# 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 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 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): """ 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(). Author: Robert Caulk, rob.caulk@gmail.com """ 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"] self.feature_parameters = config["freqai"]["feature_parameters"] # self.backtest_timerange = config["timerange"] self.time_last_trained = None self.current_time = None self.model = None self.predictions = None 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() 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 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 the model. :metadata: pair metadata coming from strategy. """ self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) self.pair = metadata["pair"] self.dh = FreqaiDataKitchen(self.config, dataframe, self.live) if self.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)) # 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 # 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.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): self.model = self.train(dataframe_train, metadata) self.dh.save_data(self.model) else: self.model = self.dh.load_data() # 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) 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.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: """ 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() file_exists = self.model_exists(metadata['pair'], training_timerange=self.freqai_info[ 'live_trained_timerange']) 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) 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 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(self.new_trained_timerange, metadata, strategy) preds, do_preds = self.predict(dataframe, metadata) self.dh.append_predictions(preds, do_preds, len(dataframe)) 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: """ 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() # 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: """ 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 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 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 == "": 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")) file_exists = path_to_modelfile.is_file() if file_exists: logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename) 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: TimeRange, 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: TimeRange, 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) 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) """