][ - # (drop_index == 0) - # ] - # self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ - # (drop_index == 0) - # ] - # self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][ - # (drop_index == 0) - # ] - - # logger.info( - # f'remove_outliers() tossed {drop_index.sum()}' - # f' test points from {len(filter_test_df)}' - # ) - - # return - - # def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]: - # """ - # standardize all data in the data_dictionary according to the training dataset - # :params: - # :data_dictionary: dictionary containing the cleaned and split training/test data/labels - # :returns: - # :data_dictionary: updated dictionary with standardized values. - # """ - # # standardize the data by training stats - # train_mean = data_dictionary["train_features"].mean() - # train_std = data_dictionary["train_features"].std() - # data_dictionary["train_features"] = ( - # data_dictionary["train_features"] - train_mean - # ) / train_std - # data_dictionary["test_features"] = ( - # data_dictionary["test_features"] - train_mean - # ) / train_std - - # train_labels_std = data_dictionary["train_labels"].std() - # train_labels_mean = data_dictionary["train_labels"].mean() - # data_dictionary["train_labels"] = ( - # data_dictionary["train_labels"] - train_labels_mean - # ) / train_labels_std - # data_dictionary["test_labels"] = ( - # data_dictionary["test_labels"] - train_labels_mean - # ) / train_labels_std - - # for item in train_std.keys(): - # self.data[item + "_std"] = train_std[item] - # self.data[item + "_mean"] = train_mean[item] - - # self.data["labels_std"] = train_labels_std - # self.data["labels_mean"] = train_labels_mean - - # return data_dictionary - - # def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame: - # """ - # Normalizes a set of data using the mean and standard deviation from - # the associated training data. - # :params: - # :df: Dataframe to be standardized - # """ - - # for item in df.keys(): - # df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"] - - # return df diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 833fb50d6..0c32a625d 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -491,9 +491,6 @@ class IFreqaiModel(ABC): model = self.train(unfiltered_dataframe, pair, dk) - dk.get_current_trade_database() - self.analyze_trade_database(dk, pair) - self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts dk.set_new_model_names(pair, new_trained_timerange) self.dd.pair_dict[pair]["first"] = False @@ -612,20 +609,3 @@ class IFreqaiModel(ABC): :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index) """ - - def analyze_trade_database(self, dk: FreqaiDataKitchen, pair: str) -> None: - """ - User analyzes the trade database here and returns summary stats which will be passed back - to the strategy for reinforcement learning or for additional adaptive metrics for use - in entry/exit signals. Store these metrics in dk.data['extra_returns_per_train'] and - they will format themselves into the dataframe as an additional column in the user - strategy. User has access to the current trade database in dk.trade_database_df. - """ - # if dk.trade_database_df.empty: - # logger.warning(f'No trades found for {pair} to analyze DB') - # return - - # total_profit = dk.trade_database_df['close_profit_abs'].sum() - # dk.data['extra_returns_per_train']['total_profit'] = total_profit - - return From f7502bcc923044a60d2bad31d5c3cd25ef12f56d Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 11:35:24 +0000 Subject: [PATCH 327/518] slightly update dca_short test --- tests/test_integration.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_integration.py b/tests/test_integration.py index 40fdb4277..6a11b13f4 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -291,7 +291,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None: 'freqtrade.exchange.Exchange', fetch_ticker=ticker_usdt, get_fee=fee, - amount_to_precision=lambda s, x, y: y, + amount_to_precision=lambda s, x, y: round(y, 4), price_to_precision=lambda s, x, y: y, ) @@ -303,6 +303,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None: assert len(trade.orders) == 1 assert pytest.approx(trade.stake_amount) == 60 assert trade.open_rate == 2.02 + assert trade.orders[0].amount == trade.amount # No adjustment freqtrade.process() trade = Trade.get_trades().first() @@ -331,8 +332,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None: trade = Trade.get_trades().first() assert len(trade.orders) == 2 assert pytest.approx(trade.stake_amount) == 120 - # assert trade.orders[0].amount == 30 - assert trade.orders[1].amount == 60 / ticker_usdt_modif['ask'] + assert trade.orders[1].amount == round(60 / ticker_usdt_modif['ask'], 4) assert trade.amount == trade.orders[0].amount + trade.orders[1].amount assert trade.nr_of_successful_entries == 2 @@ -344,7 +344,7 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None: assert trade.is_open is False # assert trade.orders[0].amount == 30 assert trade.orders[0].side == 'sell' - assert trade.orders[1].amount == 60 / ticker_usdt_modif['ask'] + assert trade.orders[1].amount == round(60 / ticker_usdt_modif['ask'], 4) # Sold everything assert trade.orders[-1].side == 'buy' assert trade.orders[2].amount == trade.amount From dd4e44931e9a909ed5a6d04ab6e51ff26beb37c0 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 15:02:52 +0000 Subject: [PATCH 328/518] Improve NAN handling in RPC module --- freqtrade/rpc/rpc.py | 25 +++++++++++++++---------- tests/rpc/test_rpc.py | 40 ---------------------------------------- 2 files changed, 15 insertions(+), 50 deletions(-) diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index 9f2c8cf37..ede8a9d3c 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -239,12 +239,15 @@ class RPC: trade.pair, side='exit', is_short=trade.is_short, refresh=False) except (PricingError, ExchangeError): current_rate = NAN - if len(trade.select_filled_orders(trade.entry_side)) > 0: - trade_profit = trade.calc_profit(current_rate) - profit_str = f'{trade.calc_profit_ratio(current_rate):.2%}' + trade_profit = NAN + profit_str = f'{NAN:.2%}' else: - trade_profit = 0.0 - profit_str = f'{0.0:.2f}' + if trade.nr_of_successful_entries > 0: + trade_profit = trade.calc_profit(current_rate) + profit_str = f'{trade.calc_profit_ratio(current_rate):.2%}' + else: + trade_profit = 0.0 + profit_str = f'{0.0:.2f}' direction_str = ('S' if trade.is_short else 'L') if nonspot else '' if self._fiat_converter: fiat_profit = self._fiat_converter.convert_amount( @@ -424,8 +427,6 @@ class RPC: for trade in trades: current_rate: float = 0.0 - if not trade.open_rate: - continue if trade.close_date: durations.append((trade.close_date - trade.open_date).total_seconds()) @@ -447,9 +448,13 @@ class RPC: trade.pair, side='exit', is_short=trade.is_short, refresh=False) except (PricingError, ExchangeError): current_rate = NAN - profit_ratio = trade.calc_profit_ratio(rate=current_rate) - profit_abs = trade.calc_profit( - rate=trade.close_rate or current_rate) + trade.realized_profit + if isnan(current_rate): + profit_ratio = NAN + profit_abs = NAN + else: + profit_ratio = trade.calc_profit_ratio(rate=current_rate) + profit_abs = trade.calc_profit( + rate=trade.close_rate or current_rate) + trade.realized_profit profit_all_coin.append(profit_abs) profit_all_ratio.append(profit_ratio) diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 4c580c3c2..1a2428fe7 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -461,46 +461,6 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None: assert isnan(stats['profit_all_coin']) -# Test that rpc_trade_statistics can handle trades that lacks -# trade.open_rate (it is set to None) -def test_rpc_trade_statistics_closed(mocker, default_conf_usdt, ticker, fee): - mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price', - return_value=1.1) - mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock()) - mocker.patch.multiple( - 'freqtrade.exchange.Exchange', - fetch_ticker=ticker, - get_fee=fee, - ) - - freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt) - patch_get_signal(freqtradebot) - stake_currency = default_conf_usdt['stake_currency'] - fiat_display_currency = default_conf_usdt['fiat_display_currency'] - - rpc = RPC(freqtradebot) - - # Create some test data - create_mock_trades_usdt(fee) - - for trade in Trade.query.order_by(Trade.id).all(): - trade.open_rate = None - - stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency) - assert stats['profit_closed_coin'] == 0 - assert stats['profit_closed_percent_mean'] == 0 - assert stats['profit_closed_fiat'] == 0 - assert stats['profit_all_coin'] == 0 - assert stats['profit_all_percent_mean'] == 0 - assert stats['profit_all_fiat'] == 0 - assert stats['trade_count'] == 7 - assert stats['first_trade_date'] == '2 days ago' - assert stats['latest_trade_date'] == '17 minutes ago' - assert stats['avg_duration'] == '0:00:00' - assert stats['best_pair'] == 'XRP/USDT' - assert stats['best_rate'] == 10.0 - - def test_rpc_balance_handle_error(default_conf, mocker): mock_balance = { 'BTC': { From de690b0a698f6c65ce1e4cebb6b2fc673df9b3af Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 20:08:40 +0200 Subject: [PATCH 329/518] Use PEP440 compatible versioning --- freqtrade/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/__init__.py b/freqtrade/__init__.py index f8be8f66f..2572c03f1 100644 --- a/freqtrade/__init__.py +++ b/freqtrade/__init__.py @@ -1,5 +1,5 @@ """ Freqtrade bot """ -__version__ = 'develop' +__version__ = '2022.8.dev' if 'dev' in __version__: try: From cc885e25ac048cba4df69b8a2bd8787d728dd69d Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 20:16:07 +0200 Subject: [PATCH 330/518] Improve NAN Handling in RPC --- freqtrade/rpc/rpc.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index ede8a9d3c..ed7f13a96 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -179,8 +179,10 @@ class RPC: else: current_rate = trade.close_rate if len(trade.select_filled_orders(trade.entry_side)) > 0: - current_profit = trade.calc_profit_ratio(current_rate) - current_profit_abs = trade.calc_profit(current_rate) + current_profit = trade.calc_profit_ratio( + current_rate) if not isnan(current_rate) else NAN + current_profit_abs = trade.calc_profit( + current_rate) if not isnan(current_rate) else NAN current_profit_fiat: Optional[float] = None # Calculate fiat profit if self._fiat_converter: From aa1bf2adbd0b0f1796b7ad734123cea173bd43aa Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 12 Aug 2022 06:43:34 +0200 Subject: [PATCH 331/518] Try fix windows testfailure --- tests/plugins/test_protections.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py index 4cebb6492..07541735d 100644 --- a/tests/plugins/test_protections.py +++ b/tests/plugins/test_protections.py @@ -335,6 +335,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=110, min_ago_close=20, profit_rate=0.8, )) + Trade.commit() # Locks due to 2nd trade assert freqtrade.protections.global_stop() != only_per_side @@ -342,6 +343,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert PairLocks.is_pair_locked('XRP/BTC', side='long') assert PairLocks.is_pair_locked('XRP/BTC', side='*') != only_per_side assert not PairLocks.is_global_lock() + Trade.commit() @pytest.mark.usefixtures("init_persistence") From d93bb82193fed27d255bb5386e2c6b31c7702bca Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 12 Aug 2022 08:19:40 +0200 Subject: [PATCH 332/518] Add more Commits to failing test --- tests/plugins/test_protections.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py index 07541735d..7a21b2ed6 100644 --- a/tests/plugins/test_protections.py +++ b/tests/plugins/test_protections.py @@ -305,6 +305,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): min_ago_open=800, min_ago_close=450, profit_rate=0.9, )) + Trade.commit() # Not locked with 1 trade assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') @@ -316,6 +317,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): min_ago_open=200, min_ago_close=120, profit_rate=0.9, )) + Trade.commit() # Not locked with 1 trade (first trade is outside of lookback_period) assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') @@ -327,6 +329,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=20, min_ago_close=10, profit_rate=1.15, is_short=True )) + Trade.commit() assert freqtrade.protections.stop_per_pair('XRP/BTC') != only_per_side assert not PairLocks.is_pair_locked('XRP/BTC', side='*') assert PairLocks.is_pair_locked('XRP/BTC', side='long') == only_per_side From b427c7ff136fb017d75eb5f59b8adf98dfb306ac Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 12 Aug 2022 07:28:19 +0000 Subject: [PATCH 333/518] Use diff. close time to avoid buggy behavior --- tests/plugins/test_protections.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py index 7a21b2ed6..8a5356b3e 100644 --- a/tests/plugins/test_protections.py +++ b/tests/plugins/test_protections.py @@ -336,7 +336,7 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): Trade.query.session.add(generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, - min_ago_open=110, min_ago_close=20, profit_rate=0.8, + min_ago_open=110, min_ago_close=21, profit_rate=0.8, )) Trade.commit() From fb4b73ce8961dd9db34ad5b94d7484ca80ffc06b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 12 Aug 2022 12:03:44 +0200 Subject: [PATCH 334/518] ensure dates are saved --- freqtrade/freqai/data_kitchen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 5e64d165d..0cff9c90e 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -202,7 +202,7 @@ class FreqaiDataKitchen: labels = unfiltered_dataframe.filter(label_list, axis=1) drop_index_labels = pd.isnull(labels).any(1) drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0) - dates = unfiltered_dataframe.filter('date', axis=1) + dates = unfiltered_dataframe['date'] filtered_dataframe = filtered_dataframe[ (drop_index == 0) & (drop_index_labels == 0) ] # dropping values From 923f73a5161dd861be3ae2885856a0b0ac54b640 Mon Sep 17 00:00:00 2001 From: ecoppen <51025241+ecoppen@users.noreply.github.com> Date: Fri, 12 Aug 2022 19:56:46 +0100 Subject: [PATCH 335/518] nobase -> baseonly --- freqtrade/rpc/telegram.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index c3b41a907..88222608e 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -1371,7 +1371,7 @@ class Telegram(RPCHandler): if context.args: if "sorted" in context.args: whitelist['whitelist'] = sorted(whitelist['whitelist']) - if "nobase" in context.args: + if "baseonly" in context.args: whitelist['whitelist'] = [pair.split("/")[0] for pair in whitelist['whitelist']] message = f"Using whitelist `{whitelist['method']}` with {whitelist['length']} pairs\n" @@ -1493,8 +1493,8 @@ class Telegram(RPCHandler): "*/fx |all:* `Alias to /forceexit`\n" f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}" "*/delete :* `Instantly delete the given trade in the database`\n" - "*/whitelist [sorted] [nobase]:* `Show current whitelist. Optionally in " - "order and/or without the base currency.`\n" + "*/whitelist [sorted] [baseonly]:* `Show current whitelist. Optionally in " + "order and/or only displaying the base currency of each pairing.`\n" "*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " "to the blacklist.` \n" "*/blacklist_delete [pairs]| /bl_delete [pairs]:* " From ccc0ad6f642aa875c06bc39bb34a646c07cddb10 Mon Sep 17 00:00:00 2001 From: ecoppen <51025241+ecoppen@users.noreply.github.com> Date: Fri, 12 Aug 2022 19:58:41 +0100 Subject: [PATCH 336/518] fix - reload whitelist Should fix the issue, if not I'll move development to a different computer and get local testing running properly. --- tests/rpc/test_rpc_telegram.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index 8049a10e9..cd26e744f 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -1461,21 +1461,25 @@ def test_whitelist_static(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in sorted(msg_mock.call_args_list[0][0][0])) context = MagicMock() - context.args = ['nobase'] + context.args = ['baseonly'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() - context.args = ['nobase', 'sorted'] + context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) + def test_whitelist_dynamic(default_conf, update, mocker) -> None: mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True)) default_conf['pairlists'] = [{'method': 'VolumePairList', @@ -1491,21 +1495,25 @@ def test_whitelist_dynamic(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in sorted(msg_mock.call_args_list[0][0][0])) context = MagicMock() - context.args = ['nobase'] + context.args = ['baseonly'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() - context.args = ['nobase', 'sorted'] + context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() + telegram._whitelist(update=update, context=MagicMock()) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) + def test_blacklist_static(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) From 2312b86a66bbe31a1aa28e52e82b74db77bbdbbc Mon Sep 17 00:00:00 2001 From: ecoppen <51025241+ecoppen@users.noreply.github.com> Date: Fri, 12 Aug 2022 19:59:08 +0100 Subject: [PATCH 337/518] Update telegram-usage.md Add the optional arguments to the documentation. --- docs/telegram-usage.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md index a690e18b9..add889681 100644 --- a/docs/telegram-usage.md +++ b/docs/telegram-usage.md @@ -187,7 +187,7 @@ official commands. You can ask at any moment for help with `/help`. | `/stats` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells | `/exits` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells | `/entries` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells -| `/whitelist` | Show the current whitelist +| `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing. | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist. | `/edge` | Show validated pairs by Edge if it is enabled. | `/help` | Show help message From 0e61c2d05717617dc80df252af5bfc5da60238bf Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 10 Aug 2022 20:31:03 +0200 Subject: [PATCH 338/518] Replace Decimal with FtPrecise in trade_model --- freqtrade/persistence/trade_model.py | 26 +++++++++++++------------- tests/exchange/test_ccxt_precise.py | 2 ++ 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 3ea6ddf2d..4797ba99d 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -3,7 +3,6 @@ This module contains the class to persist trades into SQLite """ import logging from datetime import datetime, timedelta, timezone -from decimal import Decimal from math import isclose from typing import Any, Dict, List, Optional @@ -17,6 +16,7 @@ from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE +from freqtrade.util import FtPrecise logger = logging.getLogger(__name__) @@ -694,8 +694,8 @@ class LocalTrade(): Calculate the open_rate including open_fee. :return: Price in of the open trade incl. Fees """ - open_trade = Decimal(amount) * Decimal(open_rate) - fees = open_trade * Decimal(self.fee_open) + open_trade = FtPrecise(amount) * FtPrecise(open_rate) + fees = open_trade * FtPrecise(self.fee_open) if self.is_short: return float(open_trade - fees) else: @@ -708,30 +708,30 @@ class LocalTrade(): """ self.open_trade_value = self._calc_open_trade_value(self.amount, self.open_rate) - def calculate_interest(self) -> Decimal: + def calculate_interest(self) -> FtPrecise: """ Calculate interest for this trade. Only applicable for Margin trading. """ - zero = Decimal(0.0) + zero = FtPrecise(0.0) # If nothing was borrowed if self.trading_mode != TradingMode.MARGIN or self.has_no_leverage: return zero open_date = self.open_date.replace(tzinfo=None) now = (self.close_date or datetime.now(timezone.utc)).replace(tzinfo=None) - sec_per_hour = Decimal(3600) - total_seconds = Decimal((now - open_date).total_seconds()) + sec_per_hour = FtPrecise(3600) + total_seconds = FtPrecise((now - open_date).total_seconds()) hours = total_seconds / sec_per_hour or zero - rate = Decimal(self.interest_rate) - borrowed = Decimal(self.borrowed) + rate = FtPrecise(self.interest_rate) + borrowed = FtPrecise(self.borrowed) return interest(exchange_name=self.exchange, borrowed=borrowed, rate=rate, hours=hours) - def _calc_base_close(self, amount: Decimal, rate: float, fee: float) -> Decimal: + def _calc_base_close(self, amount: FtPrecise, rate: float, fee: float) -> FtPrecise: - close_trade = amount * Decimal(rate) - fees = close_trade * Decimal(fee) + close_trade = amount * FtPrecise(rate) + fees = close_trade * FtPrecise(fee) if self.is_short: return close_trade + fees @@ -747,7 +747,7 @@ class LocalTrade(): if rate is None and not self.close_rate: return 0.0 - amount1 = Decimal(amount or self.amount) + amount1 = FtPrecise(amount or self.amount) trading_mode = self.trading_mode or TradingMode.SPOT if trading_mode == TradingMode.SPOT: diff --git a/tests/exchange/test_ccxt_precise.py b/tests/exchange/test_ccxt_precise.py index 8b599093f..5542ac8d2 100644 --- a/tests/exchange/test_ccxt_precise.py +++ b/tests/exchange/test_ccxt_precise.py @@ -78,3 +78,5 @@ def test_FtPrecise(): assert FtPrecise(-213) == '-213' assert str(FtPrecise(-213)) == '-213' assert FtPrecise(213.2) == '213.2' + assert float(FtPrecise(213.2)) == 213.2 + assert float(FtPrecise(-213.2)) == -213.2 From da253f12fe598a5285c6f3c462b585d8248289d7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 10 Aug 2022 20:45:54 +0200 Subject: [PATCH 339/518] Bump CCXT to required version --- requirements.txt | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 4fa6e60a0..06c3ca44f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.1 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.91.93 +ccxt==1.92.9 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 diff --git a/setup.py b/setup.py index 7aa56bf81..95671d3b8 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ setup( ], install_requires=[ # from requirements.txt - 'ccxt>=1.83.12', + 'ccxt>=1.92.9', 'SQLAlchemy', 'python-telegram-bot>=13.4', 'arrow>=0.17.0', From 902afc2f027de1c269925401c5a6b1f5d1b05f8e Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 10 Aug 2022 20:56:04 +0200 Subject: [PATCH 340/518] Use FtPrecise in interest calculation --- freqtrade/leverage/interest.py | 22 +++++++++++----------- tests/leverage/test_interest.py | 12 ++++++------ tests/rpc/test_rpc_apiserver.py | 2 +- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/freqtrade/leverage/interest.py b/freqtrade/leverage/interest.py index 367df5821..ddeea2b42 100644 --- a/freqtrade/leverage/interest.py +++ b/freqtrade/leverage/interest.py @@ -1,20 +1,20 @@ -from decimal import Decimal from math import ceil from freqtrade.exceptions import OperationalException +from freqtrade.util import FtPrecise -one = Decimal(1.0) -four = Decimal(4.0) -twenty_four = Decimal(24.0) +one = FtPrecise(1.0) +four = FtPrecise(4.0) +twenty_four = FtPrecise(24.0) def interest( exchange_name: str, - borrowed: Decimal, - rate: Decimal, - hours: Decimal -) -> Decimal: + borrowed: FtPrecise, + rate: FtPrecise, + hours: FtPrecise +) -> FtPrecise: """ Equation to calculate interest on margin trades @@ -31,13 +31,13 @@ def interest( """ exchange_name = exchange_name.lower() if exchange_name == "binance": - return borrowed * rate * ceil(hours) / twenty_four + return borrowed * rate * FtPrecise(ceil(hours)) / twenty_four elif exchange_name == "kraken": # Rounded based on https://kraken-fees-calculator.github.io/ - return borrowed * rate * (one + ceil(hours / four)) + return borrowed * rate * (one + FtPrecise(ceil(hours / four))) elif exchange_name == "ftx": # As Explained under #Interest rates section in # https://help.ftx.com/hc/en-us/articles/360053007671-Spot-Margin-Trading-Explainer - return borrowed * rate * ceil(hours) / twenty_four + return borrowed * rate * FtPrecise(ceil(hours)) / twenty_four else: raise OperationalException(f"Leverage not available on {exchange_name} with freqtrade") diff --git a/tests/leverage/test_interest.py b/tests/leverage/test_interest.py index 6b189ce50..7bdf4c2f0 100644 --- a/tests/leverage/test_interest.py +++ b/tests/leverage/test_interest.py @@ -1,14 +1,14 @@ -from decimal import Decimal from math import isclose import pytest from freqtrade.leverage import interest +from freqtrade.util import FtPrecise -ten_mins = Decimal(1 / 6) -five_hours = Decimal(5.0) -twentyfive_hours = Decimal(25.0) +ten_mins = FtPrecise(1 / 6) +five_hours = FtPrecise(5.0) +twentyfive_hours = FtPrecise(25.0) @pytest.mark.parametrize('exchange,interest_rate,hours,expected', [ @@ -28,11 +28,11 @@ twentyfive_hours = Decimal(25.0) ('ftx', 0.00025, twentyfive_hours, 0.015625), ]) def test_interest(exchange, interest_rate, hours, expected): - borrowed = Decimal(60.0) + borrowed = FtPrecise(60.0) assert isclose(interest( exchange_name=exchange, borrowed=borrowed, - rate=Decimal(interest_rate), + rate=FtPrecise(interest_rate), hours=hours ), expected) diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 25343ead6..eb3172704 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -892,7 +892,7 @@ def test_api_performance(botclient, fee): assert_response(rc) assert len(rc.json()) == 2 assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61, 'profit_pct': 7.61, - 'profit_ratio': 0.07609203, 'profit_abs': 0.01872279}, + 'profit_ratio': 0.07609203, 'profit_abs': 0.0187228}, {'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57, 'profit_pct': -5.57, 'profit_ratio': -0.05570419, 'profit_abs': -0.1150375}] From 3bcb47d75d33d57885babee10bbac1c3a8af8e1c Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 06:34:12 +0200 Subject: [PATCH 341/518] Remove usage of Decimal --- freqtrade/freqtradebot.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 0dbeb2e44..e3ef01dd5 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -5,7 +5,6 @@ import copy import logging import traceback from datetime import datetime, time, timedelta, timezone -from decimal import Decimal from math import isclose from threading import Lock from typing import Any, Dict, List, Optional, Tuple @@ -33,6 +32,7 @@ from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.rpc import RPCManager from freqtrade.strategy.interface import IStrategy from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper +from freqtrade.util import FtPrecise from freqtrade.wallets import Wallets @@ -565,7 +565,7 @@ class FreqtradeBot(LoggingMixin): if stake_amount is not None and stake_amount < 0.0: # We should decrease our position - amount = abs(float(Decimal(stake_amount) / Decimal(current_exit_rate))) + amount = abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate))) if amount > trade.amount: # This is currently ineffective as remaining would become < min tradable # Fixing this would require checking for 0.0 there - From 9513c39a171a16df17250bb4f0571d4cd50b357b Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 06:38:14 +0200 Subject: [PATCH 342/518] Fix migration rounding test --- tests/test_persistence.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index eea6d6fa1..8a3a18577 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1387,7 +1387,8 @@ def test_migrate_new(mocker, default_conf, fee, caplog): assert log_has("trying trades_bak2", caplog) assert log_has("Running database migration for trades - backup: trades_bak2, orders_bak0", caplog) - assert trade.open_trade_value == trade._calc_open_trade_value(trade.amount, trade.open_rate) + assert round(trade.open_trade_value, 15) == trade._calc_open_trade_value( + trade.amount, trade.open_rate) assert trade.close_profit_abs is None orders = trade.orders From e3a5b97b451c0a59eec8df4dfc60a5de9c36e4cf Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 11 Aug 2022 06:51:34 +0200 Subject: [PATCH 343/518] Update recalc_from_trades to use FtPrecise --- freqtrade/persistence/trade_model.py | 33 ++++++++++++++-------------- tests/test_integration.py | 2 +- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 4797ba99d..faed1eab5 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -827,11 +827,11 @@ class LocalTrade(): return float(f"{profit_ratio:.8f}") def recalc_trade_from_orders(self, is_closing: bool = False): - - current_amount = 0.0 - current_stake = 0.0 + ZERO = FtPrecise(0.0) + current_amount = FtPrecise(0.0) + current_stake = FtPrecise(0.0) total_stake = 0.0 # Total stake after all buy orders (does not subtract!) - avg_price = 0.0 + avg_price = FtPrecise(0.0) close_profit = 0.0 close_profit_abs = 0.0 @@ -839,28 +839,29 @@ class LocalTrade(): if o.ft_is_open or not o.filled: continue - tmp_amount = o.safe_amount_after_fee - tmp_price = o.safe_price + tmp_amount = FtPrecise(o.safe_amount_after_fee) + tmp_price = FtPrecise(o.safe_price) is_exit = o.ft_order_side != self.entry_side - side = -1 if is_exit else 1 - if tmp_amount > 0.0 and tmp_price is not None: + side = FtPrecise(-1 if is_exit else 1) + if tmp_amount > ZERO and tmp_price is not None: current_amount += tmp_amount * side price = avg_price if is_exit else tmp_price current_stake += price * tmp_amount * side - if current_amount > 0: + if current_amount > ZERO: avg_price = current_stake / current_amount if is_exit: # Process partial exits exit_rate = o.safe_price exit_amount = o.safe_amount_after_fee - profit = self.calc_profit(rate=exit_rate, amount=exit_amount, open_rate=avg_price) + profit = self.calc_profit(rate=exit_rate, amount=exit_amount, + open_rate=float(avg_price)) close_profit_abs += profit close_profit = self.calc_profit_ratio( exit_rate, amount=exit_amount, open_rate=avg_price) - if current_amount <= 0: + if current_amount <= ZERO: profit = close_profit_abs else: total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price) @@ -870,13 +871,13 @@ class LocalTrade(): self.realized_profit = close_profit_abs self.close_profit_abs = profit - if current_amount > 0: + if current_amount > ZERO: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. - self.open_rate = current_stake / current_amount - self.stake_amount = current_stake / (self.leverage or 1.0) - self.amount = current_amount - self.fee_open_cost = self.fee_open * current_stake + self.open_rate = float(current_stake / current_amount) + self.stake_amount = float(current_stake) / (self.leverage or 1.0) + self.amount = float(current_amount) + self.fee_open_cost = self.fee_open * float(current_stake) self.recalc_open_trade_value() if self.stop_loss_pct is not None and self.open_rate is not None: self.adjust_stop_loss(self.open_rate, self.stop_loss_pct) diff --git a/tests/test_integration.py b/tests/test_integration.py index 6a11b13f4..b970e1c89 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -189,7 +189,7 @@ def test_forcebuy_last_unlimited(default_conf, ticker, fee, mocker, balance_rati assert len(trades) == 5 for trade in trades: - assert trade.stake_amount == result1 + assert pytest.approx(trade.stake_amount) == result1 # Reset trade open order id's trade.open_order_id = None trades = Trade.get_open_trades() From f6545ebdb8a82cedce065779873d395631aff07f Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:10:03 +0200 Subject: [PATCH 344/518] Disallow backtesting with --strategy-list for now. --- freqtrade/optimize/backtesting.py | 3 +++ tests/freqai/test_freqai_backtsting.py | 33 ++++++++++++++++++++++++++ 2 files changed, 36 insertions(+) create mode 100644 tests/freqai/test_freqai_backtsting.py diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 86c1822f7..c00f30686 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -89,6 +89,9 @@ class Backtesting: self.dataprovider = DataProvider(self.config, self.exchange) if self.config.get('strategy_list'): + if self.config.get('freqai'): + raise OperationalException( + "You can't use strategy_list and freqai at the same time.") for strat in list(self.config['strategy_list']): stratconf = deepcopy(self.config) stratconf['strategy'] = strat diff --git a/tests/freqai/test_freqai_backtsting.py b/tests/freqai/test_freqai_backtsting.py new file mode 100644 index 000000000..bb0a71e2c --- /dev/null +++ b/tests/freqai/test_freqai_backtsting.py @@ -0,0 +1,33 @@ +from pathlib import Path +from unittest.mock import PropertyMock + +import pytest + +from freqtrade.commands.optimize_commands import start_backtesting +from freqtrade.exceptions import OperationalException +from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, patch_exchange, + patched_configuration_load_config_file) + + +def test_backtest_start_backtest_list_freqai(freqai_conf, mocker, testdatadir): + # Tests detail-data loading + patch_exchange(mocker) + + mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist', + PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT'])) + # mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock) + + patched_configuration_load_config_file(mocker, freqai_conf) + + args = [ + 'backtesting', + '--config', 'config.json', + '--datadir', str(testdatadir), + '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'), + '--timeframe', '1h', + '--strategy-list', CURRENT_TEST_STRATEGY + ] + args = get_args(args) + with pytest.raises(OperationalException, + match=r"You can't use strategy_list and freqai at the same time\."): + start_backtesting(args) From 49989012abae22740a6a65ccc5ba2685c036aa91 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:20:58 +0200 Subject: [PATCH 345/518] Bump catboost requirement to latest --- requirements-freqai.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-freqai.txt b/requirements-freqai.txt index 5e67a5539..26e4617af 100644 --- a/requirements-freqai.txt +++ b/requirements-freqai.txt @@ -4,5 +4,5 @@ # Required for freqai scikit-learn==1.1.2 joblib==1.1.0 -catboost==1.0.4; platform_machine != 'aarch64' +catboost==1.0.6; platform_machine != 'aarch64' lightgbm==3.3.2 From 3b827ee60a6f9194c825a7ee472bfbc4815e4fe6 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:24:04 +0200 Subject: [PATCH 346/518] Add "freqai.enabled" flag to disable freqAI via config flag aligns with how other optional modules work in freqtrade. --- config_examples/config_freqai.example.json | 1 + freqtrade/constants.py | 16 ++++++++++------ freqtrade/optimize/backtesting.py | 4 ++-- freqtrade/plugins/pairlist/pairlist_helpers.py | 2 +- freqtrade/strategy/interface.py | 2 +- tests/freqai/conftest.py | 1 + 6 files changed, 16 insertions(+), 10 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index e9fc50a4a..aeb1cb13d 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -52,6 +52,7 @@ } ], "freqai": { + "enabled": true, "startup_candles": 10000, "purge_old_models": true, "train_period_days": 15, diff --git a/freqtrade/constants.py b/freqtrade/constants.py index b5d6b4b8e..ddbc84fa9 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -486,6 +486,7 @@ CONF_SCHEMA = { "freqai": { "type": "object", "properties": { + "enabled": {"type": "boolean", "default": False}, "keras": {"type": "boolean", "default": False}, "conv_width": {"type": "integer", "default": 2}, "train_period_days": {"type": "integer", "default": 0}, @@ -525,12 +526,15 @@ CONF_SCHEMA = { }, }, }, - "required": ["train_period_days", - "backtest_period_days", - "identifier", - "feature_parameters", - "data_split_parameters", - "model_training_parameters"] + "required": [ + "enabled", + "train_period_days", + "backtest_period_days", + "identifier", + "feature_parameters", + "data_split_parameters", + "model_training_parameters" + ] }, }, } diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index c00f30686..7691b96f1 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -89,7 +89,7 @@ class Backtesting: self.dataprovider = DataProvider(self.config, self.exchange) if self.config.get('strategy_list'): - if self.config.get('freqai'): + if self.config.get('freqai', {}).get('enabled', False): raise OperationalException( "You can't use strategy_list and freqai at the same time.") for strat in list(self.config['strategy_list']): @@ -210,7 +210,7 @@ class Backtesting: """ self.progress.init_step(BacktestState.DATALOAD, 1) - if self.config.get('freqai') is not None: + if self.config.get('freqai', {}).get('enabled', False): startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0)) if not startup_candles: raise OperationalException('FreqAI backtesting module requires user set ' diff --git a/freqtrade/plugins/pairlist/pairlist_helpers.py b/freqtrade/plugins/pairlist/pairlist_helpers.py index a07a0f783..f1eca6df0 100644 --- a/freqtrade/plugins/pairlist/pairlist_helpers.py +++ b/freqtrade/plugins/pairlist/pairlist_helpers.py @@ -44,7 +44,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str], def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]: expanded_pairs = expand_pairlist(config['pairs'], markets) - if config.get('freqai', {}): + if config.get('freqai', {}).get('enabled', False): corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist'] expanded_pairs += [pair for pair in corr_pairlist if pair not in config['pairs']] diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index cda8a1cbc..d10699e3b 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -146,7 +146,7 @@ class IStrategy(ABC, HyperStrategyMixin): self._ft_informative.append((informative_data, cls_method)) def load_freqAI_model(self) -> None: - if self.config.get('freqai', None): + if self.config.get('freqai', {}).get('enabled', False): # Import here to avoid importing this if freqAI is disabled from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index ee02cc097..63f3ef2c7 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -17,6 +17,7 @@ def freqai_conf(default_conf, tmpdir): freqaiconf = deepcopy(default_conf) freqaiconf.update( { + "enabled": True, "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", "user_data_dir": Path(tmpdir), From 3918f4abbd7ed975e113477f09bd231ceb8f2fab Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:27:56 +0200 Subject: [PATCH 347/518] Simplify strategy interface by removing explicit self.freqai_info assignment --- docs/freqai.md | 2 -- freqtrade/strategy/interface.py | 1 + freqtrade/templates/FreqaiExampleStrategy.py | 2 -- 3 files changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index c495ba24b..eb76ab1a4 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -401,8 +401,6 @@ The FreqAI strategy requires the user to include the following lines of code in def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - self.freqai_info = self.config["freqai"] - # All indicators must be populated by populate_any_indicators() for live functionality # to work correctly. diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index d10699e3b..760d852c4 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -151,6 +151,7 @@ class IStrategy(ABC, HyperStrategyMixin): from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver self.freqai = FreqaiModelResolver.load_freqaimodel(self.config) + self.freqai_info = self.config["freqai"] def ft_bot_start(self, **kwargs) -> None: """ diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 01f947a6a..d8584d5f9 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -180,8 +180,6 @@ class FreqaiExampleStrategy(IStrategy): def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - self.freqai_info = self.config["freqai"] - # All indicators must be populated by populate_any_indicators() for live functionality # to work correctly. From c190d57f1a67ffd3a6735365bbe3ee592c445a6e Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:48:59 +0200 Subject: [PATCH 348/518] Test populate_any_indicator interface --- tests/strategy/test_interface.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index 4257b2cf9..8b4ba5f03 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -290,6 +290,18 @@ def test_advise_all_indicators(default_conf, testdatadir) -> None: assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed +def test_populate_any_indicators(default_conf, testdatadir) -> None: + strategy = StrategyResolver.load_strategy(default_conf) + + timerange = TimeRange.parse_timerange('1510694220-1510700340') + data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange, + fill_up_missing=True) + processed = strategy.populate_any_indicators('UNITTEST/BTC', data, '5m') + assert processed == data + assert id(processed) == id(data) + assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed + + def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None: strategy = StrategyResolver.load_strategy(default_conf) aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators') From b682fc446e10c3df0aee91776df7a6e0ccebf851 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:53:18 +0200 Subject: [PATCH 349/518] Graciously fail if strategy has freqAI code, but freqAI is not enabled. --- freqtrade/strategy/interface.py | 7 +++++++ tests/strategy/test_interface.py | 7 +++++++ 2 files changed, 14 insertions(+) diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 760d852c4..45609beaa 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -152,6 +152,13 @@ class IStrategy(ABC, HyperStrategyMixin): self.freqai = FreqaiModelResolver.load_freqaimodel(self.config) self.freqai_info = self.config["freqai"] + else: + # Gracious failures if freqAI is disabled but "start" is called. + class DummyClass(): + def start(self, *args, **kwargs): + raise OperationalException( + 'freqAI is not enabled. Please enable it in your config to use this strategy.') + self.freqai = DummyClass() # type: ignore def ft_bot_start(self, **kwargs) -> None: """ diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index 8b4ba5f03..83f7d19b7 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -302,6 +302,13 @@ def test_populate_any_indicators(default_conf, testdatadir) -> None: assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed +def test_freqai_not_initialized(default_conf) -> None: + strategy = StrategyResolver.load_strategy(default_conf) + strategy.ft_bot_start() + with pytest.raises(OperationalException, match=r'freqAI is not enabled\.'): + strategy.freqai.start() + + def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None: strategy = StrategyResolver.load_strategy(default_conf) aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators') From 1ac6ec1446a02a30d0a840e2181fcd2dcf6de932 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 09:56:21 +0200 Subject: [PATCH 350/518] Fix failing test... --- tests/freqai/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 63f3ef2c7..6ace13677 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -17,7 +17,6 @@ def freqai_conf(default_conf, tmpdir): freqaiconf = deepcopy(default_conf) freqaiconf.update( { - "enabled": True, "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", "user_data_dir": Path(tmpdir), @@ -26,6 +25,7 @@ def freqai_conf(default_conf, tmpdir): "freqaimodel_path": "freqai/prediction_models", "timerange": "20180110-20180115", "freqai": { + "enabled": True, "startup_candles": 10000, "purge_old_models": True, "train_period_days": 5, From 5aaab75d1cf177e10cd4ee0bd575084f9a6a2910 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 10:18:57 +0200 Subject: [PATCH 351/518] Add test for dynamic_pairlist_expand --- freqtrade/plugins/pairlist/pairlist_helpers.py | 4 ++-- freqtrade/strategy/interface.py | 3 ++- tests/plugins/test_pairlist.py | 18 +++++++++++++++++- 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/freqtrade/plugins/pairlist/pairlist_helpers.py b/freqtrade/plugins/pairlist/pairlist_helpers.py index f1eca6df0..0cec734fb 100644 --- a/freqtrade/plugins/pairlist/pairlist_helpers.py +++ b/freqtrade/plugins/pairlist/pairlist_helpers.py @@ -1,5 +1,5 @@ import re -from typing import List +from typing import Any, Dict, List def expand_pairlist(wildcardpl: List[str], available_pairs: List[str], @@ -42,7 +42,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str], return result -def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]: +def dynamic_expand_pairlist(config: Dict[str, Any], markets: List[str]) -> List[str]: expanded_pairs = expand_pairlist(config['pairs'], markets) if config.get('freqai', {}).get('enabled', False): corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist'] diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 45609beaa..f41369189 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -157,7 +157,8 @@ class IStrategy(ABC, HyperStrategyMixin): class DummyClass(): def start(self, *args, **kwargs): raise OperationalException( - 'freqAI is not enabled. Please enable it in your config to use this strategy.') + 'freqAI is not enabled. ' + 'Please enable it in your config to use this strategy.') self.freqai = DummyClass() # type: ignore def ft_bot_start(self, **kwargs) -> None: diff --git a/tests/plugins/test_pairlist.py b/tests/plugins/test_pairlist.py index c56f405e2..d3d08dc8c 100644 --- a/tests/plugins/test_pairlist.py +++ b/tests/plugins/test_pairlist.py @@ -12,7 +12,7 @@ from freqtrade.constants import AVAILABLE_PAIRLISTS from freqtrade.enums import CandleType, RunMode from freqtrade.exceptions import OperationalException from freqtrade.persistence import Trade -from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist +from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist from freqtrade.plugins.pairlistmanager import PairListManager from freqtrade.resolvers import PairListResolver from tests.conftest import (create_mock_trades_usdt, get_patched_exchange, get_patched_freqtradebot, @@ -1282,6 +1282,22 @@ def test_expand_pairlist(wildcardlist, pairs, expected): expand_pairlist(wildcardlist, pairs) else: assert sorted(expand_pairlist(wildcardlist, pairs)) == sorted(expected) + conf = { + 'pairs': wildcardlist, + 'freqai': { + "enabled": True, + "feature_parameters": { + "include_corr_pairlist": [ + "BTC/USDT:USDT", + "XRP/BUSD", + ] + } + } + } + assert sorted(dynamic_expand_pairlist(conf, pairs)) == sorted(expected + [ + "BTC/USDT:USDT", + "XRP/BUSD", + ]) @pytest.mark.parametrize('wildcardlist,pairs,expected', [ From 0b92c30abd3ec1262b15ecfce1a6b2cbea0e5d6b Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 10:19:46 +0200 Subject: [PATCH 352/518] Fix typo in test file --- .../{test_freqai_backtsting.py => test_freqai_backtesting.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/freqai/{test_freqai_backtsting.py => test_freqai_backtesting.py} (100%) diff --git a/tests/freqai/test_freqai_backtsting.py b/tests/freqai/test_freqai_backtesting.py similarity index 100% rename from tests/freqai/test_freqai_backtsting.py rename to tests/freqai/test_freqai_backtesting.py From 82ac8cb41ff400e45306f3e6fbaf9fd1ebf9638d Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 10:48:57 +0200 Subject: [PATCH 353/518] Add freqai backtesting_load test --- tests/freqai/test_freqai_backtesting.py | 32 +++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/tests/freqai/test_freqai_backtesting.py b/tests/freqai/test_freqai_backtesting.py index bb0a71e2c..a92742b86 100644 --- a/tests/freqai/test_freqai_backtesting.py +++ b/tests/freqai/test_freqai_backtesting.py @@ -1,16 +1,18 @@ +from copy import deepcopy +from datetime import datetime, timezone from pathlib import Path -from unittest.mock import PropertyMock +from unittest.mock import MagicMock, PropertyMock import pytest from freqtrade.commands.optimize_commands import start_backtesting from freqtrade.exceptions import OperationalException -from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, patch_exchange, +from freqtrade.optimize.backtesting import Backtesting +from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, log_has_re, patch_exchange, patched_configuration_load_config_file) -def test_backtest_start_backtest_list_freqai(freqai_conf, mocker, testdatadir): - # Tests detail-data loading +def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir): patch_exchange(mocker) mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist', @@ -31,3 +33,25 @@ def test_backtest_start_backtest_list_freqai(freqai_conf, mocker, testdatadir): with pytest.raises(OperationalException, match=r"You can't use strategy_list and freqai at the same time\."): start_backtesting(args) + + +def test_freqai_backtest_load_data(freqai_conf, mocker, caplog): + patch_exchange(mocker) + + now = datetime.now(timezone.utc) + mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist', + PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT'])) + mocker.patch('freqtrade.optimize.backtesting.history.load_data') + mocker.patch('freqtrade.optimize.backtesting.history.get_timerange', return_value=(now, now)) + backtesting = Backtesting(deepcopy(freqai_conf)) + backtesting.load_bt_data() + + assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog) + + del freqai_conf['freqai']['startup_candles'] + backtesting = Backtesting(freqai_conf) + with pytest.raises(OperationalException, + match=r'FreqAI backtesting module.*startup_candles in config.'): + backtesting.load_bt_data() + + Backtesting.cleanup() From d36da95941e497492420d1dfe3c0db5c0d9db528 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 11:07:58 +0200 Subject: [PATCH 354/518] Fix bad import --- tests/freqai/test_freqai_backtesting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/freqai/test_freqai_backtesting.py b/tests/freqai/test_freqai_backtesting.py index a92742b86..273791609 100644 --- a/tests/freqai/test_freqai_backtesting.py +++ b/tests/freqai/test_freqai_backtesting.py @@ -1,7 +1,7 @@ from copy import deepcopy from datetime import datetime, timezone from pathlib import Path -from unittest.mock import MagicMock, PropertyMock +from unittest.mock import PropertyMock import pytest From e09fbe9e53b23b54ad1ee1b9c65535cf0f2f4915 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 11:17:22 +0200 Subject: [PATCH 355/518] Improve test resiliance --- tests/test_freqtradebot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index fb5fd38d8..eb4804167 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -3451,7 +3451,7 @@ def test_execute_trade_exit_down_stoploss_on_exchange_dry_run( trade.stop_loss = 2.0 * 1.01 if is_short else 2.0 * 0.99 freqtrade.execute_trade_exit( - trade=trade, limit=(ticker_usdt_sell_up if is_short else ticker_usdt_sell_down())['bid'], + trade=trade, limit=trade.stop_loss, exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS)) assert rpc_mock.call_count == 2 From 7c18ec4053f3f3f44a2acfa0ac7a87e4f36e38d9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 11:24:55 +0200 Subject: [PATCH 356/518] Add missing key to "full" config sample --- config_examples/config_full.example.json | 1 + 1 file changed, 1 insertion(+) diff --git a/config_examples/config_full.example.json b/config_examples/config_full.example.json index e2e9a16fd..f076ffaf1 100644 --- a/config_examples/config_full.example.json +++ b/config_examples/config_full.example.json @@ -5,6 +5,7 @@ "tradable_balance_ratio": 0.99, "fiat_display_currency": "USD", "amount_reserve_percent": 0.05, + "available_capital": 1000, "amend_last_stake_amount": false, "last_stake_amount_min_ratio": 0.5, "dry_run": true, From 7075b00e201e0d6dc331ecd3478780035f540374 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 11:37:23 +0200 Subject: [PATCH 357/518] Remove odd dry run stoploss behavior closes #7208 --- freqtrade/freqtradebot.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 0dbeb2e44..95f884056 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1475,12 +1475,6 @@ class FreqtradeBot(LoggingMixin): ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION): exit_type = 'stoploss' - # if stoploss is on exchange and we are on dry_run mode, - # we consider the sell price stop price - if (self.config['dry_run'] and exit_type == 'stoploss' - and self.strategy.order_types['stoploss_on_exchange']): - limit = trade.stoploss_or_liquidation - # set custom_exit_price if available proposed_limit_rate = limit current_profit = trade.calc_profit_ratio(limit) From 61acbf21d0372a87401884fdbfa03c8a16a6264e Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 15:46:06 +0200 Subject: [PATCH 358/518] Fix broken telegram tests --- tests/rpc/test_rpc_telegram.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index cd26e744f..a30115bd9 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -1461,21 +1461,21 @@ def test_whitelist_static(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) + telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" - "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in sorted(msg_mock.call_args_list[0][0][0])) + "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) + telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) + telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) @@ -1495,22 +1495,22 @@ def test_whitelist_dynamic(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) - assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" - "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in sorted(msg_mock.call_args_list[0][0][0])) + telegram._whitelist(update=update, context=context) + assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" + "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) - assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" + telegram._whitelist(update=update, context=context) + assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=MagicMock()) - assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" + telegram._whitelist(update=update, context=context) + assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) From 23cc21ce59093df511f6fcc89ae22ec0ab5829cb Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 9 Aug 2022 17:31:38 +0200 Subject: [PATCH 359/518] add predict_proba to base classifier, improve historic predictions handling --- freqtrade/freqai/data_drawer.py | 10 +++-- freqtrade/freqai/data_kitchen.py | 11 +++++ .../prediction_models/CatboostClassifier.py | 41 +++++++++++++++++-- 3 files changed, 54 insertions(+), 8 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index ec49c7050..0dacd4ee7 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -358,10 +358,12 @@ class FreqaiDataDrawer: dk.find_features(dataframe) - if self.freqai_info.get('predict_proba', []): - full_labels = dk.label_list + self.freqai_info['predict_proba'] - else: - full_labels = dk.label_list + added_labels = [] + if dk.unique_classes: + for label in dk.unique_classes: + added_labels += dk.unique_classes[label] + + full_labels = dk.label_list + added_labels for label in full_labels: dataframe[label] = 0 diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 0cff9c90e..3eb89ce6d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -90,6 +90,7 @@ class FreqaiDataKitchen: self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {}) self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1) self.train_dates: DataFrame = pd.DataFrame() + self.unique_classes: Dict[str, list] = {} def set_paths( self, @@ -977,6 +978,8 @@ class FreqaiDataKitchen: informative=corr_dataframes[i][tf] ) + self.get_unique_classes_from_labels(dataframe) + return dataframe def fit_labels(self) -> None: @@ -1003,3 +1006,11 @@ class FreqaiDataKitchen: col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%") ] return dataframe[to_keep] + + def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None: + + self.find_features(dataframe) + + for key in self.label_list: + if dataframe[key].dtype == object: + self.unique_classes[key] = dataframe[key].dropna().unique() diff --git a/freqtrade/freqai/prediction_models/CatboostClassifier.py b/freqtrade/freqai/prediction_models/CatboostClassifier.py index ac1386eee..7a4b06557 100644 --- a/freqtrade/freqai/prediction_models/CatboostClassifier.py +++ b/freqtrade/freqai/prediction_models/CatboostClassifier.py @@ -1,10 +1,12 @@ import logging -from typing import Any, Dict - +from typing import Any, Dict, Tuple +import pandas as pd +from pandas import DataFrame from catboost import CatBoostClassifier, Pool - +import numpy.typing as npt +import numpy as np from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel - +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) @@ -39,3 +41,34 @@ class CatboostClassifier(BaseRegressionModel): cbr.fit(train_data) return cbr + + def predict( + self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False + ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :pred_df: dataframe containing the predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + dk.find_features(unfiltered_dataframe) + filtered_dataframe, _ = dk.filter_features( + unfiltered_dataframe, dk.training_features_list, training_filter=False + ) + filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) + dk.data_dictionary["prediction_features"] = filtered_dataframe + + self.data_cleaning_predict(dk, filtered_dataframe) + + predictions = self.model.predict(dk.data_dictionary["prediction_features"]) + pred_df = DataFrame(predictions, columns=dk.label_list) + + predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"]) + pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_) + + pred_df = pd.concat([pred_df, pred_df_prob], axis=1) + + return (pred_df, dk.do_predict) From b1b76a2dbede53ad6d00fc3c2f06c10176f7cdda Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 10 Aug 2022 15:16:50 +0200 Subject: [PATCH 360/518] debug classifier with predict proba --- freqtrade/freqai/data_drawer.py | 7 +------ freqtrade/freqai/data_kitchen.py | 11 ++++++++++- freqtrade/freqai/freqai_interface.py | 18 ++++++++++-------- .../prediction_models/BaseRegressionModel.py | 9 --------- 4 files changed, 21 insertions(+), 24 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 0dacd4ee7..db1206a39 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -358,12 +358,7 @@ class FreqaiDataDrawer: dk.find_features(dataframe) - added_labels = [] - if dk.unique_classes: - for label in dk.unique_classes: - added_labels += dk.unique_classes[label] - - full_labels = dk.label_list + added_labels + full_labels = dk.label_list + dk.unique_class_list for label in full_labels: dataframe[label] = 0 diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 3eb89ce6d..2ba8c5ecf 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -91,6 +91,7 @@ class FreqaiDataKitchen: self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1) self.train_dates: DataFrame = pd.DataFrame() self.unique_classes: Dict[str, list] = {} + self.unique_class_list: list = [] def set_paths( self, @@ -338,7 +339,7 @@ class FreqaiDataKitchen: """ for label in df.columns: - if df[label].dtype == object: + if df[label].dtype == object or label in self.unique_class_list: continue df[label] = ( (df[label] + 1) @@ -995,6 +996,10 @@ class FreqaiDataKitchen: f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label]) self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1] + # incase targets are classifications + for label in self.unique_class_list: + self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0 + return def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame: @@ -1014,3 +1019,7 @@ class FreqaiDataKitchen: for key in self.label_list: if dataframe[key].dtype == object: self.unique_classes[key] = dataframe[key].dropna().unique() + + if self.unique_classes: + for label in self.unique_classes: + self.unique_class_list += list(self.unique_classes[label]) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 0c32a625d..e7dd6c714 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -320,6 +320,8 @@ class IFreqaiModel(ABC): # allows FreqUI to show full return values. pred_df, do_preds = self.predict(dataframe, dk) self.dd.set_initial_return_values(pair, dk, pred_df, do_preds) + if pair not in self.dd.historic_predictions: + self.set_initial_historic_predictions(pred_df, dk, pair) dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe) return elif self.dk.check_if_model_expired(trained_timestamp): @@ -336,6 +338,9 @@ class IFreqaiModel(ABC): # historical accuracy reasons. pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False) + self.dd.save_historic_predictions_to_disk() + if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: + self.fit_live_predictions(dk) self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe)) dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe) @@ -503,7 +508,7 @@ class IFreqaiModel(ABC): self.dd.purge_old_models() def set_initial_historic_predictions( - self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str + self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str ) -> None: """ This function is called only if the datadrawer failed to load an @@ -528,12 +533,6 @@ class IFreqaiModel(ABC): :param: dk: FreqaiDataKitchen = object containing methods for data analysis :param: pair: str = current pair """ - num_candles = self.freqai_info.get('fit_live_predictions_candles', 600) - if not num_candles: - num_candles = 600 - df_tail = df.tail(num_candles) - trained_predictions = model.predict(df_tail) - pred_df = DataFrame(trained_predictions, columns=dk.label_list) pred_df = dk.denormalize_labels_from_metadata(pred_df) @@ -560,9 +559,12 @@ class IFreqaiModel(ABC): """ import scipy as spy + # add classes from classifier label types if used + full_labels = dk.label_list + dk.unique_class_list + num_candles = self.freqai_info.get("fit_live_predictions_candles", 100) dk.data["labels_mean"], dk.data["labels_std"] = {}, {} - for label in dk.label_list: + for label in full_labels: if self.dd.historic_predictions[dk.pair][label].dtype == object: continue f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles)) diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py index a3bd82a8f..2ef175a2e 100644 --- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py +++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py @@ -62,15 +62,6 @@ class BaseRegressionModel(IFreqaiModel): model = self.fit(data_dictionary) - if pair not in self.dd.historic_predictions: - self.set_initial_historic_predictions( - data_dictionary['train_features'], model, dk, pair) - - if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: - self.fit_live_predictions(dk) - - self.dd.save_historic_predictions_to_disk() - logger.info(f"--------------------done training {pair}--------------------") return model From 1f192be43bd448172f0eecb5cf5c0b1109f1b40a Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 10 Aug 2022 17:30:57 +0200 Subject: [PATCH 361/518] avoid denormalizing labels twice --- freqtrade/freqai/freqai_interface.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index e7dd6c714..d8356702c 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -534,8 +534,6 @@ class IFreqaiModel(ABC): :param: pair: str = current pair """ - pred_df = dk.denormalize_labels_from_metadata(pred_df) - self.dd.historic_predictions[pair] = pred_df hist_preds_df = self.dd.historic_predictions[pair] From 7d448fd4ac44b724d4f54fd0c85032242a517639 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 10 Aug 2022 19:44:22 +0200 Subject: [PATCH 362/518] allow fit_live_predictions access to current pair --- freqtrade/freqai/freqai_interface.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index d8356702c..502d5ac34 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -340,7 +340,7 @@ class IFreqaiModel(ABC): self.dd.save_historic_predictions_to_disk() if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: - self.fit_live_predictions(dk) + self.fit_live_predictions(dk, pair) self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe)) dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe) @@ -551,7 +551,7 @@ class IFreqaiModel(ABC): for return_str in dk.data['extra_returns_per_train']: hist_preds_df[return_str] = 0 - def fit_live_predictions(self, dk: FreqaiDataKitchen) -> None: + def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None: """ Fit the labels with a gaussian distribution """ From 3e38c1b0bd80498f0e6c7a03727653e24a148db6 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 12 Aug 2022 13:13:08 +0200 Subject: [PATCH 363/518] take dynamic sized tail off historic_predictions as return dataframe to strategy. --- freqtrade/freqai/data_drawer.py | 114 ++++++++++++++------------- freqtrade/freqai/freqai_interface.py | 12 ++- 2 files changed, 69 insertions(+), 57 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index db1206a39..0644ddbc9 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -241,48 +241,48 @@ class FreqaiDataDrawer: """ # dynamic df returned to strategy and plotted in frequi - mrv_df = self.model_return_values[pair] = pd.DataFrame() + # mrv_df = self.model_return_values[pair] = pd.DataFrame() # if user reused `identifier` in config and has historical predictions collected, load them # so that frequi remains uninterrupted after a crash hist_df = self.historic_predictions - if pair in hist_df: - len_diff = len(hist_df[pair].index) - len(pred_df.index) - if len_diff < 0: - df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]], - ignore_index=True, keys=hist_df[pair].keys()) - else: - df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True) - df_concat = df_concat.fillna(0) - self.model_return_values[pair] = df_concat - logger.info(f'Setting initial FreqUI plots from historical data for {pair}.') - + # if pair in hist_df: + len_diff = len(hist_df[pair].index) - len(pred_df.index) + if len_diff < 0: + df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]], + ignore_index=True, keys=hist_df[pair].keys()) else: - for label in pred_df.columns: - mrv_df[label] = pred_df[label] - if mrv_df[label].dtype == object: - continue - mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label] - mrv_df[f"{label}_std"] = dk.data["labels_std"][label] + df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True) + df_concat = df_concat.fillna(0) + self.model_return_values[pair] = df_concat + logger.info(f'Setting initial FreqUI plots from historical data for {pair}.') - if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: - mrv_df["DI_values"] = dk.DI_values + # else: + # for label in pred_df.columns: + # mrv_df[label] = pred_df[label] + # if mrv_df[label].dtype == object: + # continue + # mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label] + # mrv_df[f"{label}_std"] = dk.data["labels_std"][label] - mrv_df["do_predict"] = do_preds + # if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: + # mrv_df["DI_values"] = dk.DI_values - if dk.data['extra_returns_per_train']: - rets = dk.data['extra_returns_per_train'] - for return_str in rets: - mrv_df[return_str] = rets[return_str] + # mrv_df["do_predict"] = do_preds - # for keras type models, the conv_window needs to be prepended so - # viewing is correct in frequi - if self.freqai_info.get('keras', False): - n_lost_points = self.freqai_info.get('conv_width', 2) - zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))), - columns=mrv_df.columns) - self.model_return_values[pair] = pd.concat( - [zeros_df, mrv_df], axis=0, ignore_index=True) + # if dk.data['extra_returns_per_train']: + # rets = dk.data['extra_returns_per_train'] + # for return_str in rets: + # mrv_df[return_str] = rets[return_str] + + # # for keras type models, the conv_window needs to be prepended so + # # viewing is correct in frequi + # if self.freqai_info.get('keras', False): + # n_lost_points = self.freqai_info.get('conv_width', 2) + # zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))), + # columns=mrv_df.columns) + # self.model_return_values[pair] = pd.concat( + # [zeros_df, mrv_df], axis=0, ignore_index=True) def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: NDArray[np.int_], @@ -292,23 +292,23 @@ class FreqaiDataDrawer: # own return array in the same shape, we need to figure out how the size has changed # and adapt our stored/returned info accordingly. - length_difference = len(self.model_return_values[pair]) - len_df - i = 0 + # length_difference = len(self.model_return_values[pair]) - len_df + # i = 0 - if length_difference == 0: - i = 1 - elif length_difference > 0: - i = length_difference + 1 + # if length_difference == 0: + # i = 1 + # elif length_difference > 0: + # i = length_difference + 1 - df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i) + # df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i) - if pair in self.historic_predictions: - hp_df = self.historic_predictions[pair] - # here are some pandas hula hoops to accommodate the possibility of a series - # or dataframe depending number of labels requested by user - nan_df = pd.DataFrame(np.nan, index=hp_df.index[-2:] + 2, columns=hp_df.columns) - hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0) - self.historic_predictions[pair] = hp_df[:-1] + # if pair in self.historic_predictions: + df = self.historic_predictions[pair] + # here are some pandas hula hoops to accommodate the possibility of a series + # or dataframe depending number of labels requested by user + nan_df = pd.DataFrame(np.nan, index=df.index[-2:] + 2, columns=df.columns) + df = pd.concat([df, nan_df], ignore_index=True, axis=0) + df = self.historic_predictions[pair] = df[:-1] # incase user adds additional "predictions" e.g. predict_proba output: for label in predictions.columns: @@ -328,16 +328,18 @@ class FreqaiDataDrawer: for return_str in rets: df[return_str].iloc[-1] = rets[return_str] - # append the new predictions to persistent storage - if pair in self.historic_predictions: - for key in df.keys(): - self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1] + self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True) - if length_difference < 0: - prepend_df = pd.DataFrame( - np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns - ) - df = pd.concat([prepend_df, df], axis=0) + # # append the new predictions to persistent storage + # if pair in self.historic_predictions: + # for key in df.keys(): + # self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1] + + # if length_difference < 0: + # prepend_df = pd.DataFrame( + # np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns + # ) + # df = pd.concat([prepend_df, df], axis=0) def attach_return_values_to_return_dataframe( self, pair: str, dataframe: DataFrame) -> DataFrame: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 502d5ac34..4306b4879 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -319,9 +319,10 @@ class IFreqaiModel(ABC): # first predictions are made on entire historical candle set coming from strategy. This # allows FreqUI to show full return values. pred_df, do_preds = self.predict(dataframe, dk) - self.dd.set_initial_return_values(pair, dk, pred_df, do_preds) if pair not in self.dd.historic_predictions: self.set_initial_historic_predictions(pred_df, dk, pair) + self.dd.set_initial_return_values(pair, dk, pred_df, do_preds) + dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe) return elif self.dk.check_if_model_expired(trained_timestamp): @@ -551,6 +552,15 @@ class IFreqaiModel(ABC): for return_str in dk.data['extra_returns_per_train']: hist_preds_df[return_str] = 0 + # # for keras type models, the conv_window needs to be prepended so + # # viewing is correct in frequi + if self.freqai_info.get('keras', False): + n_lost_points = self.freqai_info.get('conv_width', 2) + zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))), + columns=hist_preds_df.columns) + self.model_return_values[pair] = pd.concat( + [zeros_df, hist_preds_df], axis=0, ignore_index=True) + def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None: """ Fit the labels with a gaussian distribution From 31be707cc838433ddd35a8c4d404e725c9e4274a Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 12 Aug 2022 16:12:28 +0200 Subject: [PATCH 364/518] clean up code, add docstrings --- freqtrade/freqai/data_drawer.py | 86 ++++++---------------------- freqtrade/freqai/freqai_interface.py | 4 +- 2 files changed, 19 insertions(+), 71 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 0644ddbc9..02ef156bd 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -232,85 +232,43 @@ class FreqaiDataDrawer: # send pair to end of queue self.pair_dict[pair]["priority"] = len(self.pair_dict) - def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen, - pred_df: DataFrame, do_preds: ArrayLike) -> None: + def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None: """ - Set the initial return values to a persistent dataframe. This avoids needing to repredict on - historical candles, and also stores historical predictions despite retrainings (so stored - predictions are true predictions, not just inferencing on trained data) + Set the initial return values to the historical predictions dataframe. This avoids needing + to repredict on historical candles, and also stores historical predictions despite + retrainings (so stored predictions are true predictions, not just inferencing on trained + data) """ - # dynamic df returned to strategy and plotted in frequi - # mrv_df = self.model_return_values[pair] = pd.DataFrame() - - # if user reused `identifier` in config and has historical predictions collected, load them - # so that frequi remains uninterrupted after a crash hist_df = self.historic_predictions - # if pair in hist_df: len_diff = len(hist_df[pair].index) - len(pred_df.index) if len_diff < 0: df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]], - ignore_index=True, keys=hist_df[pair].keys()) + ignore_index=True, keys=hist_df[pair].keys()) else: df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True) df_concat = df_concat.fillna(0) self.model_return_values[pair] = df_concat - logger.info(f'Setting initial FreqUI plots from historical data for {pair}.') - - # else: - # for label in pred_df.columns: - # mrv_df[label] = pred_df[label] - # if mrv_df[label].dtype == object: - # continue - # mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label] - # mrv_df[f"{label}_std"] = dk.data["labels_std"][label] - - # if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: - # mrv_df["DI_values"] = dk.DI_values - - # mrv_df["do_predict"] = do_preds - - # if dk.data['extra_returns_per_train']: - # rets = dk.data['extra_returns_per_train'] - # for return_str in rets: - # mrv_df[return_str] = rets[return_str] - - # # for keras type models, the conv_window needs to be prepended so - # # viewing is correct in frequi - # if self.freqai_info.get('keras', False): - # n_lost_points = self.freqai_info.get('conv_width', 2) - # zeros_df = DataFrame(np.zeros((n_lost_points, len(mrv_df.columns))), - # columns=mrv_df.columns) - # self.model_return_values[pair] = pd.concat( - # [zeros_df, mrv_df], axis=0, ignore_index=True) def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: NDArray[np.int_], dk: FreqaiDataKitchen, len_df: int) -> None: - - # strat seems to feed us variable sized dataframes - and since we are trying to build our - # own return array in the same shape, we need to figure out how the size has changed - # and adapt our stored/returned info accordingly. - - # length_difference = len(self.model_return_values[pair]) - len_df - # i = 0 - - # if length_difference == 0: - # i = 1 - # elif length_difference > 0: - # i = length_difference + 1 - - # df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i) - - # if pair in self.historic_predictions: + """ + Append model predictions to historic predictions dataframe, then set the + strategy return dataframe to the tail of the historic predictions. The length of + the tail is equivalent to the length of the dataframe that entered FreqAI from + the strategy originally. Doing this allows FreqUI to always display the correct + historic predictions. + """ df = self.historic_predictions[pair] + # here are some pandas hula hoops to accommodate the possibility of a series # or dataframe depending number of labels requested by user nan_df = pd.DataFrame(np.nan, index=df.index[-2:] + 2, columns=df.columns) df = pd.concat([df, nan_df], ignore_index=True, axis=0) df = self.historic_predictions[pair] = df[:-1] - # incase user adds additional "predictions" e.g. predict_proba output: + # model outputs and associated statistics for label in predictions.columns: df[label].iloc[-1] = predictions[label].iloc[-1] if df[label].dtype == object: @@ -318,11 +276,12 @@ class FreqaiDataDrawer: df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label] df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label] + # outlier indicators df["do_predict"].iloc[-1] = do_preds[-1] - if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: df["DI_values"].iloc[-1] = dk.DI_values[-1] + # extra values the user added within custom prediction model if dk.data['extra_returns_per_train']: rets = dk.data['extra_returns_per_train'] for return_str in rets: @@ -330,17 +289,6 @@ class FreqaiDataDrawer: self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True) - # # append the new predictions to persistent storage - # if pair in self.historic_predictions: - # for key in df.keys(): - # self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1] - - # if length_difference < 0: - # prepend_df = pd.DataFrame( - # np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns - # ) - # df = pd.concat([prepend_df, df], axis=0) - def attach_return_values_to_return_dataframe( self, pair: str, dataframe: DataFrame) -> DataFrame: """ diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4306b4879..374fcea2f 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -321,7 +321,7 @@ class IFreqaiModel(ABC): pred_df, do_preds = self.predict(dataframe, dk) if pair not in self.dd.historic_predictions: self.set_initial_historic_predictions(pred_df, dk, pair) - self.dd.set_initial_return_values(pair, dk, pred_df, do_preds) + self.dd.set_initial_return_values(pair, pred_df) dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe) return @@ -558,7 +558,7 @@ class IFreqaiModel(ABC): n_lost_points = self.freqai_info.get('conv_width', 2) zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))), columns=hist_preds_df.columns) - self.model_return_values[pair] = pd.concat( + self.dd.historic_predictions[pair] = pd.concat( [zeros_df, hist_preds_df], axis=0, ignore_index=True) def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None: From 58de20af0f1226dd5b3985d16d72bd979ec9fcf5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sat, 13 Aug 2022 20:07:31 +0200 Subject: [PATCH 365/518] make BaseClassifierModel. Add predict_proba to lightgbm --- freqtrade/freqai/data_drawer.py | 2 +- .../prediction_models/BaseClassifierModel.py | 99 +++++++++++++++++++ .../prediction_models/CatboostClassifier.py | 45 ++------- .../prediction_models/LightGBMClassifier.py | 4 +- 4 files changed, 108 insertions(+), 42 deletions(-) create mode 100644 freqtrade/freqai/prediction_models/BaseClassifierModel.py diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 02ef156bd..4ba55a0ec 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -12,7 +12,7 @@ import pandas as pd import rapidjson from joblib import dump, load from joblib.externals import cloudpickle -from numpy.typing import ArrayLike, NDArray +from numpy.typing import NDArray from pandas import DataFrame from freqtrade.configuration import TimeRange diff --git a/freqtrade/freqai/prediction_models/BaseClassifierModel.py b/freqtrade/freqai/prediction_models/BaseClassifierModel.py new file mode 100644 index 000000000..2edbf3b51 --- /dev/null +++ b/freqtrade/freqai/prediction_models/BaseClassifierModel.py @@ -0,0 +1,99 @@ +import logging +from typing import Any, Tuple + +import numpy as np +import numpy.typing as npt +import pandas as pd +from pandas import DataFrame + +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.freqai.freqai_interface import IFreqaiModel + + +logger = logging.getLogger(__name__) + + +class BaseClassifierModel(IFreqaiModel): + """ + Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.). + User *must* inherit from this class and set fit() and predict(). See example scripts + such as prediction_models/CatboostPredictionModel.py for guidance. + """ + + def train( + self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen + ) -> Any: + """ + Filter the training data and train a model to it. Train makes heavy use of the datakitchen + for storing, saving, loading, and analyzing the data. + :param unfiltered_dataframe: Full dataframe for the current training period + :param metadata: pair metadata from strategy. + :return: + :model: Trained model which can be used to inference (self.predict) + """ + + logger.info("-------------------- Starting training " f"{pair} --------------------") + + # filter the features requested by user in the configuration file and elegantly handle NaNs + features_filtered, labels_filtered = dk.filter_features( + unfiltered_dataframe, + dk.training_features_list, + dk.label_list, + training_filter=True, + ) + + start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d") + end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d") + logger.info(f"-------------------- Training on data from {start_date} to " + f"{end_date}--------------------") + # split data into train/test data. + data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) + if not self.freqai_info.get('fit_live_predictions', 0) or not self.live: + dk.fit_labels() + # normalize all data based on train_dataset only + data_dictionary = dk.normalize_data(data_dictionary) + + # optional additional data cleaning/analysis + self.data_cleaning_train(dk) + + logger.info( + f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features" + ) + logger.info(f'Training model on {len(data_dictionary["train_features"])} data points') + + model = self.fit(data_dictionary) + + logger.info(f"--------------------done training {pair}--------------------") + + return model + + def predict( + self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False + ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: + """ + Filter the prediction features data and predict with it. + :param: unfiltered_dataframe: Full dataframe for the current backtest period. + :return: + :pred_df: dataframe containing the predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + dk.find_features(unfiltered_dataframe) + filtered_dataframe, _ = dk.filter_features( + unfiltered_dataframe, dk.training_features_list, training_filter=False + ) + filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) + dk.data_dictionary["prediction_features"] = filtered_dataframe + + self.data_cleaning_predict(dk, filtered_dataframe) + + predictions = self.model.predict(dk.data_dictionary["prediction_features"]) + pred_df = DataFrame(predictions, columns=dk.label_list) + + predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"]) + pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_) + + pred_df = pd.concat([pred_df, pred_df_prob], axis=1) + + return (pred_df, dk.do_predict) diff --git a/freqtrade/freqai/prediction_models/CatboostClassifier.py b/freqtrade/freqai/prediction_models/CatboostClassifier.py index 7a4b06557..b88b28b25 100644 --- a/freqtrade/freqai/prediction_models/CatboostClassifier.py +++ b/freqtrade/freqai/prediction_models/CatboostClassifier.py @@ -1,17 +1,15 @@ import logging -from typing import Any, Dict, Tuple -import pandas as pd -from pandas import DataFrame +from typing import Any, Dict + from catboost import CatBoostClassifier, Pool -import numpy.typing as npt -import numpy as np -from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel -from freqtrade.freqai.data_kitchen import FreqaiDataKitchen + +from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel + logger = logging.getLogger(__name__) -class CatboostClassifier(BaseRegressionModel): +class CatboostClassifier(BaseClassifierModel): """ User created prediction model. The class needs to override three necessary functions, predict(), train(), fit(). The class inherits ModelHandler which @@ -41,34 +39,3 @@ class CatboostClassifier(BaseRegressionModel): cbr.fit(train_data) return cbr - - def predict( - self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False - ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: - """ - Filter the prediction features data and predict with it. - :param: unfiltered_dataframe: Full dataframe for the current backtest period. - :return: - :pred_df: dataframe containing the predictions - :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove - data (NaNs) or felt uncertain about data (PCA and DI index) - """ - - dk.find_features(unfiltered_dataframe) - filtered_dataframe, _ = dk.filter_features( - unfiltered_dataframe, dk.training_features_list, training_filter=False - ) - filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe) - dk.data_dictionary["prediction_features"] = filtered_dataframe - - self.data_cleaning_predict(dk, filtered_dataframe) - - predictions = self.model.predict(dk.data_dictionary["prediction_features"]) - pred_df = DataFrame(predictions, columns=dk.label_list) - - predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"]) - pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_) - - pred_df = pd.concat([pred_df, pred_df_prob], axis=1) - - return (pred_df, dk.do_predict) diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifier.py b/freqtrade/freqai/prediction_models/LightGBMClassifier.py index 782dbce35..bafb16a39 100644 --- a/freqtrade/freqai/prediction_models/LightGBMClassifier.py +++ b/freqtrade/freqai/prediction_models/LightGBMClassifier.py @@ -3,13 +3,13 @@ from typing import Any, Dict from lightgbm import LGBMClassifier -from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel +from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel logger = logging.getLogger(__name__) -class LightGBMClassifier(BaseRegressionModel): +class LightGBMClassifier(BaseClassifierModel): """ User created prediction model. The class needs to override three necessary functions, predict(), train(), fit(). The class inherits ModelHandler which From 7a2b4dbb990b1c3abf022c2809c46965508d87fa Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 20:16:36 +0200 Subject: [PATCH 366/518] Fix docs edit button --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index a43322f78..45f9aba27 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,6 +1,7 @@ site_name: Freqtrade site_url: https://www.freqtrade.io/ repo_url: https://github.com/freqtrade/freqtrade +edit_uri: edit/develop/docs/ use_directory_urls: True nav: - Home: index.md From 8d9284a524a15cbce39eb3b82df53b1e21c3c3a6 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 13 Aug 2022 20:16:36 +0200 Subject: [PATCH 367/518] Fix docs edit button --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index a43322f78..45f9aba27 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,6 +1,7 @@ site_name: Freqtrade site_url: https://www.freqtrade.io/ repo_url: 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transform="matrix(1.0227138,0,0,1.0923454,-180.74242,54.998164)" /> diff --git a/docs/freqai.md b/docs/freqai.md index eb76ab1a4..093637479 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -6,16 +6,16 @@ FreqAI is a module designed to automate a variety of tasks associated with train Among the the features included: -* **Self-adaptive retraining**: automatically retrain models during live deployments to self-adapt to the market in an unsupervised manner. +* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner. * **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies. * **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. * **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining. * **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available. -* **Smart outlier removal**: remove outliers automatically from training and prediction sets using a variety of outlier detection techniques. -* **Crash resilience**: automatic model storage to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs). -* **Automated data normalization**: automatically normalize the data automatically in a smart and statistically safe way. -* **Automatic data download**: automatically compute the data download timerange and downloads data accordingly (in live deployments). -* **Clean the incoming data of NaNs in a safe way before training and prediction. +* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques. +* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs). +* **Automated data normalization**: normalize the data in a smart and statistically safe way. +* **Automatic data download**: compute the data download timerange and update historic data (in live deployments). +* **Clean incoming data** safe NaN handling before training and prediction. * **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis. * **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades. @@ -412,10 +412,75 @@ The FreqAI strategy requires the user to include the following lines of code in dataframe = self.freqai.start(dataframe, metadata, self) return dataframe + + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name and merge features + from user indicated timeframes in the configuration file. User controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e. user should not prepend any supporting metrics + (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. + """ + + coint = pair.split('/')[0] + + with self.freqai.lock: + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + return df + + ``` -The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds -the feature set with a proper naming convention for the IFreqaiModel to use later. +Notice how the `populate_any_indicators()` is where the user adds their own features and labels (more information [here](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`. ### Setting classifier targets diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py index afb439cbf..04eff045f 100644 --- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py +++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py @@ -24,11 +24,11 @@ class BaseTensorFlowModel(IFreqaiModel): for storing, saving, loading, and analyzing the data. :param unfiltered_dataframe: Full dataframe for the current training period :param metadata: pair metadata from strategy. - :returns: + :return: :model: Trained model which can be used to inference (self.predict) """ - logger.info("--------------------Starting training " f"{pair} --------------------") + logger.info("-------------------- Starting training " f"{pair} --------------------") # filter the features requested by user in the configuration file and elegantly handle NaNs features_filtered, labels_filtered = dk.filter_features( @@ -38,9 +38,14 @@ class BaseTensorFlowModel(IFreqaiModel): training_filter=True, ) + start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d") + end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d") + logger.info(f"-------------------- Training on data from {start_date} to " + f"{end_date}--------------------") # split data into train/test data. data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) - + if not self.freqai_info.get('fit_live_predictions', 0) or not self.live: + dk.fit_labels() # normalize all data based on train_dataset only data_dictionary = dk.normalize_data(data_dictionary) @@ -54,17 +59,6 @@ class BaseTensorFlowModel(IFreqaiModel): model = self.fit(data_dictionary) - if pair not in self.dd.historic_predictions: - self.set_initial_historic_predictions( - data_dictionary['train_features'], model, dk, pair) - - if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: - self.fit_live_predictions(dk) - else: - dk.fit_labels() - - self.dd.save_historic_predictions_to_disk() - logger.info(f"--------------------done training {pair}--------------------") return model From 044cf8bb2e19b7c96406eb12d434b4ee8800a31a Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 08:41:25 +0200 Subject: [PATCH 369/518] Allow new whitelist combination in "button" commands --- freqtrade/rpc/telegram.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 88222608e..8ce2fa2e4 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -120,7 +120,8 @@ class Telegram(RPCHandler): r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+', r'/stats$', r'/count$', r'/locks$', r'/balance$', r'/stopbuy$', r'/reload_config$', r'/show_config$', - r'/logs$', r'/whitelist$', r'/blacklist$', r'/bl_delete$', + r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$', + r'/blacklist$', r'/bl_delete$', r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$', r'/forcebuy$', r'/forcelong$', r'/forceshort$', r'/forcesell$', r'/forceexit$', From 24f1dc4ecc08ac30fac3f507e342e8d560d65a8c Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 09:06:04 +0200 Subject: [PATCH 370/518] Update patched ta-lib install for conda --- .gitignore | 1 + build_helpers/install_ta-lib.sh | 16 +++++++++++----- build_helpers/install_ta-lib_conda.sh | 21 --------------------- docs/installation.md | 7 +++++-- 4 files changed, 17 insertions(+), 28 deletions(-) delete mode 100644 build_helpers/install_ta-lib_conda.sh diff --git a/.gitignore b/.gitignore index d6cec5225..065ac1f68 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ user_data/notebooks/* freqtrade-plot.html freqtrade-profit-plot.html freqtrade/rpc/api_server/ui/* +build_helpers/ta-lib/* # Macos related .DS_Store diff --git a/build_helpers/install_ta-lib.sh b/build_helpers/install_ta-lib.sh index 00c4417ae..581c1cd41 100755 --- a/build_helpers/install_ta-lib.sh +++ b/build_helpers/install_ta-lib.sh @@ -4,7 +4,7 @@ else INSTALL_LOC=${1} fi echo "Installing to ${INSTALL_LOC}" -if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then +if [ -n "$2" ] && [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then tar zxvf ta-lib-0.4.0-src.tar.gz cd ta-lib \ && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \ @@ -17,11 +17,17 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then cd .. && rm -rf ./ta-lib/ exit 1 fi - which sudo && sudo make install || make install - if [ -x "$(command -v apt-get)" ]; then - echo "Updating library path using ldconfig" - sudo ldconfig + if [ -z "$2" ]; then + which sudo && sudo make install || make install + if [ -x "$(command -v apt-get)" ]; then + echo "Updating library path using ldconfig" + sudo ldconfig + fi + else + # Don't install with sudo + make install fi + cd .. && rm -rf ./ta-lib/ else echo "TA-lib already installed, skipping installation" diff --git a/build_helpers/install_ta-lib_conda.sh b/build_helpers/install_ta-lib_conda.sh deleted file mode 100644 index 27f8a50a1..000000000 --- a/build_helpers/install_ta-lib_conda.sh +++ /dev/null @@ -1,21 +0,0 @@ -if ! [[ -v CONDA_PREFIX ]]; then - echo "The conda environment is not activated." - exit 1 -fi - -INSTALL_LOC=${CONDA_PREFIX} -echo "Installing to ${INSTALL_LOC}" -tar zxvf ta-lib-0.4.0-src.tar.gz -cd ta-lib \ -&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \ -&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \ -&& curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \ -&& ./configure --prefix=${INSTALL_LOC}/ \ -&& make -if [ $? -ne 0 ]; then - echo "Failed building ta-lib." - cd .. && rm -rf ./ta-lib/ - exit 1 -fi -make install || make install -cd .. && rm -rf ./ta-lib/ diff --git a/docs/installation.md b/docs/installation.md index 29511384c..9dd14274a 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -326,11 +326,14 @@ python3 -m pip install --upgrade pip python3 -m pip install -e . ``` -Patch conda libta-lib +Patch conda libta-lib (Linux only) ```bash +# Ensure that the environment is active! +conda activate freqtrade-conda + cd build_helpers -bash install_ta-lib_conda.sh +bash install_ta-lib.sh ${CONDA_PREFIX} nosudo ``` ### Congratulations From e7513c96b3a3795e1d7c38f59e519e3e41d54337 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 09:36:38 +0200 Subject: [PATCH 371/518] install py-find-1st from conda forge closes #7193 --- environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 19f3c7f5a..d6d85de9d 100644 --- a/environment.yml +++ b/environment.yml @@ -9,6 +9,7 @@ dependencies: - pandas - pip + - py-find-1st - aiohttp - SQLAlchemy - python-telegram-bot @@ -64,7 +65,7 @@ dependencies: - pip: - pycoingecko - - py_find_1st + # - py_find_1st - tables - pytest-random-order - ccxt From a5e96881f42c0e44b4faf3c278bee6dfcde453e4 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 17:08:29 +0200 Subject: [PATCH 372/518] slightly update doc wording --- docs/freqai.md | 211 ++++++++++++++++++++++++------------------------- 1 file changed, 103 insertions(+), 108 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 093637479..6e81d9f28 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -8,7 +8,7 @@ Among the the features included: * **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner. * **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies. -* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. +* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. * **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining. * **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available. * **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques. @@ -37,7 +37,7 @@ The example strategy, example prediction model, and example config can all be fo The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way a typical Freqtrade strategy is created) as well as target values which look into the future. -FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible. +FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible. An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage. @@ -66,7 +66,7 @@ directly influence nodal weights within the model. ## Install prerequisites -The normal Freqtrade install process will ask the user if they wish to install `FreqAI` dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with: +The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with: ``` bash pip install -r requirements-freqai.txt @@ -75,21 +75,21 @@ pip install -r requirements-freqai.txt !!! Note Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform. -## Configuring the bot +## Configuring FreqAI ### Parameter table -The table below will list all configuration parameters available for `FreqAI`. +The table below will list all configuration parameters available for FreqAI. Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. | Parameter | Description | |------------|-------------| -| `freqai` | **Required.** The dictionary containing all the parameters for controlling FreqAI.
**Datatype:** dictionary. +| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI.
**Datatype:** dictionary. | `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** string. | `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window).
**Datatype:** positive integer. | `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. -| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. **Datatype:** Float > 0. +| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible.
**Datatype:** Float > 0. | `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default.
**Datatype:** boolean. | `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
**Datatype:** positive integer. | `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics.
**Datatype:** positive integer. @@ -101,11 +101,11 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set.
**Datatype:** list of timeframes (strings). | `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not.
**Datatype:** positive integer. | `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user.
**Datatype:** positive integer. -| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained more [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** positive float (typically below 1). +| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** positive float (typically below 1). | `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process).
**Datatype:** positive float (typically below 1). | `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
**Datatype:** boolean. | `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
**Datatype:** boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reprodicibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary.
**Datatype:** dictionary. | `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
**Datatype:** positive integer. | `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
**Datatype:** positive integer. | `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
**Datatype:** list of positive integers. @@ -113,7 +113,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)
**Datatype:** dictionary. | `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** positive float below 1. -| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False. **Datatype:** boolean. +| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False.
**Datatype:** boolean. | | **Model training parameters** | `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model.
**Datatype:** dictionary. | `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
**Datatype:** integer. @@ -123,8 +123,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
**Datatype:** boolean. | `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
**Datatype:** integer. - ### Important FreqAI dataframe key patterns + Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`): | DataFrame Key | Description | @@ -137,51 +137,50 @@ Here are the values the user can expect to include/use inside the typical strate ### Example config file -The user interface is isolated to the typical config file. A typical FreqAI -config setup could include: +The user interface is isolated to the typical config file. A typical FreqAI config setup could include: ```json "freqai": { - "startup_candles": 10000, - "purge_old_models": true, - "train_period_days" : 30, - "backtest_period_days" : 7, - "identifier" : "unique-id", - "feature_parameters" : { - "include_timeframes" : ["5m","15m","4h"], - "include_corr_pairlist": [ - "ETH/USD", - "LINK/USD", - "BNB/USD" - ], - "label_period_candles": 24, - "include_shifted_candles": 2, - "weight_factor": 0, - "indicator_max_period_candles": 20, - "indicator_periods_candles": [10, 20] - }, - "data_split_parameters" : { - "test_size": 0.25, - "random_state": 42 - }, - "model_training_parameters" : { - "n_estimators": 100, - "random_state": 42, - "learning_rate": 0.02, - "task_type": "CPU", - }, - } + "startup_candles": 10000, + "purge_old_models": true, + "train_period_days": 30, + "backtest_period_days": 7, + "identifier" : "unique-id", + "feature_parameters" : { + "include_timeframes": ["5m","15m","4h"], + "include_corr_pairlist": [ + "ETH/USD", + "LINK/USD", + "BNB/USD" + ], + "label_period_candles": 24, + "include_shifted_candles": 2, + "weight_factor": 0, + "indicator_max_period_candles": 20, + "indicator_periods_candles": [10, 20] + }, + "data_split_parameters" : { + "test_size": 0.25, + "random_state": 42 + }, + "model_training_parameters" : { + "n_estimators": 100, + "random_state": 42, + "learning_rate": 0.02, + "task_type": "CPU", + }, + } ``` ### Feature engineering -Features are added by the user inside the `populate_any_indicators()` method of the strategy +Features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%` and labels are added by prepending `&`. There are some important components/structures that the user *must* include when building their feature set. As shown below, `with self.freqai.lock:` must be used to ensure thread safety - especially when using third party libraries for indicator construction such as TA-lib. Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). -This is where the user will add single features and labels to their feature set to avoid duplication from +This is where the user will add single features and labels to their feature set to avoid duplication from various configuration parameters which multiply the feature set such as `include_timeframes`. ```python @@ -275,42 +274,9 @@ The user of the present example does not wish to pass the `bb_lowerband` as a fe and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the model for training/prediction and has therefore prepended it with `%`. -Note: features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()` -will fail in live/dry mode. If the user wishes to add generalized features that are not associated with -a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` -(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`: - -```python - def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): - - ... - - # Add generalized indicators here (because in live, it will call only this function to populate - # indicators for retraining). Notice how we ensure not to add them multiple times by associating - # these generalized indicators to the basepair/timeframe - if set_generalized_indicators: - df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 - df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) -``` - -(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) - -The `include_timeframes` from the example config above are the timeframes of each `populate_any_indicator()` - included metric for inclusion in the feature set. In the present case, the user is asking for the -`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included -in the feature set. +The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` +included metric for inclusion in the feature set. In the present case, the user is asking for the +`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. In addition, the user can ask for each of these features to be included from informative pairs using the `include_corr_pairlist`. This means that the present feature @@ -324,7 +290,40 @@ FreqAI to include the the past 2 candles for each of the features included in th In total, the number of features the present user has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` -_3 * 3 * 3 * 2 * 2 = 108_. +$3 * 3 * 3 * 2 * 2 = 108$. + +!!! Note + Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()` + will fail in live/dry mode. If the user wishes to add generalized features that are not associated with + a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`: + + ```python + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): + + ... + + # Add generalized indicators here (because in live, it will call only this function to populate + # indicators for retraining). Notice how we ensure not to add them multiple times by associating + # these generalized indicators to the basepair/timeframe + if set_generalized_indicators: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + ``` + + (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) ### Deciding the sliding training window and backtesting duration @@ -370,12 +369,11 @@ Backtesting mode requires the user to have the data pre-downloaded (unlike dry/l If this command has never been executed with the existing config file, then it will train a new model for each pair, for each backtesting window within the bigger `--timerange`. ---- !!! Note "Model reuse" - Once the training is completed, the user can execute this again with the same config file and - FreqAI will find the trained models and load them instead of spending time training. This is useful + Once the training is completed, the user can execute this again with the same config file and + FreqAI will find the trained models and load them instead of spending time training. This is useful if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user - *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`. + *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`. This way, the user can return to using any model they wish by simply changing the `identifier`. --- @@ -430,7 +428,7 @@ The FreqAI strategy requires the user to include the following lines of code in :param coin: the name of the coin which will modify the feature names. """ - coint = pair.split('/')[0] + coin = pair.split('/')[0] with self.freqai.lock: if informative is None: @@ -480,7 +478,7 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features and labels (more information [here](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`. ### Setting classifier targets @@ -490,7 +488,6 @@ FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostC df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') ``` - ### Running the model live FreqAI can be run dry/live using the following command @@ -499,8 +496,8 @@ FreqAI can be run dry/live using the following command freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` -By default, FreqAI will not find find any existing models and will start by training a new one -given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. +By default, FreqAI will not find any existing models and will start by training a new one +given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse the same `identifier` parameter @@ -514,7 +511,7 @@ the same `identifier` parameter In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained, -and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. +and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. ## Data analysis techniques @@ -522,7 +519,7 @@ and if a full `live_retrain_hours` has elapsed since the end of the loaded model Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries. -Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. Meanwhile, FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly +Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly than past data via an exponential function: $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ @@ -545,8 +542,8 @@ data point and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ -is the number of features i.e. the length of the vector $X$. The -characteristic distance, $\overline{d}$ for a set of training data points is simply the mean +is the number of features i.e. the length of the vector $X$. +The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: $$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ @@ -574,8 +571,7 @@ to low levels of certainty. Activating the Dissimilarity Index can be achieved w } ``` -The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the -trained model. +The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model. ### Reducing data dimensionality with Principal Component Analysis @@ -609,7 +605,7 @@ FreqAI will train an SVM on the training data (or components if the user activat ### Clustering the training data and removing outliers with DBSCAN -The user can tell FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points. +The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points. ```json "freqai": { @@ -655,7 +651,7 @@ The follower will load models created by the leader and inference them to obtain FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running -for extended periods of time with high frequency retraining should set `purge_old_models` in their +for extended periods of time with high frequency retraining should set `purge_old_models` in their config: ```json @@ -694,7 +690,7 @@ By default, FreqAI computes this based on training data and it assumes the label These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population of *historical predictions* for creating the dynamic target instead of the trained labels, the user can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles -the user wishes to use to generate target statistics. +the user wishes to use to generate target statistics. ```json "freqai": { @@ -703,17 +699,17 @@ the user wishes to use to generate target statistics. ``` If the user sets this value, FreqAI will initially use the predictions from the training data set -and then subsequently begin introducing real prediction data as it is generated. FreqAI will save +and then subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if the user stops and restarts with the same `identifier`. ## Extra returns per train Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain. -Users can include these metrics by assigining them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction +Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. -The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is -the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training. -Another example is shown below if the user wants to use live metrics from the trade databse. +The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is +the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training. +Another example is shown below if the user wants to use live metrics from the trade database. The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes: @@ -726,10 +722,9 @@ The user needs to set the standard dictionary in the config so FreqAI can return These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs a default initial value - this is the value that will be returned. - ## Building an IFreqaiModel -FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. +FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. However, users can customize and create their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. @@ -755,8 +750,8 @@ This file structure is heavily controlled and read by the `FreqaiDataKitchen()` and should therefore not be modified. ## Credits -FreqAI was developed by a group of individuals who all contributed specific skillsets to the -project. + +FreqAI was developed by a group of individuals who all contributed specific skillsets to the project. Conception and software development: Robert Caulk @robcaulk From 3a9ec76c919d619756328b9f12ec10256b64e42c Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 17:19:50 +0200 Subject: [PATCH 373/518] Move "freqai.lock" to backend to simplify user interface --- docs/freqai.md | 184 +++++++++--------- freqtrade/freqai/data_kitchen.py | 36 ++-- freqtrade/templates/FreqaiExampleStrategy.py | 177 +++++++++-------- .../strategy/strats/freqai_test_classifier.py | 69 ++++--- .../strats/freqai_test_multimodel_strat.py | 105 +++++----- tests/strategy/strats/freqai_test_strat.py | 83 ++++---- 6 files changed, 323 insertions(+), 331 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 6e81d9f28..6631c271e 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -177,8 +177,6 @@ The user interface is isolated to the typical config file. A typical FreqAI conf Features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%` and labels are added by prepending `&`. There are some important components/structures that the user *must* include when building their feature set. -As shown below, `with self.freqai.lock:` must be used to ensure thread safety - especially when using third -party libraries for indicator construction such as TA-lib. Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). This is where the user will add single features and labels to their feature set to avoid duplication from various configuration parameters which multiply the feature set such as `include_timeframes`. @@ -203,69 +201,68 @@ various configuration parameters which multiply the feature set such as `include coint = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - bollinger = qtpylib.bollinger_bands( - qtpylib.typical_price(informative), window=t, stds=2.2 - ) - informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] - informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] - informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] + bollinger = qtpylib.bollinger_bands( + qtpylib.typical_price(informative), window=t, stds=2.2 + ) + informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] + informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] + informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] - informative[f"%-{coin}bb_width-period_{t}"] = ( - informative[f"{coin}bb_upperband-period_{t}"] - - informative[f"{coin}bb_lowerband-period_{t}"] - ) / informative[f"{coin}bb_middleband-period_{t}"] - informative[f"%-{coin}close-bb_lower-period_{t}"] = ( - informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] - ) + informative[f"%-{coin}bb_width-period_{t}"] = ( + informative[f"{coin}bb_upperband-period_{t}"] + - informative[f"{coin}bb_lowerband-period_{t}"] + ) / informative[f"{coin}bb_middleband-period_{t}"] + informative[f"%-{coin}close-bb_lower-period_{t}"] = ( + informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] + ) - informative[f"%-{coin}relative_volume-period_{t}"] = ( - informative["volume"] / informative["volume"].rolling(t).mean() - ) + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) return df ``` @@ -430,48 +427,47 @@ The FreqAI strategy requires the user to include the following lines of code in coin = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) return df diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 2ba8c5ecf..2378dcdd8 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -961,23 +961,25 @@ class FreqaiDataKitchen: for tf in tfs: if tf == tfs[-1]: sgi = True # doing this last allows user to use all tf raw prices in labels - dataframe = strategy.populate_any_indicators( - pair, - dataframe.copy(), - tf, - informative=base_dataframes[tf], - set_generalized_indicators=sgi - ) - if pairs: - for i in pairs: - if pair in i: - continue # dont repeat anything from whitelist - dataframe = strategy.populate_any_indicators( - i, - dataframe.copy(), - tf, - informative=corr_dataframes[i][tf] - ) + with strategy.freqai.lock: + dataframe = strategy.populate_any_indicators( + pair, + dataframe.copy(), + tf, + informative=base_dataframes[tf], + set_generalized_indicators=sgi + ) + with strategy.freqai.lock: + if pairs: + for i in pairs: + if pair in i: + continue # dont repeat anything from whitelist + dataframe = strategy.populate_any_indicators( + i, + dataframe.copy(), + tf, + informative=corr_dataframes[i][tf] + ) self.get_unique_classes_from_labels(dataframe) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index d8584d5f9..5810e7881 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -82,99 +82,98 @@ class FreqaiExampleStrategy(IStrategy): coin = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) - informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) + informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - bollinger = qtpylib.bollinger_bands( - qtpylib.typical_price(informative), window=t, stds=2.2 - ) - informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] - informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] - informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] + bollinger = qtpylib.bollinger_bands( + qtpylib.typical_price(informative), window=t, stds=2.2 + ) + informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] + informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] + informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] - informative[f"%-{coin}bb_width-period_{t}"] = ( - informative[f"{coin}bb_upperband-period_{t}"] - - informative[f"{coin}bb_lowerband-period_{t}"] - ) / informative[f"{coin}bb_middleband-period_{t}"] - informative[f"%-{coin}close-bb_lower-period_{t}"] = ( - informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] - ) + informative[f"%-{coin}bb_width-period_{t}"] = ( + informative[f"{coin}bb_upperband-period_{t}"] + - informative[f"{coin}bb_lowerband-period_{t}"] + ) / informative[f"{coin}bb_middleband-period_{t}"] + informative[f"%-{coin}close-bb_lower-period_{t}"] = ( + informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] + ) - informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t) + informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t) - informative[f"%-{coin}relative_volume-period_{t}"] = ( - informative["volume"] / informative["volume"].rolling(t).mean() - ) + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) - informative[f"%-{coin}pct-change"] = informative["close"].pct_change() - informative[f"%-{coin}raw_volume"] = informative["volume"] - informative[f"%-{coin}raw_price"] = informative["close"] + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + informative[f"%-{coin}raw_price"] = informative["close"] - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - # user adds targets here by prepending them with &- (see convention below) - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) + # user adds targets here by prepending them with &- (see convention below) + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) - # Classifiers are typically set up with strings as targets: - # df['&s-up_or_down'] = np.where( df["close"].shift(-100) > - # df["close"], 'up', 'down') + # Classifiers are typically set up with strings as targets: + # df['&s-up_or_down'] = np.where( df["close"].shift(-100) > + # df["close"], 'up', 'down') - # If user wishes to use multiple targets, they can add more by - # appending more columns with '&'. User should keep in mind that multi targets - # requires a multioutput prediction model such as - # templates/CatboostPredictionMultiModel.py, + # If user wishes to use multiple targets, they can add more by + # appending more columns with '&'. User should keep in mind that multi targets + # requires a multioutput prediction model such as + # templates/CatboostPredictionMultiModel.py, - # df["&-s_range"] = ( - # df["close"] - # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - # .max() - # - - # df["close"] - # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - # .min() - # ) + # df["&-s_range"] = ( + # df["close"] + # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + # .max() + # - + # df["close"] + # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + # .min() + # ) return df @@ -252,12 +251,11 @@ class FreqaiExampleStrategy(IStrategy): "prediction" + entry_tag not in pair_dict[pair] or pair_dict[pair]['extras']["prediction" + entry_tag] == 0 ): - with self.freqai.lock: - pair_dict[pair]['extras']["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) - if not follow_mode: - self.freqai.dd.save_drawer_to_disk() - else: - self.freqai.dd.save_follower_dict_to_disk() + pair_dict[pair]['extras']["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) + if not follow_mode: + self.freqai.dd.save_drawer_to_disk() + else: + self.freqai.dd.save_follower_dict_to_disk() roi_price = pair_dict[pair]['extras']["prediction" + entry_tag] roi_time = self.max_roi_time_long.value @@ -296,12 +294,11 @@ class FreqaiExampleStrategy(IStrategy): else: pair_dict = self.freqai.dd.follower_dict - with self.freqai.lock: - pair_dict[pair]['extras']["prediction" + entry_tag] = 0 - if not follow_mode: - self.freqai.dd.save_drawer_to_disk() - else: - self.freqai.dd.save_follower_dict_to_disk() + pair_dict[pair]['extras']["prediction" + entry_tag] = 0 + if not follow_mode: + self.freqai.dd.save_drawer_to_disk() + else: + self.freqai.dd.save_follower_dict_to_disk() return True diff --git a/tests/strategy/strats/freqai_test_classifier.py b/tests/strategy/strats/freqai_test_classifier.py index 0a2ce793a..a1e8cb6bf 100644 --- a/tests/strategy/strats/freqai_test_classifier.py +++ b/tests/strategy/strats/freqai_test_classifier.py @@ -63,48 +63,47 @@ class freqai_test_classifier(IStrategy): coin = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"%-{coin}pct-change"] = informative["close"].pct_change() - informative[f"%-{coin}raw_volume"] = informative["volume"] - informative[f"%-{coin}raw_price"] = informative["close"] + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + informative[f"%-{coin}raw_price"] = informative["close"] - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df['&s-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down') + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df['&s-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down') return df diff --git a/tests/strategy/strats/freqai_test_multimodel_strat.py b/tests/strategy/strats/freqai_test_multimodel_strat.py index 4f8877f14..cd3327da9 100644 --- a/tests/strategy/strats/freqai_test_multimodel_strat.py +++ b/tests/strategy/strats/freqai_test_multimodel_strat.py @@ -62,67 +62,66 @@ class freqai_test_multimodel_strat(IStrategy): coin = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"%-{coin}pct-change"] = informative["close"].pct_change() - informative[f"%-{coin}raw_volume"] = informative["volume"] - informative[f"%-{coin}raw_price"] = informative["close"] + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + informative[f"%-{coin}raw_price"] = informative["close"] - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) - df["&-s_range"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .max() - - - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .min() - ) + df["&-s_range"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .max() + - + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .min() + ) return df diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py index e239a30b0..792a3952f 100644 --- a/tests/strategy/strats/freqai_test_strat.py +++ b/tests/strategy/strats/freqai_test_strat.py @@ -62,55 +62,54 @@ class freqai_test_strat(IStrategy): coin = pair.split('/')[0] - with self.freqai.lock: - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - informative[f"%-{coin}pct-change"] = informative["close"].pct_change() - informative[f"%-{coin}raw_volume"] = informative["volume"] - informative[f"%-{coin}raw_price"] = informative["close"] + informative[f"%-{coin}pct-change"] = informative["close"].pct_change() + informative[f"%-{coin}raw_volume"] = informative["volume"] + informative[f"%-{coin}raw_price"] = informative["close"] - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) return df From a29402dddee2e1d90fd212c7bbae9d2ee142dc82 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 17:23:14 +0200 Subject: [PATCH 374/518] Rename and move analysis_lock to data_kitchen --- freqtrade/freqai/data_kitchen.py | 7 +++++-- freqtrade/freqai/freqai_interface.py | 1 - 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 2378dcdd8..730ef2767 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -3,6 +3,7 @@ import datetime import logging import shutil from pathlib import Path +from threading import Lock from typing import Any, Dict, List, Tuple import numpy as np @@ -70,6 +71,8 @@ class FreqaiDataKitchen: self.model_filename: str = "" self.live = live self.pair = pair + self.analysis_lock = Lock() + self.svm_model: linear_model.SGDOneClassSVM = None self.keras: bool = self.freqai_config.get("keras", False) self.set_all_pairs() @@ -961,7 +964,7 @@ class FreqaiDataKitchen: for tf in tfs: if tf == tfs[-1]: sgi = True # doing this last allows user to use all tf raw prices in labels - with strategy.freqai.lock: + with self.analysis_lock: dataframe = strategy.populate_any_indicators( pair, dataframe.copy(), @@ -969,7 +972,7 @@ class FreqaiDataKitchen: informative=base_dataframes[tf], set_generalized_indicators=sgi ) - with strategy.freqai.lock: + with self.analysis_lock: if pairs: for i in pairs: if pair in i: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 374fcea2f..4c9cf1c05 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -70,7 +70,6 @@ class IFreqaiModel(ABC): self.set_full_path() self.follow_mode: bool = self.freqai_info.get("follow_mode", False) self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode) - self.lock = threading.Lock() self.identifier: str = self.freqai_info.get("identifier", "no_id_provided") self.scanning = False self.keras: bool = self.freqai_info.get("keras", False) From d442383a154b37a7b63682540a26d50aefaf92ef Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 18:17:17 +0200 Subject: [PATCH 375/518] Fix ta-lib install script --- build_helpers/install_ta-lib.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_helpers/install_ta-lib.sh b/build_helpers/install_ta-lib.sh index 581c1cd41..079d578b4 100755 --- a/build_helpers/install_ta-lib.sh +++ b/build_helpers/install_ta-lib.sh @@ -4,7 +4,7 @@ else INSTALL_LOC=${1} fi echo "Installing to ${INSTALL_LOC}" -if [ -n "$2" ] && [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then +if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then tar zxvf ta-lib-0.4.0-src.tar.gz cd ta-lib \ && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \ From 464d99808fbcb6e4d22a782ab662daecdfaa5460 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 18:22:01 +0200 Subject: [PATCH 376/518] Update doc table formatting --- docs/freqai.md | 2 +- freqtrade/freqai/freqai_interface.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 6631c271e..cde3ea0f7 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -133,7 +133,7 @@ Here are the values the user can expect to include/use inside the typical strate | `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
**Datatype:** float. | `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
**Datatype:** integer between -1 and 2. | `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
**Datatype:** float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). Note: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set).
**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. ### Example config file diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4c9cf1c05..e48de1fb8 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -500,7 +500,7 @@ class IFreqaiModel(ABC): dk.set_new_model_names(pair, new_trained_timerange) self.dd.pair_dict[pair]["first"] = False if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning: - with self.lock: + with dk.analysis_lock: self.dd.pair_to_end_of_training_queue(pair) self.dd.save_data(model, pair, dk) From ad846cdb76914d8881dd33fc8b7a8c031836fb6b Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 14 Aug 2022 20:24:29 +0200 Subject: [PATCH 377/518] fix lock bug, update docstring --- freqtrade/freqai/data_drawer.py | 13 +++++++------ freqtrade/freqai/data_kitchen.py | 9 ++++----- freqtrade/freqai/freqai_interface.py | 29 +++++++++++++++------------- 3 files changed, 27 insertions(+), 24 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 4ba55a0ec..7a0ddd7ff 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -38,8 +38,7 @@ class FreqaiDataDrawer: """ Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving /loading to/from disk. - This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is - reinstantiated for each coin. + This object remains persistent throughout live/dry. Record of contribution: FreqAI was developed by a group of individuals who all contributed specific skillsets to the @@ -56,7 +55,7 @@ class FreqaiDataDrawer: Beta testing and bug reporting: @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm - Juha Nykänen @suikula, Wagner Costa @wagnercosta + Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert """ def __init__(self, full_path: Path, config: dict, follow_mode: bool = False): @@ -85,6 +84,7 @@ class FreqaiDataDrawer: self.load_historic_predictions_from_disk() self.training_queue: Dict[str, int] = {} self.history_lock = threading.Lock() + self.save_lock = threading.Lock() self.old_DBSCAN_eps: Dict[str, float] = {} self.empty_pair_dict: pair_info = { "model_filename": "", "trained_timestamp": 0, @@ -145,9 +145,10 @@ class FreqaiDataDrawer: """ Save data drawer full of all pair model metadata in present model folder. """ - with open(self.pair_dictionary_path, 'w') as fp: - rapidjson.dump(self.pair_dict, fp, default=self.np_encoder, - number_mode=rapidjson.NM_NATIVE) + with self.save_lock: + with open(self.pair_dictionary_path, 'w') as fp: + rapidjson.dump(self.pair_dict, fp, default=self.np_encoder, + number_mode=rapidjson.NM_NATIVE) def save_follower_dict_to_disk(self): """ diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 730ef2767..cf0af6c08 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -3,7 +3,6 @@ import datetime import logging import shutil from pathlib import Path -from threading import Lock from typing import Any, Dict, List, Tuple import numpy as np @@ -35,6 +34,9 @@ class FreqaiDataKitchen: Class designed to analyze data for a single pair. Employed by the IFreqaiModel class. Functionalities include holding, saving, loading, and analyzing the data. + This object is not persistent, it is reinstantiated for each coin, each time the coin + model needs to be inferenced or trained. + Record of contribution: FreqAI was developed by a group of individuals who all contributed specific skillsets to the project. @@ -50,7 +52,7 @@ class FreqaiDataKitchen: Beta testing and bug reporting: @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm - Juha Nykänen @suikula, Wagner Costa @wagnercosta + Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert """ def __init__( @@ -71,7 +73,6 @@ class FreqaiDataKitchen: self.model_filename: str = "" self.live = live self.pair = pair - self.analysis_lock = Lock() self.svm_model: linear_model.SGDOneClassSVM = None self.keras: bool = self.freqai_config.get("keras", False) @@ -964,7 +965,6 @@ class FreqaiDataKitchen: for tf in tfs: if tf == tfs[-1]: sgi = True # doing this last allows user to use all tf raw prices in labels - with self.analysis_lock: dataframe = strategy.populate_any_indicators( pair, dataframe.copy(), @@ -972,7 +972,6 @@ class FreqaiDataKitchen: informative=base_dataframes[tf], set_generalized_indicators=sgi ) - with self.analysis_lock: if pairs: for i in pairs: if pair in i: diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index e48de1fb8..902604989 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -12,7 +12,7 @@ import numpy as np import pandas as pd from numpy.typing import NDArray from pandas import DataFrame - +from threading import Lock from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode from freqtrade.exceptions import OperationalException @@ -52,7 +52,7 @@ class IFreqaiModel(ABC): Beta testing and bug reporting: @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm - Juha Nykänen @suikula, Wagner Costa @wagnercosta + Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert """ def __init__(self, config: Dict[str, Any]) -> None: @@ -81,6 +81,7 @@ class IFreqaiModel(ABC): self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist")) self.last_trade_database_summary: DataFrame = {} self.current_trade_database_summary: DataFrame = {} + self.analysis_lock = Lock() def assert_config(self, config: Dict[str, Any]) -> None: @@ -114,10 +115,10 @@ class IFreqaiModel(ABC): elif not self.follow_mode: self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"]) logger.info(f"Training {len(self.dk.training_timeranges)} timeranges") - - dataframe = self.dk.use_strategy_to_populate_indicators( - strategy, prediction_dataframe=dataframe, pair=metadata["pair"] - ) + with self.analysis_lock: + dataframe = self.dk.use_strategy_to_populate_indicators( + strategy, prediction_dataframe=dataframe, pair=metadata["pair"] + ) dk = self.start_backtesting(dataframe, metadata, self.dk) dataframe = dk.remove_features_from_df(dk.return_dataframe) @@ -289,9 +290,10 @@ class IFreqaiModel(ABC): # load the model and associated data into the data kitchen self.model = self.dd.load_data(metadata["pair"], dk) - dataframe = self.dk.use_strategy_to_populate_indicators( - strategy, prediction_dataframe=dataframe, pair=metadata["pair"] - ) + with self.analysis_lock: + dataframe = self.dk.use_strategy_to_populate_indicators( + strategy, prediction_dataframe=dataframe, pair=metadata["pair"] + ) if not self.model: logger.warning( @@ -485,9 +487,10 @@ class IFreqaiModel(ABC): data_load_timerange, pair, dk ) - unfiltered_dataframe = dk.use_strategy_to_populate_indicators( - strategy, corr_dataframes, base_dataframes, pair - ) + with self.analysis_lock: + unfiltered_dataframe = dk.use_strategy_to_populate_indicators( + strategy, corr_dataframes, base_dataframes, pair + ) unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe) @@ -500,7 +503,7 @@ class IFreqaiModel(ABC): dk.set_new_model_names(pair, new_trained_timerange) self.dd.pair_dict[pair]["first"] = False if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning: - with dk.analysis_lock: + with self.analysis_lock: self.dd.pair_to_end_of_training_queue(pair) self.dd.save_data(model, pair, dk) From 8961b8d56042545b566d2ef5fea1cb34e2ebdb35 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 14 Aug 2022 16:41:50 +0200 Subject: [PATCH 378/518] merge in inference timer and historic predictions handling improvements. --- freqtrade/freqai/data_drawer.py | 25 ++++++++++--------- freqtrade/freqai/freqai_interface.py | 36 ++++++++++++++++++++++++---- 2 files changed, 46 insertions(+), 15 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 7a0ddd7ff..c8dbdf5e5 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -85,6 +85,7 @@ class FreqaiDataDrawer: self.training_queue: Dict[str, int] = {} self.history_lock = threading.Lock() self.save_lock = threading.Lock() + self.pair_dict_lock = threading.Lock() self.old_DBSCAN_eps: Dict[str, float] = {} self.empty_pair_dict: pair_info = { "model_filename": "", "trained_timestamp": 0, @@ -228,10 +229,11 @@ class FreqaiDataDrawer: def pair_to_end_of_training_queue(self, pair: str) -> None: # march all pairs up in the queue - for p in self.pair_dict: - self.pair_dict[p]["priority"] -= 1 - # send pair to end of queue - self.pair_dict[pair]["priority"] = len(self.pair_dict) + with self.pair_dict_lock: + for p in self.pair_dict: + self.pair_dict[p]["priority"] -= 1 + # send pair to end of queue + self.pair_dict[pair]["priority"] = len(self.pair_dict) def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None: """ @@ -261,13 +263,14 @@ class FreqaiDataDrawer: the strategy originally. Doing this allows FreqUI to always display the correct historic predictions. """ - df = self.historic_predictions[pair] - # here are some pandas hula hoops to accommodate the possibility of a series - # or dataframe depending number of labels requested by user - nan_df = pd.DataFrame(np.nan, index=df.index[-2:] + 2, columns=df.columns) - df = pd.concat([df, nan_df], ignore_index=True, axis=0) - df = self.historic_predictions[pair] = df[:-1] + index = self.historic_predictions[pair].index[-1:] + columns = self.historic_predictions[pair].columns + + nan_df = pd.DataFrame(np.nan, index=index, columns=columns) + self.historic_predictions[pair] = pd.concat( + [self.historic_predictions[pair], nan_df], ignore_index=True, axis=0) + df = self.historic_predictions[pair] # model outputs and associated statistics for label in predictions.columns: @@ -523,7 +526,7 @@ class FreqaiDataDrawer: history_data[pair][tf] = pd.concat( [ history_data[pair][tf], - strategy.dp.get_pair_dataframe(pair, tf).iloc[index:], + df_dp.iloc[index:], ], ignore_index=True, axis=0, diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 902604989..5471422c5 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -7,11 +7,11 @@ import time from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, Tuple - import numpy as np import pandas as pd from numpy.typing import NDArray from pandas import DataFrame +from freqtrade.exchange import timeframe_to_seconds from threading import Lock from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode @@ -82,6 +82,9 @@ class IFreqaiModel(ABC): self.last_trade_database_summary: DataFrame = {} self.current_trade_database_summary: DataFrame = {} self.analysis_lock = Lock() + self.inference_time: float = 0 + self.begin_time: float = 0 + self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe']) def assert_config(self, config: Dict[str, Any]) -> None: @@ -104,6 +107,7 @@ class IFreqaiModel(ABC): self.dd.set_pair_dict_info(metadata) if self.live: + self.inference_timer('start') self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"]) dk = self.start_live(dataframe, metadata, strategy, self.dk) @@ -123,6 +127,8 @@ class IFreqaiModel(ABC): dataframe = dk.remove_features_from_df(dk.return_dataframe) del dk + if self.live: + self.inference_timer('stop') return dataframe @threaded @@ -155,6 +161,8 @@ class IFreqaiModel(ABC): new_trained_timerange, pair, strategy, dk, data_load_timerange ) + self.dd.save_historic_predictions_to_disk() + def start_backtesting( self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen ) -> FreqaiDataKitchen: @@ -340,7 +348,6 @@ class IFreqaiModel(ABC): # historical accuracy reasons. pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False) - self.dd.save_historic_predictions_to_disk() if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live: self.fit_live_predictions(dk, pair) self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe)) @@ -503,8 +510,7 @@ class IFreqaiModel(ABC): dk.set_new_model_names(pair, new_trained_timerange) self.dd.pair_dict[pair]["first"] = False if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning: - with self.analysis_lock: - self.dd.pair_to_end_of_training_queue(pair) + self.dd.pair_to_end_of_training_queue(pair) self.dd.save_data(model, pair, dk) if self.freqai_info.get("purge_old_models", False): @@ -582,6 +588,28 @@ class IFreqaiModel(ABC): return + def inference_timer(self, do='start'): + """ + Timer designed to track the cumulative time spent in FreqAI for one pass through + the whitelist. This will check if the time spent is more than 1/4 the time + of a single candle, and if so, it will warn the user of degraded performance + """ + if do == 'start': + self.pair_it += 1 + self.begin_time = time.time() + elif do == 'stop': + end = time.time() + self.inference_time += (end - self.begin_time) + if self.pair_it == self.total_pairs: + logger.info( + f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds') + if self.inference_time > 0.25 * self.base_tf_seconds: + logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to' + ' avoid blinding open trades and degrading performance.') + self.pair_it = 0 + self.inference_time = 0 + return + # Following methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModel.py for an example. From 006b11e5d5d7c8ca388dd9dd7ad765cd22da1ea6 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 14 Aug 2022 21:42:55 +0200 Subject: [PATCH 379/518] fix leftover bug in indicator population --- freqtrade/freqai/data_kitchen.py | 34 ++++++++++++++++---------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index cf0af6c08..35f51baed 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -965,23 +965,23 @@ class FreqaiDataKitchen: for tf in tfs: if tf == tfs[-1]: sgi = True # doing this last allows user to use all tf raw prices in labels - dataframe = strategy.populate_any_indicators( - pair, - dataframe.copy(), - tf, - informative=base_dataframes[tf], - set_generalized_indicators=sgi - ) - if pairs: - for i in pairs: - if pair in i: - continue # dont repeat anything from whitelist - dataframe = strategy.populate_any_indicators( - i, - dataframe.copy(), - tf, - informative=corr_dataframes[i][tf] - ) + dataframe = strategy.populate_any_indicators( + pair, + dataframe.copy(), + tf, + informative=base_dataframes[tf], + set_generalized_indicators=sgi + ) + if pairs: + for i in pairs: + if pair in i: + continue # dont repeat anything from whitelist + dataframe = strategy.populate_any_indicators( + i, + dataframe.copy(), + tf, + informative=corr_dataframes[i][tf] + ) self.get_unique_classes_from_labels(dataframe) From 3f6d427084b8d2a7b6def504b3083b3d816623f5 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 14 Aug 2022 21:46:37 +0200 Subject: [PATCH 380/518] add a check for number of training features in tests --- tests/freqai/test_freqai_interface.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index a36f80280..3de8561ed 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -71,6 +71,7 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf): assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() + assert(len(freqai.dk.data['training_features_list']) == 26) shutil.rmtree(Path(freqai.dk.full_path)) From 4b708caa6a17aff2da89c126728fd78308f3428d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:19 +0000 Subject: [PATCH 381/518] Bump sqlalchemy from 1.4.39 to 1.4.40 Bumps [sqlalchemy](https://github.com/sqlalchemy/sqlalchemy) from 1.4.39 to 1.4.40. - [Release notes](https://github.com/sqlalchemy/sqlalchemy/releases) - [Changelog](https://github.com/sqlalchemy/sqlalchemy/blob/main/CHANGES.rst) - [Commits](https://github.com/sqlalchemy/sqlalchemy/commits) --- updated-dependencies: - dependency-name: sqlalchemy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4fa6e60a0..d2527858a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ ccxt==1.91.93 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 -SQLAlchemy==1.4.39 +SQLAlchemy==1.4.40 python-telegram-bot==13.13 arrow==1.2.2 cachetools==4.2.2 From 96c279f86c9293eb4ad31454b230a93c118385ca Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:22 +0000 Subject: [PATCH 382/518] Bump nbconvert from 6.5.0 to 6.5.3 Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 6.5.0 to 6.5.3. - [Release notes](https://github.com/jupyter/nbconvert/releases) - [Commits](https://github.com/jupyter/nbconvert/compare/6.5...6.5.3) --- updated-dependencies: - dependency-name: nbconvert dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index 977b7b9ec..af6cd0ed6 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -19,7 +19,7 @@ isort==5.10.1 time-machine==2.7.1 # Convert jupyter notebooks to markdown documents -nbconvert==6.5.0 +nbconvert==6.5.3 # mypy types types-cachetools==5.2.1 From 025ff27dd2f4abbddf06848dfac0c6e167230276 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:29 +0000 Subject: [PATCH 383/518] Bump numpy from 1.23.1 to 1.23.2 Bumps [numpy](https://github.com/numpy/numpy) from 1.23.1 to 1.23.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.23.1...v1.23.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4fa6e60a0..545fa092e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy==1.23.1 +numpy==1.23.2 pandas==1.4.3 pandas-ta==0.3.14b From baa0af68b214c83c1adc769c062430e3e87c3009 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:33 +0000 Subject: [PATCH 384/518] Bump plotly from 5.9.0 to 5.10.0 Bumps [plotly](https://github.com/plotly/plotly.py) from 5.9.0 to 5.10.0. - [Release notes](https://github.com/plotly/plotly.py/releases) - [Changelog](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md) - [Commits](https://github.com/plotly/plotly.py/compare/v5.9.0...v5.10.0) --- updated-dependencies: - dependency-name: plotly dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements-plot.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-plot.txt b/requirements-plot.txt index 0f6ae94c2..80cd3f4f2 100644 --- a/requirements-plot.txt +++ b/requirements-plot.txt @@ -1,4 +1,4 @@ # Include all requirements to run the bot. -r requirements.txt -plotly==5.9.0 +plotly==5.10.0 From 2581acd75e90cd2b5dbf95a7f61759b6915da013 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:36 +0000 Subject: [PATCH 385/518] Bump filelock from 3.7.1 to 3.8.0 Bumps [filelock](https://github.com/tox-dev/py-filelock) from 3.7.1 to 3.8.0. - [Release notes](https://github.com/tox-dev/py-filelock/releases) - [Changelog](https://github.com/tox-dev/py-filelock/blob/main/docs/changelog.rst) - [Commits](https://github.com/tox-dev/py-filelock/compare/3.7.1...3.8.0) --- updated-dependencies: - dependency-name: filelock dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements-hyperopt.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt index 11b9511f2..020ccdda8 100644 --- a/requirements-hyperopt.txt +++ b/requirements-hyperopt.txt @@ -5,5 +5,5 @@ scipy==1.9.0 scikit-learn==1.1.2 scikit-optimize==0.9.0 -filelock==3.7.1 +filelock==3.8.0 progressbar2==4.0.0 From bc79027cf41911632a813e195d81d49ad058fe4e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:51 +0000 Subject: [PATCH 386/518] Bump mkdocs-material from 8.3.9 to 8.4.0 Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 8.3.9 to 8.4.0. - [Release notes](https://github.com/squidfunk/mkdocs-material/releases) - [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG) - [Commits](https://github.com/squidfunk/mkdocs-material/compare/8.3.9...8.4.0) --- updated-dependencies: - dependency-name: mkdocs-material dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- docs/requirements-docs.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index 205516d6d..a53e909e0 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,6 +1,6 @@ markdown==3.3.7 mkdocs==1.3.1 -mkdocs-material==8.3.9 +mkdocs-material==8.4.0 mdx_truly_sane_lists==1.3 pymdown-extensions==9.5 jinja2==3.1.2 From 6fb5fbdd30b3e9bda8b46a6213de2f807a39f708 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 03:01:56 +0000 Subject: [PATCH 387/518] Bump orjson from 3.7.11 to 3.7.12 Bumps [orjson](https://github.com/ijl/orjson) from 3.7.11 to 3.7.12. - [Release notes](https://github.com/ijl/orjson/releases) - [Changelog](https://github.com/ijl/orjson/blob/master/CHANGELOG.md) - [Commits](https://github.com/ijl/orjson/compare/3.7.11...3.7.12) --- updated-dependencies: - dependency-name: orjson dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4fa6e60a0..5bf2b8f6b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,7 +28,7 @@ py_find_1st==1.1.5 # Load ticker files 30% faster python-rapidjson==1.8 # Properly format api responses -orjson==3.7.11 +orjson==3.7.12 # Notify systemd sdnotify==0.3.2 From dba7d7fd65e42517931a059b169d73cbc7477a84 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Aug 2022 04:29:20 +0000 Subject: [PATCH 388/518] Bump ccxt from 1.91.93 to 1.92.20 Bumps [ccxt](https://github.com/ccxt/ccxt) from 1.91.93 to 1.92.20. - [Release notes](https://github.com/ccxt/ccxt/releases) - [Changelog](https://github.com/ccxt/ccxt/blob/master/exchanges.cfg) - [Commits](https://github.com/ccxt/ccxt/compare/1.91.93...1.92.20) --- updated-dependencies: - dependency-name: ccxt dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ad012ae28..fec5885cb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.2 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.91.93 +ccxt==1.92.20 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 From 398b2946b58b9365bf8d9272dc53283249b5c4f5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 06:49:28 +0200 Subject: [PATCH 389/518] Update test formatting --- tests/freqai/test_freqai_interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index 3de8561ed..792ffc467 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -71,7 +71,7 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf): assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() - assert(len(freqai.dk.data['training_features_list']) == 26) + assert len(freqai.dk.data['training_features_list']) == 26 shutil.rmtree(Path(freqai.dk.full_path)) From c7e171921588166094ca06cd17f582a1d9229a8f Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 06:53:02 +0200 Subject: [PATCH 390/518] Fix interface import sorting --- freqtrade/freqai/freqai_interface.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 5471422c5..49e4ce5c3 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -6,16 +6,18 @@ import threading import time from abc import ABC, abstractmethod from pathlib import Path +from threading import Lock from typing import Any, Dict, Tuple + import numpy as np import pandas as pd from numpy.typing import NDArray from pandas import DataFrame -from freqtrade.exchange import timeframe_to_seconds -from threading import Lock + from freqtrade.configuration import TimeRange from freqtrade.enums import RunMode from freqtrade.exceptions import OperationalException +from freqtrade.exchange import timeframe_to_seconds from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.strategy.interface import IStrategy From 6c32331740101cdf5c1b21b4952a47f761ba4d2f Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 08:43:58 +0200 Subject: [PATCH 391/518] Move precision calculations to standalone functions --- freqtrade/exchange/exchange.py | 90 +++++++++++++++++++--------- freqtrade/persistence/trade_model.py | 2 +- 2 files changed, 62 insertions(+), 30 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 18598e92d..67423bd9b 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -683,42 +683,18 @@ class Exchange: def amount_to_precision(self, pair: str, amount: float) -> float: """ Returns the amount to buy or sell to a precision the Exchange accepts - Re-implementation of ccxt internal methods - ensuring we can test the result is correct - based on our definitions. - """ - if self.markets[pair]['precision']['amount'] is not None: - amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE, - precision=self.markets[pair]['precision']['amount'], - counting_mode=self.precisionMode, - )) - return amount + """ + return amount_to_precision(amount, self.markets[pair]['precision']['amount'], + self.precisionMode) def price_to_precision(self, pair: str, price: float) -> float: """ Returns the price rounded up to the precision the Exchange accepts. - Partial Re-implementation of ccxt internal method decimal_to_precision(), - which does not support rounding up - TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and - align with amount_to_precision(). Rounds up """ - if self.markets[pair]['precision']['price']: - # price = float(decimal_to_precision(price, rounding_mode=ROUND, - # precision=self.markets[pair]['precision']['price'], - # counting_mode=self.precisionMode, - # )) - if self.precisionMode == TICK_SIZE: - precision = FtPrecise(self.markets[pair]['precision']['price']) - price_str = FtPrecise(price) - missing = price_str % precision - if not missing == FtPrecise("0"): - price = round(float(str(price_str - missing + precision)), 14) - else: - symbol_prec = self.markets[pair]['precision']['price'] - big_price = price * pow(10, symbol_prec) - price = ceil(big_price) / pow(10, symbol_prec) - return price + return price_to_precision(price, self.markets[pair]['precision']['price'], + self.precisionMode) def price_get_one_pip(self, pair: str, price: float) -> float: """ @@ -2849,3 +2825,59 @@ def market_is_active(market: Dict) -> bool: # See https://github.com/ccxt/ccxt/issues/4874, # https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520 return market.get('active', True) is not False + + +def amount_to_precision(amount: float, amount_precision: Optional[float], + precisionMode: int) -> float: + """ + Returns the amount to buy or sell to a precision the Exchange accepts + Re-implementation of ccxt internal methods - ensuring we can test the result is correct + based on our definitions. + :param amount: amount to truncate + :param amount_precision: amount precision to use. + should be retrieved from markets[pair]['precision']['amount'] + :param precisionMode: precision mode to use. Should be used from precisionMode + one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE + :return: truncated amount + """ + if amount_precision is not None: + amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE, + precision=amount_precision, + counting_mode=precisionMode, + )) + + return amount + + +def price_to_precision(price: float, price_precision: Optional[float], + precisionMode: int) -> float: + """ + Returns the price rounded up to the precision the Exchange accepts. + Partial Re-implementation of ccxt internal method decimal_to_precision(), + which does not support rounding up + TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and + align with amount_to_precision(). + !!! Rounds up + :param price: price to convert + :param price_precision: price precision to use. Used from markets[pair]['precision']['price'] + :param precisionMode: precision mode to use. Should be used from precisionMode + one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE + :return: price rounded up to the precision the Exchange accepts + + """ + if price_precision: + # price = float(decimal_to_precision(price, rounding_mode=ROUND, + # precision=price_precision, + # counting_mode=self.precisionMode, + # )) + if precisionMode == TICK_SIZE: + precision = FtPrecise(price_precision) + price_str = FtPrecise(price) + missing = price_str % precision + if not missing == FtPrecise("0"): + price = round(float(str(price_str - missing + precision)), 14) + else: + symbol_prec = price_precision + big_price = price * pow(10, symbol_prec) + price = ceil(big_price) / pow(10, symbol_prec) + return price diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index faed1eab5..864b4fde9 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -826,7 +826,7 @@ class LocalTrade(): return float(f"{profit_ratio:.8f}") - def recalc_trade_from_orders(self, is_closing: bool = False): + def recalc_trade_from_orders(self, *, is_closing: bool = False): ZERO = FtPrecise(0.0) current_amount = FtPrecise(0.0) current_stake = FtPrecise(0.0) From 15e85797c2e3df415ecfd82da942dc96909893c0 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 08:51:15 +0200 Subject: [PATCH 392/518] Simplify to_precision tests and imports --- freqtrade/exchange/__init__.py | 13 ++--- freqtrade/exchange/okx.py | 3 +- tests/exchange/test_exchange.py | 85 ++++++++++----------------------- 3 files changed, 33 insertions(+), 68 deletions(-) diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index 2b9ed47ea..cb63c6b9a 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -9,12 +9,13 @@ from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bittrex import Bittrex from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro -from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges, - is_exchange_known_ccxt, is_exchange_officially_supported, - market_is_active, timeframe_to_minutes, timeframe_to_msecs, - timeframe_to_next_date, timeframe_to_prev_date, - timeframe_to_seconds, validate_exchange, - validate_exchanges) +from freqtrade.exchange.exchange import (amount_to_precision, available_exchanges, ccxt_exchanges, + date_minus_candles, is_exchange_known_ccxt, + is_exchange_officially_supported, market_is_active, + price_to_precision, timeframe_to_minutes, + timeframe_to_msecs, timeframe_to_next_date, + timeframe_to_prev_date, timeframe_to_seconds, + validate_exchange, validate_exchanges) from freqtrade.exchange.ftx import Ftx from freqtrade.exchange.gateio import Gateio from freqtrade.exchange.hitbtc import Hitbtc diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py index afd7a672f..540e76fca 100644 --- a/freqtrade/exchange/okx.py +++ b/freqtrade/exchange/okx.py @@ -7,9 +7,8 @@ from freqtrade.constants import BuySell from freqtrade.enums import MarginMode, TradingMode from freqtrade.enums.candletype import CandleType from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError -from freqtrade.exchange import Exchange +from freqtrade.exchange import Exchange, date_minus_candles from freqtrade.exchange.common import retrier -from freqtrade.exchange.exchange import date_minus_candles logger = logging.getLogger(__name__) diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index bbe424430..6ad4a72c6 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -14,12 +14,12 @@ from pandas import DataFrame from freqtrade.enums import CandleType, MarginMode, TradingMode from freqtrade.exceptions import (DDosProtection, DependencyException, InvalidOrderException, OperationalException, PricingError, TemporaryError) -from freqtrade.exchange import Binance, Bittrex, Exchange, Kraken +from freqtrade.exchange import (Binance, Bittrex, Exchange, Kraken, amount_to_precision, + date_minus_candles, market_is_active, price_to_precision, + timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date, + timeframe_to_prev_date, timeframe_to_seconds) from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, API_RETRY_COUNT, calculate_backoff, remove_credentials) -from freqtrade.exchange.exchange import (date_minus_candles, market_is_active, timeframe_to_minutes, - timeframe_to_msecs, timeframe_to_next_date, - timeframe_to_prev_date, timeframe_to_seconds) from freqtrade.resolvers.exchange_resolver import ExchangeResolver from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re, num_log_has_re @@ -279,62 +279,35 @@ def test_validate_order_time_in_force(default_conf, mocker, caplog): ex.validate_order_time_in_force(tif2) -@pytest.mark.parametrize("amount,precision_mode,precision,contract_size,expected,trading_mode", [ - (2.34559, 2, 4, 1, 2.3455, 'spot'), - (2.34559, 2, 5, 1, 2.34559, 'spot'), - (2.34559, 2, 3, 1, 2.345, 'spot'), - (2.9999, 2, 3, 1, 2.999, 'spot'), - (2.9909, 2, 3, 1, 2.990, 'spot'), - (2.9909, 2, 0, 1, 2, 'spot'), - (29991.5555, 2, 0, 1, 29991, 'spot'), - (29991.5555, 2, -1, 1, 29990, 'spot'), - (29991.5555, 2, -2, 1, 29900, 'spot'), +@pytest.mark.parametrize("amount,precision_mode,precision,expected", [ + (2.34559, 2, 4, 2.3455), + (2.34559, 2, 5, 2.34559), + (2.34559, 2, 3, 2.345), + (2.9999, 2, 3, 2.999), + (2.9909, 2, 3, 2.990), + (2.9909, 2, 0, 2), + (29991.5555, 2, 0, 29991), + (29991.5555, 2, -1, 29990), + (29991.5555, 2, -2, 29900), # Tests for Tick-size - (2.34559, 4, 0.0001, 1, 2.3455, 'spot'), - (2.34559, 4, 0.00001, 1, 2.34559, 'spot'), - (2.34559, 4, 0.001, 1, 2.345, 'spot'), - (2.9999, 4, 0.001, 1, 2.999, 'spot'), - (2.9909, 4, 0.001, 1, 2.990, 'spot'), - (2.9909, 4, 0.005, 0.01, 2.99, 'futures'), - (2.9999, 4, 0.005, 10, 2.995, 'futures'), + (2.34559, 4, 0.0001, 2.3455), + (2.34559, 4, 0.00001, 2.34559), + (2.34559, 4, 0.001, 2.345), + (2.9999, 4, 0.001, 2.999), + (2.9909, 4, 0.001, 2.990), + (2.9909, 4, 0.005, 2.99), + (2.9999, 4, 0.005, 2.995), ]) -def test_amount_to_precision( - default_conf, - mocker, - amount, - precision_mode, - precision, - contract_size, - expected, - trading_mode -): +def test_amount_to_precision(amount, precision_mode, precision, expected,): """ Test rounds down """ - - markets = PropertyMock(return_value={ - 'ETH/BTC': { - 'contractSize': contract_size, - 'precision': { - 'amount': precision - } - } - }) - - default_conf['trading_mode'] = trading_mode - default_conf['margin_mode'] = 'isolated' - - exchange = get_patched_exchange(mocker, default_conf, id="binance") # digits counting mode # DECIMAL_PLACES = 2 # SIGNIFICANT_DIGITS = 3 # TICK_SIZE = 4 - mocker.patch('freqtrade.exchange.Exchange.precisionMode', - PropertyMock(return_value=precision_mode)) - mocker.patch('freqtrade.exchange.Exchange.markets', markets) - pair = 'ETH/BTC' - assert exchange.amount_to_precision(pair, amount) == expected + assert amount_to_precision(amount, precision, precision_mode) == expected @pytest.mark.parametrize("price,precision_mode,precision,expected", [ @@ -359,21 +332,13 @@ def test_amount_to_precision( (0.000000003483, 4, 1e-12, 0.000000003483), ]) -def test_price_to_precision(default_conf, mocker, price, precision_mode, precision, expected): - """Test price to precision""" - markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'price': precision}}}) - - exchange = get_patched_exchange(mocker, default_conf, id="binance") - mocker.patch('freqtrade.exchange.Exchange.markets', markets) +def test_price_to_precision(price, precision_mode, precision, expected): # digits counting mode # DECIMAL_PLACES = 2 # SIGNIFICANT_DIGITS = 3 # TICK_SIZE = 4 - mocker.patch('freqtrade.exchange.Exchange.precisionMode', - PropertyMock(return_value=precision_mode)) - pair = 'ETH/BTC' - assert exchange.price_to_precision(pair, price) == expected + assert price_to_precision(price, precision, precision_mode) == expected @pytest.mark.parametrize("price,precision_mode,precision,expected", [ From 22241c55d530dc31addbbe3f4db2280b3e33324d Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 19:56:25 +0200 Subject: [PATCH 393/518] Add methods to get precision_amount from markets --- freqtrade/exchange/exchange.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 67423bd9b..1af555e5e 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -680,21 +680,33 @@ class Exchange: """ return endpoint in self._api.has and self._api.has[endpoint] + def get_precision_amount(self, pair: str) -> Optional[float]: + """ + Returns the amount precision of the exchange. + :param pair: + """ + return self.markets[pair].get('precision', {}).get('amount', None) + + def get_precision_price(self, pair: str) -> Optional[float]: + """ + Returns the price precision of the exchange. + :param pair: + """ + return self.markets[pair].get('precision', {}).get('price', None) + def amount_to_precision(self, pair: str, amount: float) -> float: """ Returns the amount to buy or sell to a precision the Exchange accepts """ - return amount_to_precision(amount, self.markets[pair]['precision']['amount'], - self.precisionMode) + return amount_to_precision(amount, self.get_precision_amount(pair), self.precisionMode) def price_to_precision(self, pair: str, price: float) -> float: """ Returns the price rounded up to the precision the Exchange accepts. Rounds up """ - return price_to_precision(price, self.markets[pair]['precision']['price'], - self.precisionMode) + return price_to_precision(price, self.get_precision_price(pair), self.precisionMode) def price_get_one_pip(self, pair: str, price: float) -> float: """ From c3f159bd5749428f8e1bc0b623ffc2a747844dc7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 19:58:40 +0200 Subject: [PATCH 394/518] Add precision fields to database --- freqtrade/freqtradebot.py | 16 +++++++++++++++- freqtrade/optimize/backtesting.py | 3 +++ freqtrade/persistence/migrations.py | 13 ++++++++++--- freqtrade/persistence/trade_model.py | 13 +++++++++++-- 4 files changed, 39 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index e3ef01dd5..8efc6ab59 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -159,6 +159,8 @@ class FreqtradeBot(LoggingMixin): performs startup tasks """ self.rpc.startup_messages(self.config, self.pairlists, self.protections) + # Update older trades with precision and precision mode + self.startup_backpopulate_precision() if not self.edge: # Adjust stoploss if it was changed Trade.stoploss_reinitialization(self.strategy.stoploss) @@ -286,6 +288,15 @@ class FreqtradeBot(LoggingMixin): else: return 0.0 + def startup_backpopulate_precision(self): + + trades = Trade.get_trades([Trade.precision_mode.is_(None)]) + for trade in trades: + trade.precision_mode = self.exchange.precisionMode + trade.amount_precision = self.exchange.get_precision_amount(trade.pair) + trade.price_precision = self.exchange.get_precision_price(trade.pair) + Trade.commit() + def startup_update_open_orders(self): """ Updates open orders based on order list kept in the database. @@ -738,7 +749,10 @@ class FreqtradeBot(LoggingMixin): leverage=leverage, is_short=is_short, trading_mode=self.trading_mode, - funding_fees=funding_fees + funding_fees=funding_fees, + amount_precision=self.exchange.get_precision_amount(pair), + price_precision=self.exchange.get_precision_price(pair), + precision_mode=self.exchange.precisionMode, ) else: # This is additional buy, we reset fee_open_currency so timeout checking can work diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 029946cfb..795d20644 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -837,6 +837,9 @@ class Backtesting: trading_mode=self.trading_mode, leverage=leverage, # interest_rate=interest_rate, + amount_precision=self.exchange.get_precision_amount(pair), + price_precision=self.exchange.get_precision_price(pair), + precision_mode=self.exchange.precisionMode, orders=[], ) diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py index 81757a7de..e54675f16 100644 --- a/freqtrade/persistence/migrations.py +++ b/freqtrade/persistence/migrations.py @@ -130,6 +130,10 @@ def migrate_trades_and_orders_table( get_column_def(cols, 'sell_order_status', 'null')) amount_requested = get_column_def(cols, 'amount_requested', 'amount') + amount_precision = get_column_def(cols, 'amount_precision', 'null') + price_precision = get_column_def(cols, 'price_precision', 'null') + precision_mode = get_column_def(cols, 'precision_mode', 'null') + # Schema migration necessary with engine.begin() as connection: connection.execute(text(f"alter table trades rename to {trade_back_name}")) @@ -156,7 +160,8 @@ def migrate_trades_and_orders_table( max_rate, min_rate, exit_reason, exit_order_status, strategy, enter_tag, timeframe, open_trade_value, close_profit_abs, trading_mode, leverage, liquidation_price, is_short, - interest_rate, funding_fees, realized_profit + interest_rate, funding_fees, realized_profit, + amount_precision, price_precision, precision_mode ) select id, lower(exchange), pair, {base_currency} base_currency, {stake_currency} stake_currency, @@ -182,7 +187,9 @@ def migrate_trades_and_orders_table( {open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs, {trading_mode} trading_mode, {leverage} leverage, {liquidation_price} liquidation_price, {is_short} is_short, {interest_rate} interest_rate, - {funding_fees} funding_fees, {realized_profit} realized_profit + {funding_fees} funding_fees, {realized_profit} realized_profit, + {amount_precision} amount_precision, {price_precision} price_precision, + {precision_mode} precision_mode from {trade_back_name} """)) @@ -300,7 +307,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None: # Migrates both trades and orders table! # if ('orders' not in previous_tables # or not has_column(cols_orders, 'stop_price')): - if not has_column(cols_trades, 'realized_profit'): + if not has_column(cols_trades, 'precision_mode'): logger.info(f"Running database migration for trades - " f"backup: {table_back_name}, {order_table_bak_name}") migrate_trades_and_orders_table( diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 864b4fde9..436919bb1 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -14,6 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException +from freqtrade.exchange import amount_to_precision, price_to_precision from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -292,6 +293,9 @@ class LocalTrade(): timeframe: Optional[int] = None trading_mode: TradingMode = TradingMode.SPOT + amount_precision: Optional[float] = None + price_precision: Optional[float] = None + precision_mode: Optional[int] = None # Leverage trading properties liquidation_price: Optional[float] = None @@ -874,9 +878,11 @@ class LocalTrade(): if current_amount > ZERO: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. - self.open_rate = float(current_stake / current_amount) + self.open_rate = price_to_precision(float(current_stake / current_amount), + self.price_precision, self.precision_mode) + self.amount = amount_to_precision(float(current_amount), + self.amount_precision, self.precision_mode) self.stake_amount = float(current_stake) / (self.leverage or 1.0) - self.amount = float(current_amount) self.fee_open_cost = self.fee_open * float(current_stake) self.recalc_open_trade_value() if self.stop_loss_pct is not None and self.open_rate is not None: @@ -1120,6 +1126,9 @@ class Trade(_DECL_BASE, LocalTrade): timeframe = Column(Integer, nullable=True) trading_mode = Column(Enum(TradingMode), nullable=True) + amount_precision = Column(Float) + price_precision = Column(Float) + precision_mode = Column(Integer) # Leverage trading properties leverage = Column(Float, nullable=True, default=1.0) From e6af9a6903f4036c65ef2bfe8055c5eaf160eda3 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 20:00:15 +0200 Subject: [PATCH 395/518] Allow empty precisionMode on conversions --- freqtrade/exchange/exchange.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 1af555e5e..cb1e5405a 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2840,7 +2840,7 @@ def market_is_active(market: Dict) -> bool: def amount_to_precision(amount: float, amount_precision: Optional[float], - precisionMode: int) -> float: + precisionMode: Optional[int]) -> float: """ Returns the amount to buy or sell to a precision the Exchange accepts Re-implementation of ccxt internal methods - ensuring we can test the result is correct @@ -2852,7 +2852,7 @@ def amount_to_precision(amount: float, amount_precision: Optional[float], one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE :return: truncated amount """ - if amount_precision is not None: + if amount_precision is not None and precisionMode is not None: amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE, precision=amount_precision, counting_mode=precisionMode, @@ -2862,7 +2862,7 @@ def amount_to_precision(amount: float, amount_precision: Optional[float], def price_to_precision(price: float, price_precision: Optional[float], - precisionMode: int) -> float: + precisionMode: Optional[int]) -> float: """ Returns the price rounded up to the precision the Exchange accepts. Partial Re-implementation of ccxt internal method decimal_to_precision(), @@ -2877,7 +2877,7 @@ def price_to_precision(price: float, price_precision: Optional[float], :return: price rounded up to the precision the Exchange accepts """ - if price_precision: + if price_precision is not None and precisionMode is not None: # price = float(decimal_to_precision(price, rounding_mode=ROUND, # precision=price_precision, # counting_mode=self.precisionMode, From f2b6ff910f975beca136311816117bc79dc0a93f Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 20:05:22 +0200 Subject: [PATCH 396/518] Accept wrong pair in get_precision_amount --- freqtrade/exchange/exchange.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index cb1e5405a..8605e984a 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -685,14 +685,14 @@ class Exchange: Returns the amount precision of the exchange. :param pair: """ - return self.markets[pair].get('precision', {}).get('amount', None) + return self.markets.get(pair, {}).get('precision', {}).get('amount', None) def get_precision_price(self, pair: str) -> Optional[float]: """ Returns the price precision of the exchange. :param pair: """ - return self.markets[pair].get('precision', {}).get('price', None) + return self.markets.get(pair, {}).get('precision', {}).get('price', None) def amount_to_precision(self, pair: str, amount: float) -> float: """ From c0bdb7181019940c62c7ec93aa28de6a020c1185 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 20:06:29 +0200 Subject: [PATCH 397/518] Update docstring --- freqtrade/exchange/exchange.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 8605e984a..7629b1e01 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -683,14 +683,16 @@ class Exchange: def get_precision_amount(self, pair: str) -> Optional[float]: """ Returns the amount precision of the exchange. - :param pair: + :param pair: Pair to get precision for + :return: precision for amount or None. Must be used in combination with precisionMode """ return self.markets.get(pair, {}).get('precision', {}).get('amount', None) def get_precision_price(self, pair: str) -> Optional[float]: """ Returns the price precision of the exchange. - :param pair: + :param pair: Pair to get precision for + :return: precision for price or None. Must be used in combination with precisionMode """ return self.markets.get(pair, {}).get('precision', {}).get('price', None) From e8187974277f0cdc92be13233ea65397097f2039 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 15 Aug 2022 20:29:05 +0200 Subject: [PATCH 398/518] Minor fix in amount_to_precision logic --- freqtrade/exchange/exchange.py | 4 +++- tests/rpc/test_rpc.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 7629b1e01..f4a06d929 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2855,8 +2855,10 @@ def amount_to_precision(amount: float, amount_precision: Optional[float], :return: truncated amount """ if amount_precision is not None and precisionMode is not None: + precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision + # precision must be an int for non-ticksize inputs. amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE, - precision=amount_precision, + precision=precision, counting_mode=precisionMode, )) diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 1a2428fe7..64cad6daf 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -761,7 +761,7 @@ def test_rpc_force_exit(default_conf, ticker, fee, mocker) -> None: # and trade amount is updated rpc._rpc_force_exit('3') assert cancel_order_mock.call_count == 1 - assert trade.amount == filled_amount + assert pytest.approx(trade.amount) == filled_amount mocker.patch( 'freqtrade.exchange.Exchange.fetch_order', From e4b7bcaeabf029b934da2b9f5ea580d5f4552ee0 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 08:01:07 +0200 Subject: [PATCH 399/518] Fix some tests --- tests/test_freqtradebot.py | 2 +- tests/test_integration.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index fb5fd38d8..8d005826e 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -2172,7 +2172,7 @@ def test_handle_trade( assert trade.close_rate == (2.0 if is_short else 2.2) assert pytest.approx(trade.close_profit) == close_profit - assert trade.calc_profit(trade.close_rate) == 5.685 + assert pytest.approx(trade.calc_profit(trade.close_rate)) == 5.685 assert trade.close_date is not None assert trade.exit_reason == 'sell_signal1' diff --git a/tests/test_integration.py b/tests/test_integration.py index b970e1c89..0b2639879 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -263,7 +263,7 @@ def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None: assert trade.orders[0].amount == 30 assert trade.orders[1].amount == 60 / ticker_usdt_modif['bid'] - assert trade.amount == trade.orders[0].amount + trade.orders[1].amount + assert pytest.approx(trade.amount) == trade.orders[0].amount + trade.orders[1].amount assert trade.nr_of_successful_buys == 2 assert trade.nr_of_successful_entries == 2 From 1dd56e35d502ebf31a25584eed226b5f040f70a8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 08:21:02 +0200 Subject: [PATCH 400/518] Ensure comparisions align when closing a trade --- freqtrade/persistence/trade_model.py | 7 ++++--- tests/test_integration.py | 10 ++++------ 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 436919bb1..25b290bfb 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -875,13 +875,14 @@ class LocalTrade(): self.realized_profit = close_profit_abs self.close_profit_abs = profit - if current_amount > ZERO: + current_amount_tr = amount_to_precision(float(current_amount), + self.amount_precision, self.precision_mode) + if current_amount_tr > 0.0: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. self.open_rate = price_to_precision(float(current_stake / current_amount), self.price_precision, self.precision_mode) - self.amount = amount_to_precision(float(current_amount), - self.amount_precision, self.precision_mode) + self.amount = current_amount_tr self.stake_amount = float(current_stake) / (self.leverage or 1.0) self.fee_open_cost = self.fee_open * float(current_stake) self.recalc_open_trade_value() diff --git a/tests/test_integration.py b/tests/test_integration.py index 0b2639879..dd3488f81 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -220,8 +220,6 @@ def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None: 'freqtrade.exchange.Exchange', fetch_ticker=ticker_usdt, get_fee=fee, - amount_to_precision=lambda s, x, y: y, - price_to_precision=lambda s, x, y: y, ) patch_get_signal(freqtrade) @@ -249,7 +247,7 @@ def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None: assert len(trade.orders) == 2 for o in trade.orders: assert o.status == "closed" - assert trade.stake_amount == 120 + assert pytest.approx(trade.stake_amount) == 120 # Open-rate averaged between 2.0 and 2.0 * 0.995 assert trade.open_rate < 2.0 @@ -259,9 +257,9 @@ def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None: freqtrade.process() trade = Trade.get_trades().first() assert len(trade.orders) == 2 - assert trade.stake_amount == 120 + assert pytest.approx(trade.stake_amount) == 120 assert trade.orders[0].amount == 30 - assert trade.orders[1].amount == 60 / ticker_usdt_modif['bid'] + assert pytest.approx(trade.orders[1].amount) == 60 / ticker_usdt_modif['bid'] assert pytest.approx(trade.amount) == trade.orders[0].amount + trade.orders[1].amount assert trade.nr_of_successful_buys == 2 @@ -274,7 +272,7 @@ def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None: assert trade.is_open is False assert trade.orders[0].amount == 30 assert trade.orders[0].side == 'buy' - assert trade.orders[1].amount == 60 / ticker_usdt_modif['bid'] + assert pytest.approx(trade.orders[1].amount) == 60 / ticker_usdt_modif['bid'] # Sold everything assert trade.orders[-1].side == 'sell' assert trade.orders[2].amount == trade.amount From a5b438e41e5a8a882f4ff9ff6acfe2c7b0db372b Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 08:43:03 +0200 Subject: [PATCH 401/518] Run price_to_precision for dry-run orders --- freqtrade/exchange/exchange.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 18598e92d..d50007dab 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -1019,7 +1019,8 @@ class Exchange: time_in_force: str = 'gtc', ) -> Dict: if self._config['dry_run']: - dry_order = self.create_dry_run_order(pair, ordertype, side, amount, rate, leverage) + dry_order = self.create_dry_run_order( + pair, ordertype, side, amount, self.price_to_precision(pair, rate), leverage) return dry_order params = self._get_params(side, ordertype, leverage, reduceOnly, time_in_force) From 2fb7a3091d99a400e53c7ccb4dd38de5702aca7a Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 09:32:31 +0200 Subject: [PATCH 402/518] Improve backfill of precisions --- freqtrade/freqtradebot.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 0895e5cb0..1b133f88b 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -292,6 +292,8 @@ class FreqtradeBot(LoggingMixin): trades = Trade.get_trades([Trade.precision_mode.is_(None)]) for trade in trades: + if trade.exchange != self.exchange.id: + continue trade.precision_mode = self.exchange.precisionMode trade.amount_precision = self.exchange.get_precision_amount(trade.pair) trade.price_precision = self.exchange.get_precision_price(trade.pair) From a73e4f8e41f7bd6fd97cb788f297828533cd95c0 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 09:49:53 +0200 Subject: [PATCH 403/518] Truncate amount before comparing for closure --- freqtrade/persistence/trade_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 25b290bfb..66b0b2ddb 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -621,7 +621,8 @@ class LocalTrade(): else: logger.warning( f'Got different open_order_id {self.open_order_id} != {order.order_id}') - if isclose(order.safe_amount_after_fee, self.amount, abs_tol=MATH_CLOSE_PREC): + amount_tr = amount_to_precision(self.amount, self.amount_precision, self.precision_mode) + if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC): self.close(order.safe_price) else: self.recalc_trade_from_orders() From 15a1c59a9130ebf63b508e0c22557506b342e2f9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 10:15:10 +0200 Subject: [PATCH 404/518] Backtesting should cache precisionMode --- freqtrade/optimize/backtesting.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index c8b6ee97d..c0d1a8a2f 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -131,6 +131,7 @@ class Backtesting: self.fee = config['fee'] else: self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0]) + self.precision_mode = self.exchange.precisionMode self.timerange = TimeRange.parse_timerange( None if self.config.get('timerange') is None else str(self.config.get('timerange'))) @@ -851,7 +852,7 @@ class Backtesting: # interest_rate=interest_rate, amount_precision=self.exchange.get_precision_amount(pair), price_precision=self.exchange.get_precision_price(pair), - precision_mode=self.exchange.precisionMode, + precision_mode=self.self.precision_mode, orders=[], ) From b8c1cf01074cc6efe906ad59ed67058c49520431 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 10:19:19 +0200 Subject: [PATCH 405/518] Fix test leakage if config is available --- tests/commands/test_commands.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/tests/commands/test_commands.py b/tests/commands/test_commands.py index d6e80675e..9df6acf75 100644 --- a/tests/commands/test_commands.py +++ b/tests/commands/test_commands.py @@ -638,7 +638,7 @@ def test_get_ui_download_url_direct(mocker): x, last_version = get_ui_download_url('0.0.3') -def test_download_data_keyboardInterrupt(mocker, caplog, markets): +def test_download_data_keyboardInterrupt(mocker, markets): dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data', MagicMock(side_effect=KeyboardInterrupt)) patch_exchange(mocker) @@ -651,12 +651,15 @@ def test_download_data_keyboardInterrupt(mocker, caplog, markets): "--pairs", "ETH/BTC", "XRP/BTC", ] with pytest.raises(SystemExit): - start_download_data(get_args(args)) + pargs = get_args(args) + pargs['config'] = None + + start_download_data(pargs) assert dl_mock.call_count == 1 -def test_download_data_timerange(mocker, caplog, markets): +def test_download_data_timerange(mocker, markets): dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data', MagicMock(return_value=["ETH/BTC", "XRP/BTC"])) patch_exchange(mocker) @@ -672,7 +675,9 @@ def test_download_data_timerange(mocker, caplog, markets): ] with pytest.raises(OperationalException, match=r"--days and --timerange are mutually.*"): - start_download_data(get_args(args)) + pargs = get_args(args) + pargs['config'] = None + start_download_data(pargs) assert dl_mock.call_count == 0 args = [ @@ -681,7 +686,9 @@ def test_download_data_timerange(mocker, caplog, markets): "--pairs", "ETH/BTC", "XRP/BTC", "--days", "20", ] - start_download_data(get_args(args)) + pargs = get_args(args) + pargs['config'] = None + start_download_data(pargs) assert dl_mock.call_count == 1 # 20days ago days_ago = arrow.get(arrow.now().shift(days=-20).date()).int_timestamp @@ -694,7 +701,9 @@ def test_download_data_timerange(mocker, caplog, markets): "--pairs", "ETH/BTC", "XRP/BTC", "--timerange", "20200101-" ] - start_download_data(get_args(args)) + pargs = get_args(args) + pargs['config'] = None + start_download_data(pargs) assert dl_mock.call_count == 1 assert dl_mock.call_args_list[0][1]['timerange'].startts == arrow.Arrow( From 96d2f61812d02a5f38fb1c4d25c8c176a0514eb9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 10:22:59 +0200 Subject: [PATCH 406/518] Properly round timestamps to avoid constant log messages --- freqtrade/plugins/pairlist/VolumePairList.py | 22 ++++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/freqtrade/plugins/pairlist/VolumePairList.py b/freqtrade/plugins/pairlist/VolumePairList.py index cd16a46a3..e364e1a69 100644 --- a/freqtrade/plugins/pairlist/VolumePairList.py +++ b/freqtrade/plugins/pairlist/VolumePairList.py @@ -4,14 +4,14 @@ Volume PairList provider Provides dynamic pair list based on trade volumes """ import logging +from datetime import datetime, timedelta, timezone from typing import Any, Dict, List -import arrow from cachetools import TTLCache from freqtrade.constants import ListPairsWithTimeframes from freqtrade.exceptions import OperationalException -from freqtrade.exchange import timeframe_to_minutes +from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date from freqtrade.misc import format_ms_time from freqtrade.plugins.pairlist.IPairList import IPairList @@ -158,16 +158,16 @@ class VolumePairList(IPairList): filtered_tickers: List[Dict[str, Any]] = [{'symbol': k} for k in pairlist] # get lookback period in ms, for exchange ohlcv fetch - since_ms = int(arrow.utcnow() - .floor('minute') - .shift(minutes=-(self._lookback_period * self._tf_in_min) - - self._tf_in_min) - .int_timestamp) * 1000 + since_ms = int(timeframe_to_prev_date( + self._lookback_timeframe, + datetime.now(timezone.utc) + timedelta( + minutes=-(self._lookback_period * self._tf_in_min) - self._tf_in_min) + ).timestamp()) * 1000 - to_ms = int(arrow.utcnow() - .floor('minute') - .shift(minutes=-self._tf_in_min) - .int_timestamp) * 1000 + to_ms = int(timeframe_to_prev_date( + self._lookback_timeframe, + datetime.now(timezone.utc) - timedelta(minutes=self._tf_in_min) + ).timestamp()) * 1000 # todo: utc date output for starting date self.log_once(f"Using volume range of {self._lookback_period} candles, timeframe: " From fa89368c027c5233e2d9368ee5dadcd6f374945d Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 10:59:43 +0200 Subject: [PATCH 407/518] Add test for precision backpopulation --- freqtrade/optimize/backtesting.py | 2 +- tests/conftest_trades_usdt.py | 16 ++++++++-------- tests/plugins/test_pairlist.py | 6 +++--- tests/rpc/test_rpc.py | 2 +- tests/test_freqtradebot.py | 31 ++++++++++++++++++++++++++++--- 5 files changed, 41 insertions(+), 16 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index c0d1a8a2f..6528481d5 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -852,7 +852,7 @@ class Backtesting: # interest_rate=interest_rate, amount_precision=self.exchange.get_precision_amount(pair), price_precision=self.exchange.get_precision_price(pair), - precision_mode=self.self.precision_mode, + precision_mode=self.precision_mode, orders=[], ) diff --git a/tests/conftest_trades_usdt.py b/tests/conftest_trades_usdt.py index 9a89eaca6..d54a416ef 100644 --- a/tests/conftest_trades_usdt.py +++ b/tests/conftest_trades_usdt.py @@ -81,7 +81,7 @@ def mock_trade_usdt_1(fee, is_short: bool): def mock_order_usdt_2(is_short: bool): return { 'id': f'1235_{direc(is_short)}', - 'symbol': 'ETC/USDT', + 'symbol': 'NEO/USDT', 'status': 'closed', 'side': entry_side(is_short), 'type': 'limit', @@ -95,7 +95,7 @@ def mock_order_usdt_2(is_short: bool): def mock_order_usdt_2_exit(is_short: bool): return { 'id': f'12366_{direc(is_short)}', - 'symbol': 'ETC/USDT', + 'symbol': 'NEO/USDT', 'status': 'closed', 'side': exit_side(is_short), 'type': 'limit', @@ -111,7 +111,7 @@ def mock_trade_usdt_2(fee, is_short: bool): Closed trade... """ trade = Trade( - pair='ETC/USDT', + pair='NEO/USDT', stake_amount=200.0, amount=100.0, amount_requested=100.0, @@ -132,10 +132,10 @@ def mock_trade_usdt_2(fee, is_short: bool): close_date=datetime.now(tz=timezone.utc) - timedelta(minutes=2), is_short=is_short, ) - o = Order.parse_from_ccxt_object(mock_order_usdt_2(is_short), 'ETC/USDT', entry_side(is_short)) + o = Order.parse_from_ccxt_object(mock_order_usdt_2(is_short), 'NEO/USDT', entry_side(is_short)) trade.orders.append(o) o = Order.parse_from_ccxt_object( - mock_order_usdt_2_exit(is_short), 'ETC/USDT', exit_side(is_short)) + mock_order_usdt_2_exit(is_short), 'NEO/USDT', exit_side(is_short)) trade.orders.append(o) return trade @@ -205,7 +205,7 @@ def mock_trade_usdt_3(fee, is_short: bool): def mock_order_usdt_4(is_short: bool): return { 'id': f'prod_buy_12345_{direc(is_short)}', - 'symbol': 'ETC/USDT', + 'symbol': 'NEO/USDT', 'status': 'open', 'side': entry_side(is_short), 'type': 'limit', @@ -221,7 +221,7 @@ def mock_trade_usdt_4(fee, is_short: bool): Simulate prod entry """ trade = Trade( - pair='ETC/USDT', + pair='NEO/USDT', stake_amount=20.0, amount=10.0, amount_requested=10.01, @@ -236,7 +236,7 @@ def mock_trade_usdt_4(fee, is_short: bool): timeframe=5, is_short=is_short, ) - o = Order.parse_from_ccxt_object(mock_order_usdt_4(is_short), 'ETC/USDT', entry_side(is_short)) + o = Order.parse_from_ccxt_object(mock_order_usdt_4(is_short), 'NEO/USDT', entry_side(is_short)) trade.orders.append(o) return trade diff --git a/tests/plugins/test_pairlist.py b/tests/plugins/test_pairlist.py index d3d08dc8c..5974bee89 100644 --- a/tests/plugins/test_pairlist.py +++ b/tests/plugins/test_pairlist.py @@ -735,7 +735,7 @@ def test_PerformanceFilter_lookback(mocker, default_conf_usdt, fee, caplog) -> N with time_machine.travel("2021-09-01 05:00:00 +00:00") as t: create_mock_trades_usdt(fee) pm.refresh_pairlist() - assert pm.whitelist == ['XRP/USDT'] + assert pm.whitelist == ['XRP/USDT', 'NEO/USDT'] assert log_has_re(r'Removing pair .* since .* is below .*', caplog) # Move to "outside" of lookback window, so original sorting is restored. @@ -762,8 +762,8 @@ def test_PerformanceFilter_keep_mid_order(mocker, default_conf_usdt, fee, caplog with time_machine.travel("2021-09-01 05:00:00 +00:00") as t: create_mock_trades_usdt(fee) pm.refresh_pairlist() - assert pm.whitelist == ['XRP/USDT', 'ETC/USDT', 'ETH/USDT', 'LTC/USDT', - 'NEO/USDT', 'TKN/USDT', 'ADA/USDT', ] + assert pm.whitelist == ['XRP/USDT', 'NEO/USDT', 'ETH/USDT', 'LTC/USDT', + 'TKN/USDT', 'ADA/USDT', 'ETC/USDT', ] # assert log_has_re(r'Removing pair .* since .* is below .*', caplog) # Move to "outside" of lookback window, so original sorting is restored. diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 64cad6daf..e28f6510d 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -830,7 +830,7 @@ def test_performance_handle(default_conf_usdt, ticker, fee, mocker) -> None: res = rpc._rpc_performance() assert len(res) == 3 - assert res[0]['pair'] == 'ETC/USDT' + assert res[0]['pair'] == 'NEO/USDT' assert res[0]['count'] == 1 assert res[0]['profit_pct'] == 5.0 diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index ec52aeedd..0be469b75 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -23,9 +23,9 @@ from freqtrade.persistence import Order, PairLocks, Trade from freqtrade.persistence.models import PairLock from freqtrade.plugins.protections.iprotection import ProtectionReturn from freqtrade.worker import Worker -from tests.conftest import (create_mock_trades, get_patched_freqtradebot, get_patched_worker, - log_has, log_has_re, patch_edge, patch_exchange, patch_get_signal, - patch_wallet, patch_whitelist) +from tests.conftest import (create_mock_trades, create_mock_trades_usdt, get_patched_freqtradebot, + get_patched_worker, log_has, log_has_re, patch_edge, patch_exchange, + patch_get_signal, patch_wallet, patch_whitelist) from tests.conftest_trades import (MOCK_TRADE_COUNT, entry_side, exit_side, mock_order_1, mock_order_2, mock_order_2_sell, mock_order_3, mock_order_3_sell, mock_order_4, mock_order_5_stoploss, mock_order_6_sell) @@ -4888,6 +4888,31 @@ def test_startup_update_open_orders(mocker, default_conf_usdt, fee, caplog, is_s assert hto_mock.call_args_list[1][0][0]['status'] == 'canceled' +@pytest.mark.usefixtures("init_persistence") +def test_startup_backpopulate_precision(mocker, default_conf_usdt, fee, caplog): + freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) + create_mock_trades_usdt(fee) + + trades = Trade.get_trades().all() + trades[-1].exchange = 'some_other_exchange' + for trade in trades: + assert trade.price_precision is None + assert trade.amount_precision is None + assert trade.precision_mode is None + + freqtrade.startup_backpopulate_precision() + trades = Trade.get_trades().all() + for trade in trades: + if trade.exchange == 'some_other_exchange': + assert trade.price_precision is None + assert trade.amount_precision is None + assert trade.precision_mode is None + else: + assert trade.price_precision is not None + assert trade.amount_precision is not None + assert trade.precision_mode is not None + + @pytest.mark.usefixtures("init_persistence") @pytest.mark.parametrize("is_short", [False, True]) def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short): From 4c0fda400fcd6dd299a7ad36ea255740543bf181 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Tue, 16 Aug 2022 11:41:53 +0200 Subject: [PATCH 408/518] fix input shape warning for LGBMClassifier, add sample_weights/eval_weights --- .../freqai/prediction_models/LightGBMClassifier.py | 13 +++++++++---- .../freqai/prediction_models/LightGBMRegressor.py | 6 +++++- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifier.py b/freqtrade/freqai/prediction_models/LightGBMClassifier.py index bafb16a39..4ac2c448b 100644 --- a/freqtrade/freqai/prediction_models/LightGBMClassifier.py +++ b/freqtrade/freqai/prediction_models/LightGBMClassifier.py @@ -26,13 +26,18 @@ class LightGBMClassifier(BaseClassifierModel): if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: eval_set = None + test_weights = None else: - eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) - X = data_dictionary["train_features"] - y = data_dictionary["train_labels"] + eval_set = (data_dictionary["test_features"].to_numpy(), + data_dictionary["test_labels"].to_numpy()[:, 0]) + test_weights = data_dictionary["test_weights"] + X = data_dictionary["train_features"].to_numpy() + y = data_dictionary["train_labels"].to_numpy()[:, 0] + train_weights = data_dictionary["train_weights"] model = LGBMClassifier(**self.model_training_parameters) - model.fit(X=X, y=y, eval_set=eval_set) + model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, + eval_sample_weight=[test_weights]) return model diff --git a/freqtrade/freqai/prediction_models/LightGBMRegressor.py b/freqtrade/freqai/prediction_models/LightGBMRegressor.py index f72792611..2431fd2ad 100644 --- a/freqtrade/freqai/prediction_models/LightGBMRegressor.py +++ b/freqtrade/freqai/prediction_models/LightGBMRegressor.py @@ -27,13 +27,17 @@ class LightGBMRegressor(BaseRegressionModel): if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: eval_set = None + eval_weights = None else: eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) + eval_weights = data_dictionary["test_weights"] X = data_dictionary["train_features"] y = data_dictionary["train_labels"] + train_weights = data_dictionary["train_weights"] model = LGBMRegressor(**self.model_training_parameters) - model.fit(X=X, y=y, eval_set=eval_set) + model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, + eval_sample_weight=[eval_weights]) return model From 1ac81aa316df73f648479a554096b15e9540d525 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 17:06:25 +0200 Subject: [PATCH 409/518] Show message if fee update failed due to missing closes #7234 --- freqtrade/freqtradebot.py | 3 ++ tests/test_freqtradebot.py | 70 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 73 insertions(+) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 95f884056..277836f61 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1866,6 +1866,9 @@ class FreqtradeBot(LoggingMixin): if fee_rate is not None and fee_rate < 0.02: # Only update if fee-rate is < 2% trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', '')) + else: + logger.warning( + f"Not updating {order.get('side', '')}-fee - rate: {fee_rate}, {fee_currency}.") if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC): # * Leverage could be a cause for this warning diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index eb4804167..c7772ebfc 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -4553,6 +4553,76 @@ def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker): assert freqtrade.get_real_amount(trade, order, order_obj) == amount +def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, caplog): + limit_buy_order_usdt = deepcopy(buy_order_fee) + + # Fees amount in "POINT" + trades = [{ + "info": { + }, + "id": "some_trade_id", + "timestamp": 1660092505903, + "datetime": "2022-08-10T00:48:25.903Z", + "symbol": "CEL/USDT", + "order": "some_order_id", + "type": None, + "side": "sell", + "takerOrMaker": "taker", + "price": 1.83255, + "amount": 83.126, + "cost": 152.3325513, + "fee": { + "currency": "POINT", + "cost": 0.3046651026 + }, + "fees": [ + { + "cost": "0", + "currency": "USDT" + }, + { + "cost": "0", + "currency": "GT" + }, + { + "cost": "0.3046651026", + "currency": "POINT" + } + ] + }] + + mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=trades) + amount = float(sum(x['amount'] for x in trades)) + trade = Trade( + pair='CEL/USDT', + amount=amount, + exchange='binance', + fee_open=fee.return_value, + fee_close=fee.return_value, + open_rate=0.245441, + open_order_id="123456" + ) + limit_buy_order_usdt['amount'] = amount + freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt) + + order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') + res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) + assert res == amount + assert trade.fee_open_currency is None + assert trade.fee_open_cost is None + message = "Not updating buy-fee - rate: None, POINT." + assert log_has(message, caplog) + caplog.clear() + freqtrade.config['exchange']['unknown_fee_rate'] = 1 + res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) + assert res == amount + assert trade.fee_open_currency == 'POINT' + assert pytest.approx(trade.fee_open_cost) == 0.3046651026 + assert trade.fee_open == 0.002 + assert trade.fee_open != fee.return_value + assert not log_has(message, caplog) + + @pytest.mark.parametrize('amount,fee_abs,wallet,amount_exp', [ (8.0, 0.0, 10, 8), (8.0, 0.0, 0, 8), From a1e4fbf31366e68afefee4b385aa2ad20bf282ac Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 08:43:03 +0200 Subject: [PATCH 410/518] Run price_to_precision for dry-run orders --- freqtrade/exchange/exchange.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 18598e92d..d50007dab 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -1019,7 +1019,8 @@ class Exchange: time_in_force: str = 'gtc', ) -> Dict: if self._config['dry_run']: - dry_order = self.create_dry_run_order(pair, ordertype, side, amount, rate, leverage) + dry_order = self.create_dry_run_order( + pair, ordertype, side, amount, self.price_to_precision(pair, rate), leverage) return dry_order params = self._get_params(side, ordertype, leverage, reduceOnly, time_in_force) From 5dde011b311bb7262e3756a6b578fff8b9202875 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 17:23:05 +0200 Subject: [PATCH 411/518] Add unknown_fee_rate to full sample config --- config_examples/config_full.example.json | 1 + 1 file changed, 1 insertion(+) diff --git a/config_examples/config_full.example.json b/config_examples/config_full.example.json index f076ffaf1..74457d2b6 100644 --- a/config_examples/config_full.example.json +++ b/config_examples/config_full.example.json @@ -93,6 +93,7 @@ "secret": "your_exchange_secret", "password": "", "log_responses": false, + // "unknown_fee_rate": 1, "ccxt_config": {}, "ccxt_async_config": {}, "pair_whitelist": [ From ea6bc47d7a57e810c5b46bb931c720806fc711de Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 17:26:53 +0200 Subject: [PATCH 412/518] gateio default configs should specify unknown_fee_rate --- freqtrade/templates/subtemplates/exchange_gateio.j2 | 12 ++++++++++++ freqtrade/templates/subtemplates/exchange_generic.j2 | 2 -- 2 files changed, 12 insertions(+), 2 deletions(-) create mode 100644 freqtrade/templates/subtemplates/exchange_gateio.j2 diff --git a/freqtrade/templates/subtemplates/exchange_gateio.j2 b/freqtrade/templates/subtemplates/exchange_gateio.j2 new file mode 100644 index 000000000..63dff052f --- /dev/null +++ b/freqtrade/templates/subtemplates/exchange_gateio.j2 @@ -0,0 +1,12 @@ +"exchange": { + "name": "{{ exchange_name | lower }}", + "key": "{{ exchange_key }}", + "secret": "{{ exchange_secret }}", + "unknown_fee_rate": 1, + "ccxt_config": {}, + "ccxt_async_config": {}, + "pair_whitelist": [ + ], + "pair_blacklist": [ + ] +} diff --git a/freqtrade/templates/subtemplates/exchange_generic.j2 b/freqtrade/templates/subtemplates/exchange_generic.j2 index 08b11f365..01f637638 100644 --- a/freqtrade/templates/subtemplates/exchange_generic.j2 +++ b/freqtrade/templates/subtemplates/exchange_generic.j2 @@ -5,9 +5,7 @@ "ccxt_config": {}, "ccxt_async_config": {}, "pair_whitelist": [ - ], "pair_blacklist": [ - ] } From 3b44dc52e1dc8325dbf539468e0df0e5cd0e2ce0 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 18:10:48 +0200 Subject: [PATCH 413/518] Minor corrections --- freqtrade/persistence/trade_model.py | 6 +++--- tests/test_persistence.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 66b0b2ddb..3317d0ceb 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -1128,9 +1128,9 @@ class Trade(_DECL_BASE, LocalTrade): timeframe = Column(Integer, nullable=True) trading_mode = Column(Enum(TradingMode), nullable=True) - amount_precision = Column(Float) - price_precision = Column(Float) - precision_mode = Column(Integer) + amount_precision = Column(Float, nullable=True) + price_precision = Column(Float, nullable=True) + precision_mode = Column(Integer, nullable=True) # Leverage trading properties leverage = Column(Float, nullable=True, default=1.0) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 8a3a18577..f68791b72 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1387,7 +1387,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog): assert log_has("trying trades_bak2", caplog) assert log_has("Running database migration for trades - backup: trades_bak2, orders_bak0", caplog) - assert round(trade.open_trade_value, 15) == trade._calc_open_trade_value( + assert pytest.approx(trade.open_trade_value) == trade._calc_open_trade_value( trade.amount, trade.open_rate) assert trade.close_profit_abs is None From 24690c1918428f695b6d45bc20e6c3c7ec1c1128 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 18:16:23 +0200 Subject: [PATCH 414/518] Don't convert open_rate to precision this may cause more problems than it solves. --- freqtrade/persistence/trade_model.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 3317d0ceb..1c96d9b76 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -14,7 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import amount_to_precision, price_to_precision +from freqtrade.exchange import amount_to_precision from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -881,8 +881,7 @@ class LocalTrade(): if current_amount_tr > 0.0: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. - self.open_rate = price_to_precision(float(current_stake / current_amount), - self.price_precision, self.precision_mode) + self.open_rate = float(current_stake / current_amount) self.amount = current_amount_tr self.stake_amount = float(current_stake) / (self.leverage or 1.0) self.fee_open_cost = self.fee_open * float(current_stake) From 0b0e7eaf96756d60ba6b8736536bc5729a25fcbe Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 19:43:41 +0200 Subject: [PATCH 415/518] Mutex TTL Cache accesses which can be accessed by multiple threads Apparently, cachetools is (intentionally) not threadsafe when using the Caches directly. It's therefore recommended to wrap these with an explicit lock to avoid problems. source: https://github.com/tkem/cachetools/issues/245 closes #7215 --- freqtrade/exchange/exchange.py | 24 ++++++++++++++++-------- freqtrade/optimize/hyperopt.py | 1 + 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index d50007dab..d74a01f8b 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -116,6 +116,7 @@ class Exchange: self._last_markets_refresh: int = 0 # Cache for 10 minutes ... + self._cache_lock = Lock() self._fetch_tickers_cache: TTLCache = TTLCache(maxsize=2, ttl=60 * 10) # Cache values for 1800 to avoid frequent polling of the exchange for prices # Caching only applies to RPC methods, so prices for open trades are still @@ -1388,12 +1389,14 @@ class Exchange: if not self.exchange_has('fetchBidsAsks'): return {} if cached: - tickers = self._fetch_tickers_cache.get('fetch_bids_asks') + with self._cache_lock: + tickers = self._fetch_tickers_cache.get('fetch_bids_asks') if tickers: return tickers try: tickers = self._api.fetch_bids_asks(symbols) - self._fetch_tickers_cache['fetch_bids_asks'] = tickers + with self._cache_lock: + self._fetch_tickers_cache['fetch_bids_asks'] = tickers return tickers except ccxt.NotSupported as e: raise OperationalException( @@ -1414,12 +1417,14 @@ class Exchange: :return: fetch_tickers result """ if cached: - tickers = self._fetch_tickers_cache.get('fetch_tickers') + with self._cache_lock: + tickers = self._fetch_tickers_cache.get('fetch_tickers') if tickers: return tickers try: tickers = self._api.fetch_tickers(symbols) - self._fetch_tickers_cache['fetch_tickers'] = tickers + with self._cache_lock: + self._fetch_tickers_cache['fetch_tickers'] = tickers return tickers except ccxt.NotSupported as e: raise OperationalException( @@ -1528,7 +1533,8 @@ class Exchange: cache_rate: TTLCache = self._entry_rate_cache if side == "entry" else self._exit_rate_cache if not refresh: - rate = cache_rate.get(pair) + with self._cache_lock: + rate = cache_rate.get(pair) # Check if cache has been invalidated if rate: logger.debug(f"Using cached {side} rate for {pair}.") @@ -1573,7 +1579,8 @@ class Exchange: if rate is None: raise PricingError(f"{name}-Rate for {pair} was empty.") - cache_rate[pair] = rate + with self._cache_lock: + cache_rate[pair] = rate return rate @@ -1581,8 +1588,9 @@ class Exchange: entry_rate = None exit_rate = None if not refresh: - entry_rate = self._entry_rate_cache.get(pair) - exit_rate = self._exit_rate_cache.get(pair) + with self._cache_lock: + entry_rate = self._entry_rate_cache.get(pair) + exit_rate = self._exit_rate_cache.get(pair) if entry_rate: logger.debug(f"Using cached buy rate for {pair}.") if exit_rate: diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 566412f29..cbcf39131 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -483,6 +483,7 @@ class Hyperopt: self.backtesting.exchange._api_async = None self.backtesting.exchange.loop = None # type: ignore self.backtesting.exchange._loop_lock = None # type: ignore + self.backtesting.exchange._cache_lock = None # type: ignore # self.backtesting.exchange = None # type: ignore self.backtesting.pairlists = None # type: ignore From 8d182768f94fb0d26225a7218fbcaa63f43bc738 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 09:51:28 +0200 Subject: [PATCH 416/518] stoploss should also use trimmed prices --- freqtrade/persistence/trade_model.py | 10 ++++++---- tests/rpc/test_rpc.py | 22 +++++++++++----------- tests/test_freqtradebot.py | 16 ++++++---------- 3 files changed, 23 insertions(+), 25 deletions(-) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 1c96d9b76..b954fee20 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -14,7 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import amount_to_precision +from freqtrade.exchange import amount_to_precision, price_to_precision from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -527,9 +527,10 @@ class LocalTrade(): """ Method used internally to set self.stop_loss. """ + stop_loss_norm = price_to_precision(stop_loss, self.price_precision, self.precision_mode) if not self.stop_loss: - self.initial_stop_loss = stop_loss - self.stop_loss = stop_loss + self.initial_stop_loss = stop_loss_norm + self.stop_loss = stop_loss_norm self.stop_loss_pct = -1 * abs(percent) self.stoploss_last_update = datetime.utcnow() @@ -557,7 +558,8 @@ class LocalTrade(): # no stop loss assigned yet if self.initial_stop_loss_pct is None or refresh: self.__set_stop_loss(new_loss, stoploss) - self.initial_stop_loss = new_loss + self.initial_stop_loss = price_to_precision( + new_loss, self.price_precision, self.precision_mode) self.initial_stop_loss_pct = -1 * abs(stoploss) # evaluate if the stop loss needs to be updated diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index e28f6510d..7b42bf083 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -96,20 +96,20 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'profit_pct': -0.41, 'profit_abs': -4.09e-06, 'profit_fiat': ANY, - 'stop_loss_abs': 9.882e-06, + 'stop_loss_abs': 9.89e-06, 'stop_loss_pct': -10.0, 'stop_loss_ratio': -0.1, 'stoploss_order_id': None, 'stoploss_last_update': ANY, 'stoploss_last_update_timestamp': ANY, - 'initial_stop_loss_abs': 9.882e-06, + 'initial_stop_loss_abs': 9.89e-06, 'initial_stop_loss_pct': -10.0, 'initial_stop_loss_ratio': -0.1, - 'stoploss_current_dist': -1.1080000000000002e-06, - 'stoploss_current_dist_ratio': -0.10081893, - 'stoploss_current_dist_pct': -10.08, - 'stoploss_entry_dist': -0.00010475, - 'stoploss_entry_dist_ratio': -0.10448878, + 'stoploss_current_dist': pytest.approx(-1.0999999e-06), + 'stoploss_current_dist_ratio': -0.10009099, + 'stoploss_current_dist_pct': -10.01, + 'stoploss_entry_dist': -0.00010402, + 'stoploss_entry_dist_ratio': -0.10376381, 'open_order': None, 'realized_profit': 0.0, 'exchange': 'binance', @@ -181,20 +181,20 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'profit_pct': ANY, 'profit_abs': ANY, 'profit_fiat': ANY, - 'stop_loss_abs': 9.882e-06, + 'stop_loss_abs': 9.89e-06, 'stop_loss_pct': -10.0, 'stop_loss_ratio': -0.1, 'stoploss_order_id': None, 'stoploss_last_update': ANY, 'stoploss_last_update_timestamp': ANY, - 'initial_stop_loss_abs': 9.882e-06, + 'initial_stop_loss_abs': 9.89e-06, 'initial_stop_loss_pct': -10.0, 'initial_stop_loss_ratio': -0.1, 'stoploss_current_dist': ANY, 'stoploss_current_dist_ratio': ANY, 'stoploss_current_dist_pct': ANY, - 'stoploss_entry_dist': -0.00010475, - 'stoploss_entry_dist_ratio': -0.10448878, + 'stoploss_entry_dist': -0.00010402, + 'stoploss_entry_dist_ratio': -0.10376381, 'open_order': None, 'exchange': 'binance', 'realized_profit': 0.0, diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index 0be469b75..c3d738075 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -4,7 +4,6 @@ import logging import time from copy import deepcopy -from math import isclose from typing import List from unittest.mock import ANY, MagicMock, PropertyMock, patch @@ -12,7 +11,7 @@ import arrow import pytest from pandas import DataFrame -from freqtrade.constants import CANCEL_REASON, MATH_CLOSE_PREC, UNLIMITED_STAKE_AMOUNT +from freqtrade.constants import CANCEL_REASON, UNLIMITED_STAKE_AMOUNT from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RPCMessageType, RunMode, SignalDirection, State) from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError, @@ -569,7 +568,7 @@ def test_process_trade_creation(default_conf_usdt, ticker_usdt, limit_order, lim assert trade.open_date is not None assert trade.exchange == 'binance' assert trade.open_rate == ticker_usdt.return_value[ticker_side] - assert isclose(trade.amount, 60 / ticker_usdt.return_value[ticker_side]) + assert pytest.approx(trade.amount) == 60 / ticker_usdt.return_value[ticker_side] assert log_has( f'{"Short" if is_short else "Long"} signal found: about create a new trade for ETH/USDT ' @@ -1801,7 +1800,7 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, caplog, # stoploss initially at 20% as edge dictated it. assert freqtrade.handle_trade(trade) is False assert freqtrade.handle_stoploss_on_exchange(trade) is False - assert isclose(trade.stop_loss, 1.76) + assert pytest.approx(trade.stop_loss) == 1.76 cancel_order_mock = MagicMock() stoploss_order_mock = MagicMock() @@ -1818,7 +1817,7 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, caplog, assert freqtrade.handle_stoploss_on_exchange(trade) is False # stoploss should remain the same - assert isclose(trade.stop_loss, 1.76) + assert pytest.approx(trade.stop_loss) == 1.76 # stoploss on exchange should not be canceled cancel_order_mock.assert_not_called() @@ -4524,11 +4523,8 @@ def test_get_real_amount_wrong_amount_rounding(default_conf_usdt, trades_for_ord order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy') # Amount changes by fee amount. - assert isclose( - freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj), - amount - (amount * 0.001), - abs_tol=MATH_CLOSE_PREC, - ) + assert pytest.approx(freqtrade.get_real_amount( + trade, limit_buy_order_usdt, order_obj)) == amount - (amount * 0.001) def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker): From c8d7c2caac6d4d8ba13896579a05d55dcd15a159 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 09:26:02 +0200 Subject: [PATCH 417/518] Update CI to build and push freqAI images --- build_helpers/publish_docker_arm64.sh | 8 ++++++++ build_helpers/publish_docker_multi.sh | 4 ++++ docker/Dockerfile.freqai | 8 -------- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/build_helpers/publish_docker_arm64.sh b/build_helpers/publish_docker_arm64.sh index 70f99e54b..4c66f4483 100755 --- a/build_helpers/publish_docker_arm64.sh +++ b/build_helpers/publish_docker_arm64.sh @@ -6,10 +6,12 @@ export DOCKER_BUILDKIT=1 # Replace / with _ to create a valid tag TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG_PLOT=${TAG}_plot +TAG_FREQAI=${TAG}_freqai TAG_PI="${TAG}_pi" TAG_ARM=${TAG}_arm TAG_PLOT_ARM=${TAG_PLOT}_arm +TAG_FREQAI_ARM=${TAG_FREQAI}_arm CACHE_IMAGE=freqtradeorg/freqtrade_cache echo "Running for ${TAG}" @@ -38,8 +40,10 @@ fi docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot . +docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai . docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM +docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM # Run backtest docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3 @@ -53,6 +57,7 @@ docker images # docker push ${IMAGE_NAME} docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM +docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM docker push ${CACHE_IMAGE}:$TAG_ARM # Create multi-arch image @@ -66,6 +71,9 @@ docker manifest push -p ${IMAGE_NAME}:${TAG} docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT} docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT} +docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI} +docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI} + # Tag as latest for develop builds if [ "${TAG}" = "develop" ]; then docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG} diff --git a/build_helpers/publish_docker_multi.sh b/build_helpers/publish_docker_multi.sh index fd5f0ef93..1168a63f6 100755 --- a/build_helpers/publish_docker_multi.sh +++ b/build_helpers/publish_docker_multi.sh @@ -5,6 +5,7 @@ # Replace / with _ to create a valid tag TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG_PLOT=${TAG}_plot +TAG_FREQAI=${TAG}_plot TAG_PI="${TAG}_pi" PI_PLATFORM="linux/arm/v7" @@ -49,8 +50,10 @@ fi docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot . +docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai . docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT +docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI # Run backtest docker run --rm -v $(pwd)/config_examples/config_bittrex.example.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3 @@ -64,6 +67,7 @@ docker images docker push ${CACHE_IMAGE} docker push ${CACHE_IMAGE}:$TAG_PLOT +docker push ${CACHE_IMAGE}:$TAG_FREQAI docker push ${CACHE_IMAGE}:$TAG diff --git a/docker/Dockerfile.freqai b/docker/Dockerfile.freqai index 98e6e31d5..9a2f75700 100644 --- a/docker/Dockerfile.freqai +++ b/docker/Dockerfile.freqai @@ -2,16 +2,8 @@ ARG sourceimage=freqtradeorg/freqtrade ARG sourcetag=develop FROM ${sourceimage}:${sourcetag} -USER root - -RUN apt-get install -y libgomp1 - -USER ftuser - # Install dependencies COPY requirements-freqai.txt /freqtrade/ RUN pip install -r requirements-freqai.txt --user --no-cache-dir -# Temporary step - as the source image will contain the wrong (non-freqai) sourcecode -COPY --chown=ftuser:ftuser . /freqtrade/ From 463cf66881eb1b05075e8e9475d1c8604f68aca8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 16 Aug 2022 16:39:29 +0200 Subject: [PATCH 418/518] Fix bad image name --- build_helpers/publish_docker_multi.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_helpers/publish_docker_multi.sh b/build_helpers/publish_docker_multi.sh index 1168a63f6..c13732003 100755 --- a/build_helpers/publish_docker_multi.sh +++ b/build_helpers/publish_docker_multi.sh @@ -5,7 +5,7 @@ # Replace / with _ to create a valid tag TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG_PLOT=${TAG}_plot -TAG_FREQAI=${TAG}_plot +TAG_FREQAI=${TAG}_freqai TAG_PI="${TAG}_pi" PI_PLATFORM="linux/arm/v7" From 819bc71941c58e8d59d1e34269b4706811903f1c Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 10:35:56 +0200 Subject: [PATCH 419/518] Update docs for freqai docker container --- docs/freqai.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/freqai.md b/docs/freqai.md index cde3ea0f7..b22e1cd31 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -75,6 +75,12 @@ pip install -r requirements-freqai.txt !!! Note Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform. +### Usage with docker + +For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`. +As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. +This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. + ## Configuring FreqAI ### Parameter table From e0883a4ea014233c20750922fdddcb0fc0c86176 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 10:55:59 +0200 Subject: [PATCH 420/518] Improve doc wording --- docs/strategy-customization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md index def3b77f1..221c863ec 100644 --- a/docs/strategy-customization.md +++ b/docs/strategy-customization.md @@ -630,7 +630,7 @@ The strategy might look something like this: *Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.* -Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least! +Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least! Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use. @@ -732,7 +732,7 @@ if self.dp: data returned from the exchange and add appropriate error handling / defaults. !!! Warning "Warning about backtesting" - This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results. + This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results. ### Send Notification From e7902bffa07a6bf8cc34c3c3d954978ac596f9c8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 10:57:25 +0200 Subject: [PATCH 421/518] Remove checks for dataprovider existance - it's available in all modes. --- docs/strategy-customization.md | 39 ++++++++++++++------------------- freqtrade/strategy/interface.py | 12 +++------- 2 files changed, 20 insertions(+), 31 deletions(-) diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md index 221c863ec..260e253c4 100644 --- a/docs/strategy-customization.md +++ b/docs/strategy-customization.md @@ -617,9 +617,8 @@ Please always check the mode of operation to select the correct method to get da ### *available_pairs* ``` python -if self.dp: - for pair, timeframe in self.dp.available_pairs: - print(f"available {pair}, {timeframe}") +for pair, timeframe in self.dp.available_pairs: + print(f"available {pair}, {timeframe}") ``` ### *current_whitelist()* @@ -653,10 +652,9 @@ This is where calling `self.dp.current_whitelist()` comes in handy. ``` python # fetch live / historical candle (OHLCV) data for the first informative pair -if self.dp: - inf_pair, inf_timeframe = self.informative_pairs()[0] - informative = self.dp.get_pair_dataframe(pair=inf_pair, - timeframe=inf_timeframe) +inf_pair, inf_timeframe = self.informative_pairs()[0] +informative = self.dp.get_pair_dataframe(pair=inf_pair, + timeframe=inf_timeframe) ``` !!! Warning "Warning about backtesting" @@ -671,10 +669,9 @@ It can also be used in specific callbacks to get the signal that caused the acti ``` python # fetch current dataframe -if self.dp: - if self.dp.runmode.value in ('live', 'dry_run'): - dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'], - timeframe=self.timeframe) +if self.dp.runmode.value in ('live', 'dry_run'): + dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'], + timeframe=self.timeframe) ``` !!! Note "No data available" @@ -684,11 +681,10 @@ if self.dp: ### *orderbook(pair, maximum)* ``` python -if self.dp: - if self.dp.runmode.value in ('live', 'dry_run'): - ob = self.dp.orderbook(metadata['pair'], 1) - dataframe['best_bid'] = ob['bids'][0][0] - dataframe['best_ask'] = ob['asks'][0][0] +if self.dp.runmode.value in ('live', 'dry_run'): + ob = self.dp.orderbook(metadata['pair'], 1) + dataframe['best_bid'] = ob['bids'][0][0] + dataframe['best_ask'] = ob['asks'][0][0] ``` The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows: @@ -717,12 +713,11 @@ Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using t ### *ticker(pair)* ``` python -if self.dp: - if self.dp.runmode.value in ('live', 'dry_run'): - ticker = self.dp.ticker(metadata['pair']) - dataframe['last_price'] = ticker['last'] - dataframe['volume24h'] = ticker['quoteVolume'] - dataframe['vwap'] = ticker['vwap'] +if self.dp.runmode.value in ('live', 'dry_run'): + ticker = self.dp.ticker(metadata['pair']) + dataframe['last_price'] = ticker['last'] + dataframe['volume24h'] = ticker['quoteVolume'] + dataframe['vwap'] = ticker['vwap'] ``` !!! Warning diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index f41369189..79dbd4c69 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -617,9 +617,6 @@ class IStrategy(ABC, HyperStrategyMixin): ) informative_pairs.append(pair_tf) else: - if not self.dp: - raise OperationalException('@informative decorator with unspecified asset ' - 'requires DataProvider instance.') for pair in self.dp.current_whitelist(): informative_pairs.append((pair, inf_data.timeframe, candle_type)) return list(set(informative_pairs)) @@ -713,10 +710,9 @@ class IStrategy(ABC, HyperStrategyMixin): # Defs that only make change on new candle data. dataframe = self.analyze_ticker(dataframe, metadata) self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date'] - if self.dp: - self.dp._set_cached_df( - pair, self.timeframe, dataframe, - candle_type=self.config.get('candle_type_def', CandleType.SPOT)) + self.dp._set_cached_df( + pair, self.timeframe, dataframe, + candle_type=self.config.get('candle_type_def', CandleType.SPOT)) else: logger.debug("Skipping TA Analysis for already analyzed candle") dataframe[SignalType.ENTER_LONG.value] = 0 @@ -737,8 +733,6 @@ class IStrategy(ABC, HyperStrategyMixin): The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`. :param pair: Pair to analyze. """ - if not self.dp: - raise OperationalException("DataProvider not found.") dataframe = self.dp.ohlcv( pair, self.timeframe, candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) From b9667f50cfe20377078243acaa211ab7589d4f19 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 14:05:12 +0200 Subject: [PATCH 422/518] Fix random test failure --- tests/test_freqtradebot.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index c3d738075..a7ab6c614 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -4143,6 +4143,7 @@ def test_trailing_stop_loss_positive( 'last': enter_price + (-0.06 if is_short else 0.06), }) ) + caplog.clear() # stop-loss not reached, adjusted stoploss assert freqtrade.handle_trade(trade) is False caplog_text = (f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: " From c615e1bc629fb048525bc9bb15bc6d2dd6a13537 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 14:31:40 +0200 Subject: [PATCH 423/518] Avoid loop error during ccxt tests --- freqtrade/exchange/exchange.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index d74a01f8b..0a7609148 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2255,10 +2255,14 @@ class Exchange: coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)] + async def gather_results(): + return await asyncio.gather(*input_coro, return_exceptions=True) + for input_coro in chunks(coros, 100): - results = self.loop.run_until_complete( - asyncio.gather(*input_coro, return_exceptions=True)) + with self._loop_lock: + results = self.loop.run_until_complete(gather_results()) + for symbol, res in results: tiers[symbol] = res From 3983368228c2b7ba572a743a71f5d376f871ff3c Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 14:51:48 +0200 Subject: [PATCH 424/518] gateio futures is showing base currency in volume! --- freqtrade/exchange/gateio.py | 1 + 1 file changed, 1 insertion(+) diff --git a/freqtrade/exchange/gateio.py b/freqtrade/exchange/gateio.py index 6df3425d2..c6ed0c66c 100644 --- a/freqtrade/exchange/gateio.py +++ b/freqtrade/exchange/gateio.py @@ -34,6 +34,7 @@ class Gateio(Exchange): _ft_has_futures: Dict = { "needs_trading_fees": True, + "ohlcv_volume_currency": "base", "fee_cost_in_contracts": False, # Set explicitly to false for clarity "order_props_in_contracts": ['amount', 'filled', 'remaining'], } From 72c34291e322bc9470eda6112c0dc104667cb0e8 Mon Sep 17 00:00:00 2001 From: longyu Date: Wed, 17 Aug 2022 15:09:10 +0200 Subject: [PATCH 425/518] newline --- freqtrade/freqai/data_kitchen.py | 1 + 1 file changed, 1 insertion(+) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 35f51baed..b49df5a4d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -508,6 +508,7 @@ class FreqaiDataKitchen: self.data_dictionary["train_features"], n_jobs=self.thread_count) avg_mean_dist = pairwise.mean(axis=1).mean() + return avg_mean_dist def use_SVM_to_remove_outliers(self, predict: bool) -> None: From 9c38c27eede56022a71f08a31bd8c880793db225 Mon Sep 17 00:00:00 2001 From: longyu Date: Wed, 17 Aug 2022 15:09:57 +0200 Subject: [PATCH 426/518] ignore sample itself distance for avg_mean_dist computation --- freqtrade/freqai/data_kitchen.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b49df5a4d..85041515a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -454,7 +454,6 @@ class FreqaiDataKitchen: logger.info("reduced feature dimension by %s", n_components - n_keep_components) logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_)) train_components = pca2.transform(self.data_dictionary["train_features"]) - test_components = pca2.transform(self.data_dictionary["test_features"]) self.data_dictionary["train_features"] = pd.DataFrame( data=train_components, @@ -468,6 +467,7 @@ class FreqaiDataKitchen: self.training_features_list = self.data_dictionary["train_features"].columns if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0: + test_components = pca2.transform(self.data_dictionary["test_features"]) self.data_dictionary["test_features"] = pd.DataFrame( data=test_components, columns=["PC" + str(i) for i in range(0, n_keep_components)], @@ -506,7 +506,10 @@ class FreqaiDataKitchen: # logger.info("computing average mean distance for all training points") pairwise = pairwise_distances( self.data_dictionary["train_features"], n_jobs=self.thread_count) - avg_mean_dist = pairwise.mean(axis=1).mean() + # remove the diagonal distances which are itself distances ~0 + np.fill_diagonal(pairwise, np.NaN) + pairwise = pairwise.reshape(-1, 1) + avg_mean_dist = pairwise[~np.isnan(pairwise)].mean() return avg_mean_dist From 66910bfe63e86edf54b86d6bfd87852153c8ef0a Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 17 Aug 2022 10:04:09 +0200 Subject: [PATCH 427/518] Don't fail if mark candles are missing closes #7239 --- freqtrade/data/history/history_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index c972c841c..6e003c17b 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -56,7 +56,7 @@ def load_pair_history(pair: str, fill_missing=fill_up_missing, drop_incomplete=drop_incomplete, startup_candles=startup_candles, - candle_type=candle_type + candle_type=candle_type, ) @@ -97,14 +97,14 @@ def load_data(datadir: Path, fill_up_missing=fill_up_missing, startup_candles=startup_candles, data_handler=data_handler, - candle_type=candle_type + candle_type=candle_type, ) if not hist.empty: result[pair] = hist else: if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None: logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]") - result[pair] = DataFrame(columns=["open", "close", "high", "low", "volume"]) + result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"]) if fail_without_data and not result: raise OperationalException("No data found. Terminating.") From f70b0bab809c57219a9bd0b7f6b42e86f7773064 Mon Sep 17 00:00:00 2001 From: longyu Date: Wed, 17 Aug 2022 23:49:20 +0200 Subject: [PATCH 428/518] remove line --- freqtrade/freqai/data_kitchen.py | 1 - 1 file changed, 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 85041515a..421b30bf5 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -511,7 +511,6 @@ class FreqaiDataKitchen: pairwise = pairwise.reshape(-1, 1) avg_mean_dist = pairwise[~np.isnan(pairwise)].mean() - return avg_mean_dist def use_SVM_to_remove_outliers(self, predict: bool) -> None: From 0461a89348c0a69c43f8d15d7ef87e78d4b5eb97 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 07:20:49 +0200 Subject: [PATCH 429/518] Fix test failures --- freqtrade/data/history/history_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index 6e003c17b..cba1b60db 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -104,7 +104,8 @@ def load_data(datadir: Path, else: if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None: logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]") - result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"]) + elif candle_type not in (CandleType.SPOT, CandleType.FUTURES): + result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"]) if fail_without_data and not result: raise OperationalException("No data found. Terminating.") From 18fab86431f8ed67c3d56a4633a559e14e745dd7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 08:32:15 +0200 Subject: [PATCH 430/518] Add dock segment about webserver mode for docker --- docs/utils.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/docs/utils.md b/docs/utils.md index 0dd88b242..5646365e4 100644 --- a/docs/utils.md +++ b/docs/utils.md @@ -611,6 +611,26 @@ Common arguments: ``` +### Webserver mode - docker + +You can also use webserver mode via docker. +Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default. +You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port. + +Alternatively, you can reconfigure the docker-compose file to have the command updated: + +``` yml + command: > + webserver + --config /freqtrade/user_data/config.json +``` + +You can now use `docker-compose up` to start the webserver. +This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`). + +!!! Tip + Don't forget to reset the command back to the trade command if you want to start a live or dry-run bot. + ## Show previous Backtest results Allows you to show previous backtest results. From 46e8d9a5e4e6f0c071d26473d3bda9e27f5070f3 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 09:09:37 +0200 Subject: [PATCH 431/518] Reduce verbosity when whitelist is empty --- freqtrade/freqtradebot.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index a214efd76..4e3af64ea 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -418,7 +418,7 @@ class FreqtradeBot(LoggingMixin): whitelist = copy.deepcopy(self.active_pair_whitelist) if not whitelist: - logger.info("Active pair whitelist is empty.") + self.log_once("Active pair whitelist is empty.", logger.info) return trades_created # Remove pairs for currently opened trades from the whitelist for trade in Trade.get_open_trades(): @@ -427,8 +427,8 @@ class FreqtradeBot(LoggingMixin): logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: - logger.info("No currency pair in active pair whitelist, " - "but checking to exit open trades.") + self.log_once("No currency pair in active pair whitelist, " + "but checking to exit open trades.", logger.info) return trades_created if PairLocks.is_global_lock(side='*'): # This only checks for total locks (both sides). From 075e9b852601eb87ed6421a7acf9bc7d59e7d134 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 09:52:03 +0200 Subject: [PATCH 432/518] Log Exchange responses for set_leverage --- freqtrade/exchange/exchange.py | 6 ++++-- freqtrade/wallets.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index dbc3447be..ef5b96096 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2377,7 +2377,8 @@ class Exchange: return try: - self._api.set_leverage(symbol=pair, leverage=leverage) + res = self._api.set_leverage(symbol=pair, leverage=leverage) + self._log_exchange_response('set_leverage', res) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: @@ -2441,7 +2442,8 @@ class Exchange: return try: - self._api.set_margin_mode(margin_mode.value, pair, params) + res = self._api.set_margin_mode(margin_mode.value, pair, params) + self._log_exchange_response('set_margin_mode', res) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: diff --git a/freqtrade/wallets.py b/freqtrade/wallets.py index 14e5a6743..41115c72e 100644 --- a/freqtrade/wallets.py +++ b/freqtrade/wallets.py @@ -148,7 +148,7 @@ class Wallets: # Position is not open ... continue size = self._exchange._contracts_to_amount(symbol, position['contracts']) - collateral = position['collateral'] + collateral = position['collateral'] or 0.0 leverage = position['leverage'] self._positions[symbol] = PositionWallet( symbol, position=size, From fde469a2534d627bb1f5e1535c9a271259e02fcc Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 14:53:44 +0200 Subject: [PATCH 433/518] Remove unnecessary check --- freqtrade/exchange/exchange.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index ef5b96096..e415e2b6c 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2406,7 +2406,6 @@ class Exchange: if self.trading_mode in TradingMode.SPOT: return None elif ( - self.margin_mode == MarginMode.ISOLATED and self.trading_mode == TradingMode.FUTURES ): wallet_balance = (amount * open_rate) / leverage @@ -2422,7 +2421,7 @@ class Exchange: return isolated_liq else: raise OperationalException( - "Freqtrade only supports isolated futures for leverage trading") + "Freqtrade currently only supports futures for leverage trading.") def funding_fee_cutoff(self, open_date: datetime): """ From 85b43ec1a1193c6c6a2997b1bc6272bdd660dc18 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 15:23:58 +0200 Subject: [PATCH 434/518] Remove double-check for "isolated margin" --- freqtrade/exchange/exchange.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index e415e2b6c..c15481ca5 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2600,7 +2600,7 @@ class Exchange: """ if self.trading_mode == TradingMode.SPOT: return None - elif (self.trading_mode != TradingMode.FUTURES and self.margin_mode != MarginMode.ISOLATED): + elif (self.trading_mode != TradingMode.FUTURES): raise OperationalException( f"{self.name} does not support {self.margin_mode.value} {self.trading_mode.value}") From 1fada53dddc2418d89d88392d0e557974fd8690c Mon Sep 17 00:00:00 2001 From: longyu Date: Thu, 18 Aug 2022 19:40:00 +0200 Subject: [PATCH 435/518] remove newline --- freqtrade/templates/FreqaiExampleStrategy.py | 1 - 1 file changed, 1 deletion(-) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 5810e7881..ae33a3697 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -174,7 +174,6 @@ class FreqaiExampleStrategy(IStrategy): # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) # .min() # ) - return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: From 70a77ba3d975e1cdfa5626bd0af55ff7e2b3cfb9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 18 Aug 2022 20:07:41 +0200 Subject: [PATCH 436/518] Check for "last" availability in PrecisionFilter closes #7250 --- freqtrade/plugins/pairlist/PrecisionFilter.py | 5 +++++ tests/plugins/test_pairlist.py | 3 +++ 2 files changed, 8 insertions(+) diff --git a/freqtrade/plugins/pairlist/PrecisionFilter.py b/freqtrade/plugins/pairlist/PrecisionFilter.py index 521f38635..dcd153d8e 100644 --- a/freqtrade/plugins/pairlist/PrecisionFilter.py +++ b/freqtrade/plugins/pairlist/PrecisionFilter.py @@ -51,6 +51,11 @@ class PrecisionFilter(IPairList): :param ticker: ticker dict as returned from ccxt.fetch_tickers() :return: True if the pair can stay, false if it should be removed """ + if ticker.get('last', None) is None: + self.log_once(f"Removed {ticker['symbol']} from whitelist, because " + "ticker['last'] is empty (Usually no trade in the last 24h).", + logger.info) + return False stop_price = ticker['last'] * self._stoploss # Adjust stop-prices to precision diff --git a/tests/plugins/test_pairlist.py b/tests/plugins/test_pairlist.py index 5974bee89..48a0f81cb 100644 --- a/tests/plugins/test_pairlist.py +++ b/tests/plugins/test_pairlist.py @@ -366,6 +366,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf): ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, {"method": "PrecisionFilter"}], "BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC']), + ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, + {"method": "PrecisionFilter"}], + "USDT", ['ETH/USDT', 'NANO/USDT']), # PriceFilter and VolumePairList ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}, {"method": "PriceFilter", "low_price_ratio": 0.03}], From 76a3e97e05616677871d2b4f32fc7cdce614834a Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 06:39:51 +0200 Subject: [PATCH 437/518] Add migrations end message closes #7251 --- freqtrade/persistence/migrations.py | 6 ++++++ tests/test_persistence.py | 1 + 2 files changed, 7 insertions(+) diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py index e54675f16..311554359 100644 --- a/freqtrade/persistence/migrations.py +++ b/freqtrade/persistence/migrations.py @@ -307,7 +307,9 @@ def check_migrate(engine, decl_base, previous_tables) -> None: # Migrates both trades and orders table! # if ('orders' not in previous_tables # or not has_column(cols_orders, 'stop_price')): + migrating = False if not has_column(cols_trades, 'precision_mode'): + migrating = True logger.info(f"Running database migration for trades - " f"backup: {table_back_name}, {order_table_bak_name}") migrate_trades_and_orders_table( @@ -315,6 +317,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None: order_table_bak_name, cols_orders) if not has_column(cols_pairlocks, 'side'): + migrating = True logger.info(f"Running database migration for pairlocks - " f"backup: {pairlock_table_bak_name}") @@ -329,3 +332,6 @@ def check_migrate(engine, decl_base, previous_tables) -> None: set_sqlite_to_wal(engine) fix_old_dry_orders(engine) + + if migrating: + logger.info("Database migration finished.") diff --git a/tests/test_persistence.py b/tests/test_persistence.py index f68791b72..bfba58706 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1387,6 +1387,7 @@ def test_migrate_new(mocker, default_conf, fee, caplog): assert log_has("trying trades_bak2", caplog) assert log_has("Running database migration for trades - backup: trades_bak2, orders_bak0", caplog) + assert log_has("Database migration finished.", caplog) assert pytest.approx(trade.open_trade_value) == trade._calc_open_trade_value( trade.amount, trade.open_rate) assert trade.close_profit_abs is None From 42eb5085150bfd3068834f0a3b7f1ea2ff377bc4 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 6 Aug 2022 20:05:35 +0200 Subject: [PATCH 438/518] Attempt fix of #7184 --- freqtrade/persistence/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/persistence/models.py b/freqtrade/persistence/models.py index f0fa05343..7f851322e 100644 --- a/freqtrade/persistence/models.py +++ b/freqtrade/persistence/models.py @@ -53,7 +53,7 @@ def init_db(db_url: str) -> None: # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope # Scoped sessions proxy requests to the appropriate thread-local session. # We should use the scoped_session object - not a seperately initialized version - Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True)) + Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=False)) Trade.query = Trade._session.query_property() Order.query = Trade._session.query_property() PairLock.query = Trade._session.query_property() From 47b31435342d88b99de7142eb4dbd84f8dbcc8ec Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 09:10:54 +0200 Subject: [PATCH 439/518] Simplify and fix some tests --- tests/plugins/test_protections.py | 94 ++++++++++++++++--------------- tests/test_freqtradebot.py | 14 +++++ tests/test_persistence.py | 3 + 3 files changed, 67 insertions(+), 44 deletions(-) diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py index 8a5356b3e..acfe124a8 100644 --- a/tests/plugins/test_protections.py +++ b/tests/plugins/test_protections.py @@ -67,6 +67,8 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool, trade.close(open_rate * (2 - profit_rate if is_short else profit_rate)) trade.exit_reason = exit_reason + Trade.query.session.add(trade) + Trade.commit() return trade @@ -125,33 +127,33 @@ def test_stoploss_guard(mocker, default_conf, fee, caplog, is_short): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, is_short=is_short, - )) + ) assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() # This trade does not count, as it's closed too long ago - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'BCH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=250, min_ago_close=100, is_short=is_short, - )) + ) - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=240, min_ago_close=30, is_short=is_short, - )) + ) # 3 Trades closed - but the 2nd has been closed too long ago. assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'LTC/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=180, min_ago_close=30, is_short=is_short, - )) + ) assert freqtrade.protections.global_stop() assert log_has_re(message, caplog) @@ -186,25 +188,25 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) assert not freqtrade.protections.stop_per_pair(pair) assert not freqtrade.protections.global_stop() assert not log_has_re(message, caplog) caplog.clear() # This trade does not count, as it's closed too long ago - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=250, min_ago_close=100, profit_rate=0.9, is_short=is_short - )) + ) # Trade does not count for per pair stop as it's the wrong pair. - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=240, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) # 3 Trades closed - but the 2nd has been closed too long ago. assert not freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair @@ -216,10 +218,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair caplog.clear() # Trade does not count potentially, as it's in the wrong direction - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=150, min_ago_close=25, profit_rate=0.9, is_short=not is_short - )) + ) freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair assert PairLocks.is_pair_locked(pair, side=check_side) != (only_per_side and only_per_pair) @@ -231,10 +233,10 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair caplog.clear() # 2nd Trade that counts with correct pair - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( pair, fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=180, min_ago_close=30, profit_rate=0.9, is_short=is_short - )) + ) freqtrade.protections.stop_per_pair(pair) assert freqtrade.protections.global_stop() != only_per_pair @@ -259,20 +261,20 @@ def test_CooldownPeriod(mocker, default_conf, fee, caplog): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=30, - )) + ) assert not freqtrade.protections.global_stop() assert freqtrade.protections.stop_per_pair('XRP/BTC') assert PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=205, min_ago_close=35, - )) + ) assert not freqtrade.protections.global_stop() assert not PairLocks.is_pair_locked('ETH/BTC') @@ -300,10 +302,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not log_has_re(message, caplog) caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=800, min_ago_close=450, profit_rate=0.9, - )) + ) Trade.commit() # Not locked with 1 trade @@ -312,10 +314,10 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=200, min_ago_close=120, profit_rate=0.9, - )) + ) Trade.commit() # Not locked with 1 trade (first trade is outside of lookback_period) @@ -325,19 +327,19 @@ def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side): assert not PairLocks.is_global_lock() # Add positive trade - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=20, min_ago_close=10, profit_rate=1.15, is_short=True - )) + ) Trade.commit() assert freqtrade.protections.stop_per_pair('XRP/BTC') != only_per_side assert not PairLocks.is_pair_locked('XRP/BTC', side='*') assert PairLocks.is_pair_locked('XRP/BTC', side='long') == only_per_side - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=110, min_ago_close=21, profit_rate=0.8, - )) + ) Trade.commit() # Locks due to 2nd trade @@ -365,36 +367,38 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog): assert not freqtrade.protections.stop_per_pair('XRP/BTC') caplog.clear() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) - Trade.query.session.add(generate_mock_trade( + ) + generate_mock_trade( 'ETH/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) - Trade.query.session.add(generate_mock_trade( + ) + generate_mock_trade( 'NEO/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1000, min_ago_close=900, profit_rate=1.1, - )) + ) + Trade.commit() # No losing trade yet ... so max_drawdown will raise exception assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=500, min_ago_close=400, profit_rate=0.9, - )) + ) # Not locked with one trade assert not freqtrade.protections.global_stop() assert not freqtrade.protections.stop_per_pair('XRP/BTC') assert not PairLocks.is_pair_locked('XRP/BTC') assert not PairLocks.is_global_lock() - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.STOP_LOSS.value, min_ago_open=1200, min_ago_close=1100, profit_rate=0.5, - )) + ) + Trade.commit() # Not locked with 1 trade (2nd trade is outside of lookback_period) assert not freqtrade.protections.global_stop() @@ -404,20 +408,22 @@ def test_MaxDrawdown(mocker, default_conf, fee, caplog): assert not log_has_re(message, caplog) # Winning trade ... (should not lock, does not change drawdown!) - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=320, min_ago_close=410, profit_rate=1.5, - )) + ) + Trade.commit() assert not freqtrade.protections.global_stop() assert not PairLocks.is_global_lock() caplog.clear() # Add additional negative trade, causing a loss of > 15% - Trade.query.session.add(generate_mock_trade( + generate_mock_trade( 'XRP/BTC', fee.return_value, False, exit_reason=ExitType.ROI.value, min_ago_open=20, min_ago_close=10, profit_rate=0.8, - )) + ) + Trade.commit() assert not freqtrade.protections.stop_per_pair('XRP/BTC') # local lock not supported assert not PairLocks.is_pair_locked('XRP/BTC') diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index ace77a3b6..a1a16c039 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -677,6 +677,7 @@ def test_process_trade_no_whitelist_pair(default_conf_usdt, ticker_usdt, limit_b open_rate=0.001, exchange='binance', )) + Trade.commit() assert pair not in freqtrade.active_pair_whitelist freqtrade.process() @@ -2414,6 +2415,7 @@ def test_manage_open_orders_entry_usercustom( open_trade.orders[0].side = 'sell' if is_short else 'buy' open_trade.orders[0].ft_order_side = 'sell' if is_short else 'buy' Trade.query.session.add(open_trade) + Trade.commit() # Ensure default is to return empty (so not mocked yet) freqtrade.manage_open_orders() @@ -2472,6 +2474,7 @@ def test_manage_open_orders_entry( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False) freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234) @@ -2509,6 +2512,7 @@ def test_adjust_entry_cancel( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # Timeout to not interfere freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False) @@ -2549,6 +2553,7 @@ def test_adjust_entry_maintain_replace( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # Timeout to not interfere freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False) @@ -2601,6 +2606,7 @@ def test_check_handle_cancelled_buy( open_trade.orders = [] open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit freqtrade.manage_open_orders() @@ -2631,6 +2637,7 @@ def test_manage_open_orders_buy_exception( open_trade.is_short = is_short Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit freqtrade.manage_open_orders() @@ -2672,6 +2679,7 @@ def test_manage_open_orders_exit_usercustom( open_trade_usdt.is_open = False Trade.query.session.add(open_trade_usdt) + Trade.commit() # Ensure default is false freqtrade.manage_open_orders() assert cancel_order_mock.call_count == 0 @@ -2754,6 +2762,7 @@ def test_manage_open_orders_exit( open_trade_usdt.is_short = is_short Trade.query.session.add(open_trade_usdt) + Trade.commit() freqtrade.strategy.check_exit_timeout = MagicMock(return_value=False) freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False) @@ -2794,6 +2803,7 @@ def test_check_handle_cancelled_exit( open_trade_usdt.is_short = is_short Trade.query.session.add(open_trade_usdt) + Trade.commit() # check it does cancel sell orders over the time limit freqtrade.manage_open_orders() @@ -2830,6 +2840,7 @@ def test_manage_open_orders_partial( freqtrade = FreqtradeBot(default_conf_usdt) prior_stake = open_trade.stake_amount Trade.query.session.add(open_trade) + Trade.commit() # check it does cancel buy orders over the time limit # note this is for a partially-complete buy order @@ -2874,6 +2885,7 @@ def test_manage_open_orders_partial_fee( open_trade.fee_open = fee() open_trade.fee_close = fee() Trade.query.session.add(open_trade) + Trade.commit() # cancelling a half-filled order should update the amount to the bought amount # and apply fees if necessary. freqtrade.manage_open_orders() @@ -2923,6 +2935,7 @@ def test_manage_open_orders_partial_except( open_trade.fee_open = fee() open_trade.fee_close = fee() Trade.query.session.add(open_trade) + Trade.commit() # cancelling a half-filled order should update the amount to the bought amount # and apply fees if necessary. freqtrade.manage_open_orders() @@ -2961,6 +2974,7 @@ def test_manage_open_orders_exception(default_conf_usdt, ticker_usdt, open_trade freqtrade = FreqtradeBot(default_conf_usdt) Trade.query.session.add(open_trade_usdt) + Trade.commit() caplog.clear() freqtrade.manage_open_orders() diff --git a/tests/test_persistence.py b/tests/test_persistence.py index bfba58706..50d0788ca 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1886,6 +1886,7 @@ def test_stoploss_reinitialization(default_conf, fee): assert trade.initial_stop_loss == 0.95 assert trade.initial_stop_loss_pct == -0.05 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(0.06) @@ -1947,6 +1948,7 @@ def test_stoploss_reinitialization_leverage(default_conf, fee): assert trade.initial_stop_loss == 0.98 assert trade.initial_stop_loss_pct == -0.1 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(0.15) @@ -2008,6 +2010,7 @@ def test_stoploss_reinitialization_short(default_conf, fee): assert trade.initial_stop_loss == 1.02 assert trade.initial_stop_loss_pct == -0.1 Trade.query.session.add(trade) + Trade.commit() # Lower stoploss Trade.stoploss_reinitialization(-0.15) trades = Trade.get_open_trades() From 975bf8fe88ce480f1bdeadfdde094384e9201a2b Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 09:23:53 +0200 Subject: [PATCH 440/518] Update Docstring to match actual return values --- freqtrade/data/history/hdf5datahandler.py | 2 +- freqtrade/data/history/idatahandler.py | 2 +- freqtrade/data/history/jsondatahandler.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/freqtrade/data/history/hdf5datahandler.py b/freqtrade/data/history/hdf5datahandler.py index dadc9c7e6..1aa7f2883 100644 --- a/freqtrade/data/history/hdf5datahandler.py +++ b/freqtrade/data/history/hdf5datahandler.py @@ -28,7 +28,7 @@ class HDF5DataHandler(IDataHandler): Returns a list of all pairs with ohlcv data available in this datadir :param datadir: Directory to search for ohlcv files :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) + :return: List of Tuples of (pair, timeframe, CandleType) """ if trading_mode == TradingMode.FUTURES: datadir = datadir.joinpath('futures') diff --git a/freqtrade/data/history/idatahandler.py b/freqtrade/data/history/idatahandler.py index 07dc7c763..17ee0918e 100644 --- a/freqtrade/data/history/idatahandler.py +++ b/freqtrade/data/history/idatahandler.py @@ -46,7 +46,7 @@ class IDataHandler(ABC): Returns a list of all pairs with ohlcv data available in this datadir :param datadir: Directory to search for ohlcv files :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) + :return: List of Tuples of (pair, timeframe, CandleType) """ @classmethod diff --git a/freqtrade/data/history/jsondatahandler.py b/freqtrade/data/history/jsondatahandler.py index 83ec183df..c000cd15e 100644 --- a/freqtrade/data/history/jsondatahandler.py +++ b/freqtrade/data/history/jsondatahandler.py @@ -30,7 +30,7 @@ class JsonDataHandler(IDataHandler): Returns a list of all pairs with ohlcv data available in this datadir :param datadir: Directory to search for ohlcv files :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe) + :return: List of Tuples of (pair, timeframe, CandleType) """ if trading_mode == 'futures': datadir = datadir.joinpath('futures') From b420614d65b1fa70b4139bcbd90d85076dab2d0f Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 09:33:07 +0200 Subject: [PATCH 441/518] Reduce code duplication in datahandlers --- freqtrade/data/history/hdf5datahandler.py | 23 ----------------------- freqtrade/data/history/idatahandler.py | 13 ++++++++++++- freqtrade/data/history/jsondatahandler.py | 22 ---------------------- 3 files changed, 12 insertions(+), 46 deletions(-) diff --git a/freqtrade/data/history/hdf5datahandler.py b/freqtrade/data/history/hdf5datahandler.py index 1aa7f2883..b91e9c608 100644 --- a/freqtrade/data/history/hdf5datahandler.py +++ b/freqtrade/data/history/hdf5datahandler.py @@ -21,29 +21,6 @@ class HDF5DataHandler(IDataHandler): _columns = DEFAULT_DATAFRAME_COLUMNS - @classmethod - def ohlcv_get_available_data( - cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: - """ - Returns a list of all pairs with ohlcv data available in this datadir - :param datadir: Directory to search for ohlcv files - :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe, CandleType) - """ - if trading_mode == TradingMode.FUTURES: - datadir = datadir.joinpath('futures') - _tmp = [ - re.search( - cls._OHLCV_REGEX, p.name - ) for p in datadir.glob("*.h5") - ] - return [ - ( - cls.rebuild_pair_from_filename(match[1]), - cls.rebuild_timeframe_from_filename(match[2]), - CandleType.from_string(match[3]) - ) for match in _tmp if match and len(match.groups()) > 1] - @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]: """ diff --git a/freqtrade/data/history/idatahandler.py b/freqtrade/data/history/idatahandler.py index 17ee0918e..eb2441abe 100644 --- a/freqtrade/data/history/idatahandler.py +++ b/freqtrade/data/history/idatahandler.py @@ -39,7 +39,6 @@ class IDataHandler(ABC): raise NotImplementedError() @classmethod - @abstractmethod def ohlcv_get_available_data( cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: """ @@ -48,6 +47,18 @@ class IDataHandler(ABC): :param trading_mode: trading-mode to be used :return: List of Tuples of (pair, timeframe, CandleType) """ + if trading_mode == TradingMode.FUTURES: + datadir = datadir.joinpath('futures') + _tmp = [ + re.search( + cls._OHLCV_REGEX, p.name + ) for p in datadir.glob(f"*.{cls._get_file_extension()}")] + return [ + ( + cls.rebuild_pair_from_filename(match[1]), + cls.rebuild_timeframe_from_filename(match[2]), + CandleType.from_string(match[3]) + ) for match in _tmp if match and len(match.groups()) > 1] @classmethod @abstractmethod diff --git a/freqtrade/data/history/jsondatahandler.py b/freqtrade/data/history/jsondatahandler.py index c000cd15e..9f54f0746 100644 --- a/freqtrade/data/history/jsondatahandler.py +++ b/freqtrade/data/history/jsondatahandler.py @@ -23,28 +23,6 @@ class JsonDataHandler(IDataHandler): _use_zip = False _columns = DEFAULT_DATAFRAME_COLUMNS - @classmethod - def ohlcv_get_available_data( - cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes: - """ - Returns a list of all pairs with ohlcv data available in this datadir - :param datadir: Directory to search for ohlcv files - :param trading_mode: trading-mode to be used - :return: List of Tuples of (pair, timeframe, CandleType) - """ - if trading_mode == 'futures': - datadir = datadir.joinpath('futures') - _tmp = [ - re.search( - cls._OHLCV_REGEX, p.name - ) for p in datadir.glob(f"*.{cls._get_file_extension()}")] - return [ - ( - cls.rebuild_pair_from_filename(match[1]), - cls.rebuild_timeframe_from_filename(match[2]), - CandleType.from_string(match[3]) - ) for match in _tmp if match and len(match.groups()) > 1] - @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]: """ From 277245c69dca73b9714e28f2aedf392adf2c874c Mon Sep 17 00:00:00 2001 From: longyu Date: Fri, 19 Aug 2022 12:39:00 +0200 Subject: [PATCH 442/518] remove line --- freqtrade/freqai/data_kitchen.py | 1 - 1 file changed, 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 421b30bf5..af2f65696 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -510,7 +510,6 @@ class FreqaiDataKitchen: np.fill_diagonal(pairwise, np.NaN) pairwise = pairwise.reshape(-1, 1) avg_mean_dist = pairwise[~np.isnan(pairwise)].mean() - return avg_mean_dist def use_SVM_to_remove_outliers(self, predict: bool) -> None: From cfa5b3f12ca9f882b8f70b4fa0951fc46baa30d4 Mon Sep 17 00:00:00 2001 From: longyu Date: Fri, 19 Aug 2022 12:39:08 +0200 Subject: [PATCH 443/518] add new line --- freqtrade/freqai/data_kitchen.py | 1 + 1 file changed, 1 insertion(+) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index af2f65696..421b30bf5 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -510,6 +510,7 @@ class FreqaiDataKitchen: np.fill_diagonal(pairwise, np.NaN) pairwise = pairwise.reshape(-1, 1) avg_mean_dist = pairwise[~np.isnan(pairwise)].mean() + return avg_mean_dist def use_SVM_to_remove_outliers(self, predict: bool) -> None: From 521381ebf026032ff9d5e2cf7bbeb0a9b430f486 Mon Sep 17 00:00:00 2001 From: longyu Date: Fri, 19 Aug 2022 12:40:03 +0200 Subject: [PATCH 444/518] undo example strategy newline --- freqtrade/templates/FreqaiExampleStrategy.py | 1 + 1 file changed, 1 insertion(+) diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index ae33a3697..5810e7881 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -174,6 +174,7 @@ class FreqaiExampleStrategy(IStrategy): # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) # .min() # ) + return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: From 7d84ef2e2c9fdcbc3ca5a5eebdb6d9e18af14535 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 13:44:39 +0200 Subject: [PATCH 445/518] Remove unused imports --- freqtrade/data/history/hdf5datahandler.py | 5 ++--- freqtrade/data/history/jsondatahandler.py | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/freqtrade/data/history/hdf5datahandler.py b/freqtrade/data/history/hdf5datahandler.py index b91e9c608..135d97c79 100644 --- a/freqtrade/data/history/hdf5datahandler.py +++ b/freqtrade/data/history/hdf5datahandler.py @@ -7,9 +7,8 @@ import numpy as np import pandas as pd from freqtrade.configuration import TimeRange -from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, - ListPairsWithTimeframes, TradeList) -from freqtrade.enums import CandleType, TradingMode +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList +from freqtrade.enums import CandleType from .idatahandler import IDataHandler diff --git a/freqtrade/data/history/jsondatahandler.py b/freqtrade/data/history/jsondatahandler.py index 9f54f0746..a62e5e381 100644 --- a/freqtrade/data/history/jsondatahandler.py +++ b/freqtrade/data/history/jsondatahandler.py @@ -8,9 +8,9 @@ from pandas import DataFrame, read_json, to_datetime from freqtrade import misc from freqtrade.configuration import TimeRange -from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList from freqtrade.data.converter import trades_dict_to_list -from freqtrade.enums import CandleType, TradingMode +from freqtrade.enums import CandleType from .idatahandler import IDataHandler From b7553d20d41f46dd9eba335330c649ceb83b1fb7 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 13:44:31 +0200 Subject: [PATCH 446/518] Get min/max data in list-data command --- freqtrade/commands/arguments.py | 2 +- freqtrade/commands/cli_options.py | 5 ++++ freqtrade/commands/data_commands.py | 37 +++++++++++++++++------- freqtrade/configuration/configuration.py | 3 ++ freqtrade/data/history/idatahandler.py | 14 ++++++++- tests/commands/test_commands.py | 21 ++++++++++++++ 6 files changed, 69 insertions(+), 13 deletions(-) diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 48a423be4..266095cfa 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -69,7 +69,7 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "tradin ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"] -ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode"] +ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode", "show_timerange"] ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive", "timerange", "download_trades", "exchange", "timeframes", diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index f85b75af1..706f28f02 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -434,6 +434,11 @@ AVAILABLE_CLI_OPTIONS = { help='Storage format for downloaded trades data. (default: `jsongz`).', choices=constants.AVAILABLE_DATAHANDLERS, ), + "show_timerange": Arg( + '--show-timerange', + help='Show timerange available for available data. (May take a while to calculate).', + action='store_true', + ), "exchange": Arg( '--exchange', help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). ' diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 311590e64..36a86bece 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -5,6 +5,7 @@ from datetime import datetime, timedelta from typing import Any, Dict, List from freqtrade.configuration import TimeRange, setup_utils_configuration +from freqtrade.constants import DATETIME_PRINT_FORMAT from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data, refresh_backtest_trades_data) @@ -177,17 +178,31 @@ def start_list_data(args: Dict[str, Any]) -> None: paircombs = [comb for comb in paircombs if comb[0] in args['pairs']] print(f"Found {len(paircombs)} pair / timeframe combinations.") - groupedpair = defaultdict(list) - for pair, timeframe, candle_type in sorted( - paircombs, - key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) - ): - groupedpair[(pair, candle_type)].append(timeframe) + if not config.get('show_timerange'): + groupedpair = defaultdict(list) + for pair, timeframe, candle_type in sorted( + paircombs, + key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) + ): + groupedpair[(pair, candle_type)].append(timeframe) - if groupedpair: + if groupedpair: + print(tabulate([ + (pair, ', '.join(timeframes), candle_type) + for (pair, candle_type), timeframes in groupedpair.items() + ], + headers=("Pair", "Timeframe", "Type"), + tablefmt='psql', stralign='right')) + else: + paircombs1 = [( + pair, timeframe, candle_type, + *dhc.ohlcv_data_min_max(pair, timeframe, candle_type) + ) for pair, timeframe, candle_type in paircombs] print(tabulate([ - (pair, ', '.join(timeframes), candle_type) - for (pair, candle_type), timeframes in groupedpair.items() - ], - headers=("Pair", "Timeframe", "Type"), + (pair, timeframe, candle_type, + start.strftime(DATETIME_PRINT_FORMAT), + end.strftime(DATETIME_PRINT_FORMAT)) + for pair, timeframe, candle_type, start, end in paircombs1 + ], + headers=("Pair", "Timeframe", "Type", 'From', 'To'), tablefmt='psql', stralign='right')) diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py index b4f36aa3c..0db585acc 100644 --- a/freqtrade/configuration/configuration.py +++ b/freqtrade/configuration/configuration.py @@ -426,6 +426,9 @@ class Configuration: self._args_to_config(config, argname='dataformat_trades', logstring='Using "{}" to store trades data.') + self._args_to_config(config, argname='show_timerange', + logstring='Detected --show-timerange') + def _process_data_options(self, config: Dict[str, Any]) -> None: self._args_to_config(config, argname='new_pairs_days', logstring='Detected --new-pairs-days: {}') diff --git a/freqtrade/data/history/idatahandler.py b/freqtrade/data/history/idatahandler.py index eb2441abe..846bcc607 100644 --- a/freqtrade/data/history/idatahandler.py +++ b/freqtrade/data/history/idatahandler.py @@ -9,7 +9,7 @@ from abc import ABC, abstractmethod from copy import deepcopy from datetime import datetime, timezone from pathlib import Path -from typing import List, Optional, Type +from typing import List, Optional, Tuple, Type from pandas import DataFrame @@ -84,6 +84,18 @@ class IDataHandler(ABC): :return: None """ + def ohlcv_data_min_max(self, pair: str, timeframe: str, + candle_type: CandleType) -> Tuple[datetime, datetime]: + """ + Returns the min and max timestamp for the given pair and timeframe. + :param pair: Pair to get min/max for + :param timeframe: Timeframe to get min/max for + :param candle_type: Any of the enum CandleType (must match trading mode!) + :return: (min, max) + """ + data = self._ohlcv_load(pair, timeframe, None, candle_type) + return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime() + @abstractmethod def _ohlcv_load(self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType diff --git a/tests/commands/test_commands.py b/tests/commands/test_commands.py index 9df6acf75..28515a28a 100644 --- a/tests/commands/test_commands.py +++ b/tests/commands/test_commands.py @@ -1430,6 +1430,27 @@ def test_start_list_data(testdatadir, capsys): assert "\n| XRP/USDT | 1h | futures |\n" in captured.out assert "\n| XRP/USDT | 1h, 8h | mark |\n" in captured.out + args = [ + "list-data", + "--data-format-ohlcv", + "json", + "--pairs", "XRP/ETH", + "--datadir", + str(testdatadir), + "--show-timerange", + ] + pargs = get_args(args) + pargs['config'] = None + start_list_data(pargs) + captured = capsys.readouterr() + assert "Found 2 pair / timeframe combinations." in captured.out + assert ("\n| Pair | Timeframe | Type | From | To |\n" + in captured.out) + assert "UNITTEST/BTC" not in captured.out + assert ( + "\n| XRP/ETH | 1m | spot | 2019-10-11 00:00:00 | 2019-10-13 11:19:00 |\n" + in captured.out) + @pytest.mark.usefixtures("init_persistence") def test_show_trades(mocker, fee, capsys, caplog): From 16af10a5bc79abb6c823b9e8145df1738113c1a5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 14:05:27 +0200 Subject: [PATCH 447/518] Update notebook sample with simplified datadir configuration closes #7252 --- docs/strategy_analysis_example.md | 4 ++-- .../templates/strategy_analysis_example.ipynb | 15 ++++++++++----- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/docs/strategy_analysis_example.md b/docs/strategy_analysis_example.md index fbfce37d1..1526ea038 100644 --- a/docs/strategy_analysis_example.md +++ b/docs/strategy_analysis_example.md @@ -14,7 +14,7 @@ from freqtrade.configuration import Configuration # Initialize empty configuration object config = Configuration.from_files([]) -# Optionally, use existing configuration file +# Optionally (recommended), use existing configuration file # config = Configuration.from_files(["config.json"]) # Define some constants @@ -22,7 +22,7 @@ config["timeframe"] = "5m" # Name of the strategy class config["strategy"] = "SampleStrategy" # Location of the data -data_location = Path(config['user_data_dir'], 'data', 'binance') +data_location = config['datadir'] # Pair to analyze - Only use one pair here pair = "BTC/USDT" ``` diff --git a/freqtrade/templates/strategy_analysis_example.ipynb b/freqtrade/templates/strategy_analysis_example.ipynb index a7430c225..77444a023 100644 --- a/freqtrade/templates/strategy_analysis_example.ipynb +++ b/freqtrade/templates/strategy_analysis_example.ipynb @@ -30,7 +30,7 @@ "\n", "# Initialize empty configuration object\n", "config = Configuration.from_files([])\n", - "# Optionally, use existing configuration file\n", + "# Optionally (recommended), use existing configuration file\n", "# config = Configuration.from_files([\"config.json\"])\n", "\n", "# Define some constants\n", @@ -38,7 +38,7 @@ "# Name of the strategy class\n", "config[\"strategy\"] = \"SampleStrategy\"\n", "# Location of the data\n", - "data_location = Path(config['user_data_dir'], 'data', 'binance')\n", + "data_location = config['datadir']\n", "# Pair to analyze - Only use one pair here\n", "pair = \"BTC/USDT\"" ] @@ -365,7 +365,7 @@ "metadata": { "file_extension": ".py", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 64-bit ('trade_397')", "language": "python", "name": "python3" }, @@ -379,7 +379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" }, "mimetype": "text/x-python", "name": "python", @@ -427,7 +427,12 @@ ], "window_display": false }, - "version": 3 + "version": 3, + "vscode": { + "interpreter": { + "hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c" + } + } }, "nbformat": 4, "nbformat_minor": 4 From 1c6f96657974f69fbf97ea135740f180393d0239 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:03:03 +0200 Subject: [PATCH 448/518] Hyperopt: simplify parameter "can_optimize" handling --- freqtrade/strategy/parameters.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/freqtrade/strategy/parameters.py b/freqtrade/strategy/parameters.py index 83dd41de9..e64a1e4c0 100644 --- a/freqtrade/strategy/parameters.py +++ b/freqtrade/strategy/parameters.py @@ -57,6 +57,12 @@ class BaseParameter(ABC): Get-space - will be used by Hyperopt to get the hyperopt Space """ + def can_optimize(self): + return ( + self.in_space + and self.optimize + ) + class NumericParameter(BaseParameter): """ Internal parameter used for Numeric purposes """ @@ -133,7 +139,7 @@ class IntParameter(NumericParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive return range(self.low, self.high + 1) else: @@ -212,7 +218,7 @@ class DecimalParameter(NumericParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): low = int(self.low * pow(10, self._decimals)) high = int(self.high * pow(10, self._decimals)) + 1 return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)] @@ -261,7 +267,7 @@ class CategoricalParameter(BaseParameter): Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid calculating 100ds of indicators. """ - if self.in_space and self.optimize: + if self.can_optimize(): return self.opt_range else: return [self.value] From 08ef5ad2d8f12100e17beec5c4c23d26d5e51434 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:11:43 +0200 Subject: [PATCH 449/518] Add HyperoptState enum and container class --- freqtrade/enums/__init__.py | 1 + freqtrade/enums/hyperoptstate.py | 12 ++++++++++++ freqtrade/optimize/hyperopt_tools.py | 15 +++++++++++++++ 3 files changed, 28 insertions(+) create mode 100644 freqtrade/enums/hyperoptstate.py diff --git a/freqtrade/enums/__init__.py b/freqtrade/enums/__init__.py index e50ebc4a4..d2f5474fc 100644 --- a/freqtrade/enums/__init__.py +++ b/freqtrade/enums/__init__.py @@ -3,6 +3,7 @@ from freqtrade.enums.backteststate import BacktestState from freqtrade.enums.candletype import CandleType from freqtrade.enums.exitchecktuple import ExitCheckTuple from freqtrade.enums.exittype import ExitType +from freqtrade.enums.hyperoptstate import HyperoptState from freqtrade.enums.marginmode import MarginMode from freqtrade.enums.ordertypevalue import OrderTypeValues from freqtrade.enums.rpcmessagetype import RPCMessageType diff --git a/freqtrade/enums/hyperoptstate.py b/freqtrade/enums/hyperoptstate.py new file mode 100644 index 000000000..332b3354d --- /dev/null +++ b/freqtrade/enums/hyperoptstate.py @@ -0,0 +1,12 @@ +from enum import Enum + + +class HyperoptState(Enum): + """ Hyperopt states """ + STARTUP = 1 + DATALOAD = 2 + INDICATORS = 3 + OPTIMIZE = 0 + + def __str__(self): + return f"{self.name.lower()}" diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py index ab6ef013b..7f339eec1 100755 --- a/freqtrade/optimize/hyperopt_tools.py +++ b/freqtrade/optimize/hyperopt_tools.py @@ -13,6 +13,7 @@ from colorama import Fore, Style from pandas import isna, json_normalize from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES +from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs @@ -32,6 +33,20 @@ def hyperopt_serializer(x): return str(x) +class HyperoptStateContainer(): + """ Singleton class to track state of hyperopt""" + __state = HyperoptState.OPTIMIZE + + @classmethod + def set_state(cls, value: HyperoptState): + cls.__state = value + + @classmethod + @property + def state(cls) -> HyperoptState: + return cls.__state + + class HyperoptTools(): @staticmethod From 09f8904545b35454534cf2866216ec4e656c4381 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:12:55 +0200 Subject: [PATCH 450/518] Extract analysis to separate method --- freqtrade/optimize/hyperopt.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index cbcf39131..ebeb7eb25 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -406,6 +406,14 @@ class Hyperopt: def _set_random_state(self, random_state: Optional[int]) -> int: return random_state or random.randint(1, 2**16 - 1) + def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]: + preprocessed = self.backtesting.strategy.advise_all_indicators(data) + + # Trim startup period from analyzed dataframe to get correct dates for output. + processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup) + self.min_date, self.max_date = get_timerange(processed) + return processed + def prepare_hyperopt_data(self) -> None: data, timerange = self.backtesting.load_bt_data() self.backtesting.load_bt_data_detail() @@ -413,9 +421,7 @@ class Hyperopt: preprocessed = self.backtesting.strategy.advise_all_indicators(data) - # Trim startup period from analyzed dataframe to get correct dates for output. - processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup) - self.min_date, self.max_date = get_timerange(processed) + preprocessed = self.advise_and_trim(data) logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' From bc359675a2bebf55e79e72d3e6c5c54bf519e159 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:19:43 +0200 Subject: [PATCH 451/518] Add --analyze-per-epoch - moving populate_analysis to the epoch process --- docs/hyperopt.md | 10 +++++-- freqtrade/commands/arguments.py | 2 +- freqtrade/commands/cli_options.py | 7 +++++ freqtrade/configuration/configuration.py | 3 +++ freqtrade/optimize/hyperopt.py | 34 +++++++++++++++++------- freqtrade/strategy/parameters.py | 4 +++ 6 files changed, 48 insertions(+), 12 deletions(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index c9ec30056..a07bab9de 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -40,7 +40,8 @@ pip install -r requirements-hyperopt.txt ``` usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME] [--strategy-path PATH] - [--recursive-strategy-search] [-i TIMEFRAME] + [--recursive-strategy-search] [--freqaimodel NAME] + [--freqaimodel-path PATH] [-i TIMEFRAME] [--timerange TIMERANGE] [--data-format-ohlcv {json,jsongz,hdf5}] [--max-open-trades INT] @@ -53,7 +54,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--print-all] [--no-color] [--print-json] [-j JOBS] [--random-state INT] [--min-trades INT] [--hyperopt-loss NAME] [--disable-param-export] - [--ignore-missing-spaces] + [--ignore-missing-spaces] [--analyze-per-epoch] optional arguments: -h, --help show this help message and exit @@ -129,6 +130,7 @@ optional arguments: --ignore-missing-spaces, --ignore-unparameterized-spaces Suppress errors for any requested Hyperopt spaces that do not contain any parameters. + --analyze-per-epoch Run populate_indicators once per epoch. Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). @@ -154,6 +156,10 @@ Strategy arguments: --recursive-strategy-search Recursively search for a strategy in the strategies folder. + --freqaimodel NAME Specify a custom freqaimodels. + --freqaimodel-path PATH + Specify additional lookup path for freqaimodels. + ``` ### Hyperopt checklist diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 48a423be4..05a6a2ca3 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -34,7 +34,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path", "print_colorized", "print_json", "hyperopt_jobs", "hyperopt_random_state", "hyperopt_min_trades", "hyperopt_loss", "disableparamexport", - "hyperopt_ignore_missing_space"] + "hyperopt_ignore_missing_space", "analyze_per_epoch"] ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"] diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index f85b75af1..51a501d7c 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -255,6 +255,13 @@ AVAILABLE_CLI_OPTIONS = { nargs='+', default='default', ), + "analyze_per_epoch": Arg( + '--analyze-per-epoch', + help='Run populate_indicators once per epoch.', + action='store_true', + default=False, + ), + "print_all": Arg( '--print-all', help='Print all results, not only the best ones.', diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py index b4f36aa3c..41b31b022 100644 --- a/freqtrade/configuration/configuration.py +++ b/freqtrade/configuration/configuration.py @@ -302,6 +302,9 @@ class Configuration: self._args_to_config(config, argname='spaces', logstring='Parameter -s/--spaces detected: {}') + self._args_to_config(config, argname='analyze_per_epoch', + logstring='Parameter --analyze-per-epoch detected.') + self._args_to_config(config, argname='print_all', logstring='Parameter --print-all detected ...') diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index ebeb7eb25..fea2a672f 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -24,13 +24,15 @@ from pandas import DataFrame from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN from freqtrade.data.converter import trim_dataframes from freqtrade.data.history import get_timerange +from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, file_dump_json, plural from freqtrade.optimize.backtesting import Backtesting # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules from freqtrade.optimize.hyperopt_auto import HyperOptAuto from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss -from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer +from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools, + hyperopt_serializer) from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver @@ -74,10 +76,14 @@ class Hyperopt: self.dimensions: List[Dimension] = [] self.config = config + self.min_date: datetime + self.max_date: datetime self.backtesting = Backtesting(self.config) self.pairlist = self.backtesting.pairlists.whitelist self.custom_hyperopt: HyperOptAuto + self.analyze_per_epoch = self.config.get('analyze_per_epoch', False) + HyperoptStateContainer.set_state(HyperoptState.STARTUP) if not self.config.get('hyperopt'): self.custom_hyperopt = HyperOptAuto(self.config) @@ -290,6 +296,7 @@ class Hyperopt: Called once per epoch to optimize whatever is configured. Keep this function as optimized as possible! """ + HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE) backtest_start_time = datetime.now(timezone.utc) params_dict = self._get_params_dict(self.dimensions, raw_params) @@ -321,6 +328,10 @@ class Hyperopt: with self.data_pickle_file.open('rb') as f: processed = load(f, mmap_mode='r') + if self.analyze_per_epoch: + # Data is not yet analyzed, rerun populate_indicators. + processed = self.advise_and_trim(processed) + bt_results = self.backtesting.backtest( processed=processed, start_date=self.min_date, @@ -415,19 +426,24 @@ class Hyperopt: return processed def prepare_hyperopt_data(self) -> None: - data, timerange = self.backtesting.load_bt_data() + HyperoptStateContainer.set_state(HyperoptState.DATALOAD) + data, self.timerange = self.backtesting.load_bt_data() self.backtesting.load_bt_data_detail() logger.info("Dataload complete. Calculating indicators") - preprocessed = self.backtesting.strategy.advise_all_indicators(data) + if not self.analyze_per_epoch: + HyperoptStateContainer.set_state(HyperoptState.INDICATORS) - preprocessed = self.advise_and_trim(data) + preprocessed = self.advise_and_trim(data) - logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' - f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' - f'({(self.max_date - self.min_date).days} days)..') - # Store non-trimmed data - will be trimmed after signal generation. - dump(preprocessed, self.data_pickle_file) + logger.info(f'Hyperopting with data from ' + f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' + f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' + f'({(self.max_date - self.min_date).days} days)..') + # Store non-trimmed data - will be trimmed after signal generation. + dump(preprocessed, self.data_pickle_file) + else: + dump(data, self.data_pickle_file) def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]: """ diff --git a/freqtrade/strategy/parameters.py b/freqtrade/strategy/parameters.py index e64a1e4c0..c6037ae0b 100644 --- a/freqtrade/strategy/parameters.py +++ b/freqtrade/strategy/parameters.py @@ -7,6 +7,9 @@ from abc import ABC, abstractmethod from contextlib import suppress from typing import Any, Optional, Sequence, Union +from freqtrade.enums.hyperoptstate import HyperoptState +from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer + with suppress(ImportError): from skopt.space import Integer, Real, Categorical @@ -61,6 +64,7 @@ class BaseParameter(ABC): return ( self.in_space and self.optimize + and HyperoptStateContainer.state != HyperoptState.OPTIMIZE ) From 733f716819b2c9ecaf303a357744005eba38ff5b Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:22:43 +0200 Subject: [PATCH 452/518] Update documentation --- docs/hyperopt.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index a07bab9de..d230c091e 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -191,7 +191,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid ### Hyperopt execution logic -Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators. +Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified. Hyperopt will then spawn into different processes (number of processors, or `-j `), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined. @@ -434,7 +434,8 @@ While this strategy is most likely too simple to provide consistent profit, it s ??? Hint "Performance tip" By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter. While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values). - You should however try to use space ranges as small as possible. Every new column will require more memory, and every possibility hyperopt can try will increase the search space. + As this also has Performance implications, hyperopt provides `--analyze-per-epoch` - which will move the execution of `populate_indicators()` to the epoch process. This will implicitly also change the `.range` functionality to only return the actually used value. + You should however try to use space ranges as small as possible. ## Optimizing protections @@ -885,6 +886,7 @@ To combat these, you have multiple options: * Avoid using `--timeframe-detail` (this loads a lot of additional data into memory). * Reduce the number of parallel processes (`-j `). * Increase the memory of your machine. +* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality. ## The objective has been evaluated at this point before. From 1389c8f5b617f767b566b579dc8048be37cde97b Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:34:10 +0200 Subject: [PATCH 453/518] Update documentation with show-timerange option --- docs/data-download.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/data-download.md b/docs/data-download.md index 681fb717d..55c2ad738 100644 --- a/docs/data-download.md +++ b/docs/data-download.md @@ -374,6 +374,7 @@ usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--data-format-ohlcv {json,jsongz,hdf5}] [-p PAIRS [PAIRS ...]] [--trading-mode {spot,margin,futures}] + [--show-timerange] optional arguments: -h, --help show this help message and exit @@ -387,6 +388,8 @@ optional arguments: separated. --trading-mode {spot,margin,futures} Select Trading mode + --show-timerange Show timerange available for available data. (May take + a while to calculate). Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). From b9d48c32782040ebe0754f4b11cf1dd481264235 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:40:06 +0200 Subject: [PATCH 454/518] use numbers in HyperoptState properly ... --- freqtrade/enums/hyperoptstate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/enums/hyperoptstate.py b/freqtrade/enums/hyperoptstate.py index 332b3354d..6716e123a 100644 --- a/freqtrade/enums/hyperoptstate.py +++ b/freqtrade/enums/hyperoptstate.py @@ -6,7 +6,7 @@ class HyperoptState(Enum): STARTUP = 1 DATALOAD = 2 INDICATORS = 3 - OPTIMIZE = 0 + OPTIMIZE = 4 def __str__(self): return f"{self.name.lower()}" From 9c6b745f06e50bc19d3bef6fb6e5dbb790158272 Mon Sep 17 00:00:00 2001 From: elintornquist <107926911+elintornquist@users.noreply.github.com> Date: Wed, 17 Aug 2022 22:35:26 +0200 Subject: [PATCH 455/518] Restructure and improve doc, add fiq --- docs/assets/freqai_algo.png | Bin 1018784 -> 17562663 bytes docs/assets/freqai_moving-window.png | Bin 0 -> 3435842 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learning model to generate market forecasts given a set of input features. -Among the the features included: +Features include: -* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner. -* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies. -* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing. -* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining. -* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available. -* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques. -* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs). -* **Automated data normalization**: normalize the data in a smart and statistically safe way. +* **Self-adaptive retraining**: retrain models during [live deployments](#running-the-model-live) to self-adapt to the market in an unsupervised manner. +* **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies. +* **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing. +* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining. +* **Modifiability**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples are currently available. +* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal). +* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs. +* **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way. * **Automatic data download**: compute the data download timerange and update historic data (in live deployments). -* **Clean incoming data** safe NaN handling before training and prediction. -* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis. -* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades. +* **Cleaning of incoming data**: handle NaNs safely before training and prediction. +* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](#reducing-data-dimensionality-with-principal-component-analysis). +* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](#setting-up-a-follower) inference the models and handle trades. ## Quick start -The easiest way to quickly test FreqAI is to run it in dry run with the following command +The easiest way to quickly test FreqAI is to run it in dry mode with the following command ```bash freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates ``` -where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading. +The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. -The example strategy, example prediction model, and example config can all be found in -`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, +The example strategy, example prediction model, and example config can be found in +`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and `config_examples/config_freqai.example.json`, respectively. ## General approach -The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way -a typical Freqtrade strategy is created) as well as target values which look into the future. -FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible. +The user provides FreqAI with a set of custom *base* indicators (the same way as in a typical Freqtrade strategy) as well as target values (*labels*). +FreqAI trains a model to predict the target values based on the input of custom indicators, for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as up to date as possible. -An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage. +An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage. ![freqai-algo](assets/freqai_algo.png) -## Background and vocabulary +### Important machine learning vocabulary -**Features** are the quantities with which a model is trained. $X_i$ represents the -vector of all features for a single candle. In FreqAI, the user -builds the features from anything they can construct in the strategy. +**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user +builds the feature sets from anything they can construct in the strategy. -**Labels** are the target values with which the weights inside a model are trained -toward. Each set of features is associated with a single label, which is also -defined within the strategy by the user. These labels intentionally look into the -future, and are not available to the model during dryrun/live/backtesting. +**Labels** - the target values that a model is trained +toward. Each set of features is associated with a single label that is +defined by the user within the strategy. These labels intentionally look into the +future, and are not available to the model during dry/live/backtesting. -**Training** refers to the process of feeding individual feature sets into the -model with associated labels with the goal of matching input feature sets to associated labels. +**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the +model with the goal of matching input feature sets to associated labels. -**Train data** is a subset of the historic data which is fed to the model during -training to adjust weights. This data directly influences weight connections in the model. +**Train data** - a subset of the historic data that is fed to the model during +training. This data directly influences weight connections in the model. -**Test data** is a subset of the historic data which is used to evaluate the -intermediate performance of the model during training. This data does not -directly influence nodal weights within the model. +**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model. ## Install prerequisites @@ -81,69 +77,76 @@ For docker users, a dedicated tag with freqAI dependencies is available as `:fre As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. -## Configuring FreqAI +## Setting up FreqAI ### Parameter table -The table below will list all configuration parameters available for FreqAI. +The table below will list all configuration parameters available for FreqAI, sorted based on where they should be placed in the config. Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. | Parameter | Description | |------------|-------------| -| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI.
    **Datatype:** dictionary. -| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data.
    **Datatype:** string. -| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window).
    **Datatype:** positive integer. -| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
    **Datatype:** Float. -| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible.
    **Datatype:** Float > 0. -| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default.
    **Datatype:** boolean. -| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
    **Datatype:** positive integer. -| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics.
    **Datatype:** positive integer. -| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`.
    **Datatype:** boolean. -| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire.
    **Datatype:** positive integer. -| | **Feature Parameters** -| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering)
    **Datatype:** dictionary. -| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` will be created for each coin in this list, and that set of features is added to the base asset feature set.
    **Datatype:** list of assets (strings). -| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set.
    **Datatype:** list of timeframes (strings). -| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not.
    **Datatype:** positive integer. -| `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user.
    **Datatype:** positive integer. -| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index).
    **Datatype:** positive float (typically below 1). -| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process).
    **Datatype:** positive float (typically below 1). -| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA.
    **Datatype:** boolean. -| `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points.
    **Datatype:** boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary.
    **Datatype:** dictionary. -| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing.
    **Datatype:** positive integer. -| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN
    **Datatype:** positive integer. -| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set.
    **Datatype:** list of positive integers. -| `use_DBSCAN_to_remove_outliers` | Inactive by default. If true, FreqAI clusters data using DBSCAN to identify and remove outliers from training and prediction data.
    **Datatype:** float (fraction of 1). +| | **General configuration parameters** +| `freqai` | **Required.**
    The parent dictionary containing all the parameters for controlling FreqAI.
    **Datatype:** Dictionary. +| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period.
    **Datatype:** Positive integer. +| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk).
    **Datatype:** Boolean. Default: `False`. +| `train_period_days` | **Required.**
    Number of days to use for the training data (width of the sliding window).
    **Datatype:** Positive integer. +| `backtest_period_days` | **Required.**
    Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
    **Datatype:** Float. +| `identifier` | **Required.**
    A unique name for the current model. This can be reused to reload pre-trained models/data.
    **Datatype:** String. +| `live_retrain_hours` | Frequency of retraining during dry/live runs.
    Default set to 0, which means the model will retrain as often as possible.
    **Datatype:** Float > 0. +| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
    Defaults set to 0, which means models never expire.
    **Datatype:** Positive integer. +| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set.
    **Datatype:** Positive integer. +| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models.
    **Datatype:** Boolean. Default: `False`. +| | **Feature parameters** +| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering).
    **Datatype:** Dictionary. +| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set.
    **Datatype:** List of timeframes (strings). +| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set.
    **Datatype:** List of assets (strings). +| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not.
    **Datatype:** Positive integer. +| `include_shifted_candles` | Add features from previous candles to subsequent candles to add historical information. FreqAI takes all features from the `include_shifted_candles` previous candles, duplicates and shifts them so that the information is available for the subsequent candle.
    **Datatype:** Positive integer. +| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process).
    **Datatype:** Positive float (typically < 1). +| `indicator_max_period_candles` | The maximum period used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points that should be downloaded so that the first data point does not have a NaN.
    **Datatype:** Positive integer. +| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set.
    **Datatype:** List of positive integers. +| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model)
    **Datatype:** Positive integer. +| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
    **Datatype:** Boolean. +| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index).
    **Datatype:** Positive float (typically < 1). +| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Boolean. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Dictionary. +| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan).
    **Datatype:** Boolean. | | **Data split parameters** -| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)
    **Datatype:** dictionary. -| `test_size` | Fraction of data that should be used for testing instead of training.
    **Datatype:** positive float below 1. -| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False.
    **Datatype:** boolean. +| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
    **Datatype:** Dictionary. +| `test_size` | Fraction of data that should be used for testing instead of training.
    **Datatype:** Positive float < 1. +| `shuffle` | Shuffle the training data points during training. Typically, for time series forecasting, this is set to `False`.
    | | **Model training parameters** -| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model.
    **Datatype:** dictionary. -| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit
    **Datatype:** integer. -| `learning_rate` | A common parameter among regressors which sets the boosting learning rate.
    **Datatype:** float. -| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`.
    **Datatype:** float. +| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If the user selects a different model, this dictionary can contain any parameter from that model.
    **Datatype:** Dictionary.**Datatype:** Boolean. +| `n_estimators` | The number of boosted trees to fit in regression.
    **Datatype:** Integer. +| `learning_rate` | Boosting learning rate during regression.
    **Datatype:** Float. +| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
    **Datatype:** Float. | | **Extraneous parameters** -| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false`
    **Datatype:** boolean. -| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2
    **Datatype:** integer. +| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards.
    **Datatype:** Boolean. Default: `False`. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
    **Datatype:** Integer. Default: 2. -### Important FreqAI dataframe key patterns +### Important dataframe key patterns -Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`): +Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`): | DataFrame Key | Description | |------------|-------------| -| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
    **Datatype:** depends on the output of the model. -| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
    **Datatype:** float. -| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
    **Datatype:** integer between -1 and 2. -| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
    **Datatype:** float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set).
    **Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
    **Datatype:** depends on the output of the model. +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
    **Datatype:** Depends on the output of the model. +| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
    **Datatype:** Float. +| `df['do_predict']` | Indication of prediction confidence. The return value is integer between -1 and 2 that lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the prediction is above the user-defined threshold (prediction seems to be an outlier), it will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the prediction was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the prediction an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
    **Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index values that indicate the level of confidence the user should have in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
    **Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
    **Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
    **Datatype:** Depends on the output of the model. + +### File structure + +`user_data_dir/models/` contains all the data associated with the trainings and backtests. +This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()` +and should therefore not be modified. ### Example config file -The user interface is isolated to the typical config file. A typical FreqAI config setup could include: +The user interface is isolated to the typical Freqtrade config file. A typical FreqAI config setup could include: ```json "freqai": { @@ -178,212 +181,9 @@ The user interface is isolated to the typical config file. A typical FreqAI conf } ``` -### Feature engineering +## Building a FreqAI strategy -Features are added by the user inside the `populate_any_indicators()` method of the strategy -by prepending indicators with `%` and labels are added by prepending `&`. -There are some important components/structures that the user *must* include when building their feature set. -Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). -This is where the user will add single features and labels to their feature set to avoid duplication from -various configuration parameters which multiply the feature set such as `include_timeframes`. - -```python - def populate_any_indicators( - self, pair, df, tf, informative=None, set_generalized_indicators=False - ): - """ - Function designed to automatically generate, name and merge features - from user indicated timeframes in the configuration file. User controls the indicators - passed to the training/prediction by prepending indicators with `'%-' + coin ` - (see convention below). I.e. user should not prepend any supporting metrics - (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the - model. - :param pair: pair to be used as informative - :param df: strategy dataframe which will receive merges from informatives - :param tf: timeframe of the dataframe which will modify the feature names - :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. - """ - - coint = pair.split('/')[0] - - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) - - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - - bollinger = qtpylib.bollinger_bands( - qtpylib.typical_price(informative), window=t, stds=2.2 - ) - informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] - informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] - informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] - - informative[f"%-{coin}bb_width-period_{t}"] = ( - informative[f"{coin}bb_upperband-period_{t}"] - - informative[f"{coin}bb_lowerband-period_{t}"] - ) / informative[f"{coin}bb_middleband-period_{t}"] - informative[f"%-{coin}close-bb_lower-period_{t}"] = ( - informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] - ) - - informative[f"%-{coin}relative_volume-period_{t}"] = ( - informative["volume"] / informative["volume"].rolling(t).mean() - ) - - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) - - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) - - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - - return df -``` - -The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model, -and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the -model for training/prediction and has therefore prepended it with `%`. - -The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` -included metric for inclusion in the feature set. In the present case, the user is asking for the -`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. - -In addition, the user can ask for each of these features to be included from -informative pairs using the `include_corr_pairlist`. This means that the present feature -set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of -`ETH/USD`, `LINK/USD`, and `BNB/USD`. - -`include_shifted_candles` is another user controlled parameter which indicates the number of previous -candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells -FreqAI to include the the past 2 candles for each of the features included in the dataset. - -In total, the number of features the present user has created is: - -length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` -$3 * 3 * 3 * 2 * 2 = 108$. - -!!! Note - Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()` - will fail in live/dry mode. If the user wishes to add generalized features that are not associated with - a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` - (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`: - - ```python - def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): - - ... - - # Add generalized indicators here (because in live, it will call only this function to populate - # indicators for retraining). Notice how we ensure not to add them multiple times by associating - # these generalized indicators to the basepair/timeframe - if set_generalized_indicators: - df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 - df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - ``` - - (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`) - -### Deciding the sliding training window and backtesting duration - -Users define the backtesting timerange with the typical `--timerange` parameter in the user -configuration file. `train_period_days` is the duration of the sliding training window, while -`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be -a float to indicate sub daily retraining in live/dry mode). In the present example, -the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days. -This means that if the user sets `--timerange 20210501-20210701`, -FreqAI will train 8 separate models (because the full range comprises 8 weeks), -and then backtest the subsequent week associated with each of the 8 training -data set timerange months. Users can think of this as a "sliding window" which -emulates FreqAI retraining itself once per week in live using the previous -month of data. - -In live, the required training data is automatically computed and downloaded. However, in backtesting -the user must manually enter the required number of `startup_candles` in the config. This value -is used to increase the available data to FreqAI and should be sufficient to enable all indicators -to be NaN free at the beginning of the first training timerange. This boils down to identifying the -highest timeframe (`4h` in present example) and the longest indicator period (25 in present example) -and adding this to the `train_period_days`. The units need to be in the base candle time frame: - -`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488. - -!!! Note - In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live. - -!!! Note - Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. - -## Running FreqAI - -### Backtesting - -The FreqAI backtesting module can be executed with the following command: - -```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 -``` - -Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). - -If this command has never been executed with the existing config file, then it will train a new model -for each pair, for each backtesting window within the bigger `--timerange`. - -!!! Note "Model reuse" - Once the training is completed, the user can execute this again with the same config file and - FreqAI will find the trained models and load them instead of spending time training. This is useful - if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user - *wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`. - This way, the user can return to using any model they wish by simply changing the `identifier`. - ---- - -### Building a freqai strategy - -The FreqAI strategy requires the user to include the following lines of code in the strategy: +The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy: ```python @@ -480,29 +280,214 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and targets ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. -### Setting classifier targets +## Creating a dynamic target -FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings: +The `target_roi` describes the target (label) the model has been trained on and allows the model to know what magnitude of a target to be expecting during prediction. +As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed +by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used +*during the most recent training*. +By default, FreqAI computes this based on the training data and assumes that the labels follow a Gaussian distribution. +This is a big assumption that the user should consider when creating their labels. + +If the user wants to consider the population +of *historical predictions* for creating the dynamic target instead of the trained labels, the user +can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles +the user wishes to use to generate target statistics. + +```json + "freqai": { + "fit_live_prediction_candles": 300, + } +``` + +If the user sets this value, FreqAI will initially use the predictions from the training data +and subsequently begin introducing real prediction data as it is generated. FreqAI will save +this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. + +## Building an IFreqaiModel + +FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. +However, the user can customize and create their own prediction models using the `IFreqaiModel` class. +The user is encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. + +## Feature engineering + +Features are added by the user inside the `populate_any_indicators()` method of the strategy +by prepending indicators with `%`, and labels with `&`. + +There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below: + +```python + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name, and merge features + from user-indicated timeframes in the configuration file. The user controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e., the user should not prepend any supporting metrics + (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. + """ + + coint = pair.split('/')[0] + + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + bollinger = qtpylib.bollinger_bands( + qtpylib.typical_price(informative), window=t, stds=2.2 + ) + informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] + informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] + informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] + + informative[f"%-{coin}bb_width-period_{t}"] = ( + informative[f"{coin}bb_upperband-period_{t}"] + - informative[f"{coin}bb_lowerband-period_{t}"] + ) / informative[f"{coin}bb_middleband-period_{t}"] + informative[f"%-{coin}close-bb_lower-period_{t}"] = ( + informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] + ) + + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + return df +``` + +In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model, +and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the +model for training/prediction and has therefore prepended it with `%`. + +The `include_timeframes` in the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the +`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. + +The user can ask for each of the defined features to be included also from +informative pairs using the `include_corr_pairlist`. This means that the feature +set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`). + +`include_shifted_candles` indicates the number of previous +candles to include in the feature set. For example, `include_shifted_candles: 2` tells +FreqAI to include the past 2 candles for each of the features in the feature set. + +In total, the number of features the user of the presented example strat has created is: +length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` + $= 3 * 3 * 3 * 2 * 2 = 108$. + +Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). +This is where the user will add single features and labels to their feature set to avoid duplication of them from +various configuration parameters that multiply the feature set, such as `include_timeframes`. + +!!! Note + Features **must** be defined in `populate_any_indicators()`. Definint features in `populate_indicators()` + will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with + a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): + + ```python + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): + + ... + + # Add generalized indicators here (because in live, it will call only this function to populate + # indicators for retraining). Notice how we ensure not to add them multiple times by associating + # these generalized indicators to the basepair/timeframe + if set_generalized_indicators: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + ``` + + (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) + +## Target/label creation + +FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings: ```python df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') ``` +## Running FreqAI + +There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. + +![freqai-window](assets/freqai_moving-window.png) + ### Running the model live -FreqAI can be run dry/live using the following command +FreqAI can be run dry/live using the following command: ```bash freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` By default, FreqAI will not find any existing models and will start by training a new one -given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours. +based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, userthe user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. -If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse -the same `identifier` parameter +If the user wishes to start a dry/live run from a saved backtest model, the user only needs to reuse +the same `identifier` parameter: ```json "freqai": { @@ -513,127 +498,106 @@ the same `identifier` parameter In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained, -and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain. +and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain. -## Data analysis techniques +### Backtesting -### Controlling the model learning process +The FreqAI backtesting module can be executed with the following command: -Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries. +```bash +freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 +``` -Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly -than past data via an exponential function: +Backtesting mode requires the user to have the data pre-downloaded (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration). -$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ +If this command has never been executed with the existing config file, it will train a new model +for each pair, for each backtesting window within the expanded `--timerange`. -where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. +!!! Note "Model reuse" + Once the training is completed, the user can execute the backtesting again with the same config file and + FreqAI will find the trained models and load them instead of spending time training. This is useful + if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user + *wants* to retrain a new model with the same config file, then they should simply change the `identifier`. + This way, the user can return to using any model they wish by simply specifying the `identifier`. -![weight-factor](assets/weights_factor.png) +--- -`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. +### Deciding the size of the sliding training window and backtesting duration -Finally, `label_period_candles` defines the offset used for the `labels`. In the present example, -the user is asking for `labels` that are 24 candles in the future. +The user defines the backtesting timerange with the typical `--timerange` parameter in the +configuration file. The duration of the sliding training window is set by `train_period_days`, whilst +`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be +a float to indicate sub-daily retraining in live/dry mode). In the presented example config, +the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. +This means that if the user sets `--timerange 20210501-20210701`, +FreqAI will train have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. -### Removing outliers with the Dissimilarity Index +In live mode, the required training data is automatically computed and downloaded. However, in backtesting mode, +the user must manually enter the required number of `startup_candles` in the config. This value +is used to increase the data to FreqAI, which should be sufficient to enable all indicators +to be NaN free at the beginning of the first training. This is done by identifying the +longest timeframe (`4h` in presented example config) and the longest indicator period (`20` days in presented example config) +and adding this to the `train_period_days`. The units need to be in the base candle time frame: +`startup_candles` = ( 4 hours * 20 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 9360. -The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each -prediction by the model. To do so, FreqAI measures the distance between each training -data point and all other training data points: +!!! Note + In dry/live mode, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live mode. -$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ +!!! Note + Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run. -where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ -is the number of features i.e. the length of the vector $X$. -The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean -of the average distances: +### Defining model expirations -$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ - -$\overline{d}$ quantifies the spread of the training data, which is compared to -the distance between the new prediction feature vectors, $X_k$ and all the training -data: - -$$ d_k = \arg \min d_{k,i} $$ - -which enables the estimation of a Dissimilarity Index: - -$$ DI_k = d_k/\overline{d} $$ - -Equity and crypto markets suffer from a high level of non-patterned noise in the -form of outlier data points. The dissimilarity index allows predictions which -are outliers and not existent in the model feature space, to be thrown out due -to low levels of certainty. Activating the Dissimilarity Index can be achieved with: +During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main +Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training +on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. +This may be undesirable if the characteristic time scale (the trade duration target) for a strategy +is less than 4 hours. The user can decide to only make trade entries if the model is less than +a certain number of hours old by setting the `expiration_hours` in the config file: ```json "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } + "expiration_hours": 0.5, } ``` -The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model. +In the presented example config, the user will only allow predictions on models that are less than 1/2 hours +old. -### Reducing data dimensionality with Principal Component Analysis +### Purging old model data -Users can reduce the dimensionality of their features by activating the `principal_component_analysis`: +FreqAI stores new model files each time it retrains. These files become obsolete as new models +are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running +for extended periods of time with high frequency retraining should enable `purge_old_models` in their +config: ```json "freqai": { - "feature_parameters" : { - "principal_component_analysis": true - } + "purge_old_models": true, } ``` -Which will perform PCA on the features and reduce the dimensionality of the data so that the explained -variance of the data set is >= 0.999. +This will automatically purge all models older than the two most recently trained ones. -### Removing outliers using a Support Vector Machine (SVM) +### Returning additional info from training -The user can tell FreqAI to remove outlier data points from the training/test data sets by setting: +The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. +The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction +model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. +The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is +the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. +An example, where the user wants to use live metrics from the trade database, is shown below: ```json "freqai": { - "feature_parameters" : { - "use_SVM_to_remove_outliers": true - } + "extra_returns_per_train": {"total_profit": 4} } ``` -FreqAI will train an SVM on the training data (or components if the user activated -`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space. +The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs +a default initial value, this is the value that will be returned. -### Clustering the training data and removing outliers with DBSCAN - -The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points. - -```json - "freqai": { - "feature_parameters" : { - "use_DBSCAN_to_remove_outliers": true - } - } -``` - -### Stratifying the data - -The user can stratify the training/testing data using: - -```json - "freqai": { - "feature_parameters" : { - "stratify_training_data": 3 - } - } -``` - -which will split the data chronologically so that every Xth data points is a testing data point. In the -present example, the user is asking for every third data point in the dataframe to be used for -testing, the other points are used for training. - -## Setting up a follower +### Setting up a follower The user can define: @@ -646,89 +610,141 @@ The user can define: to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the -`identifier: "example"` already running or launching simultaneously as the present follower. +`identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. The follower will load models created by the leader and inference them to obtain predictions. -## Purging old model data +## Data manipulation techniques -FreqAI stores new model files each time it retrains. These files become obsolete as new models -are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running -for extended periods of time with high frequency retraining should set `purge_old_models` in their -config: +### Feature normalization + +The feature set created by the user is automatically normalized to the training data. +This includes all test data and unseen prediction data (dry/live/backtest). + +### Reducing data dimensionality with Principal Component Analysis + +Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: ```json "freqai": { - "purge_old_models": true, + "feature_parameters" : { + "principal_component_analysis": true + } } ``` -which will automatically purge all models older than the two most recently trained ones. +This will perform PCA on the features and reduce the dimensionality of the data so that the explained +variance of the data set is >= 0.999. -## Defining model expirations +### Stratifying the data for training and testing the model -During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main -Freqtrade bot). This means there is always an age discrepancy between models. If a user is training -on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. -This may be undesirable if the characteristic time scale (read trade duration target) for a strategy -is much less than 4 hours. The user can decide to only make trade entries if the model is less than -a certain number of hours in age by setting the `expiration_hours` in the config file: +The user can stratify (group) the training/testing data using: ```json "freqai": { - "expiration_hours": 0.5, + "feature_parameters" : { + "stratify_training_data": 3 + } } ``` -In the present example, the user will only allow predictions on models that are less than 1/2 hours -old. +This will split the data chronologically so that every Xth data point is used to test the model after training. In the +example above, the user is asking for every third data point in the dataframe to be used for +testing; the other points are used for training. -## Choosing the calculation of the `target_roi` +The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used. -As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed -by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used -*during the most recent training*. -This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data. -By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed. -These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population -of *historical predictions* for creating the dynamic target instead of the trained labels, the user -can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles -the user wishes to use to generate target statistics. +### Controlling the model learning process + +Model training parameters are unique to the machine learning library selected by the user. FreqAI allows the user to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration file (found in `config_examples/config_freqai.example.json`) show some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries. + +Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. + +FreqAI includes some additional parameters such as `weight_factor`, which allows the user to weight more recent data more strongly +than past data via an exponential function: + +$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ + +where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. + +![weight-factor](assets/weights_factor.png) + +`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. + +Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config, +the user is asking for `labels` that are 24 candles in the future. + +### Outlier removal + +#### Removing outliers with the Dissimilarity Index + +The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each +prediction made by the model. To do so, FreqAI measures the distance between each training +data point (feature vector), $X_{a}$, and all other training data points: + +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ + +where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ +is the number of features, i.e., the length of the vector $X$. +The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean +of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to +the distance between a new prediction feature vectors, $X_k$ and all the training +data: + +$$ d_k = \arg \min d_{k,i} $$ + +which enables the estimation of the Dissimilarity Index as: + +$$ DI_k = d_k/\overline{d} $$ + +Equity and crypto markets suffer from a high level of non-patterned noise in the +form of outlier data points. The DI allows predictions which +are outliers (not existent in the model feature space) to be thrown out due +to low levels of certainty. Activating the DI is done by including the following statement in the config: ```json "freqai": { - "fit_live_prediction_candles": 300, + "feature_parameters" : { + "DI_threshold": 1 + } } ``` -If the user sets this value, FreqAI will initially use the predictions from the training data set -and then subsequently begin introducing real prediction data as it is generated. FreqAI will save -this historical data to be reloaded if the user stops and restarts with the same `identifier`. +The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. -## Extra returns per train +#### Removing outliers using a Support Vector Machine (SVM) -Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain. -Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction -model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. -The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is -the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training. -Another example is shown below if the user wants to use live metrics from the trade database. - -The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes: +The user can tell FreqAI to remove outlier data points from the training/test data sets by setting: ```json "freqai": { - "extra_returns_per_train": {"total_profit": 4} + "feature_parameters" : { + "use_SVM_to_remove_outliers": true + } } ``` -These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs -a default initial value - this is the value that will be returned. +FreqAI will train an SVM on the training data (or components of it if the user activated +`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space. -## Building an IFreqaiModel +The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time. -FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. -However, users can customize and create their own prediction models using the `IFreqaiModel` class. -Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. +The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers. + +#### Removing outliers with DBSCAN + +The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_DBSCAN_to_remove_outliers": true + } + } +``` ## Additional information @@ -737,19 +753,8 @@ Users are encouraged to inherit `train()` and `predict()` to let them customize FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends -new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due -to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`. - -### Feature normalization - -The feature set created by the user is automatically normalized to the training data only. -This includes all test data and unseen prediction data (dry/live/backtest). - -### File structure - -`user_data_dir/models/` contains all the data associated with the trainings and backtests. -This file structure is heavily controlled and read by the `FreqaiDataKitchen()` -and should therefore not be modified. +new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due +to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist`. ## Credits @@ -759,7 +764,7 @@ Conception and software development: Robert Caulk @robcaulk Theoretical brainstorming: -Elin Törnquist @thorntwig +Elin Törnquist @th0rntwig Code review, software architecture brainstorming: @xmatthias From 88d6a7fbff64edcb32dbf9d5c821ca5b99fd82ca Mon Sep 17 00:00:00 2001 From: robcaulk Date: Fri, 19 Aug 2022 14:04:29 +0200 Subject: [PATCH 456/518] additional edits --- docs/freqai.md | 111 +++++++++++++++++++------------------------------ 1 file changed, 42 insertions(+), 69 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 04b9c5dc6..baaa0bf37 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -10,7 +10,7 @@ Features include: * **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies. * **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing. * **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining. -* **Modifiability**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples are currently available. +* **Modifiability**: use the generalized and robust architecture for incorporating any [machine learning library/method](#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network. * **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal). * **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs. * **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way. @@ -81,7 +81,7 @@ This image contains the regular freqAI dependencies. Similar to native installs, ### Parameter table -The table below will list all configuration parameters available for FreqAI, sorted based on where they should be placed in the config. +The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`. Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. @@ -116,7 +116,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
    **Datatype:** Dictionary. | `test_size` | Fraction of data that should be used for testing instead of training.
    **Datatype:** Positive float < 1. -| `shuffle` | Shuffle the training data points during training. Typically, for time series forecasting, this is set to `False`.
    +| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
    | | **Model training parameters** | `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If the user selects a different model, this dictionary can contain any parameter from that model.
    **Datatype:** Dictionary.**Datatype:** Boolean. | `n_estimators` | The number of boosted trees to fit in regression.
    **Datatype:** Integer. @@ -134,8 +134,8 @@ Below are the values the user can expect to include/use inside a typical strateg |------------|-------------| | `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
    **Datatype:** Depends on the output of the model. | `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
    **Datatype:** Float. -| `df['do_predict']` | Indication of prediction confidence. The return value is integer between -1 and 2 that lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the prediction is above the user-defined threshold (prediction seems to be an outlier), it will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the prediction was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the prediction an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
    **Datatype:** Integer between -1 and 2. -| `df['DI_values']` | Dissimilarity Index values that indicate the level of confidence the user should have in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
    **Datatype:** Float. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
    **Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
    **Datatype:** Float. | `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
    **Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
    **Datatype:** Depends on the output of the model. ### File structure @@ -146,10 +146,11 @@ and should therefore not be modified. ### Example config file -The user interface is isolated to the typical Freqtrade config file. A typical FreqAI config setup could include: +The user interface is isolated to the typical Freqtrade config file. A FreqAI config should include: ```json "freqai": { + "enabled": true, "startup_candles": 10000, "purge_old_models": true, "train_period_days": 30, @@ -164,19 +165,14 @@ The user interface is isolated to the typical Freqtrade config file. A typical F ], "label_period_candles": 24, "include_shifted_candles": 2, - "weight_factor": 0, "indicator_max_period_candles": 20, "indicator_periods_candles": [10, 20] }, "data_split_parameters" : { - "test_size": 0.25, - "random_state": 42 + "test_size": 0.25 }, "model_training_parameters" : { - "n_estimators": 100, - "random_state": 42, - "learning_rate": 0.02, - "task_type": "CPU", + "n_estimators": 100 }, } ``` @@ -202,9 +198,6 @@ The FreqAI strategy requires the user to include the following lines of code in def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # All indicators must be populated by populate_any_indicators() for live functionality - # to work correctly. - # the model will return all labels created by user in `populate_any_indicators` # (& appended targets), an indication of whether or not the prediction should be accepted, # the target mean/std values for each of the labels created by user in @@ -280,19 +273,19 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and targets ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. ## Creating a dynamic target -The `target_roi` describes the target (label) the model has been trained on and allows the model to know what magnitude of a target to be expecting during prediction. -As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed -by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used -*during the most recent training*. -By default, FreqAI computes this based on the training data and assumes that the labels follow a Gaussian distribution. -This is a big assumption that the user should consider when creating their labels. +The `&*_std/mean` return values describe the statistical fit of the user defined label *during the most recent training*. This value allows the user to know the rarity of a given prediction. For example, `templates/FreqaiExampleStrategy.py`, creates a `target_roi` which is based on filtering out predictions that are below a given z-score of 1.25. -If the user wants to consider the population -of *historical predictions* for creating the dynamic target instead of the trained labels, the user +```python +dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 +dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 +``` + +If the user wishes to consider the population +of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles the user wishes to use to generate target statistics. @@ -306,7 +299,7 @@ If the user sets this value, FreqAI will initially use the predictions from the and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. -## Building an IFreqaiModel +## Building a custom prediction model FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. However, the user can customize and create their own prediction models using the `IFreqaiModel` class. @@ -337,7 +330,7 @@ There are some important components/structures that the user *must* include when :param coin: the name of the coin which will modify the feature names. """ - coint = pair.split('/')[0] + coin = pair.split('/')[0] if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) @@ -429,7 +422,7 @@ This is where the user will add single features and labels to their feature set various configuration parameters that multiply the feature set, such as `include_timeframes`. !!! Note - Features **must** be defined in `populate_any_indicators()`. Definint features in `populate_indicators()` + Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()` will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): @@ -461,14 +454,16 @@ various configuration parameters that multiply the feature set, such as `include (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) -## Target/label creation +## Setting classifier targets -FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings: +FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. The user should take care to set the classes using strings: ```python df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') ``` +Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column. + ## Running FreqAI There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. @@ -484,15 +479,15 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example. ``` By default, FreqAI will not find any existing models and will start by training a new one -based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, userthe user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. +based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. -If the user wishes to start a dry/live run from a saved backtest model, the user only needs to reuse +If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse the same `identifier` parameter: ```json "freqai": { "identifier": "example", - "live_retrain_hours": 1 + "live_retrain_hours": 0.5 } ``` @@ -548,11 +543,7 @@ and adding this to the `train_period_days`. The units need to be in the base can ### Defining model expirations -During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main -Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training -on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. -This may be undesirable if the characteristic time scale (the trade duration target) for a strategy -is less than 4 hours. The user can decide to only make trade entries if the model is less than +During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than a certain number of hours old by setting the `expiration_hours` in the config file: ```json @@ -561,15 +552,11 @@ a certain number of hours old by setting the `expiration_hours` in the config fi } ``` -In the presented example config, the user will only allow predictions on models that are less than 1/2 hours -old. +In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. ### Purging old model data -FreqAI stores new model files each time it retrains. These files become obsolete as new models -are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running -for extended periods of time with high frequency retraining should enable `purge_old_models` in their -config: +FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: ```json "freqai": { @@ -582,8 +569,7 @@ This will automatically purge all models older than the two most recently traine ### Returning additional info from training The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. -The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction -model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. +The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. An example, where the user wants to use live metrics from the trade database, is shown below: @@ -594,8 +580,7 @@ An example, where the user wants to use live metrics from the trade database, is } ``` -The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs -a default initial value, this is the value that will be returned. +The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned. ### Setting up a follower @@ -608,17 +593,14 @@ The user can define: } ``` -to indicate to the bot that it should not train models, but instead should look for models trained -by a leader with the same `identifier`. In this example, the user has a leader bot with the -`identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. +to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. The follower will load models created by the leader and inference them to obtain predictions. ## Data manipulation techniques ### Feature normalization -The feature set created by the user is automatically normalized to the training data. -This includes all test data and unseen prediction data (dry/live/backtest). +The feature set created by the user is automatically normalized to the training data. This includes all test data and unseen prediction data (dry/live/backtest). ### Reducing data dimensionality with Principal Component Analysis @@ -632,8 +614,7 @@ Users can reduce the dimensionality of their features by activating the `princip } ``` -This will perform PCA on the features and reduce the dimensionality of the data so that the explained -variance of the data set is >= 0.999. +This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. ### Stratifying the data for training and testing the model @@ -677,22 +658,15 @@ the user is asking for `labels` that are 24 candles in the future. #### Removing outliers with the Dissimilarity Index -The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each -prediction made by the model. To do so, FreqAI measures the distance between each training -data point (feature vector), $X_{a}$, and all other training data points: +The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ -where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ -is the number of features, i.e., the length of the vector $X$. -The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean -of the average distances: +where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: $$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ -$\overline{d}$ quantifies the spread of the training data, which is compared to -the distance between a new prediction feature vectors, $X_k$ and all the training -data: +$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: $$ d_k = \arg \min d_{k,i} $$ @@ -750,11 +724,10 @@ The user can configure FreqAI to use DBSCAN to cluster and remove outliers from ### Common pitfalls -FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). +FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends -new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due -to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist`. +new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). ## Credits @@ -763,7 +736,7 @@ FreqAI was developed by a group of individuals who all contributed specific skil Conception and software development: Robert Caulk @robcaulk -Theoretical brainstorming: +Theoretical brainstorming, data analysis: Elin Törnquist @th0rntwig Code review, software architecture brainstorming: From 692c6bf1fdef03ad7974c399b6f40952edfb4c3c Mon Sep 17 00:00:00 2001 From: elintornquist <107926911+elintornquist@users.noreply.github.com> Date: Fri, 19 Aug 2022 18:48:22 +0200 Subject: [PATCH 457/518] Added and updated figs and fig descriptions --- docs/assets/freqai_DI.png | Bin 0 -> 39781127 bytes docs/assets/freqai_dbscan.png | Bin 0 -> 1992760 bytes docs/assets/freqai_weight-factor.png | Bin 0 -> 4893270 bytes docs/freqai.md | 43 +++++++++++++++++---------- 4 files changed, 27 insertions(+), 16 deletions(-) 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custom An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage. -![freqai-algo](assets/freqai_algo.png) +![freqai-algo](assets/freqai_algo.jpg) ### Important machine learning vocabulary @@ -469,7 +469,7 @@ Additionally, the example classifier models do not accommodate multiple labels, There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. -![freqai-window](assets/freqai_moving-window.png) +![freqai-window](assets/freqai_moving-window.jpg) ### Running the model live @@ -648,7 +648,7 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. -![weight-factor](assets/freqai_weight-factor.png) +![weight-factor](assets/freqai_weight-factor.jpg) `train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. @@ -691,7 +691,7 @@ The user can tweak the DI through the `DI_threshold` to increase or decrease the Below is a figure that describes the DI for a 3D data set. -![DI](assets/freqai_DI.png) +![DI](assets/freqai_DI.jpg) #### Removing outliers using a Support Vector Machine (SVM) @@ -728,7 +728,7 @@ DBSCAN is an unsupervised machine learning algorithm that clusters data without Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. -![dbscan](assets/freqai_dbscan.png) +![dbscan](assets/freqai_dbscan.jpg) FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. From 2403a03fcbbae135b9919e635ef8d63257610c97 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 29 Aug 2022 06:28:53 +0200 Subject: [PATCH 518/518] Version bump 2022.8 --- freqtrade/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/__init__.py b/freqtrade/__init__.py index 2572c03f1..6c5c52a04 100644 --- a/freqtrade/__init__.py +++ b/freqtrade/__init__.py @@ -1,5 +1,5 @@ """ Freqtrade bot """ -__version__ = '2022.8.dev' +__version__ = '2022.8' if 'dev' in __version__: try:

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zo+!Vn*6|VD7u3j-YzTxv2!ucggg^*{z8ec!ls?IZQAO0|Rr^F|OKnR3D2!ucggg^*{KnR3D2!ucg zgg^*{Kq~?f-Usjg+}r;&wg9afRN`500@X}@cw2?~MUf$@6WWKj@h!8c+DN(~5CS0( z0wE9rArJx~5CS0(0wE9rA+UY~BEqjMBsbu00@-?oOjk67+|wMZY{ z+Fu>B+dt}4f3NXo8v-E^0=*`1`Okm%LxaKIPn~<#+b-;NMw7V^2!Rj?fe;9R5D0+~ k2!Rj?fmsP0@z+22umAotZ`$SjbKm~cXI*y2`+oEP2Ws%JlmGw# literal 0 HcmV?d00001 diff --git a/docs/freqai.md b/docs/freqai.md index baaa0bf37..bba6faaea 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -647,7 +647,7 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. -![weight-factor](assets/weights_factor.png) +![weight-factor](assets/freqai_weight-factor.png) `train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. @@ -658,7 +658,19 @@ the user is asking for `labels` that are 24 candles in the future. #### Removing outliers with the Dissimilarity Index -The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: +The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "DI_threshold": 1 + } + } +``` + +Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. + +To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ @@ -674,24 +686,15 @@ which enables the estimation of the Dissimilarity Index as: $$ DI_k = d_k/\overline{d} $$ -Equity and crypto markets suffer from a high level of non-patterned noise in the -form of outlier data points. The DI allows predictions which -are outliers (not existent in the model feature space) to be thrown out due -to low levels of certainty. Activating the DI is done by including the following statement in the config: +The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. -```json - "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } - } -``` +Below is a figure that describes the DI for a 3D data set. -The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. +![DI](assets/freqai_DI.png) #### Removing outliers using a Support Vector Machine (SVM) -The user can tell FreqAI to remove outlier data points from the training/test data sets by setting: +The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting: ```json "freqai": { @@ -710,7 +713,7 @@ The parameter `nu`, *very* broadly, is the amount of data points that should be #### Removing outliers with DBSCAN -The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: +The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: ```json "freqai": { @@ -720,6 +723,14 @@ The user can configure FreqAI to use DBSCAN to cluster and remove outliers from } ``` +DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. + +Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. + +![dbscan](assets/freqai_dbscan.png) + +FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. + ## Additional information ### Common pitfalls From 665cf4431de06e86d3edf96114c8e3b33341cc07 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 19 Aug 2022 15:49:31 +0200 Subject: [PATCH 458/518] Add explicit test cov. for .range behavior --- tests/strategy/test_interface.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index 83f7d19b7..65ee05d71 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -12,7 +12,9 @@ from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.data.history import load_data from freqtrade.enums import ExitCheckTuple, ExitType, SignalDirection +from freqtrade.enums.hyperoptstate import HyperoptState from freqtrade.exceptions import OperationalException, StrategyError +from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer from freqtrade.optimize.space import SKDecimal from freqtrade.persistence import PairLocks, Trade from freqtrade.resolvers import StrategyResolver @@ -859,7 +861,9 @@ def test_strategy_safe_wrapper_trade_copy(fee): def test_hyperopt_parameters(): + HyperoptStateContainer.set_state(HyperoptState.INDICATORS) from skopt.space import Categorical, Integer, Real + with pytest.raises(OperationalException, match=r"Name is determined.*"): IntParameter(low=0, high=5, default=1, name='hello') @@ -937,6 +941,12 @@ def test_hyperopt_parameters(): assert list(boolpar.range) == [True, False] + HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE) + assert len(list(intpar.range)) == 1 + assert len(list(fltpar.range)) == 1 + assert len(list(catpar.range)) == 1 + assert len(list(boolpar.range)) == 1 + def test_auto_hyperopt_interface(default_conf): default_conf.update({'strategy': 'HyperoptableStrategyV2'}) From aa3da092a0262a1efbf6387fbcadc928b5b04306 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 10:55:52 +0200 Subject: [PATCH 459/518] Dont' use classProperty - that's not supported on 3.8 --- freqtrade/optimize/hyperopt_tools.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py index 7f339eec1..9b022d519 100755 --- a/freqtrade/optimize/hyperopt_tools.py +++ b/freqtrade/optimize/hyperopt_tools.py @@ -35,16 +35,11 @@ def hyperopt_serializer(x): class HyperoptStateContainer(): """ Singleton class to track state of hyperopt""" - __state = HyperoptState.OPTIMIZE + state: HyperoptState = HyperoptState.OPTIMIZE @classmethod def set_state(cls, value: HyperoptState): - cls.__state = value - - @classmethod - @property - def state(cls) -> HyperoptState: - return cls.__state + cls.state = value class HyperoptTools(): From 54ddc1a4c22062def40e9cad2a3a9e13bf96ece1 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 11:24:20 +0200 Subject: [PATCH 460/518] Add --tradingmode alias --- freqtrade/commands/cli_options.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index 706f28f02..a458c1591 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -367,7 +367,7 @@ AVAILABLE_CLI_OPTIONS = { metavar='BASE_CURRENCY', ), "trading_mode": Arg( - '--trading-mode', + '--trading-mode', '--tradingmode', help='Select Trading mode', choices=constants.TRADING_MODES, ), From 0da060083614588875784b44889ebdddc70388b5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 11:24:49 +0200 Subject: [PATCH 461/518] Have backtesting respect tradable size closes #7161 --- freqtrade/optimize/backtesting.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 6528481d5..2a96037ac 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -821,7 +821,12 @@ class Backtesting: if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount): self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) - amount = round((stake_amount / propose_rate) * leverage, 8) + amount_p = (stake_amount / propose_rate) * leverage + amount = self.exchange._contracts_to_amount(pair, self.exchange.amount_to_precision( + pair, self.exchange._amount_to_contracts(pair, amount_p))) + # Backcalculate actual stake amount. + stake_amount = amount * propose_rate / leverage + is_short = (direction == 'short') # Necessary for Margin trading. Disabled until support is enabled. # interest_rate = self.exchange.get_interest_rate() From 7563050f17328713b01514e7c1feb4baff3afddd Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 11:47:15 +0200 Subject: [PATCH 462/518] Realign tests to precision backtesting --- tests/optimize/test_backtesting.py | 2 +- tests/optimize/test_backtesting_adjust_position.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index 0b964c54a..98d18c156 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -559,7 +559,7 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: default_conf_usdt['exchange']['pair_whitelist'] = ['.*'] backtesting = Backtesting(default_conf_usdt) backtesting._set_strategy(backtesting.strategylist[0]) - pair = 'UNITTEST/USDT:USDT' + pair = 'ETH/USDT:USDT' row = [ pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0), 0.001, # Open diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index 2bb7de574..0cbad29f6 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -18,6 +18,7 @@ from tests.conftest import patch_exchange def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None: default_conf['use_exit_signal'] = False mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) + mocker.patch('freqtrade.exchange.Exchange.amount_to_precision', lambda s, x, y: round(y, 8)) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) patch_exchange(mocker) From 52ec0d1046038e8de27cb04f6b527decba1a4a2b Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 11:53:15 +0200 Subject: [PATCH 463/518] Update binance Leverage tiers --- .../exchange/binance_leverage_tiers.json | 1000 +++++++++++++++-- 1 file changed, 918 insertions(+), 82 deletions(-) diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json index 1cf6ba079..eace16c05 100644 --- a/freqtrade/exchange/binance_leverage_tiers.json +++ b/freqtrade/exchange/binance_leverage_tiers.json @@ -81,6 +81,88 @@ } } ], + "1000SHIB/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "1000SHIB/USDT": [ { "tier": 1.0, @@ -1991,6 +2073,88 @@ } } ], + "AUCTION/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "AUDIO/USDT": [ { "tier": 1.0, @@ -5333,6 +5497,88 @@ } } ], + "CVX/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "DAR/USDT": [ { "tier": 1.0, @@ -7013,6 +7259,88 @@ } } ], + "ETC/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "ETC/USDT": [ { "tier": 1.0, @@ -7164,13 +7492,13 @@ "tier": 1.0, "currency": "BUSD", "minNotional": 0.0, - "maxNotional": 25000.0, + "maxNotional": 50000.0, "maintenanceMarginRate": 0.004, "maxLeverage": 50.0, "info": { "bracket": "1", "initialLeverage": "50", - "notionalCap": "25000", + "notionalCap": "50000", "notionalFloor": "0", "maintMarginRatio": "0.004", "cum": "0.0" @@ -7179,7 +7507,7 @@ { "tier": 2.0, "currency": "BUSD", - "minNotional": 25000.0, + "minNotional": 50000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.005, "maxLeverage": 25.0, @@ -7187,111 +7515,111 @@ "bracket": "2", "initialLeverage": "25", "notionalCap": "100000", - "notionalFloor": "25000", + "notionalFloor": "50000", "maintMarginRatio": "0.005", - "cum": "25.0" + "cum": "50.0" } }, { "tier": 3.0, "currency": "BUSD", "minNotional": 100000.0, - "maxNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "500000", + "notionalCap": "1000000", "notionalFloor": "100000", "maintMarginRatio": "0.01", - "cum": "525.0" + "cum": "550.0" } }, { "tier": 4.0, "currency": "BUSD", - "minNotional": 500000.0, - "maxNotional": 1500000.0, + "minNotional": 1000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "1500000", - "notionalFloor": "500000", + "notionalCap": "5000000", + "notionalFloor": "1000000", "maintMarginRatio": "0.025", - "cum": "8025.0" + "cum": "15550.0" } }, { "tier": 5.0, "currency": "BUSD", - "minNotional": 1500000.0, - "maxNotional": 4000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 6.0, "info": { "bracket": "5", "initialLeverage": "6", - "notionalCap": "4000000", - "notionalFloor": "1500000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.05", - "cum": "45525.0" + "cum": "140550.0" } }, { "tier": 6.0, "currency": "BUSD", - "minNotional": 4000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "10000000", - "notionalFloor": "4000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.1", - "cum": "245525.0" + "cum": "640550.0" } }, { "tier": 7.0, "currency": "BUSD", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 40000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "40000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.125", - "cum": "495525.0" + "cum": "1140550.0" } }, { "tier": 8.0, "currency": "BUSD", - "minNotional": 20000000.0, - "maxNotional": 40000000.0, + "minNotional": 40000000.0, + "maxNotional": 80000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "40000000", - "notionalFloor": "20000000", + "notionalCap": "80000000", + "notionalFloor": "40000000", "maintMarginRatio": "0.15", - "cum": "995525.0" + "cum": "2140550.0" } }, { "tier": 9.0, "currency": "BUSD", - "minNotional": 40000000.0, + "minNotional": 80000000.0, "maxNotional": 150000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, @@ -7299,25 +7627,25 @@ "bracket": "9", "initialLeverage": "2", "notionalCap": "150000000", - "notionalFloor": "40000000", + "notionalFloor": "80000000", "maintMarginRatio": "0.25", - "cum": "4995525.0" + "cum": "1.014055E7" } }, { "tier": 10.0, "currency": "BUSD", "minNotional": 150000000.0, - "maxNotional": 500000000.0, + "maxNotional": 300000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "10", "initialLeverage": "1", - "notionalCap": "500000000", + "notionalCap": "300000000", "notionalFloor": "150000000", "maintMarginRatio": "0.5", - "cum": "4.2495525E7" + "cum": "4.764055E7" } } ], @@ -7326,13 +7654,13 @@ "tier": 1.0, "currency": "USDT", "minNotional": 0.0, - "maxNotional": 10000.0, + "maxNotional": 100000.0, "maintenanceMarginRate": 0.005, "maxLeverage": 100.0, "info": { "bracket": "1", "initialLeverage": "100", - "notionalCap": "10000", + "notionalCap": "100000", "notionalFloor": "0", "maintMarginRatio": "0.005", "cum": "0.0" @@ -7341,119 +7669,119 @@ { "tier": 2.0, "currency": "USDT", - "minNotional": 10000.0, - "maxNotional": 100000.0, + "minNotional": 100000.0, + "maxNotional": 250000.0, "maintenanceMarginRate": 0.0065, "maxLeverage": 75.0, "info": { "bracket": "2", "initialLeverage": "75", - "notionalCap": "100000", - "notionalFloor": "10000", + "notionalCap": "250000", + "notionalFloor": "100000", "maintMarginRatio": "0.0065", - "cum": "15.0" + "cum": "150.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 500000.0, + "minNotional": 250000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 50.0, "info": { "bracket": "3", "initialLeverage": "50", - "notionalCap": "500000", - "notionalFloor": "100000", + "notionalCap": "1000000", + "notionalFloor": "250000", "maintMarginRatio": "0.01", - "cum": "365.0" + "cum": "1025.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1500000.0, + "minNotional": 1000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.02, "maxLeverage": 25.0, "info": { "bracket": "4", "initialLeverage": "25", - "notionalCap": "1500000", - "notionalFloor": "500000", + "notionalCap": "5000000", + "notionalFloor": "1000000", "maintMarginRatio": "0.02", - "cum": "5365.0" + "cum": "11025.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1500000.0, - "maxNotional": 4000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "5", "initialLeverage": "10", - "notionalCap": "4000000", - "notionalFloor": "1500000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.05", - "cum": "50365.0" + "cum": "161025.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 4000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "10000000", - "notionalFloor": "4000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.1", - "cum": "250365.0" + "cum": "661025.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 40000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "40000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.125", - "cum": "500365.0" + "cum": "1161025.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 20000000.0, - "maxNotional": 40000000.0, + "minNotional": 40000000.0, + "maxNotional": 80000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "40000000", - "notionalFloor": "20000000", + "notionalCap": "80000000", + "notionalFloor": "40000000", "maintMarginRatio": "0.15", - "cum": "1000365.0" + "cum": "2161025.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 40000000.0, + "minNotional": 80000000.0, "maxNotional": 150000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, @@ -7461,25 +7789,25 @@ "bracket": "9", "initialLeverage": "2", "notionalCap": "150000000", - "notionalFloor": "40000000", + "notionalFloor": "80000000", "maintMarginRatio": "0.25", - "cum": "5000365.0" + "cum": "1.0161025E7" } }, { "tier": 10.0, "currency": "USDT", "minNotional": 150000000.0, - "maxNotional": 500000000.0, + "maxNotional": 300000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "10", "initialLeverage": "1", - "notionalCap": "500000000", + "notionalCap": "300000000", "notionalFloor": "150000000", "maintMarginRatio": "0.5", - "cum": "4.2500365E7" + "cum": "4.7661025E7" } } ], @@ -7597,6 +7925,88 @@ } } ], + "FIL/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "FIL/USDT": [ { "tier": 1.0, @@ -9737,6 +10147,104 @@ } } ], + "INJ/USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.01", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "75.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "700.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5700.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "5", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11950.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "6", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386950.0" + } + } + ], "IOST/USDT": [ { "tier": 1.0, @@ -10521,6 +11029,170 @@ } } ], + "LDO/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], + "LEVER/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "LINA/USDT": [ { "tier": 1.0, @@ -11663,6 +12335,88 @@ } } ], + "MATIC/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "MATIC/USDT": [ { "tier": 1.0, @@ -16055,6 +16809,88 @@ } } ], + "UNI/BUSD": [ + { + "tier": 1.0, + "currency": "BUSD", + "minNotional": 0.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "0", + "maintMarginRatio": "0.025", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "BUSD", + "minNotional": 25000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "100000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "625.0" + } + }, + { + "tier": 3.0, + "currency": "BUSD", + "minNotional": 100000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "3", + "initialLeverage": "5", + "notionalCap": "250000", + "notionalFloor": "100000", + "maintMarginRatio": "0.1", + "cum": "5625.0" + } + }, + { + "tier": 4.0, + "currency": "BUSD", + "minNotional": 250000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 2.0, + "info": { + "bracket": "4", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "250000", + "maintMarginRatio": "0.125", + "cum": "11875.0" + } + }, + { + "tier": 5.0, + "currency": "BUSD", + "minNotional": 1000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "5", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "386875.0" + } + } + ], "UNI/USDT": [ { "tier": 1.0, From b6e8b9df35155e8f8fbc242961d09eded0ccc3bf Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 13:00:25 +0200 Subject: [PATCH 464/518] Use cached leverage tiers --- freqtrade/exchange/exchange.py | 50 +++++++++++++++++++++++++++++----- 1 file changed, 43 insertions(+), 7 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index c15481ca5..cfe4eab34 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -17,6 +17,7 @@ import ccxt import ccxt.async_support as ccxt_async from cachetools import TTLCache from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision +from dateutil import parser from pandas import DataFrame from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell, @@ -30,7 +31,8 @@ from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGE EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, SUPPORTED_EXCHANGES, remove_credentials, retrier, retrier_async) -from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2 +from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json, + safe_value_fallback2) from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.util import FtPrecise @@ -2207,6 +2209,7 @@ class Exchange: @retrier_async async def get_market_leverage_tiers(self, symbol: str) -> Tuple[str, List[Dict]]: + """ Leverage tiers per symbol """ try: tier = await self._api_async.fetch_market_leverage_tiers(symbol) return symbol, tier @@ -2238,12 +2241,21 @@ class Exchange: tiers: Dict[str, List[Dict]] = {} - # Be verbose here, as this delays startup by ~1 minute. - logger.info( - f"Initializing leverage_tiers for {len(symbols)} markets. " - "This will take about a minute.") + tiers_cached = self.load_cached_leverage_tiers(self._config['stake_currency']) + if tiers_cached: + tiers = tiers_cached - coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)] + coros = [ + self.get_market_leverage_tiers(symbol) + for symbol in sorted(symbols) if symbol not in tiers] + + # Be verbose here, as this delays startup by ~1 minute. + if coros: + logger.info( + f"Initializing leverage_tiers for {len(symbols)} markets. " + "This will take about a minute.") + else: + logger.info("Using cached leverage_tiers.") async def gather_results(): return await asyncio.gather(*input_coro, return_exceptions=True) @@ -2255,7 +2267,8 @@ class Exchange: for symbol, res in results: tiers[symbol] = res - + if len(coros) > 0: + self.cache_leverage_tiers(tiers, self._config['stake_currency']) logger.info(f"Done initializing {len(symbols)} markets.") return tiers @@ -2264,6 +2277,29 @@ class Exchange: else: return {} + def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None: + + filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" + data = { + "updated": datetime.now(timezone.utc), + "data": tiers, + } + file_dump_json(filename, data) + + def load_cached_leverage_tiers(self, stake_currency: str) -> Optional[Dict[str, List[Dict]]]: + filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" + if filename.is_file(): + tiers = file_load_json(filename) + updated = tiers.get('updated') + if updated: + updated_dt = parser.parse(updated) + print(updated_dt) + if updated_dt < datetime.now(timezone.utc) - timedelta(days=1): + logger.info("Cached leverage tiers are outdated. Will update.") + return None + return tiers['data'] + return None + def fill_leverage_tiers(self) -> None: """ Assigns property _leverage_tiers to a dictionary of information about the leverage From 738e95b8753bd6bbdc53eca58789b0dcc092d6bb Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 13:47:34 +0200 Subject: [PATCH 465/518] Add tests for leverage tiers caching --- freqtrade/exchange/exchange.py | 3 ++- tests/exchange/test_okx.py | 26 ++++++++++++++++++++++++-- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index cfe4eab34..7ef6858a5 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2280,6 +2280,8 @@ class Exchange: def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None: filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" + if not filename.parent.is_dir(): + filename.parent.mkdir(parents=True) data = { "updated": datetime.now(timezone.utc), "data": tiers, @@ -2293,7 +2295,6 @@ class Exchange: updated = tiers.get('updated') if updated: updated_dt = parser.parse(updated) - print(updated_dt) if updated_dt < datetime.now(timezone.utc) - timedelta(days=1): logger.info("Cached leverage tiers are outdated. Will update.") return None diff --git a/tests/exchange/test_okx.py b/tests/exchange/test_okx.py index 91c4a3368..10e087ced 100644 --- a/tests/exchange/test_okx.py +++ b/tests/exchange/test_okx.py @@ -1,4 +1,5 @@ from datetime import datetime, timedelta, timezone +from pathlib import Path from unittest.mock import MagicMock, PropertyMock import pytest @@ -6,7 +7,7 @@ import pytest from freqtrade.enums import MarginMode, TradingMode from freqtrade.enums.candletype import CandleType from freqtrade.exchange.exchange import timeframe_to_minutes -from tests.conftest import get_mock_coro, get_patched_exchange +from tests.conftest import get_mock_coro, get_patched_exchange, log_has from tests.exchange.test_exchange import ccxt_exceptionhandlers @@ -267,7 +268,10 @@ def test_additional_exchange_init_okx(default_conf, mocker): "additional_exchange_init", "fetch_accounts") -def test_load_leverage_tiers_okx(default_conf, mocker, markets): +def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, time_machine): + + default_conf['datadir'] = Path(tmpdir) + # fd_mock = mocker.patch('freqtrade.exchange.exchange.file_dump_json') api_mock = MagicMock() type(api_mock).has = PropertyMock(return_value={ 'fetchLeverageTiers': False, @@ -455,3 +459,21 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets): }, ], } + filename = (default_conf['datadir'] / + f"futures/leverage_tiers_{default_conf['stake_currency']}.json") + assert filename.is_file() + + logmsg = 'Cached leverage tiers are outdated. Will update.' + assert not log_has(logmsg, caplog) + + api_mock.fetch_market_leverage_tiers.reset_mock() + + exchange.load_leverage_tiers() + assert not log_has(logmsg, caplog) + + api_mock.fetch_market_leverage_tiers.call_count == 0 + # 2 day passes ... + time_machine.move_to(datetime.now() + timedelta(days=2)) + exchange.load_leverage_tiers() + + assert log_has(logmsg, caplog) From 4511634f3a5012266fc49ef4cd33f9833235c805 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 14:03:47 +0200 Subject: [PATCH 466/518] improve test coverage --- tests/exchange/test_exchange.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 6ad4a72c6..ec259d703 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -4791,6 +4791,20 @@ def test_load_leverage_tiers(mocker, default_conf, leverage_tiers, exchange_name ) +@pytest.mark.asyncio +@pytest.mark.parametrize('exchange_name', EXCHANGES) +async def test_get_market_leverage_tiers(mocker, default_conf, exchange_name): + default_conf['exchange']['name'] = exchange_name + await async_ccxt_exception( + mocker, + default_conf, + MagicMock(), + "get_market_leverage_tiers", + "fetch_market_leverage_tiers", + symbol='BTC/USDT:USDT' + ) + + def test_parse_leverage_tier(mocker, default_conf): exchange = get_patched_exchange(mocker, default_conf) From 5b3f0315908d2e9c2f38a28898763d968bb356f9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 14:13:15 +0200 Subject: [PATCH 467/518] Use hyperopt safe amount precision method --- freqtrade/optimize/backtesting.py | 8 ++++++-- tests/optimize/test_backtesting_adjust_position.py | 2 +- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 2a96037ac..12536c333 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -24,6 +24,7 @@ from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType TradingMode) from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds +from freqtrade.exchange.exchange import amount_to_precision from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress @@ -822,8 +823,11 @@ class Backtesting: self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) amount_p = (stake_amount / propose_rate) * leverage - amount = self.exchange._contracts_to_amount(pair, self.exchange.amount_to_precision( - pair, self.exchange._amount_to_contracts(pair, amount_p))) + amount = self.exchange._contracts_to_amount( + pair, amount_to_precision( + self.exchange._amount_to_contracts(pair, amount_p), + self.exchange.get_precision_amount(pair), self.precision_mode) + ) # Backcalculate actual stake amount. stake_amount = amount * propose_rate / leverage diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index 0cbad29f6..f0829ea1e 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -18,7 +18,7 @@ from tests.conftest import patch_exchange def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None: default_conf['use_exit_signal'] = False mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) - mocker.patch('freqtrade.exchange.Exchange.amount_to_precision', lambda s, x, y: round(y, 8)) + mocker.patch('freqtrade.optimize.backtesting.amount_to_precision', lambda x, y, z: round(x, 8)) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) patch_exchange(mocker) From 1fb2e9558ff9511d2c8a3f4e0d43caace0581a4d Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 20 Aug 2022 14:36:19 +0200 Subject: [PATCH 468/518] Disable caching of leverage tiers in ccxt compat methods --- tests/exchange/test_ccxt_compat.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/exchange/test_ccxt_compat.py b/tests/exchange/test_ccxt_compat.py index 7bb52ccaf..c3a5fe17f 100644 --- a/tests/exchange/test_ccxt_compat.py +++ b/tests/exchange/test_ccxt_compat.py @@ -137,6 +137,10 @@ def exchange_futures(request, exchange_conf, class_mocker): 'freqtrade.exchange.binance.Binance.fill_leverage_tiers') class_mocker.patch('freqtrade.exchange.exchange.Exchange.fetch_trading_fees') class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init') + class_mocker.patch('freqtrade.exchange.exchange.Exchange.load_cached_leverage_tiers', + return_value=None) + class_mocker.patch('freqtrade.exchange.exchange.Exchange.cache_leverage_tiers') + exchange = ExchangeResolver.load_exchange( request.param, exchange_conf, validate=True, load_leverage_tiers=True) From 0ec38e0cfd1549d502114172eabdbf7d3fcf3c6d Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 21 Aug 2022 08:23:07 +0200 Subject: [PATCH 469/518] Fix typo in docs --- docs/rest-api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/rest-api.md b/docs/rest-api.md index 1ec9b6c12..d9840a09c 100644 --- a/docs/rest-api.md +++ b/docs/rest-api.md @@ -317,7 +317,7 @@ whitelist ### OpenAPI interface To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration. -This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings. +This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs - but it'll depend on your settings. ### Advanced API usage using JWT tokens From 085f81ec9e3dee2e885533722ed8429d2ba19566 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 21 Aug 2022 08:24:14 +0200 Subject: [PATCH 470/518] Fix header indent on stake-size-management --- docs/strategy-callbacks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index a9b032818..8d46f42e1 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -75,7 +75,7 @@ class AwesomeStrategy(IStrategy): ``` -### Stake size management +## Stake size management Called before entering a trade, makes it possible to manage your position size when placing a new trade. From 87a3115073562e9abc7efd792569020435a01f6e Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 21 Aug 2022 10:03:04 +0200 Subject: [PATCH 471/518] Add get_open_trade_count() to simplify getting open trade count. --- freqtrade/freqtradebot.py | 2 +- freqtrade/persistence/trade_model.py | 10 ++++++++++ tests/test_freqtradebot.py | 2 -- tests/test_persistence.py | 2 ++ 4 files changed, 13 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 4e3af64ea..5b71d6ec4 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -271,7 +271,7 @@ class FreqtradeBot(LoggingMixin): Return the number of free open trades slots or 0 if max number of open trades reached """ - open_trades = len(Trade.get_open_trades()) + open_trades = Trade.get_open_trade_count() return max(0, self.config['max_open_trades'] - open_trades) def update_funding_fees(self): diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index b954fee20..0c438956a 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -1044,6 +1044,16 @@ class LocalTrade(): """ return Trade.get_trades_proxy(is_open=True) + @staticmethod + def get_open_trade_count() -> int: + """ + get open trade count + """ + if Trade.use_db: + return Trade.query.filter(Trade.is_open.is_(True)).count() + else: + return len(LocalTrade.trades_open) + @staticmethod def stoploss_reinitialization(desired_stoploss): """ diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py index a1a16c039..138527053 100644 --- a/tests/test_freqtradebot.py +++ b/tests/test_freqtradebot.py @@ -473,8 +473,6 @@ def test_create_trade_no_signal(default_conf_usdt, fee, mocker) -> None: freqtrade = FreqtradeBot(default_conf_usdt) patch_get_signal(freqtrade, enter_long=False, exit_long=False) - Trade.query = MagicMock() - Trade.query.filter = MagicMock() assert not freqtrade.create_trade('ETH/USDT') diff --git a/tests/test_persistence.py b/tests/test_persistence.py index 50d0788ca..d8973e9d0 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -1689,6 +1689,7 @@ def test_get_open(fee, is_short, use_db): create_mock_trades(fee, is_short, use_db) assert len(Trade.get_open_trades()) == 4 + assert Trade.get_open_trade_count() == 4 Trade.use_db = True @@ -1701,6 +1702,7 @@ def test_get_open_lev(fee, use_db): create_mock_trades_with_leverage(fee, use_db) assert len(Trade.get_open_trades()) == 5 + assert Trade.get_open_trade_count() == 5 Trade.use_db = True From f6d832c6d918607d6828b5c49ad857433b91d69c Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 21 Aug 2022 17:48:13 +0200 Subject: [PATCH 472/518] Add get_option to expose ft_has via method --- freqtrade/commands/data_commands.py | 2 +- freqtrade/data/history/history_utils.py | 4 ++-- freqtrade/exchange/exchange.py | 6 ++++++ freqtrade/optimize/backtesting.py | 6 +++--- freqtrade/plugins/pairlist/VolumePairList.py | 4 ++-- tests/exchange/test_exchange.py | 16 ++++++++-------- 6 files changed, 22 insertions(+), 16 deletions(-) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 36a86bece..ce26d39ab 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -81,7 +81,7 @@ def start_download_data(args: Dict[str, Any]) -> None: data_format_trades=config['dataformat_trades'], ) else: - if not exchange._ft_has.get('ohlcv_has_history', True): + if not exchange.get_option('ohlcv_has_history', True): raise OperationalException( f"Historic klines not available for {exchange.name}. " "Please use `--dl-trades` instead for this exchange " diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index cba1b60db..04d0bf6d3 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -302,8 +302,8 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes if trading_mode == 'futures': # Predefined candletype (and timeframe) depending on exchange # Downloads what is necessary to backtest based on futures data. - tf_mark = exchange._ft_has['mark_ohlcv_timeframe'] - fr_candle_type = CandleType.from_string(exchange._ft_has['mark_ohlcv_price']) + tf_mark = exchange.get_option('mark_ohlcv_timeframe') + fr_candle_type = CandleType.from_string(exchange.get_option('mark_ohlcv_price')) # All exchanges need FundingRate for futures trading. # The timeframe is aligned to the mark-price timeframe. for funding_candle_type in (CandleType.FUNDING_RATE, fr_candle_type): diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 7ef6858a5..a2944c784 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -674,6 +674,12 @@ class Exchange: f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}" ) + def get_option(self, param: str, default: Any = None) -> Any: + """ + Get parameter value from _ft_has + """ + return self._ft_has.get(param, default) + def exchange_has(self, endpoint: str) -> bool: """ Checks if exchange implements a specific API endpoint. diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 12536c333..e32eae533 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -267,7 +267,7 @@ class Backtesting: funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, - timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], + timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, @@ -279,12 +279,12 @@ class Backtesting: mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, - timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], + timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), - candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"]) + candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price")) ) # Combine data to avoid combining the data per trade. unavailable_pairs = [] diff --git a/freqtrade/plugins/pairlist/VolumePairList.py b/freqtrade/plugins/pairlist/VolumePairList.py index e364e1a69..8138a5fb6 100644 --- a/freqtrade/plugins/pairlist/VolumePairList.py +++ b/freqtrade/plugins/pairlist/VolumePairList.py @@ -73,7 +73,7 @@ class VolumePairList(IPairList): if (not self._use_range and not ( self._exchange.exchange_has('fetchTickers') - and self._exchange._ft_has["tickers_have_quoteVolume"])): + and self._exchange.get_option("tickers_have_quoteVolume"))): raise OperationalException( "Exchange does not support dynamic whitelist in this configuration. " "Please edit your config and either remove Volumepairlist, " @@ -193,7 +193,7 @@ class VolumePairList(IPairList): ) in candles else None # in case of candle data calculate typical price and quoteVolume for candle if pair_candles is not None and not pair_candles.empty: - if self._exchange._ft_has["ohlcv_volume_currency"] == "base": + if self._exchange.get_option("ohlcv_volume_currency") == "base": pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low'] + pair_candles['close']) / 3 diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index ec259d703..4f9c92efc 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -2352,10 +2352,10 @@ def test_fetch_l2_order_book(default_conf, mocker, order_book_l2, exchange_name) order_book = exchange.fetch_l2_order_book(pair='ETH/BTC', limit=val) assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == 'ETH/BTC' # Not all exchanges support all limits for orderbook - if not exchange._ft_has['l2_limit_range'] or val in exchange._ft_has['l2_limit_range']: + if not exchange.get_option('l2_limit_range') or val in exchange.get_option('l2_limit_range'): assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == val else: - next_limit = exchange.get_next_limit_in_list(val, exchange._ft_has['l2_limit_range']) + next_limit = exchange.get_next_limit_in_list(val, exchange.get_option('l2_limit_range')) assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == next_limit @@ -3311,16 +3311,16 @@ def test_merge_ft_has_dict(default_conf, mocker): ex = Kraken(default_conf) assert ex._ft_has != Exchange._ft_has_default - assert ex._ft_has['trades_pagination'] == 'id' - assert ex._ft_has['trades_pagination_arg'] == 'since' + assert ex.get_option('trades_pagination') == 'id' + assert ex.get_option('trades_pagination_arg') == 'since' # Binance defines different values ex = Binance(default_conf) assert ex._ft_has != Exchange._ft_has_default - assert ex._ft_has['stoploss_on_exchange'] - assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc'] - assert ex._ft_has['trades_pagination'] == 'id' - assert ex._ft_has['trades_pagination_arg'] == 'fromId' + assert ex.get_option('stoploss_on_exchange') + assert ex.get_option('order_time_in_force') == ['gtc', 'fok', 'ioc'] + assert ex.get_option('trades_pagination') == 'id' + assert ex.get_option('trades_pagination_arg') == 'fromId' conf = copy.deepcopy(default_conf) conf['exchange']['_ft_has_params'] = {"DeadBeef": 20, From 2dc34779d5877810d035f31a644f0a204d8868dd Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 21 Aug 2022 18:07:41 +0200 Subject: [PATCH 473/518] Fix line length --- tests/exchange/test_exchange.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 4f9c92efc..be8d4b2cf 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -2352,7 +2352,8 @@ def test_fetch_l2_order_book(default_conf, mocker, order_book_l2, exchange_name) order_book = exchange.fetch_l2_order_book(pair='ETH/BTC', limit=val) assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == 'ETH/BTC' # Not all exchanges support all limits for orderbook - if not exchange.get_option('l2_limit_range') or val in exchange.get_option('l2_limit_range'): + if (not exchange.get_option('l2_limit_range') + or val in exchange.get_option('l2_limit_range')): assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == val else: next_limit = exchange.get_next_limit_in_list(val, exchange.get_option('l2_limit_range')) From dfa7d1fc2746823ba2d38ab544cfbcaf0ffadf6e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:01:51 +0000 Subject: [PATCH 474/518] Bump fastapi from 0.79.0 to 0.79.1 Bumps [fastapi](https://github.com/tiangolo/fastapi) from 0.79.0 to 0.79.1. - [Release notes](https://github.com/tiangolo/fastapi/releases) - [Commits](https://github.com/tiangolo/fastapi/compare/0.79.0...0.79.1) --- updated-dependencies: - dependency-name: fastapi dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 77925f98b..a5e344a54 100644 --- a/requirements.txt +++ b/requirements.txt @@ -34,7 +34,7 @@ orjson==3.7.12 sdnotify==0.3.2 # API Server -fastapi==0.79.0 +fastapi==0.79.1 uvicorn==0.18.2 pyjwt==2.4.0 aiofiles==0.8.0 From 3958e53aaa63d3b94125d1f4e9b0ac2103867c82 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:01:55 +0000 Subject: [PATCH 475/518] Bump mkdocs-material from 8.4.0 to 8.4.1 Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 8.4.0 to 8.4.1. - [Release notes](https://github.com/squidfunk/mkdocs-material/releases) - [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG) - [Commits](https://github.com/squidfunk/mkdocs-material/compare/8.4.0...8.4.1) --- updated-dependencies: - dependency-name: mkdocs-material dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- docs/requirements-docs.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index a53e909e0..bffc04d1c 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,6 +1,6 @@ markdown==3.3.7 mkdocs==1.3.1 -mkdocs-material==8.4.0 +mkdocs-material==8.4.1 mdx_truly_sane_lists==1.3 pymdown-extensions==9.5 jinja2==3.1.2 From 70848a258dfa115835f311643fb50c6f05518b8f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:01:59 +0000 Subject: [PATCH 476/518] Bump types-requests from 2.28.8 to 2.28.9 Bumps [types-requests](https://github.com/python/typeshed) from 2.28.8 to 2.28.9. - [Release notes](https://github.com/python/typeshed/releases) - [Commits](https://github.com/python/typeshed/commits) --- updated-dependencies: - dependency-name: types-requests dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index 0cd4a6a6c..f1c8851b4 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -25,6 +25,6 @@ nbconvert==6.5.3 # mypy types types-cachetools==5.2.1 types-filelock==3.2.7 -types-requests==2.28.8 +types-requests==2.28.9 types-tabulate==0.8.11 types-python-dateutil==2.8.19 From eeb177110eaa1ce7037fbbaaa272fb835a52a550 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:02:03 +0000 Subject: [PATCH 477/518] Bump jsonschema from 4.9.1 to 4.14.0 Bumps [jsonschema](https://github.com/python-jsonschema/jsonschema) from 4.9.1 to 4.14.0. - [Release notes](https://github.com/python-jsonschema/jsonschema/releases) - [Changelog](https://github.com/python-jsonschema/jsonschema/blob/main/CHANGELOG.rst) - [Commits](https://github.com/python-jsonschema/jsonschema/compare/v4.9.1...v4.14.0) --- updated-dependencies: - dependency-name: jsonschema dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 77925f98b..50e7d6c8e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ arrow==1.2.2 cachetools==4.2.2 requests==2.28.1 urllib3==1.26.11 -jsonschema==4.9.1 +jsonschema==4.14.0 TA-Lib==0.4.24 technical==1.3.0 tabulate==0.8.10 From 354d3c0cda13ead678c4f65078630c0e1db398a4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:02:10 +0000 Subject: [PATCH 478/518] Bump time-machine from 2.7.1 to 2.8.1 Bumps [time-machine](https://github.com/adamchainz/time-machine) from 2.7.1 to 2.8.1. - [Release notes](https://github.com/adamchainz/time-machine/releases) - [Changelog](https://github.com/adamchainz/time-machine/blob/main/HISTORY.rst) - [Commits](https://github.com/adamchainz/time-machine/compare/2.7.1...2.8.1) --- updated-dependencies: - dependency-name: time-machine dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index 0cd4a6a6c..8316a4d46 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -17,7 +17,7 @@ pytest-mock==3.8.2 pytest-random-order==1.0.4 isort==5.10.1 # For datetime mocking -time-machine==2.7.1 +time-machine==2.8.1 # Convert jupyter notebooks to markdown documents nbconvert==6.5.3 From ff9ed1abad041a38e7552840d0ddcd221b93df9b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 22 Aug 2022 03:02:25 +0000 Subject: [PATCH 479/518] Bump ccxt from 1.92.20 to 1.92.52 Bumps [ccxt](https://github.com/ccxt/ccxt) from 1.92.20 to 1.92.52. - [Release notes](https://github.com/ccxt/ccxt/releases) - [Changelog](https://github.com/ccxt/ccxt/blob/master/exchanges.cfg) - [Commits](https://github.com/ccxt/ccxt/compare/1.92.20...1.92.52) --- updated-dependencies: - dependency-name: ccxt dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 77925f98b..23bb9ecac 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy==1.23.2 pandas==1.4.3 pandas-ta==0.3.14b -ccxt==1.92.20 +ccxt==1.92.52 # Pin cryptography for now due to rust build errors with piwheels cryptography==37.0.4 aiohttp==3.8.1 From 93d2f7fc85184da2f29f9720eb62fcac42a43e76 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 06:37:26 +0200 Subject: [PATCH 480/518] types-requests bump pre-commit --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a205f24ec..86c4ec1ad 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: additional_dependencies: - types-cachetools==5.2.1 - types-filelock==3.2.7 - - types-requests==2.28.8 + - types-requests==2.28.9 - types-tabulate==0.8.11 - types-python-dateutil==2.8.19 # stages: [push] From 015be770c33fde9405ae98f76004d0cae68c14f2 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 06:42:14 +0200 Subject: [PATCH 481/518] ccxt now defaults to base volume for all markets --- freqtrade/exchange/gateio.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/freqtrade/exchange/gateio.py b/freqtrade/exchange/gateio.py index c6ed0c66c..426a4b64d 100644 --- a/freqtrade/exchange/gateio.py +++ b/freqtrade/exchange/gateio.py @@ -25,7 +25,6 @@ class Gateio(Exchange): _ft_has: Dict = { "ohlcv_candle_limit": 1000, - "ohlcv_volume_currency": "quote", "time_in_force_parameter": "timeInForce", "order_time_in_force": ['gtc', 'ioc'], "stoploss_order_types": {"limit": "limit"}, @@ -34,7 +33,6 @@ class Gateio(Exchange): _ft_has_futures: Dict = { "needs_trading_fees": True, - "ohlcv_volume_currency": "base", "fee_cost_in_contracts": False, # Set explicitly to false for clarity "order_props_in_contracts": ['amount', 'filled', 'remaining'], } From f55d5ffd8c65dc0cc76ab2255e21551a406f5a66 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 09:18:51 +0000 Subject: [PATCH 482/518] Don't fail when `--strategy-path` is not a valid directory. closes #7264 --- freqtrade/resolvers/iresolver.py | 5 ++++- tests/strategy/test_strategy_loading.py | 4 ++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/freqtrade/resolvers/iresolver.py b/freqtrade/resolvers/iresolver.py index 74b28dffe..b99e7a94b 100644 --- a/freqtrade/resolvers/iresolver.py +++ b/freqtrade/resolvers/iresolver.py @@ -193,7 +193,10 @@ class IResolver: :return: List of dicts containing 'name', 'class' and 'location' entries """ logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'") - objects = [] + objects: List[Dict[str, Any]] = [] + if not directory.is_dir(): + logger.info(f"'{directory}' is not a directory, skipping.") + return objects for entry in directory.iterdir(): if ( recursive and entry.is_dir() diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py index 5b6f15d11..b794cdc99 100644 --- a/tests/strategy/test_strategy_loading.py +++ b/tests/strategy/test_strategy_loading.py @@ -48,6 +48,10 @@ def test_search_all_strategies_with_failed(): assert len([x for x in strategies if x['class'] is not None]) == 9 assert len([x for x in strategies if x['class'] is None]) == 1 + directory = Path(__file__).parent / "strats_nonexistingdir" + strategies = StrategyResolver.search_all_objects(directory, enum_failed=True) + assert len(strategies) == 0 + def test_load_strategy(default_conf, result): default_conf.update({'strategy': 'SampleStrategy', From 96d8882f1e6740f6c0a859c6e5f52a5a30ddb007 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Mon, 22 Aug 2022 13:30:30 +0200 Subject: [PATCH 483/518] Plug mem leak, add training timer --- freqtrade/freqai/data_drawer.py | 6 ++--- freqtrade/freqai/data_kitchen.py | 7 ------ freqtrade/freqai/freqai_interface.py | 37 +++++++++++++++++++++++++--- 3 files changed, 37 insertions(+), 13 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index c8dbdf5e5..b3060deff 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -421,7 +421,7 @@ class FreqaiDataDrawer: ) # if self.live: - self.model_dictionary[dk.model_filename] = model + self.model_dictionary[coin] = model self.pair_dict[coin]["model_filename"] = dk.model_filename self.pair_dict[coin]["data_path"] = str(dk.data_path) self.save_drawer_to_disk() @@ -460,8 +460,8 @@ class FreqaiDataDrawer: ) # try to access model in memory instead of loading object from disk to save time - if dk.live and dk.model_filename in self.model_dictionary: - model = self.model_dictionary[dk.model_filename] + if dk.live and coin in self.model_dictionary: + model = self.model_dictionary[coin] elif not dk.keras: model = load(dk.data_path / f"{dk.model_filename}_model.joblib") else: diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 421b30bf5..9351e92d6 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -873,13 +873,6 @@ class FreqaiDataKitchen: data_load_timerange.stopts = int(time) retrain = True - # logger.info( - # f"downloading data for " - # f"{(data_load_timerange.stopts-data_load_timerange.startts)/SECONDS_IN_DAY:.2f} " - # " days. " - # f"Extension of {additional_seconds/SECONDS_IN_DAY:.2f} days" - # ) - return retrain, trained_timerange, data_load_timerange def set_new_model_names(self, pair: str, trained_timerange: TimeRange): diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 49e4ce5c3..4106f24e0 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -80,12 +80,15 @@ class IFreqaiModel(ABC): logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") self.CONV_WIDTH = self.freqai_info.get("conv_width", 2) self.pair_it = 0 + self.pair_it_train = 0 self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist")) self.last_trade_database_summary: DataFrame = {} self.current_trade_database_summary: DataFrame = {} self.analysis_lock = Lock() self.inference_time: float = 0 + self.train_time: float = 0 self.begin_time: float = 0 + self.begin_time_train: float = 0 self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe']) def assert_config(self, config: Dict[str, Any]) -> None: @@ -128,11 +131,20 @@ class IFreqaiModel(ABC): dk = self.start_backtesting(dataframe, metadata, self.dk) dataframe = dk.remove_features_from_df(dk.return_dataframe) - del dk + self.clean_up() if self.live: self.inference_timer('stop') return dataframe + def clean_up(self): + """ + Objects that should be handled by GC already between coins, but + are explicitly shown here to help demonstrate the non-persistence of these + objects. + """ + self.model = None + self.dk = None + @threaded def start_scanning(self, strategy: IStrategy) -> None: """ @@ -159,9 +171,11 @@ class IFreqaiModel(ABC): dk.set_paths(pair, new_trained_timerange.stopts) if retrain: + self.train_timer('start') self.train_model_in_series( new_trained_timerange, pair, strategy, dk, data_load_timerange ) + self.train_timer('stop') self.dd.save_historic_predictions_to_disk() @@ -480,8 +494,7 @@ class IFreqaiModel(ABC): data_load_timerange: TimeRange, ): """ - Retrieve data and train model in single threaded mode (only used if model directory is empty - upon startup for dry/live ) + Retrieve data and train model. :param new_trained_timerange: TimeRange = the timerange to train the model on :param metadata: dict = strategy provided metadata :param strategy: IStrategy = user defined strategy object @@ -612,6 +625,24 @@ class IFreqaiModel(ABC): self.inference_time = 0 return + def train_timer(self, do='start'): + """ + Timer designed to track the cumulative time spent training the full pairlist in + FreqAI. + """ + if do == 'start': + self.pair_it_train += 1 + self.begin_time_train = time.time() + elif do == 'stop': + end = time.time() + self.train_time += (end - self.begin_time_train) + if self.pair_it_train == self.total_pairs: + logger.info( + f'Total time spent training pairlist {self.train_time:.2f} seconds') + self.pair_it_train = 0 + self.train_time = 0 + return + # Following methods which are overridden by user made prediction models. # See freqai/prediction_models/CatboostPredictionModel.py for an example. From 5ce1c6980356d764b240ac2fdbd7d29572cc31de Mon Sep 17 00:00:00 2001 From: th0rntwig <107926911+th0rntwig@users.noreply.github.com> Date: Mon, 22 Aug 2022 19:57:20 +0200 Subject: [PATCH 484/518] Improve DBSCAN epsilon identification (#7269) * Improve DBSCAN epsilon identification --- freqtrade/freqai/data_kitchen.py | 38 +++++++++++++++++++++++++++----- 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 9351e92d6..e480ab135 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -601,6 +601,8 @@ class FreqaiDataKitchen: is an outlier. """ + from math import cos, sin + if predict: train_ft_df = self.data_dictionary['train_features'] pred_ft_df = self.data_dictionary['prediction_features'] @@ -619,23 +621,47 @@ class FreqaiDataKitchen: else: + def normalise_distances(distances): + normalised_distances = (distances - distances.min()) / \ + (distances.max() - distances.min()) + return normalised_distances + + def rotate_point(origin, point, angle): + # rotate a point counterclockwise by a given angle (in radians) + # around a given origin + x = origin[0] + cos(angle) * (point[0] - origin[0]) - \ + sin(angle) * (point[1] - origin[1]) + y = origin[1] + sin(angle) * (point[0] - origin[0]) + \ + cos(angle) * (point[1] - origin[1]) + return (x, y) + MinPts = len(self.data_dictionary['train_features'].columns) * 2 # measure pairwise distances to train_features.shape[1]*2 nearest neighbours neighbors = NearestNeighbors( n_neighbors=MinPts, n_jobs=self.thread_count) neighbors_fit = neighbors.fit(self.data_dictionary['train_features']) distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features']) - distances = np.sort(distances, axis=0) - index_ten_pct = int(len(distances[:, 1]) * 0.1) - distances = distances[index_ten_pct:, 1] - epsilon = distances[-1] + distances = np.sort(distances, axis=0).mean(axis=1) + + normalised_distances = normalise_distances(distances) + x_range = np.linspace(0, 1, len(distances)) + line = np.linspace(normalised_distances[0], + normalised_distances[-1], len(normalised_distances)) + deflection = np.abs(normalised_distances - line) + max_deflection_loc = np.where(deflection == deflection.max())[0][0] + origin = x_range[max_deflection_loc], line[max_deflection_loc] + point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc] + rot_angle = np.pi / 4 + elbow_loc = rotate_point(origin, point, rot_angle) + + epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0] clustering = DBSCAN(eps=epsilon, min_samples=MinPts, n_jobs=int(self.thread_count)).fit( self.data_dictionary['train_features'] ) - logger.info(f'DBSCAN found eps of {epsilon}.') + logger.info(f'DBSCAN found eps of {epsilon:.2f}.') self.data['DBSCAN_eps'] = epsilon self.data['DBSCAN_min_samples'] = MinPts @@ -698,7 +724,7 @@ class FreqaiDataKitchen: if (len(do_predict) - do_predict.sum()) > 0: logger.info( - f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for " + f"DI tossed {len(do_predict) - do_predict.sum()} predictions for " "being too far from training data" ) From 5f38a574ce8159e125822a4f27ee5efec27220cc Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 20:23:19 +0200 Subject: [PATCH 485/518] Add okx broker id --- freqtrade/exchange/exchange.py | 10 +++++----- freqtrade/exchange/okx.py | 2 ++ tests/exchange/test_exchange.py | 4 ++-- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index a2944c784..06f83f4df 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -54,8 +54,8 @@ class Exchange: # Parameters to add directly to buy/sell calls (like agreeing to trading agreement) _params: Dict = {} - # Additional headers - added to the ccxt object - _headers: Dict = {} + # Additional parameters - added to the ccxt object + _ccxt_params: Dict = {} # Dict to specify which options each exchange implements # This defines defaults, which can be selectively overridden by subclasses using _ft_has @@ -242,9 +242,9 @@ class Exchange: } if ccxt_kwargs: logger.info('Applying additional ccxt config: %s', ccxt_kwargs) - if self._headers: - # Inject static headers after the above output to not confuse users. - ccxt_kwargs = deep_merge_dicts({'headers': self._headers}, ccxt_kwargs) + if self._ccxt_params: + # Inject static options after the above output to not confuse users. + ccxt_kwargs = deep_merge_dicts(self._ccxt_params, ccxt_kwargs) if ccxt_kwargs: ex_config.update(ccxt_kwargs) try: diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py index 540e76fca..80373e071 100644 --- a/freqtrade/exchange/okx.py +++ b/freqtrade/exchange/okx.py @@ -39,6 +39,8 @@ class Okx(Exchange): net_only = True + _ccxt_params: Dict = {'options': {'brokerId': 'ffb5405ad327SUDE'}} + def ohlcv_candle_limit( self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int: """ diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index be8d4b2cf..7e61f76cd 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -181,11 +181,11 @@ def test_init_ccxt_kwargs(default_conf, mocker, caplog): assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog) assert log_has(asynclogmsg, caplog) # Test additional headers case - Exchange._headers = {'hello': 'world'} + Exchange._ccxt_params = {'hello': 'world'} ex = Exchange(conf) assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog) - assert ex._api.headers == {'hello': 'world'} + assert ex._api.hello == 'world' assert ex._ccxt_config == {} Exchange._headers = {} From 1b0f37a93cc54fbd10926c1d0b3a1bddd701e71d Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 20:17:04 +0200 Subject: [PATCH 486/518] Fix documentation typo --- docs/data-download.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/data-download.md b/docs/data-download.md index 55c2ad738..be36d579d 100644 --- a/docs/data-download.md +++ b/docs/data-download.md @@ -186,7 +186,7 @@ Freqtrade currently supports 3 data-formats for both OHLCV and trades data: By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data. This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively. -To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time: +To persist this change, you should also add the following snippet to your configuration, so you don't have to insert the above arguments each time: ``` jsonc // ... From 6036018f352ff2e60a8198cf78ad65d38186843e Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 20:28:33 +0200 Subject: [PATCH 487/518] Extract contracts_to_amount and amount_to_contracts to standalone functions --- freqtrade/exchange/exchange.py | 47 +++++++++++++++++++++--------- tests/exchange/test_ccxt_compat.py | 4 +-- tests/exchange/test_exchange.py | 6 ++-- 3 files changed, 39 insertions(+), 18 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 06f83f4df..44288e696 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -408,7 +408,7 @@ class Exchange: else: return DataFrame() - def _get_contract_size(self, pair: str) -> float: + def get_contract_size(self, pair: str) -> float: if self.trading_mode == TradingMode.FUTURES: market = self.markets[pair] contract_size: float = 1.0 @@ -421,7 +421,7 @@ class Exchange: def _trades_contracts_to_amount(self, trades: List) -> List: if len(trades) > 0 and 'symbol' in trades[0]: - contract_size = self._get_contract_size(trades[0]['symbol']) + contract_size = self.get_contract_size(trades[0]['symbol']) if contract_size != 1: for trade in trades: trade['amount'] = trade['amount'] * contract_size @@ -429,7 +429,7 @@ class Exchange: def _order_contracts_to_amount(self, order: Dict) -> Dict: if 'symbol' in order and order['symbol'] is not None: - contract_size = self._get_contract_size(order['symbol']) + contract_size = self.get_contract_size(order['symbol']) if contract_size != 1: for prop in self._ft_has.get('order_props_in_contracts', []): if prop in order and order[prop] is not None: @@ -438,19 +438,13 @@ class Exchange: def _amount_to_contracts(self, pair: str, amount: float) -> float: - contract_size = self._get_contract_size(pair) - if contract_size and contract_size != 1: - return amount / contract_size - else: - return amount + contract_size = self.get_contract_size(pair) + return amount_to_contracts(amount, contract_size) def _contracts_to_amount(self, pair: str, num_contracts: float) -> float: - contract_size = self._get_contract_size(pair) - if contract_size and contract_size != 1: - return num_contracts * contract_size - else: - return num_contracts + contract_size = self.get_contract_size(pair) + return contracts_to_amount(num_contracts, contract_size) def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None: if exchange_config.get('sandbox'): @@ -2898,6 +2892,33 @@ def market_is_active(market: Dict) -> bool: return market.get('active', True) is not False +def amount_to_contracts(amount: float, contract_size: float) -> float: + """ + Convert amount to contracts. + :param amount: amount to convert + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: num-contracts + """ + if contract_size and contract_size != 1: + return amount / contract_size + else: + return amount + + +def contracts_to_amount(num_contracts: float, contract_size: float) -> float: + """ + Takes num-contracts and converts it to contract size + :param num_contracts: number of contracts + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: Amount + """ + + if contract_size and contract_size != 1: + return num_contracts * contract_size + else: + return num_contracts + + def amount_to_precision(amount: float, amount_precision: Optional[float], precisionMode: Optional[int]) -> float: """ diff --git a/tests/exchange/test_ccxt_compat.py b/tests/exchange/test_ccxt_compat.py index c3a5fe17f..29b317c3c 100644 --- a/tests/exchange/test_ccxt_compat.py +++ b/tests/exchange/test_ccxt_compat.py @@ -409,14 +409,14 @@ class TestCCXTExchange(): assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int)) assert futures_leverage >= 1.0 - def test_ccxt__get_contract_size(self, exchange_futures): + def test_ccxt_get_contract_size(self, exchange_futures): futures, futures_name = exchange_futures if futures: futures_pair = EXCHANGES[futures_name].get( 'futures_pair', EXCHANGES[futures_name]['pair'] ) - contract_size = futures._get_contract_size(futures_pair) + contract_size = futures.get_contract_size(futures_pair) assert (isinstance(contract_size, float) or isinstance(contract_size, int)) assert contract_size >= 0.0 diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 7e61f76cd..5002a33e1 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -4288,7 +4288,7 @@ def test__fetch_and_calculate_funding_fees_datetime_called( ('XLTCUSDT', 0.01, 'futures'), ('ETH/USDT:USDT', 10, 'futures') ]) -def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode): +def est__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode): api_mock = MagicMock() default_conf['trading_mode'] = trading_mode default_conf['margin_mode'] = 'isolated' @@ -4307,7 +4307,7 @@ def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_m 'contractSize': '10', } }) - size = exchange._get_contract_size(pair) + size = exchange.get_contract_size(pair) assert expected_size == size @@ -5146,7 +5146,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun mocker.patch('freqtrade.exchange.Exchange.price_to_precision', lambda s, x, y: y) exchange = get_patched_exchange(mocker, default_conf, api_mock) - exchange._get_contract_size = MagicMock(return_value=contract_size) + exchange.get_contract_size = MagicMock(return_value=contract_size) api_mock.create_order.reset_mock() order = exchange.stoploss( From 78b161e14cc262740b386758db70b79e1b88abb8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 20:32:55 +0200 Subject: [PATCH 488/518] add contract_size to database --- freqtrade/freqtradebot.py | 3 ++- freqtrade/persistence/migrations.py | 7 ++++--- freqtrade/persistence/trade_model.py | 2 ++ 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 5b71d6ec4..bb66303a8 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -290,13 +290,14 @@ class FreqtradeBot(LoggingMixin): def startup_backpopulate_precision(self): - trades = Trade.get_trades([Trade.precision_mode.is_(None)]) + trades = Trade.get_trades([Trade.contract_size.is_(None)]) for trade in trades: if trade.exchange != self.exchange.id: continue trade.precision_mode = self.exchange.precisionMode trade.amount_precision = self.exchange.get_precision_amount(trade.pair) trade.price_precision = self.exchange.get_precision_price(trade.pair) + trade.contract_size = self.exchange.get_contract_size(trade.pair) Trade.commit() def startup_update_open_orders(self): diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py index 311554359..1131c88b4 100644 --- a/freqtrade/persistence/migrations.py +++ b/freqtrade/persistence/migrations.py @@ -133,6 +133,7 @@ def migrate_trades_and_orders_table( amount_precision = get_column_def(cols, 'amount_precision', 'null') price_precision = get_column_def(cols, 'price_precision', 'null') precision_mode = get_column_def(cols, 'precision_mode', 'null') + contract_size = get_column_def(cols, 'contract_size', 'null') # Schema migration necessary with engine.begin() as connection: @@ -161,7 +162,7 @@ def migrate_trades_and_orders_table( timeframe, open_trade_value, close_profit_abs, trading_mode, leverage, liquidation_price, is_short, interest_rate, funding_fees, realized_profit, - amount_precision, price_precision, precision_mode + amount_precision, price_precision, precision_mode, contract_size ) select id, lower(exchange), pair, {base_currency} base_currency, {stake_currency} stake_currency, @@ -189,7 +190,7 @@ def migrate_trades_and_orders_table( {is_short} is_short, {interest_rate} interest_rate, {funding_fees} funding_fees, {realized_profit} realized_profit, {amount_precision} amount_precision, {price_precision} price_precision, - {precision_mode} precision_mode + {precision_mode} precision_mode, {contract_size} contract_size from {trade_back_name} """)) @@ -308,7 +309,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None: # if ('orders' not in previous_tables # or not has_column(cols_orders, 'stop_price')): migrating = False - if not has_column(cols_trades, 'precision_mode'): + if not has_column(cols_trades, 'contract_size'): migrating = True logger.info(f"Running database migration for trades - " f"backup: {table_back_name}, {order_table_bak_name}") diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 0c438956a..a46a2dd9f 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -296,6 +296,7 @@ class LocalTrade(): amount_precision: Optional[float] = None price_precision: Optional[float] = None precision_mode: Optional[int] = None + contract_size: Optional[float] = None # Leverage trading properties liquidation_price: Optional[float] = None @@ -1142,6 +1143,7 @@ class Trade(_DECL_BASE, LocalTrade): amount_precision = Column(Float, nullable=True) price_precision = Column(Float, nullable=True) precision_mode = Column(Integer, nullable=True) + contract_size = Column(Float, nullable=True) # Leverage trading properties leverage = Column(Float, nullable=True, default=1.0) From fe7108ae755733b75d76736e028d2dcfccb8769a Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 20:48:02 +0200 Subject: [PATCH 489/518] Convert amount to contracts before comparing for close closes #7279 --- freqtrade/exchange/__init__.py | 3 ++- freqtrade/exchange/exchange.py | 4 ++-- freqtrade/freqtradebot.py | 1 + freqtrade/optimize/backtesting.py | 18 +++++++++++------- freqtrade/persistence/trade_model.py | 7 ++++++- 5 files changed, 22 insertions(+), 11 deletions(-) diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index cb63c6b9a..57114a342 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -9,7 +9,8 @@ from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bittrex import Bittrex from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro -from freqtrade.exchange.exchange import (amount_to_precision, available_exchanges, ccxt_exchanges, +from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision, + available_exchanges, ccxt_exchanges, contracts_to_amount, date_minus_candles, is_exchange_known_ccxt, is_exchange_officially_supported, market_is_active, price_to_precision, timeframe_to_minutes, diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 44288e696..7d2a2f5c5 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2892,7 +2892,7 @@ def market_is_active(market: Dict) -> bool: return market.get('active', True) is not False -def amount_to_contracts(amount: float, contract_size: float) -> float: +def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float: """ Convert amount to contracts. :param amount: amount to convert @@ -2905,7 +2905,7 @@ def amount_to_contracts(amount: float, contract_size: float) -> float: return amount -def contracts_to_amount(num_contracts: float, contract_size: float) -> float: +def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float: """ Takes num-contracts and converts it to contract size :param num_contracts: number of contracts diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index bb66303a8..5791a816d 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -756,6 +756,7 @@ class FreqtradeBot(LoggingMixin): amount_precision=self.exchange.get_precision_amount(pair), price_precision=self.exchange.get_precision_price(pair), precision_mode=self.exchange.precisionMode, + contract_size=self.exchange.get_contract_size(pair), ) else: # This is additional buy, we reset fee_open_currency so timeout checking can work diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index e32eae533..e81698eef 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -24,7 +24,8 @@ from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType TradingMode) from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds -from freqtrade.exchange.exchange import amount_to_precision +from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision, + contracts_to_amount) from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress @@ -823,11 +824,13 @@ class Backtesting: self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) amount_p = (stake_amount / propose_rate) * leverage - amount = self.exchange._contracts_to_amount( - pair, amount_to_precision( - self.exchange._amount_to_contracts(pair, amount_p), - self.exchange.get_precision_amount(pair), self.precision_mode) - ) + contract_size = self.exchange.get_contract_size(pair) + precision_amount = self.exchange.get_precision_amount(pair) + amount = contracts_to_amount( + amount_to_precision( + amount_to_contracts(amount_p, contract_size), + precision_amount, self.precision_mode), + contract_size) # Backcalculate actual stake amount. stake_amount = amount * propose_rate / leverage @@ -859,9 +862,10 @@ class Backtesting: trading_mode=self.trading_mode, leverage=leverage, # interest_rate=interest_rate, - amount_precision=self.exchange.get_precision_amount(pair), + amount_precision=precision_amount, price_precision=self.exchange.get_precision_price(pair), precision_mode=self.precision_mode, + contract_size=contract_size, orders=[], ) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index a46a2dd9f..b25487993 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -15,6 +15,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exchange import amount_to_precision, price_to_precision +from freqtrade.exchange.exchange import amount_to_contracts, contracts_to_amount from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -624,7 +625,11 @@ class LocalTrade(): else: logger.warning( f'Got different open_order_id {self.open_order_id} != {order.order_id}') - amount_tr = amount_to_precision(self.amount, self.amount_precision, self.precision_mode) + amount_tr = contracts_to_amount( + amount_to_precision( + amount_to_contracts(self.amount, self.contract_size), + self.amount_precision, self.precision_mode), + self.contract_size) if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC): self.close(order.safe_price) else: From a6d78a8615a91e18d9adc830bd615bca0f1faa72 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 22 Aug 2022 21:18:02 +0200 Subject: [PATCH 490/518] initialize Since parameter properly closes #7285 --- freqtrade/data/history/history_utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index 04d0bf6d3..7a3fa4e0c 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -330,13 +330,12 @@ def _download_trades_history(exchange: Exchange, try: until = None + since = 0 if timerange: if timerange.starttype == 'date': since = timerange.startts * 1000 if timerange.stoptype == 'date': until = timerange.stopts * 1000 - else: - since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000 trades = data_handler.trades_load(pair) @@ -349,6 +348,9 @@ def _download_trades_history(exchange: Exchange, logger.info(f"Start earlier than available data. Redownloading trades for {pair}...") trades = [] + if not since: + since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000 + from_id = trades[-1][1] if trades else None if trades and since < trades[-1][0]: # Reset since to the last available point From 32faad93336f83c9969de9c955a5487c7af7f2d3 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 24 Aug 2022 20:36:08 +0200 Subject: [PATCH 491/518] Fix backtest calculation problem with DCA closes #7287 --- freqtrade/optimize/backtesting.py | 21 +++++++++++++++------ freqtrade/persistence/trade_model.py | 5 ++--- 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index e81698eef..725ff2406 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -25,7 +25,7 @@ from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision, - contracts_to_amount) + contracts_to_amount, price_to_precision) from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress @@ -658,7 +658,13 @@ class Backtesting: self.order_id_counter += 1 exit_candle_time = sell_row[DATE_IDX].to_pydatetime() order_type = self.strategy.order_types['exit'] - amount = amount or trade.amount + # amount = amount or trade.amount + amount = contracts_to_amount( + amount_to_precision( + amount_to_contracts(amount or trade.amount, trade.contract_size), + trade.amount_precision, self.precision_mode), + trade.contract_size) + rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode) order = Order( id=self.order_id_counter, ft_trade_id=trade.id, @@ -672,12 +678,12 @@ class Backtesting: side=trade.exit_side, order_type=order_type, status="open", - price=close_rate, - average=close_rate, + price=rate, + average=rate, amount=amount, filled=0, remaining=amount, - cost=amount * close_rate, + cost=amount * rate, ) trade.orders.append(order) return trade @@ -823,7 +829,10 @@ class Backtesting: if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount): self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) + precision_price = self.exchange.get_precision_price(pair) + propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode) amount_p = (stake_amount / propose_rate) * leverage + contract_size = self.exchange.get_contract_size(pair) precision_amount = self.exchange.get_precision_amount(pair) amount = contracts_to_amount( @@ -863,7 +872,7 @@ class Backtesting: leverage=leverage, # interest_rate=interest_rate, amount_precision=precision_amount, - price_precision=self.exchange.get_precision_price(pair), + price_precision=precision_price, precision_mode=self.precision_mode, contract_size=contract_size, orders=[], diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index b25487993..9ab608321 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -847,7 +847,7 @@ class LocalTrade(): avg_price = FtPrecise(0.0) close_profit = 0.0 close_profit_abs = 0.0 - + profit = None for o in self.orders: if o.ft_is_open or not o.filled: continue @@ -874,8 +874,6 @@ class LocalTrade(): close_profit_abs += profit close_profit = self.calc_profit_ratio( exit_rate, amount=exit_amount, open_rate=avg_price) - if current_amount <= ZERO: - profit = close_profit_abs else: total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price) @@ -900,6 +898,7 @@ class LocalTrade(): # Close profit abs / maximum owned # Fees are considered as they are part of close_profit_abs self.close_profit = (close_profit_abs / total_stake) * self.leverage + self.close_profit_abs = close_profit_abs def select_order_by_order_id(self, order_id: str) -> Optional[Order]: """ From 70df037690aed351b7d374d3bdac7554205b4015 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 24 Aug 2022 20:44:48 +0200 Subject: [PATCH 492/518] Improve test precision --- tests/optimize/test_backtesting.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index 98d18c156..368e368c5 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -550,6 +550,7 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=100) + mocker.patch("freqtrade.optimize.backtesting.price_to_precision", lambda p, *args: p) patch_exchange(mocker) default_conf_usdt['stake_amount'] = 300 default_conf_usdt['max_open_trades'] = 2 @@ -562,10 +563,10 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: pair = 'ETH/USDT:USDT' row = [ pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0), - 0.001, # Open - 0.0012, # High - 0.00099, # Low - 0.0011, # Close + 0.1, # Open + 0.12, # High + 0.099, # Low + 0.11, # Close 1, # enter_long 0, # exit_long 1, # enter_short @@ -580,8 +581,8 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: return_value=(0.01, 0.01)) # leverage = 5 - # ep1(trade.open_rate) = 0.001 - # position(trade.amount) = 1500000 + # ep1(trade.open_rate) = 0.1 + # position(trade.amount) = 15000 # stake_amount = 300 -> wb = 300 / 5 = 60 # mmr = 0.01 # cum_b = 0.01 @@ -591,26 +592,26 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None: # Binance, Long # liquidation_price # = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position)) - # = ((300 + 0.01) - (1 * 1500000 * 0.001)) / ((1500000 * 0.01) - (1 * 1500000)) + # = ((300 + 0.01) - (1 * 15000 * 0.1)) / ((15000 * 0.01) - (1 * 15000)) # = 0.0008080740740740741 # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1) - # = 0.0008080740740740741 + ((0.001 - 0.0008080740740740741) * 0.05 * 1) - # = 0.0008176703703703704 + # = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1) + # = 0.08176703703703704 trade = backtesting._enter_trade(pair, row=row, direction='long') - assert pytest.approx(trade.liquidation_price) == 0.00081767037 + assert pytest.approx(trade.liquidation_price) == 0.081767037 # Binance, Short # liquidation_price # = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position)) - # = ((300 + 0.01) - ((-1) * 1500000 * 0.001)) / ((1500000 * 0.01) - ((-1) * 1500000)) + # = ((300 + 0.01) - ((-1) * 15000 * 0.1)) / ((15000 * 0.01) - ((-1) * 15000)) # = 0.0011881254125412541 # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1) - # = 0.0011881254125412541 + (abs(0.001 - 0.0011881254125412541) * 0.05 * -1) - # = 0.0011787191419141915 + # = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1) + # = 0.11787191419141915 trade = backtesting._enter_trade(pair, row=row, direction='short') - assert pytest.approx(trade.liquidation_price) == 0.0011787191 + assert pytest.approx(trade.liquidation_price) == 0.11787191 # Stake-amount too high! mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=600.0) From 205ab26e9225bf8bc0b3d30d68207f30ed0af889 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 24 Aug 2022 20:54:22 +0200 Subject: [PATCH 493/518] Remove TODO in test --- tests/test_persistence.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tests/test_persistence.py b/tests/test_persistence.py index d8973e9d0..2460fde68 100644 --- a/tests/test_persistence.py +++ b/tests/test_persistence.py @@ -2894,8 +2894,8 @@ def test_order_to_ccxt(limit_buy_order_open): (('buy', 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)), (('sell', 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.17647059)), (('buy', 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.17647059)), - (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 350.0, 0.2)), - (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 950.0, 0.40)), + (('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 200.0, 0.2)), + (('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 600.0, 0.40)), ], 'end_profit': 950.0, 'end_profit_ratio': 0.283582, @@ -2960,9 +2960,8 @@ def test_recalc_trade_from_orders_dca(data) -> None: assert trade.amount == result[0] assert trade.open_rate == result[1] assert trade.stake_amount == result[2] - # TODO: enable the below. assert pytest.approx(trade.realized_profit) == result[3] - # assert pytest.approx(trade.close_profit_abs) == result[4] + assert pytest.approx(trade.close_profit_abs) == result[4] assert pytest.approx(trade.close_profit) == result[5] trade.close(price) From 9e48e6a40b917a6d24cb4084904bc1942f6285c4 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 25 Aug 2022 06:46:57 +0200 Subject: [PATCH 494/518] Update docs about precision limits in backtesting --- docs/backtesting.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/backtesting.md b/docs/backtesting.md index a7baf6932..8b2fdc345 100644 --- a/docs/backtesting.md +++ b/docs/backtesting.md @@ -561,6 +561,14 @@ BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtestin Today's minimum would be `0.001 * 22_000` - or 22\$. However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting. +#### Trading precision limits + +Most exchanges pose precision limits on both price and amounts, so you cannot buy 1.0020401 of a pair, or at a price of 1.24567123123. +Instead, these prices and amounts will be rounded or truncated (based on the exchange definition) to the defined trading precision. +The above values may for example be rounded to an amount of 1.002, and a price of 1.24567. + +These precision values are based on current exchange limits (as described in the [above section](#trading-limits-in-backtesting)), as historic precision limits are not available. + ## Improved backtest accuracy One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?). From 6636f17e0f587dfc4ee0505040cb5e9aa6b30ed8 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 25 Aug 2022 07:08:22 +0200 Subject: [PATCH 495/518] Simplify usage of amount_to_contract precision --- freqtrade/exchange/__init__.py | 10 ++++---- freqtrade/exchange/exchange.py | 23 +++++++++++++++++++ freqtrade/optimize/backtesting.py | 19 +++++---------- freqtrade/persistence/trade_model.py | 14 ++++------- .../test_backtesting_adjust_position.py | 3 ++- 5 files changed, 41 insertions(+), 28 deletions(-) diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index 57114a342..ff7ec7e04 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -9,11 +9,11 @@ from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bittrex import Bittrex from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro -from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision, - available_exchanges, ccxt_exchanges, contracts_to_amount, - date_minus_candles, is_exchange_known_ccxt, - is_exchange_officially_supported, market_is_active, - price_to_precision, timeframe_to_minutes, +from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts, + amount_to_precision, available_exchanges, ccxt_exchanges, + contracts_to_amount, date_minus_candles, + is_exchange_known_ccxt, is_exchange_officially_supported, + market_is_active, price_to_precision, timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date, timeframe_to_prev_date, timeframe_to_seconds, validate_exchange, validate_exchanges) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 7d2a2f5c5..3264b82e2 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2943,6 +2943,29 @@ def amount_to_precision(amount: float, amount_precision: Optional[float], return amount +def amount_to_contract_precision( + amount, amount_precision: Optional[float], precisionMode: Optional[int], + contract_size: Optional[float]) -> float: + """ + Returns the amount to buy or sell to a precision the Exchange accepts + including calculation to and from contracts. + Re-implementation of ccxt internal methods - ensuring we can test the result is correct + based on our definitions. + :param amount: amount to truncate + :param amount_precision: amount precision to use. + should be retrieved from markets[pair]['precision']['amount'] + :param precisionMode: precision mode to use. Should be used from precisionMode + one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE + :param contract_size: contract size - taken from exchange.get_contract_size(pair) + :return: truncated amount + """ + if amount_precision is not None and precisionMode is not None: + contracts = amount_to_contracts(amount, contract_size) + amount_p = amount_to_precision(contracts, amount_precision, precisionMode) + return contracts_to_amount(amount_p, contract_size) + return amount + + def price_to_precision(price: float, price_precision: Optional[float], precisionMode: Optional[int]) -> float: """ diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 725ff2406..248300fad 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -23,9 +23,8 @@ from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode, TradingMode) from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds -from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision, - contracts_to_amount, price_to_precision) +from freqtrade.exchange import (amount_to_contract_precision, price_to_precision, + timeframe_to_minutes, timeframe_to_seconds) from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress @@ -659,11 +658,8 @@ class Backtesting: exit_candle_time = sell_row[DATE_IDX].to_pydatetime() order_type = self.strategy.order_types['exit'] # amount = amount or trade.amount - amount = contracts_to_amount( - amount_to_precision( - amount_to_contracts(amount or trade.amount, trade.contract_size), - trade.amount_precision, self.precision_mode), - trade.contract_size) + amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision, + self.precision_mode, trade.contract_size) rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode) order = Order( id=self.order_id_counter, @@ -835,11 +831,8 @@ class Backtesting: contract_size = self.exchange.get_contract_size(pair) precision_amount = self.exchange.get_precision_amount(pair) - amount = contracts_to_amount( - amount_to_precision( - amount_to_contracts(amount_p, contract_size), - precision_amount, self.precision_mode), - contract_size) + amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode, + contract_size) # Backcalculate actual stake amount. stake_amount = amount * propose_rate / leverage diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 9ab608321..23997f835 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -14,8 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import amount_to_precision, price_to_precision -from freqtrade.exchange.exchange import amount_to_contracts, contracts_to_amount +from freqtrade.exchange import amount_to_contract_precision, price_to_precision from freqtrade.leverage import interest from freqtrade.persistence.base import _DECL_BASE from freqtrade.util import FtPrecise @@ -625,11 +624,8 @@ class LocalTrade(): else: logger.warning( f'Got different open_order_id {self.open_order_id} != {order.order_id}') - amount_tr = contracts_to_amount( - amount_to_precision( - amount_to_contracts(self.amount, self.contract_size), - self.amount_precision, self.precision_mode), - self.contract_size) + amount_tr = amount_to_contract_precision(self.amount, self.amount_precision, + self.precision_mode, self.contract_size) if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC): self.close(order.safe_price) else: @@ -882,8 +878,8 @@ class LocalTrade(): self.realized_profit = close_profit_abs self.close_profit_abs = profit - current_amount_tr = amount_to_precision(float(current_amount), - self.amount_precision, self.precision_mode) + current_amount_tr = amount_to_contract_precision( + float(current_amount), self.amount_precision, self.precision_mode, self.contract_size) if current_amount_tr > 0.0: # Trade is still open # Leverage not updated, as we don't allow changing leverage through DCA at the moment. diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index f0829ea1e..71f8cdcea 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -18,7 +18,8 @@ from tests.conftest import patch_exchange def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None: default_conf['use_exit_signal'] = False mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) - mocker.patch('freqtrade.optimize.backtesting.amount_to_precision', lambda x, y, z: round(x, 8)) + mocker.patch('freqtrade.optimize.backtesting.amount_to_contract_precision', + lambda x, *args, **kwargs: round(x, 8)) mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001) mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf')) patch_exchange(mocker) From f2a356a80c6f7581f0dc3f1bb0bd5bdcf9756818 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 25 Aug 2022 07:08:58 +0200 Subject: [PATCH 496/518] Fix some imports --- freqtrade/commands/data_commands.py | 3 +-- freqtrade/edge/edge_positioning.py | 2 +- freqtrade/freqtradebot.py | 3 +-- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index ce26d39ab..360387aa6 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -11,8 +11,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh refresh_backtest_trades_data) from freqtrade.enums import CandleType, RunMode, TradingMode from freqtrade.exceptions import OperationalException -from freqtrade.exchange import timeframe_to_minutes -from freqtrade.exchange.exchange import market_is_active +from freqtrade.exchange import market_is_active, timeframe_to_minutes from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist from freqtrade.resolvers import ExchangeResolver diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py index 2fe41a17b..af20e1645 100644 --- a/freqtrade/edge/edge_positioning.py +++ b/freqtrade/edge/edge_positioning.py @@ -15,7 +15,7 @@ from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT from freqtrade.data.history import get_timerange, load_data, refresh_data from freqtrade.enums import CandleType, ExitType, RunMode from freqtrade.exceptions import OperationalException -from freqtrade.exchange.exchange import timeframe_to_seconds +from freqtrade.exchange import timeframe_to_seconds from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.strategy.interface import IStrategy diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 5791a816d..1967de1fe 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -21,8 +21,7 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode, State, TradingMode) from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError, InvalidOrderException, PricingError) -from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds -from freqtrade.exchange.exchange import timeframe_to_next_date +from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds from freqtrade.misc import safe_value_fallback, safe_value_fallback2 from freqtrade.mixins import LoggingMixin from freqtrade.persistence import Order, PairLocks, Trade, init_db From 1fd223c815caab3ac104986701c7d9d6df281a66 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 25 Aug 2022 17:03:41 +0200 Subject: [PATCH 497/518] Update --prepend help string closes #7290 --- docs/data-download.md | 2 +- freqtrade/commands/cli_options.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/data-download.md b/docs/data-download.md index be36d579d..b72e7f337 100644 --- a/docs/data-download.md +++ b/docs/data-download.md @@ -63,7 +63,7 @@ optional arguments: `jsongz`). --trading-mode {spot,margin,futures} Select Trading mode - --prepend Allow data prepending. + --prepend Allow data prepending. (Data-appending is disabled) Common arguments: -v, --verbose Verbose mode (-vv for more, -vvv to get all messages). diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py index a458c1591..b9e9e26f7 100644 --- a/freqtrade/commands/cli_options.py +++ b/freqtrade/commands/cli_options.py @@ -455,7 +455,7 @@ AVAILABLE_CLI_OPTIONS = { ), "prepend_data": Arg( '--prepend', - help='Allow data prepending.', + help='Allow data prepending. (Data-appending is disabled)', action='store_true', ), "erase": Arg( From 53d46a0385e859e199b8aa038205169f0d38bc22 Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 25 Aug 2022 20:36:17 +0200 Subject: [PATCH 498/518] align `max_entry_position_adjustment` behavior of backtesting to live closes #7293 --- freqtrade/optimize/backtesting.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 248300fad..00cd8fa4a 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -534,12 +534,16 @@ class Backtesting: # Check if we should increase our position if stake_amount is not None and stake_amount > 0.0: - - pos_trade = self._enter_trade( - trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade) - if pos_trade is not None: - self.wallets.update() - return pos_trade + check_adjust_entry = True + if self.strategy.max_entry_position_adjustment > -1: + entry_count = trade.nr_of_successful_entries + check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment) + if check_adjust_entry: + pos_trade = self._enter_trade( + trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade) + if pos_trade is not None: + self.wallets.update() + return pos_trade if stake_amount is not None and stake_amount < 0.0: amount = abs(stake_amount) / current_rate @@ -570,12 +574,7 @@ class Backtesting: # Check if we need to adjust our current positions if self.strategy.position_adjustment_enable: - check_adjust_entry = True - if self.strategy.max_entry_position_adjustment > -1: - entry_count = trade.nr_of_successful_entries - check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment) - if check_adjust_entry: - trade = self._get_adjust_trade_entry_for_candle(trade, row) + trade = self._get_adjust_trade_entry_for_candle(trade, row) enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX] exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX] From b2d664c63c061e06ed6b136792146d57c8ffabe8 Mon Sep 17 00:00:00 2001 From: elintornquist <107926911+elintornquist@users.noreply.github.com> Date: Fri, 26 Aug 2022 18:57:27 +0200 Subject: [PATCH 499/518] Change MinPts calculation --- freqtrade/freqai/data_kitchen.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index e480ab135..102a4857e 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -635,7 +635,7 @@ class FreqaiDataKitchen: cos(angle) * (point[1] - origin[1]) return (x, y) - MinPts = len(self.data_dictionary['train_features'].columns) * 2 + MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25) # measure pairwise distances to train_features.shape[1]*2 nearest neighbours neighbors = NearestNeighbors( n_neighbors=MinPts, n_jobs=self.thread_count) From 753d1b2aad18eacfafea3f718a66747418657d4f Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 26 Aug 2022 19:34:51 +0200 Subject: [PATCH 500/518] Update leverage tier terminology to be clear and aligned with ccxt --- freqtrade/exchange/exchange.py | 25 +- freqtrade/exchange/okx.py | 2 +- tests/conftest.py | 456 ++++++++++++++++---------------- tests/exchange/test_binance.py | 104 ++++---- tests/exchange/test_exchange.py | 16 +- tests/exchange/test_okx.py | 48 ++-- 6 files changed, 326 insertions(+), 325 deletions(-) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 3264b82e2..afda824f2 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2316,10 +2316,10 @@ class Exchange: def parse_leverage_tier(self, tier) -> Dict: info = tier.get('info', {}) return { - 'min': tier['minNotional'], - 'max': tier['maxNotional'], - 'mmr': tier['maintenanceMarginRate'], - 'lev': tier['maxLeverage'], + 'minNotional': tier['minNotional'], + 'maxNotional': tier['maxNotional'], + 'maintenanceMarginRate': tier['maintenanceMarginRate'], + 'maxLeverage': tier['maxLeverage'], 'maintAmt': float(info['cum']) if 'cum' in info else None, } @@ -2348,18 +2348,18 @@ class Exchange: pair_tiers = self._leverage_tiers[pair] if stake_amount == 0: - return self._leverage_tiers[pair][0]['lev'] # Max lev for lowest amount + return self._leverage_tiers[pair][0]['maxLeverage'] # Max lev for lowest amount for tier_index in range(len(pair_tiers)): tier = pair_tiers[tier_index] - lev = tier['lev'] + lev = tier['maxLeverage'] if tier_index < len(pair_tiers) - 1: next_tier = pair_tiers[tier_index + 1] - next_floor = next_tier['min'] / next_tier['lev'] + next_floor = next_tier['minNotional'] / next_tier['maxLeverage'] if next_floor > stake_amount: # Next tier min too high for stake amount - return min((tier['max'] / stake_amount), lev) + return min((tier['maxNotional'] / stake_amount), lev) # # With the two leverage tiers below, # - a stake amount of 150 would mean a max leverage of (10000 / 150) = 66.66 @@ -2380,10 +2380,11 @@ class Exchange: # else: # if on the last tier - if stake_amount > tier['max']: # If stake is > than max tradeable amount + if stake_amount > tier['maxNotional']: + # If stake is > than max tradeable amount raise InvalidOrderException(f'Amount {stake_amount} too high for {pair}') else: - return tier['lev'] + return tier['maxLeverage'] raise OperationalException( 'Looped through all tiers without finding a max leverage. Should never be reached' @@ -2749,8 +2750,8 @@ class Exchange: pair_tiers = self._leverage_tiers[pair] for tier in reversed(pair_tiers): - if nominal_value >= tier['min']: - return (tier['mmr'], tier['maintAmt']) + if nominal_value >= tier['minNotional']: + return (tier['maintenanceMarginRate'], tier['maintAmt']) raise OperationalException("nominal value can not be lower than 0") # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py index 80373e071..f039f2b3f 100644 --- a/freqtrade/exchange/okx.py +++ b/freqtrade/exchange/okx.py @@ -146,4 +146,4 @@ class Okx(Exchange): return float('inf') pair_tiers = self._leverage_tiers[pair] - return pair_tiers[-1]['max'] / leverage + return pair_tiers[-1]['maxNotional'] / leverage diff --git a/tests/conftest.py b/tests/conftest.py index a02fc4566..fffac8e0a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3085,416 +3085,416 @@ def leverage_tiers(): return { "1000SHIB/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ], "1INCH/USDT": [ { - 'min': 0, - 'max': 5000, - 'mmr': 0.012, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 5000, + 'maintenanceMarginRate': 0.012, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 5000, - 'max': 25000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 5000, + 'maxNotional': 25000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 65.0 }, { - 'min': 25000, - 'max': 100000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 25000, + 'maxNotional': 100000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 690.0 }, { - 'min': 100000, - 'max': 250000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 100000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 5690.0 }, { - 'min': 250000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 2, + 'minNotional': 250000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 2, 'maintAmt': 11940.0 }, { - 'min': 1000000, - 'max': 100000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 1000000, + 'maxNotional': 100000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 386940.0 }, ], "AAVE/USDT": [ { - 'min': 0, - 'max': 5000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 5000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 5000, - 'max': 25000, - 'mmr': 0.02, - 'lev': 25, + 'minNotional': 5000, + 'maxNotional': 25000, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 25, 'maintAmt': 75.0 }, { - 'min': 25000, - 'max': 100000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 25000, + 'maxNotional': 100000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 700.0 }, { - 'min': 100000, - 'max': 250000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 100000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 5700.0 }, { - 'min': 250000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 2, + 'minNotional': 250000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 2, 'maintAmt': 11950.0 }, { - 'min': 10000000, - 'max': 50000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 10000000, + 'maxNotional': 50000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 386950.0 }, ], "ADA/BUSD": [ { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, - "max": 500000, - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, + "maxNotional": 500000, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, - "max": 1000000, - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, + "maxNotional": 1000000, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, - "max": 2000000, - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, + "maxNotional": 2000000, + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, - "max": 5000000, - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, + "maxNotional": 5000000, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, - "max": 30000000, - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, + "maxNotional": 30000000, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 }, ], 'BNB/BUSD': [ { - "min": 0, # stake(before leverage) = 0 - "max": 100000, # max stake(before leverage) = 5000 - "mmr": 0.025, - "lev": 20, + "minNotional": 0, # stake(before leverage) = 0 + "maxNotional": 100000, # max stake(before leverage) = 5000 + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, # stake = 10000.0 - "max": 500000, # max_stake = 50000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, # stake = 10000.0 + "maxNotional": 500000, # max_stake = 50000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, # stake = 100000.0 - "max": 1000000, # max_stake = 200000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, # stake = 100000.0 + "maxNotional": 1000000, # max_stake = 200000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, # stake = 333333.3333333333 - "max": 2000000, # max_stake = 666666.6666666666 - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, # stake = 333333.3333333333 + "maxNotional": 2000000, # max_stake = 666666.6666666666 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, # stake = 1000000.0 - "max": 5000000, # max_stake = 2500000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, # stake = 1000000.0 + "maxNotional": 5000000, # max_stake = 2500000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, # stake = 5000000.0 - "max": 30000000, # max_stake = 30000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, # stake = 5000000.0 + "maxNotional": 30000000, # max_stake = 30000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 } ], 'BNB/USDT': [ { - "min": 0, # stake = 0.0 - "max": 10000, # max_stake = 133.33333333333334 - "mmr": 0.0065, - "lev": 75, + "minNotional": 0, # stake = 0.0 + "maxNotional": 10000, # max_stake = 133.33333333333334 + "maintenanceMarginRate": 0.0065, + "maxLeverage": 75, "maintAmt": 0.0 }, { - "min": 10000, # stake = 200.0 - "max": 50000, # max_stake = 1000.0 - "mmr": 0.01, - "lev": 50, + "minNotional": 10000, # stake = 200.0 + "maxNotional": 50000, # max_stake = 1000.0 + "maintenanceMarginRate": 0.01, + "maxLeverage": 50, "maintAmt": 35.0 }, { - "min": 50000, # stake = 2000.0 - "max": 250000, # max_stake = 10000.0 - "mmr": 0.02, - "lev": 25, + "minNotional": 50000, # stake = 2000.0 + "maxNotional": 250000, # max_stake = 10000.0 + "maintenanceMarginRate": 0.02, + "maxLeverage": 25, "maintAmt": 535.0 }, { - "min": 250000, # stake = 25000.0 - "max": 1000000, # max_stake = 100000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 250000, # stake = 25000.0 + "maxNotional": 1000000, # max_stake = 100000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 8035.0 }, { - "min": 1000000, # stake = 200000.0 - "max": 2000000, # max_stake = 400000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 1000000, # stake = 200000.0 + "maxNotional": 2000000, # max_stake = 400000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 58035.0 }, { - "min": 2000000, # stake = 500000.0 - "max": 5000000, # max_stake = 1250000.0 - "mmr": 0.125, - "lev": 4, + "minNotional": 2000000, # stake = 500000.0 + "maxNotional": 5000000, # max_stake = 1250000.0 + "maintenanceMarginRate": 0.125, + "maxLeverage": 4, "maintAmt": 108035.0 }, { - "min": 5000000, # stake = 1666666.6666666667 - "max": 10000000, # max_stake = 3333333.3333333335 - "mmr": 0.15, - "lev": 3, + "minNotional": 5000000, # stake = 1666666.6666666667 + "maxNotional": 10000000, # max_stake = 3333333.3333333335 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 233035.0 }, { - "min": 10000000, # stake = 5000000.0 - "max": 20000000, # max_stake = 10000000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 10000000, # stake = 5000000.0 + "maxNotional": 20000000, # max_stake = 10000000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 1233035.0 }, { - "min": 20000000, # stake = 20000000.0 - "max": 50000000, # max_stake = 50000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 20000000, # stake = 20000000.0 + "maxNotional": 50000000, # max_stake = 50000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 6233035.0 }, ], 'BTC/USDT': [ { - "min": 0, # stake = 0.0 - "max": 50000, # max_stake = 400.0 - "mmr": 0.004, - "lev": 125, + "minNotional": 0, # stake = 0.0 + "maxNotional": 50000, # max_stake = 400.0 + "maintenanceMarginRate": 0.004, + "maxLeverage": 125, "maintAmt": 0.0 }, { - "min": 50000, # stake = 500.0 - "max": 250000, # max_stake = 2500.0 - "mmr": 0.005, - "lev": 100, + "minNotional": 50000, # stake = 500.0 + "maxNotional": 250000, # max_stake = 2500.0 + "maintenanceMarginRate": 0.005, + "maxLeverage": 100, "maintAmt": 50.0 }, { - "min": 250000, # stake = 5000.0 - "max": 1000000, # max_stake = 20000.0 - "mmr": 0.01, - "lev": 50, + "minNotional": 250000, # stake = 5000.0 + "maxNotional": 1000000, # max_stake = 20000.0 + "maintenanceMarginRate": 0.01, + "maxLeverage": 50, "maintAmt": 1300.0 }, { - "min": 1000000, # stake = 50000.0 - "max": 7500000, # max_stake = 375000.0 - "mmr": 0.025, - "lev": 20, + "minNotional": 1000000, # stake = 50000.0 + "maxNotional": 7500000, # max_stake = 375000.0 + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 16300.0 }, { - "min": 7500000, # stake = 750000.0 - "max": 40000000, # max_stake = 4000000.0 - "mmr": 0.05, - "lev": 10, + "minNotional": 7500000, # stake = 750000.0 + "maxNotional": 40000000, # max_stake = 4000000.0 + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 203800.0 }, { - "min": 40000000, # stake = 8000000.0 - "max": 100000000, # max_stake = 20000000.0 - "mmr": 0.1, - "lev": 5, + "minNotional": 40000000, # stake = 8000000.0 + "maxNotional": 100000000, # max_stake = 20000000.0 + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 2203800.0 }, { - "min": 100000000, # stake = 25000000.0 - "max": 200000000, # max_stake = 50000000.0 - "mmr": 0.125, - "lev": 4, + "minNotional": 100000000, # stake = 25000000.0 + "maxNotional": 200000000, # max_stake = 50000000.0 + "maintenanceMarginRate": 0.125, + "maxLeverage": 4, "maintAmt": 4703800.0 }, { - "min": 200000000, # stake = 66666666.666666664 - "max": 400000000, # max_stake = 133333333.33333333 - "mmr": 0.15, - "lev": 3, + "minNotional": 200000000, # stake = 66666666.666666664 + "maxNotional": 400000000, # max_stake = 133333333.33333333 + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 9703800.0 }, { - "min": 400000000, # stake = 200000000.0 - "max": 600000000, # max_stake = 300000000.0 - "mmr": 0.25, - "lev": 2, + "minNotional": 400000000, # stake = 200000000.0 + "maxNotional": 600000000, # max_stake = 300000000.0 + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 4.97038E7 }, { - "min": 600000000, # stake = 600000000.0 - "max": 1000000000, # max_stake = 1000000000.0 - "mmr": 0.5, - "lev": 1, + "minNotional": 600000000, # stake = 600000000.0 + "maxNotional": 1000000000, # max_stake = 1000000000.0 + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1.997038E8 }, ], "ZEC/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ] diff --git a/tests/exchange/test_binance.py b/tests/exchange/test_binance.py index 45f8a3817..4d1c40647 100644 --- a/tests/exchange/test_binance.py +++ b/tests/exchange/test_binance.py @@ -376,96 +376,96 @@ def test_fill_leverage_tiers_binance(default_conf, mocker): assert exchange._leverage_tiers == { 'ADA/BUSD': [ { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0 }, { - "min": 100000, - "max": 500000, - "mmr": 0.05, - "lev": 10, + "minNotional": 100000, + "maxNotional": 500000, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10, "maintAmt": 2500.0 }, { - "min": 500000, - "max": 1000000, - "mmr": 0.1, - "lev": 5, + "minNotional": 500000, + "maxNotional": 1000000, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5, "maintAmt": 27500.0 }, { - "min": 1000000, - "max": 2000000, - "mmr": 0.15, - "lev": 3, + "minNotional": 1000000, + "maxNotional": 2000000, + "maintenanceMarginRate": 0.15, + "maxLeverage": 3, "maintAmt": 77500.0 }, { - "min": 2000000, - "max": 5000000, - "mmr": 0.25, - "lev": 2, + "minNotional": 2000000, + "maxNotional": 5000000, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2, "maintAmt": 277500.0 }, { - "min": 5000000, - "max": 30000000, - "mmr": 0.5, - "lev": 1, + "minNotional": 5000000, + "maxNotional": 30000000, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1, "maintAmt": 1527500.0 } ], "ZEC/USDT": [ { - 'min': 0, - 'max': 50000, - 'mmr': 0.01, - 'lev': 50, + 'minNotional': 0, + 'maxNotional': 50000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 50, 'maintAmt': 0.0 }, { - 'min': 50000, - 'max': 150000, - 'mmr': 0.025, - 'lev': 20, + 'minNotional': 50000, + 'maxNotional': 150000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 20, 'maintAmt': 750.0 }, { - 'min': 150000, - 'max': 250000, - 'mmr': 0.05, - 'lev': 10, + 'minNotional': 150000, + 'maxNotional': 250000, + 'maintenanceMarginRate': 0.05, + 'maxLeverage': 10, 'maintAmt': 4500.0 }, { - 'min': 250000, - 'max': 500000, - 'mmr': 0.1, - 'lev': 5, + 'minNotional': 250000, + 'maxNotional': 500000, + 'maintenanceMarginRate': 0.1, + 'maxLeverage': 5, 'maintAmt': 17000.0 }, { - 'min': 500000, - 'max': 1000000, - 'mmr': 0.125, - 'lev': 4, + 'minNotional': 500000, + 'maxNotional': 1000000, + 'maintenanceMarginRate': 0.125, + 'maxLeverage': 4, 'maintAmt': 29500.0 }, { - 'min': 1000000, - 'max': 2000000, - 'mmr': 0.25, - 'lev': 2, + 'minNotional': 1000000, + 'maxNotional': 2000000, + 'maintenanceMarginRate': 0.25, + 'maxLeverage': 2, 'maintAmt': 154500.0 }, { - 'min': 2000000, - 'max': 30000000, - 'mmr': 0.5, - 'lev': 1, + 'minNotional': 2000000, + 'maxNotional': 30000000, + 'maintenanceMarginRate': 0.5, + 'maxLeverage': 1, 'maintAmt': 654500.0 }, ] diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 5002a33e1..650a20197 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -4826,10 +4826,10 @@ def test_parse_leverage_tier(mocker, default_conf): } assert exchange.parse_leverage_tier(tier) == { - "min": 0, - "max": 100000, - "mmr": 0.025, - "lev": 20, + "minNotional": 0, + "maxNotional": 100000, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20, "maintAmt": 0.0, } @@ -4855,10 +4855,10 @@ def test_parse_leverage_tier(mocker, default_conf): } assert exchange.parse_leverage_tier(tier2) == { - 'min': 0, - 'max': 2000, - 'mmr': 0.01, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 75, "maintAmt": None, } diff --git a/tests/exchange/test_okx.py b/tests/exchange/test_okx.py index 10e087ced..b475b84ff 100644 --- a/tests/exchange/test_okx.py +++ b/tests/exchange/test_okx.py @@ -414,47 +414,47 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, assert exchange._leverage_tiers == { 'ADA/USDT:USDT': [ { - 'min': 0, - 'max': 500, - 'mmr': 0.02, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 500, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 75, 'maintAmt': None }, { - 'min': 501, - 'max': 1000, - 'mmr': 0.025, - 'lev': 50, + 'minNotional': 501, + 'maxNotional': 1000, + 'maintenanceMarginRate': 0.025, + 'maxLeverage': 50, 'maintAmt': None }, { - 'min': 1001, - 'max': 2000, - 'mmr': 0.03, - 'lev': 20, + 'minNotional': 1001, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.03, + 'maxLeverage': 20, 'maintAmt': None }, ], 'ETH/USDT:USDT': [ { - 'min': 0, - 'max': 2000, - 'mmr': 0.01, - 'lev': 75, + 'minNotional': 0, + 'maxNotional': 2000, + 'maintenanceMarginRate': 0.01, + 'maxLeverage': 75, 'maintAmt': None }, { - 'min': 2001, - 'max': 4000, - 'mmr': 0.015, - 'lev': 50, + 'minNotional': 2001, + 'maxNotional': 4000, + 'maintenanceMarginRate': 0.015, + 'maxLeverage': 50, 'maintAmt': None }, { - 'min': 4001, - 'max': 8000, - 'mmr': 0.02, - 'lev': 20, + 'minNotional': 4001, + 'maxNotional': 8000, + 'maintenanceMarginRate': 0.02, + 'maxLeverage': 20, 'maintAmt': None }, ], From 01126c43f78bf932c5dbeab7e5517cbab4fe52d6 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 26 Aug 2022 20:04:36 +0200 Subject: [PATCH 501/518] Fix liquidation price tier calculation closes #7294 --- freqtrade/exchange/binance.py | 21 ++++++++++++--------- freqtrade/exchange/exchange.py | 26 +++++++++++++++----------- freqtrade/freqtradebot.py | 1 + freqtrade/optimize/backtesting.py | 1 + tests/exchange/test_exchange.py | 17 +++++++++++------ 5 files changed, 40 insertions(+), 26 deletions(-) diff --git a/freqtrade/exchange/binance.py b/freqtrade/exchange/binance.py index 37a3c419d..a5e9fd37c 100644 --- a/freqtrade/exchange/binance.py +++ b/freqtrade/exchange/binance.py @@ -137,23 +137,27 @@ class Binance(Exchange): pair: str, open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size + amount: float, + stake_amount: float, wallet_balance: float, # Or margin balance mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ + Important: Must be fetching data from cached values as this is used by backtesting! MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93 :param exchange_name: - :param open_rate: (EP1) Entry price of position + :param open_rate: Entry price of position :param is_short: True if the trade is a short, false otherwise - :param position: Absolute value of position size (in base currency) - :param wallet_balance: (WB) + :param amount: Absolute value of position size incl. leverage (in base currency) + :param stake_amount: Stake amount - Collateral in settle currency. + :param trading_mode: SPOT, MARGIN, FUTURES, etc. + :param margin_mode: Either ISOLATED or CROSS + :param wallet_balance: Amount of margin_mode in the wallet being used to trade Cross-Margin Mode: crossWalletBalance Isolated-Margin Mode: isolatedWalletBalance - :param maintenance_amt: # * Only required for Cross :param mm_ex_1: (TMM) @@ -165,12 +169,11 @@ class Binance(Exchange): """ side_1 = -1 if is_short else 1 - position = abs(position) cross_vars = upnl_ex_1 - mm_ex_1 if self.margin_mode == MarginMode.CROSS else 0.0 # mm_ratio: Binance's formula specifies maintenance margin rate which is mm_ratio * 100% # maintenance_amt: (CUM) Maintenance Amount of position - mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, position) + mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, stake_amount) if (maintenance_amt is None): raise OperationalException( @@ -182,9 +185,9 @@ class Binance(Exchange): return ( ( (wallet_balance + cross_vars + maintenance_amt) - - (side_1 * position * open_rate) + (side_1 * amount * open_rate) ) / ( - (position * mm_ratio) - (side_1 * position) + (amount * mm_ratio) - (side_1 * amount) ) ) else: diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index afda824f2..4386f47f6 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -2437,6 +2437,7 @@ class Exchange: pair: str, open_rate: float, amount: float, # quote currency, includes leverage + stake_amount: float, leverage: float, is_short: bool ) -> Optional[float]: @@ -2446,13 +2447,13 @@ class Exchange: elif ( self.trading_mode == TradingMode.FUTURES ): - wallet_balance = (amount * open_rate) / leverage isolated_liq = self.get_or_calculate_liquidation_price( pair=pair, open_rate=open_rate, is_short=is_short, - position=amount, - wallet_balance=wallet_balance, + amount=amount, + stake_amount=stake_amount, + wallet_balance=stake_amount, # In isolated mode, stake-amount = wallet size mm_ex_1=0.0, upnl_ex_1=0.0, ) @@ -2627,14 +2628,14 @@ class Exchange: # Dry-run open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size + amount: float, # Absolute value of position size + stake_amount: float, wallet_balance: float, # Or margin balance mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ Set's the margin mode on the exchange to cross or isolated for a specific pair - :param pair: base/quote currency pair (e.g. "ADA/USDT") """ if self.trading_mode == TradingMode.SPOT: return None @@ -2648,7 +2649,8 @@ class Exchange: pair=pair, open_rate=open_rate, is_short=is_short, - position=position, + amount=amount, + stake_amount=stake_amount, wallet_balance=wallet_balance, mm_ex_1=mm_ex_1, upnl_ex_1=upnl_ex_1 @@ -2677,22 +2679,24 @@ class Exchange: pair: str, open_rate: float, # Entry price of position is_short: bool, - position: float, # Absolute value of position size + amount: float, + stake_amount: float, wallet_balance: float, # Or margin balance mm_ex_1: float = 0.0, # (Binance) Cross only upnl_ex_1: float = 0.0, # (Binance) Cross only ) -> Optional[float]: """ + Important: Must be fetching data from cached values as this is used by backtesting! PERPETUAL: gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price okex: https://www.okex.com/support/hc/en-us/articles/ 360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin - Important: Must be fetching data from cached values as this is used by backtesting! :param exchange_name: :param open_rate: Entry price of position :param is_short: True if the trade is a short, false otherwise - :param position: Absolute value of position size incl. leverage (in base currency) + :param amount: Absolute value of position size incl. leverage (in base currency) + :param stake_amount: Stake amount - Collateral in settle currency. :param trading_mode: SPOT, MARGIN, FUTURES, etc. :param margin_mode: Either ISOLATED or CROSS :param wallet_balance: Amount of margin_mode in the wallet being used to trade @@ -2706,7 +2710,7 @@ class Exchange: market = self.markets[pair] taker_fee_rate = market['taker'] - mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, position) + mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount) if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED: @@ -2714,7 +2718,7 @@ class Exchange: raise OperationalException( "Freqtrade does not yet support inverse contracts") - value = wallet_balance / position + value = wallet_balance / amount mm_ratio_taker = (mm_ratio + taker_fee_rate) if is_short: diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 1967de1fe..f4731220c 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1734,6 +1734,7 @@ class FreqtradeBot(LoggingMixin): leverage=trade.leverage, pair=trade.pair, amount=trade.amount, + stake_amount=trade.stake_amount, open_rate=trade.open_rate, is_short=trade.is_short )) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 00cd8fa4a..ff30dbc2a 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -876,6 +876,7 @@ class Backtesting: pair=pair, open_rate=propose_rate, amount=amount, + stake_amount=trade.stake_amount, leverage=leverage, is_short=is_short, )) diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 650a20197..093284668 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -4132,7 +4132,8 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf): pair='NEAR/USDT:USDT', open_rate=18.884, is_short=False, - position=0.8, + amount=0.8, + stake_amount=18.884 * 0.8, wallet_balance=0.8, ) assert liq_price == 17.47 @@ -4143,7 +4144,8 @@ def test_get_or_calculate_liquidation_price(mocker, default_conf): pair='NEAR/USDT:USDT', open_rate=18.884, is_short=False, - position=0.8, + amount=0.8, + stake_amount=18.884 * 0.8, wallet_balance=0.8, ) assert liq_price == 17.540699999999998 @@ -4543,7 +4545,8 @@ def test_liquidation_price_is_none( pair='DOGE/USDT', open_rate=open_rate, is_short=is_short, - position=71200.81144, + amount=71200.81144, + stake_amount=open_rate * 71200.81144, wallet_balance=-56354.57, mm_ex_1=0.10, upnl_ex_1=0.0 @@ -4552,7 +4555,7 @@ def test_liquidation_price_is_none( @pytest.mark.parametrize( 'exchange_name, is_short, trading_mode, margin_mode, wallet_balance, ' - 'mm_ex_1, upnl_ex_1, maintenance_amt, position, open_rate, ' + 'mm_ex_1, upnl_ex_1, maintenance_amt, amount, open_rate, ' 'mm_ratio, expected', [ ("binance", False, 'futures', 'isolated', 1535443.01, 0.0, @@ -4566,7 +4569,7 @@ def test_liquidation_price_is_none( ]) def test_liquidation_price( mocker, default_conf, exchange_name, open_rate, is_short, trading_mode, - margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, position, mm_ratio, expected + margin_mode, wallet_balance, mm_ex_1, upnl_ex_1, maintenance_amt, amount, mm_ratio, expected ): default_conf['trading_mode'] = trading_mode default_conf['margin_mode'] = margin_mode @@ -4580,7 +4583,8 @@ def test_liquidation_price( wallet_balance=wallet_balance, mm_ex_1=mm_ex_1, upnl_ex_1=upnl_ex_1, - position=position, + amount=amount, + stake_amount=open_rate * amount, ), 2), expected) @@ -5111,6 +5115,7 @@ def test_get_liquidation_price( pair='ETH/USDT:USDT', open_rate=open_rate, amount=amount, + stake_amount=amount * open_rate / leverage, leverage=leverage, is_short=is_short, ) From efe4fd3e2415b605d57866567f142d30461c34b5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 26 Aug 2022 20:21:19 +0200 Subject: [PATCH 502/518] Add libgomp1 to dockerfile --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 14a67edc8..e84a4d095 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker" # Prepare environment RUN mkdir /freqtrade \ && apt-get update \ - && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev \ + && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev libgomp1 \ && apt-get clean \ && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \ && chown ftuser:ftuser /freqtrade \ From 2ef4534fee008fc91066c0d39b5a5e722e913d03 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 26 Aug 2022 20:44:36 +0200 Subject: [PATCH 503/518] Fix ccxt / longrun tests --- tests/exchange/test_ccxt_compat.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/exchange/test_ccxt_compat.py b/tests/exchange/test_ccxt_compat.py index 29b317c3c..49b7684f8 100644 --- a/tests/exchange/test_ccxt_compat.py +++ b/tests/exchange/test_ccxt_compat.py @@ -468,6 +468,7 @@ class TestCCXTExchange(): False, 100, 100, + 100, ) assert (isinstance(liquidation_price, float)) assert liquidation_price >= 0.0 @@ -478,6 +479,7 @@ class TestCCXTExchange(): False, 100, 100, + 100, ) assert (isinstance(liquidation_price, float)) assert liquidation_price >= 0.0 From 86c5ac44e4f9803dd302c2154320008037f5c50f Mon Sep 17 00:00:00 2001 From: elintornquist <107926911+elintornquist@users.noreply.github.com> Date: Fri, 26 Aug 2022 23:05:07 +0200 Subject: [PATCH 504/518] Add outlier percentage check --- freqtrade/freqai/data_kitchen.py | 37 ++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 102a4857e..c58cfa75a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -513,6 +513,19 @@ class FreqaiDataKitchen: return avg_mean_dist + def get_outlier_percentage(self, dropped_pts: npt.ArrayLike) -> float: + """ + Check if more than X% of points werer dropped during outlier detection. + """ + outlier_protection_pct = self.freqai_config["feature_parameters"].get( + "outlier_protection_percentage", 30) + outlier_pct = dropped_pts.sum() / len(dropped_pts) + if outlier_pct >= outlier_protection_pct: + self.svm_model = None + return outlier_pct + else: + return 0.0 + def use_SVM_to_remove_outliers(self, predict: bool) -> None: """ Build/inference a Support Vector Machine to detect outliers @@ -552,6 +565,14 @@ class FreqaiDataKitchen: y_pred = self.svm_model.predict(self.data_dictionary["train_features"]) dropped_points = np.where(y_pred == -1, 0, y_pred) # keep_index = np.where(y_pred == 1) + outlier_ptc = self.get_outlier_percentage(dropped_points) + if outlier_ptc: + logger.warning( + f"SVM detected >{outlier_ptc}% of the points as outliers." + f"Keeping original dataset." + ) + return + self.data_dictionary["train_features"] = self.data_dictionary["train_features"][ (y_pred == 1) ] @@ -667,6 +688,14 @@ class FreqaiDataKitchen: self.data['DBSCAN_min_samples'] = MinPts dropped_points = np.where(clustering.labels_ == -1, 1, 0) + outlier_ptc = self.get_outlier_percentage(dropped_points) + if outlier_ptc: + logger.warning( + f"DBSCAN detected >{outlier_ptc}% of the points as outliers." + f"Keeping original dataset." + ) + return + self.data_dictionary['train_features'] = self.data_dictionary['train_features'][ (clustering.labels_ != -1) ] @@ -722,6 +751,14 @@ class FreqaiDataKitchen: 0, ) + outlier_ptc = self.get_outlier_percentage(1 - do_predict) + if outlier_ptc: + logger.warning( + f"DBSCAN detected >{outlier_ptc}% of the points as outliers." + f"Keeping original dataset." + ) + return + if (len(do_predict) - do_predict.sum()) > 0: logger.info( f"DI tossed {len(do_predict) - do_predict.sum()} predictions for " From 9204f013124761d21ee1374fff4a976131ec75bb Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Aug 2022 07:23:02 +0200 Subject: [PATCH 505/518] Don't lock pairs on partial exit --- freqtrade/freqtradebot.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index f4731220c..d84d50b7c 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -1552,9 +1552,10 @@ class FreqtradeBot(LoggingMixin): trade.close_rate_requested = limit trade.exit_reason = exit_reason - # Lock pair for one candle to prevent immediate re-trading - self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), - reason='Auto lock') + if not sub_trade_amt: + # Lock pair for one candle to prevent immediate re-trading + self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc), + reason='Auto lock') self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj) # In case of market sell orders the order can be closed immediately From 2b70c3d0c00d6e90c1f5c14782e00eb86f14d52f Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Aug 2022 08:50:09 +0200 Subject: [PATCH 506/518] support price callback for partial exits in bt This will align results to how live works. closes #7292 --- docs/bot-basics.md | 2 +- docs/strategy-callbacks.md | 2 +- freqtrade/optimize/backtesting.py | 38 +++++++++++++++++-------------- 3 files changed, 23 insertions(+), 19 deletions(-) diff --git a/docs/bot-basics.md b/docs/bot-basics.md index 14823722e..3df926371 100644 --- a/docs/bot-basics.md +++ b/docs/bot-basics.md @@ -70,7 +70,7 @@ This loop will be repeated again and again until the bot is stopped. * Determine stake size by calling the `custom_stake_amount()` callback. * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested. * Call `custom_stoploss()` and `custom_exit()` to find custom exit points. - * For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle). + * For exits based on exit-signal, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle). * Generate backtest report output !!! Note diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index 8d46f42e1..32ad1b288 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -423,7 +423,7 @@ class AwesomeStrategy(IStrategy): !!! Warning "Backtesting" Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range. Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle. - `custom_exit_price()` is only called for sells of type exit_signal and Custom exit. All other exit-types will use regular backtesting prices. + `custom_exit_price()` is only called for sells of type exit_signal, Custom exit and partial exits. All other exit-types will use regular backtesting prices. ## Custom order timeout rules diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index ff30dbc2a..57b272e86 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -554,7 +554,8 @@ class Backtesting: if remaining < min_stake: # Remaining stake is too low to be sold. return trade - pos_trade = self._exit_trade(trade, row, current_rate, amount) + exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT) + pos_trade = self._get_exit_for_signal(trade, row, exit_, amount) if pos_trade is not None: order = pos_trade.orders[-1] if self._get_order_filled(order.price, row): @@ -589,14 +590,15 @@ class Backtesting: return t return None - def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple, - exit_: ExitCheckTuple) -> Optional[LocalTrade]: + def _get_exit_for_signal( + self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple, + amount: Optional[float] = None) -> Optional[LocalTrade]: exit_candle_time: datetime = row[DATE_IDX].to_pydatetime() if exit_.exit_flag: trade.close_date = exit_candle_time exit_reason = exit_.exit_reason - + amount_ = amount if amount is not None else trade.amount trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60) try: close_rate = self._get_close_rate(row, trade, exit_, trade_dur) @@ -605,7 +607,8 @@ class Backtesting: # call the custom exit price,with default value as previous close_rate current_profit = trade.calc_profit_ratio(close_rate) order_type = self.strategy.order_types['exit'] - if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT): + if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT, + ExitType.PARTIAL_EXIT): # Checks and adds an exit tag, after checking that the length of the # row has the length for an exit tag column if ( @@ -633,22 +636,23 @@ class Backtesting: # Confirm trade exit: time_in_force = self.strategy.order_time_in_force['exit'] - if (exit_.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper( - self.strategy.confirm_trade_exit, default_retval=True)( - pair=trade.pair, - trade=trade, # type: ignore[arg-type] - order_type=order_type, - amount=trade.amount, - rate=close_rate, - time_in_force=time_in_force, - sell_reason=exit_reason, # deprecated - exit_reason=exit_reason, - current_time=exit_candle_time)): + if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT) + and not strategy_safe_wrapper( + self.strategy.confirm_trade_exit, default_retval=True)( + pair=trade.pair, + trade=trade, # type: ignore[arg-type] + order_type=order_type, + amount=amount_, + rate=close_rate, + time_in_force=time_in_force, + sell_reason=exit_reason, # deprecated + exit_reason=exit_reason, + current_time=exit_candle_time)): return None trade.exit_reason = exit_reason - return self._exit_trade(trade, row, close_rate, trade.amount) + return self._exit_trade(trade, row, close_rate, amount_) return None def _exit_trade(self, trade: LocalTrade, sell_row: Tuple, From c3e74e6e8d59f002185112547f88ddb187484bee Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Aug 2022 08:55:29 +0200 Subject: [PATCH 507/518] Improve doc wording --- docs/hyperopt.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index d230c091e..6b6c2a772 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -432,10 +432,10 @@ While this strategy is most likely too simple to provide consistent profit, it s `range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space. ??? Hint "Performance tip" - By doing the calculation of all possible indicators in `populate_indicators()`, the calculation of the indicator happens only once for every parameter. - While this may slow down the hyperopt startup speed, the overall performance will increase as the Hyperopt execution itself may pick the same value for multiple epochs (changing other values). - As this also has Performance implications, hyperopt provides `--analyze-per-epoch` - which will move the execution of `populate_indicators()` to the epoch process. This will implicitly also change the `.range` functionality to only return the actually used value. - You should however try to use space ranges as small as possible. + During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value. This will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues. + + In either case, you should try to use space ranges as small as possible this will improve CPU/RAM usage in both scenarios. + ## Optimizing protections From 71f7d687832316ae5524c3a87491f50ddbfa28f9 Mon Sep 17 00:00:00 2001 From: th0rntwig Date: Sat, 27 Aug 2022 12:44:55 +0200 Subject: [PATCH 508/518] Fixed mypy error --- freqtrade/freqai/data_kitchen.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index c58cfa75a..eb9f6beb7 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -513,7 +513,7 @@ class FreqaiDataKitchen: return avg_mean_dist - def get_outlier_percentage(self, dropped_pts: npt.ArrayLike) -> float: + def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float: """ Check if more than X% of points werer dropped during outlier detection. """ @@ -568,7 +568,7 @@ class FreqaiDataKitchen: outlier_ptc = self.get_outlier_percentage(dropped_points) if outlier_ptc: logger.warning( - f"SVM detected >{outlier_ptc}% of the points as outliers." + f"SVM detected > {outlier_ptc}% of the points as outliers." f"Keeping original dataset." ) return @@ -691,7 +691,7 @@ class FreqaiDataKitchen: outlier_ptc = self.get_outlier_percentage(dropped_points) if outlier_ptc: logger.warning( - f"DBSCAN detected >{outlier_ptc}% of the points as outliers." + f"DBSCAN detected > {outlier_ptc}% of the points as outliers." f"Keeping original dataset." ) return @@ -754,7 +754,7 @@ class FreqaiDataKitchen: outlier_ptc = self.get_outlier_percentage(1 - do_predict) if outlier_ptc: logger.warning( - f"DBSCAN detected >{outlier_ptc}% of the points as outliers." + f"DI detected > {outlier_ptc}% of the points as outliers." f"Keeping original dataset." ) return From 59a723aec82bdc2c8cacbb2ebe90bb9d8d50f6e2 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 27 Aug 2022 15:12:04 +0200 Subject: [PATCH 509/518] Add /health to rest client discovered in #7299 --- docs/rest-api.md | 8 ++++++++ scripts/rest_client.py | 7 +++++++ 2 files changed, 15 insertions(+) diff --git a/docs/rest-api.md b/docs/rest-api.md index d9840a09c..cc82aadda 100644 --- a/docs/rest-api.md +++ b/docs/rest-api.md @@ -163,6 +163,8 @@ python3 scripts/rest_client.py --config rest_config.json [optional par | `strategy ` | Get specific Strategy content. **Alpha** | `available_pairs` | List available backtest data. **Alpha** | `version` | Show version. +| `sysinfo` | Show informations about the system load. +| `health` | Show bot health (last bot loop). !!! Warning "Alpha status" Endpoints labeled with *Alpha status* above may change at any time without notice. @@ -227,6 +229,11 @@ forceexit Force-exit a trade. :param tradeid: Id of the trade (can be received via status command) + :param ordertype: Order type to use (must be market or limit) + :param amount: Amount to sell. Full sell if not given + +health + Provides a quick health check of the running bot. locks Return current locks @@ -312,6 +319,7 @@ version whitelist Show the current whitelist. + ``` ### OpenAPI interface diff --git a/scripts/rest_client.py b/scripts/rest_client.py index 989e6a50d..ac6d97133 100755 --- a/scripts/rest_client.py +++ b/scripts/rest_client.py @@ -361,6 +361,13 @@ class FtRestClient(): """ return self._get("sysinfo") + def health(self): + """Provides a quick health check of the running bot. + + :return: json object + """ + return self._get("health") + def add_arguments(): parser = argparse.ArgumentParser() From b9f35cadb330763e70c52dd867ab74dc4555a94e Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 28 Aug 2022 11:32:53 +0200 Subject: [PATCH 510/518] add /stopentry alias for /stopbuy --- README.md | 2 +- docs/faq.md | 4 ++-- docs/strategy-callbacks.md | 2 +- docs/telegram-usage.md | 4 ++-- freqtrade/freqtradebot.py | 2 +- freqtrade/rpc/api_server/api_v1.py | 3 ++- freqtrade/rpc/rpc.py | 4 ++-- freqtrade/rpc/telegram.py | 34 ++++++++++++++++-------------- tests/rpc/test_rpc.py | 6 +++--- tests/rpc/test_rpc_apiserver.py | 11 ++++++++-- tests/rpc/test_rpc_telegram.py | 7 +++--- 11 files changed, 45 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 059e80cd9..0cc2364e5 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor - `/start`: Starts the trader. - `/stop`: Stops the trader. -- `/stopbuy`: Stop entering new trades. +- `/stopentry`: Stop entering new trades. - `/status |[table]`: Lists all or specific open trades. - `/profit []`: Lists cumulative profit from all finished trades, over the last n days. - `/forceexit |all`: Instantly exits the given trade (Ignoring `minimum_roi`). diff --git a/docs/faq.md b/docs/faq.md index f1542d08e..381bbceb5 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -77,9 +77,9 @@ Freqtrade will not provide incomplete candles to strategies. Using incomplete ca You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting. -### Is there a setting to only SELL the coins being held and not perform anymore BUYS? +### Is there a setting to only Exit the trades being held and not perform any new Entries? -You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades). +You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades). ### I want to run multiple bots on the same machine diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index 32ad1b288..0b8403414 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -654,7 +654,7 @@ Position adjustments will always be applied in the direction of the trade, so a Stoploss is still calculated from the initial opening price, not averaged price. Regular stoploss rules still apply (cannot move down). - While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades. + While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades. !!! Warning "Backtesting" During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected. diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md index add889681..ece8700de 100644 --- a/docs/telegram-usage.md +++ b/docs/telegram-usage.md @@ -149,7 +149,7 @@ You can create your own keyboard in `config.json`: !!! Note "Supported Commands" Only the following commands are allowed. Command arguments are not supported! - `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopbuy`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version` + `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version` ## Telegram commands @@ -161,7 +161,7 @@ official commands. You can ask at any moment for help with `/help`. |----------|-------------| | `/start` | Starts the trader | `/stop` | Stops the trader -| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules. +| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules. | `/reload_config` | Reloads the configuration file | `/show_config` | Shows part of the current configuration with relevant settings to operation | `/logs [limit]` | Show last log messages. diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index d84d50b7c..35ba6bab2 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -239,7 +239,7 @@ class FreqtradeBot(LoggingMixin): 'status': f"{len(open_trades)} open trades active.\n\n" f"Handle these trades manually on {self.exchange.name}, " - f"or '/start' the bot again and use '/stopbuy' " + f"or '/start' the bot again and use '/stopentry' " f"to handle open trades gracefully. \n" f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}", } diff --git a/freqtrade/rpc/api_server/api_v1.py b/freqtrade/rpc/api_server/api_v1.py index e0fef7be8..bf21715b7 100644 --- a/freqtrade/rpc/api_server/api_v1.py +++ b/freqtrade/rpc/api_server/api_v1.py @@ -216,9 +216,10 @@ def stop(rpc: RPC = Depends(get_rpc)): return rpc._rpc_stop() +@router.post('/stopentry', response_model=StatusMsg, tags=['botcontrol']) @router.post('/stopbuy', response_model=StatusMsg, tags=['botcontrol']) def stop_buy(rpc: RPC = Depends(get_rpc)): - return rpc._rpc_stopbuy() + return rpc._rpc_stopentry() @router.post('/reload_config', response_model=StatusMsg, tags=['botcontrol']) diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index ed7f13a96..11311f671 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -657,7 +657,7 @@ class RPC: self._freqtrade.state = State.RELOAD_CONFIG return {'status': 'Reloading config ...'} - def _rpc_stopbuy(self) -> Dict[str, str]: + def _rpc_stopentry(self) -> Dict[str, str]: """ Handler to stop buying, but handle open trades gracefully. """ @@ -665,7 +665,7 @@ class RPC: # Set 'max_open_trades' to 0 self._freqtrade.config['max_open_trades'] = 0 - return {'status': 'No more buy will occur from now. Run /reload_config to reset.'} + return {'status': 'No more entries will occur from now. Run /reload_config to reset.'} def __exec_force_exit(self, trade: Trade, ordertype: Optional[str], amount: Optional[float] = None) -> None: diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 8ce2fa2e4..8c988d570 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -114,18 +114,20 @@ class Telegram(RPCHandler): # TODO: DRY! - its not good to list all valid cmds here. But otherwise # this needs refactoring of the whole telegram module (same # problem in _help()). - valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$', - r'/trades$', r'/performance$', r'/buys', r'/entries', - r'/sells', r'/exits', r'/mix_tags', - r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+', - r'/stats$', r'/count$', r'/locks$', r'/balance$', - r'/stopbuy$', r'/reload_config$', r'/show_config$', - r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$', - r'/blacklist$', r'/bl_delete$', - r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$', - r'/forcebuy$', r'/forcelong$', r'/forceshort$', - r'/forcesell$', r'/forceexit$', - r'/edge$', r'/health$', r'/help$', r'/version$'] + valid_keys: List[str] = [ + r'/start$', r'/stop$', r'/status$', r'/status table$', + r'/trades$', r'/performance$', r'/buys', r'/entries', + r'/sells', r'/exits', r'/mix_tags', + r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+', + r'/stats$', r'/count$', r'/locks$', r'/balance$', + r'/stopbuy$', r'/stopentry$', r'/reload_config$', r'/show_config$', + r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$', + r'/blacklist$', r'/bl_delete$', + r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$', + r'/forcebuy$', r'/forcelong$', r'/forceshort$', + r'/forcesell$', r'/forceexit$', + r'/edge$', r'/health$', r'/help$', r'/version$' + ] # Create keys for generation valid_keys_print = [k.replace('$', '') for k in valid_keys] @@ -182,7 +184,7 @@ class Telegram(RPCHandler): CommandHandler(['unlock', 'delete_locks'], self._delete_locks), CommandHandler(['reload_config', 'reload_conf'], self._reload_config), CommandHandler(['show_config', 'show_conf'], self._show_config), - CommandHandler('stopbuy', self._stopbuy), + CommandHandler(['stopbuy', 'stopentry'], self._stopentry), CommandHandler('whitelist', self._whitelist), CommandHandler('blacklist', self._blacklist), CommandHandler(['blacklist_delete', 'bl_delete'], self._blacklist_delete), @@ -984,7 +986,7 @@ class Telegram(RPCHandler): self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _stopbuy(self, update: Update, context: CallbackContext) -> None: + def _stopentry(self, update: Update, context: CallbackContext) -> None: """ Handler for /stop_buy. Sets max_open_trades to 0 and gracefully sells all open trades @@ -992,7 +994,7 @@ class Telegram(RPCHandler): :param update: message update :return: None """ - msg = self._rpc._rpc_stopbuy() + msg = self._rpc._rpc_stopentry() self._send_msg(f"Status: `{msg['status']}`") @authorized_only @@ -1488,7 +1490,7 @@ class Telegram(RPCHandler): "------------\n" "*/start:* `Starts the trader`\n" "*/stop:* Stops the trader\n" - "*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" + "*/stopentry:* `Stops entering, but handles open trades gracefully` \n" "*/forceexit |all:* `Instantly exits the given trade or all trades, " "regardless of profit`\n" "*/fx |all:* `Alias to /forceexit`\n" diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 7b42bf083..8bbf75a32 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -663,7 +663,7 @@ def test_rpc_stop(mocker, default_conf) -> None: assert freqtradebot.state == State.STOPPED -def test_rpc_stopbuy(mocker, default_conf) -> None: +def test_rpc_stopentry(mocker, default_conf) -> None: mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock()) mocker.patch.multiple( 'freqtrade.exchange.Exchange', @@ -676,8 +676,8 @@ def test_rpc_stopbuy(mocker, default_conf) -> None: freqtradebot.state = State.RUNNING assert freqtradebot.config['max_open_trades'] != 0 - result = rpc._rpc_stopbuy() - assert {'status': 'No more buy will occur from now. Run /reload_config to reset.'} == result + result = rpc._rpc_stopentry() + assert {'status': 'No more entries will occur from now. Run /reload_config to reset.'} == result assert freqtradebot.config['max_open_trades'] == 0 diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 9aa965da2..5dfa77d8b 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -422,13 +422,20 @@ def test_api_reloadconf(botclient): assert ftbot.state == State.RELOAD_CONFIG -def test_api_stopbuy(botclient): +def test_api_stopentry(botclient): ftbot, client = botclient assert ftbot.config['max_open_trades'] != 0 rc = client_post(client, f"{BASE_URI}/stopbuy") assert_response(rc) - assert rc.json() == {'status': 'No more buy will occur from now. Run /reload_config to reset.'} + assert rc.json() == { + 'status': 'No more entries will occur from now. Run /reload_config to reset.'} + assert ftbot.config['max_open_trades'] == 0 + + rc = client_post(client, f"{BASE_URI}/stopentry") + assert_response(rc) + assert rc.json() == { + 'status': 'No more entries will occur from now. Run /reload_config to reset.'} assert ftbot.config['max_open_trades'] == 0 diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index a30115bd9..cde7025a7 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -103,7 +103,8 @@ def test_telegram_init(default_conf, mocker, caplog) -> None: "['stats'], ['daily'], ['weekly'], ['monthly'], " "['count'], ['locks'], ['unlock', 'delete_locks'], " "['reload_config', 'reload_conf'], ['show_config', 'show_conf'], " - "['stopbuy'], ['whitelist'], ['blacklist'], ['blacklist_delete', 'bl_delete'], " + "['stopbuy', 'stopentry'], ['whitelist'], ['blacklist'], " + "['blacklist_delete', 'bl_delete'], " "['logs'], ['edge'], ['health'], ['help'], ['version']" "]") @@ -896,10 +897,10 @@ def test_stopbuy_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) assert freqtradebot.config['max_open_trades'] != 0 - telegram._stopbuy(update=update, context=MagicMock()) + telegram._stopentry(update=update, context=MagicMock()) assert freqtradebot.config['max_open_trades'] == 0 assert msg_mock.call_count == 1 - assert 'No more buy will occur from now. Run /reload_config to reset.' \ + assert 'No more entries will occur from now. Run /reload_config to reset.' \ in msg_mock.call_args_list[0][0][0] From 22b42e91f3d1875e73780d8ce4dcbbedeac01b92 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 11:53:24 +0200 Subject: [PATCH 511/518] add new parameter to freqai doc --- docs/freqai.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/freqai.md b/docs/freqai.md index bba6faaea..78d71e2f8 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -113,6 +113,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Boolean. | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Dictionary. | `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan).
    **Datatype:** Boolean. +| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact.
    **Datatype:** float. Default: `0.3` | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
    **Datatype:** Dictionary. | `test_size` | Fraction of data that should be used for testing instead of training.
    **Datatype:** Positive float < 1. From 1e41c773a067beaf523c7bd3c67717b27fd5e732 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 12:11:29 +0200 Subject: [PATCH 512/518] fix outlier protection --- docs/freqai.md | 2 +- freqtrade/freqai/data_kitchen.py | 28 ++++++++++++++-------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 78d71e2f8..c0f764953 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -113,7 +113,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Boolean. | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
    **Datatype:** Dictionary. | `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan).
    **Datatype:** Boolean. -| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact.
    **Datatype:** float. Default: `0.3` +| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact.
    **Datatype:** float. Default: `30` | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
    **Datatype:** Dictionary. | `test_size` | Fraction of data that should be used for testing instead of training.
    **Datatype:** Positive float < 1. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index eb9f6beb7..ed3990de0 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -519,7 +519,7 @@ class FreqaiDataKitchen: """ outlier_protection_pct = self.freqai_config["feature_parameters"].get( "outlier_protection_percentage", 30) - outlier_pct = dropped_pts.sum() / len(dropped_pts) + outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100 if outlier_pct >= outlier_protection_pct: self.svm_model = None return outlier_pct @@ -563,12 +563,12 @@ class FreqaiDataKitchen: self.data_dictionary["train_features"] ) y_pred = self.svm_model.predict(self.data_dictionary["train_features"]) - dropped_points = np.where(y_pred == -1, 0, y_pred) + kept_points = np.where(y_pred == -1, 0, y_pred) # keep_index = np.where(y_pred == 1) - outlier_ptc = self.get_outlier_percentage(dropped_points) - if outlier_ptc: + outlier_pct = self.get_outlier_percentage(1 - kept_points) + if outlier_pct: logger.warning( - f"SVM detected > {outlier_ptc}% of the points as outliers." + f"SVM detected {outlier_pct:.2f}% of the points as outliers. " f"Keeping original dataset." ) return @@ -584,7 +584,7 @@ class FreqaiDataKitchen: ] logger.info( - f"SVM tossed {len(y_pred) - dropped_points.sum()}" + f"SVM tossed {len(y_pred) - kept_points.sum()}" f" train points from {len(y_pred)} total points." ) @@ -593,7 +593,7 @@ class FreqaiDataKitchen: # to reduce code duplication if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0: y_pred = self.svm_model.predict(self.data_dictionary["test_features"]) - dropped_points = np.where(y_pred == -1, 0, y_pred) + kept_points = np.where(y_pred == -1, 0, y_pred) self.data_dictionary["test_features"] = self.data_dictionary["test_features"][ (y_pred == 1) ] @@ -604,7 +604,7 @@ class FreqaiDataKitchen: ] logger.info( - f"SVM tossed {len(y_pred) - dropped_points.sum()}" + f"SVM tossed {len(y_pred) - kept_points.sum()}" f" test points from {len(y_pred)} total points." ) @@ -688,10 +688,10 @@ class FreqaiDataKitchen: self.data['DBSCAN_min_samples'] = MinPts dropped_points = np.where(clustering.labels_ == -1, 1, 0) - outlier_ptc = self.get_outlier_percentage(dropped_points) - if outlier_ptc: + outlier_pct = self.get_outlier_percentage(dropped_points) + if outlier_pct: logger.warning( - f"DBSCAN detected > {outlier_ptc}% of the points as outliers." + f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. " f"Keeping original dataset." ) return @@ -751,10 +751,10 @@ class FreqaiDataKitchen: 0, ) - outlier_ptc = self.get_outlier_percentage(1 - do_predict) - if outlier_ptc: + outlier_pct = self.get_outlier_percentage(1 - do_predict) + if outlier_pct: logger.warning( - f"DI detected > {outlier_ptc}% of the points as outliers." + f"DI detected {outlier_pct:.2f}% of the points as outliers. " f"Keeping original dataset." ) return From dd628eb525acc407fd0643aba2548d5eac6f3800 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 12:56:39 +0200 Subject: [PATCH 513/518] add tests for outlier detection and removal functions --- freqtrade/freqai/data_drawer.py | 3 +- freqtrade/freqai/data_kitchen.py | 2 +- tests/freqai/conftest.py | 47 ++++++++++++++++++++++++- tests/freqai/test_freqai_datakitchen.py | 31 ++++++++++++++-- 4 files changed, 78 insertions(+), 5 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index b3060deff..477b9e098 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -566,7 +566,7 @@ class FreqaiDataDrawer: for training according to user defined train_period_days metadata: dict = strategy furnished pair metadata """ - + import pytest with self.history_lock: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} @@ -576,6 +576,7 @@ class FreqaiDataDrawer: ) for tf in self.freqai_info["feature_parameters"].get("include_timeframes"): + # pytest.set_trace() base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf]) if pairs: for p in pairs: diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index ed3990de0..8e68c9a38 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -657,7 +657,7 @@ class FreqaiDataKitchen: return (x, y) MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25) - # measure pairwise distances to train_features.shape[1]*2 nearest neighbours + # measure pairwise distances to nearest neighbours neighbors = NearestNeighbors( n_neighbors=MinPts, n_jobs=self.thread_count) neighbors_fit = neighbors.fit(self.data_dictionary['train_features']) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 6ace13677..98f086ec9 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -2,7 +2,7 @@ from copy import deepcopy from pathlib import Path import pytest - +from unittest.mock import MagicMock from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_drawer import FreqaiDataDrawer @@ -81,6 +81,51 @@ def get_patched_freqaimodel(mocker, freqaiconf): return freqaimodel +def make_data_dictionary(mocker, freqai_conf): + freqai_conf.update({"timerange": "20180110-20180130"}) + + strategy = get_patched_freqai_strategy(mocker, freqai_conf) + exchange = get_patched_exchange(mocker, freqai_conf) + strategy.dp = DataProvider(freqai_conf, exchange) + strategy.freqai_info = freqai_conf.get("freqai", {}) + freqai = strategy.freqai + freqai.live = True + freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.pair = "ADA/BTC" + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + + freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes( + data_load_timerange, freqai.dk.pair, freqai.dk + ) + + unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators( + strategy, corr_dataframes, base_dataframes, freqai.dk.pair + ) + + unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe) + + freqai.dk.find_features(unfiltered_dataframe) + + features_filtered, labels_filtered = freqai.dk.filter_features( + unfiltered_dataframe, + freqai.dk.training_features_list, + freqai.dk.label_list, + training_filter=True, + ) + + data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered) + + data_dictionary = freqai.dk.normalize_data(data_dictionary) + + return freqai + + def get_freqai_live_analyzed_dataframe(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 9f2a2f71e..581286715 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -5,8 +5,8 @@ from pathlib import Path import pytest from freqtrade.exceptions import OperationalException -from tests.freqai.conftest import get_patched_data_kitchen - +from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary +from tests.conftest import log_has_re @pytest.mark.parametrize( "timerange, train_period_days, expected_result", @@ -66,3 +66,30 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected): dk = get_patched_data_kitchen(mocker, freqai_conf) assert dk.check_if_model_expired(timestamp) == expected shutil.rmtree(Path(dk.full_path)) + + +def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog): + freqai = make_data_dictionary(mocker, freqai_conf) + # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1}) + freqai.dk.use_DBSCAN_to_remove_outliers(predict=False) + assert log_has_re( + "DBSCAN found eps of 2.42.", + caplog, + ) + + +def test_compute_distances(mocker, freqai_conf): + freqai = make_data_dictionary(mocker, freqai_conf) + freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1}) + avg_mean_dist = freqai.dk.compute_distances() + assert round(avg_mean_dist, 2) == 2.56 + + +def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog): + freqai = make_data_dictionary(mocker, freqai_conf) + freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1}) + freqai.dk.use_SVM_to_remove_outliers(predict=False) + assert log_has_re( + "SVM detected 8.46%", + caplog, + ) From fcb5d1cb5a5419f723d94edde80abe6173d33916 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 13:01:39 +0200 Subject: [PATCH 514/518] remove debugging flag --- freqtrade/freqai/data_drawer.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 477b9e098..b6a1a15d7 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -566,7 +566,6 @@ class FreqaiDataDrawer: for training according to user defined train_period_days metadata: dict = strategy furnished pair metadata """ - import pytest with self.history_lock: corr_dataframes: Dict[Any, Any] = {} base_dataframes: Dict[Any, Any] = {} @@ -576,7 +575,6 @@ class FreqaiDataDrawer: ) for tf in self.freqai_info["feature_parameters"].get("include_timeframes"): - # pytest.set_trace() base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf]) if pairs: for p in pairs: From 6634229cc198f1e264b1fe3a56d4bd919c7216c0 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 13:21:29 +0200 Subject: [PATCH 515/518] appease the flake8 gods --- tests/freqai/test_freqai_datakitchen.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 581286715..6ef2971ee 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -8,6 +8,7 @@ from freqtrade.exceptions import OperationalException from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary from tests.conftest import log_has_re + @pytest.mark.parametrize( "timerange, train_period_days, expected_result", [ From a44a235b563fa96ca893cbd96f3e64d86aa7ff31 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 28 Aug 2022 13:47:01 +0200 Subject: [PATCH 516/518] isort imports in tests/freqai --- tests/freqai/conftest.py | 3 ++- tests/freqai/test_freqai_datakitchen.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 98f086ec9..dd148da77 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -1,8 +1,9 @@ from copy import deepcopy from pathlib import Path +from unittest.mock import MagicMock import pytest -from unittest.mock import MagicMock + from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_drawer import FreqaiDataDrawer diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 6ef2971ee..9ef955695 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -5,8 +5,8 @@ from pathlib import Path import pytest from freqtrade.exceptions import OperationalException -from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary from tests.conftest import log_has_re +from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary @pytest.mark.parametrize( From 8b0cfe12365af01ab38e7b61662d1c4283689216 Mon Sep 17 00:00:00 2001 From: th0rntwig <107926911+th0rntwig@users.noreply.github.com> Date: Sun, 28 Aug 2022 23:27:12 +0200 Subject: [PATCH 517/518] Reduce image sizes in freqai doc (#7304) --- docs/assets/freqai_DI.jpg | Bin 0 -> 314012 bytes docs/assets/freqai_DI.png | Bin 39781127 -> 0 bytes docs/assets/freqai_algo.jpg | Bin 0 -> 353611 bytes docs/assets/freqai_algo.png | Bin 17562663 -> 0 bytes docs/assets/freqai_dbscan.jpg | Bin 0 -> 67965 bytes docs/assets/freqai_dbscan.png | Bin 1992760 -> 0 bytes docs/assets/freqai_moving-window.jpg | Bin 0 -> 276574 bytes docs/assets/freqai_moving-window.png | Bin 3435842 -> 0 bytes docs/assets/freqai_weight-factor.jpg | Bin 0 -> 195825 bytes docs/assets/freqai_weight-factor.png | Bin 4893270 -> 0 bytes docs/assets/weights_factor.png | Bin 129065 -> 0 bytes docs/freqai.md | 10 +++++----- 12 files changed, 5 insertions(+), 5 deletions(-) create mode 100644 docs/assets/freqai_DI.jpg delete mode 100644 docs/assets/freqai_DI.png create mode 100644 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