mirror of
https://github.com/freqtrade/freqtrade.git
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204 lines
8.5 KiB
Python
204 lines
8.5 KiB
Python
import gc
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import logging
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, Dict, Tuple
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import numpy.typing as npt
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.enums import RunMode
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.strategy.interface import IStrategy
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pd.options.mode.chained_assignment = None
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logger = logging.getLogger(__name__)
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class IFreqaiModel(ABC):
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"""
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Class containing all tools for training and prediction in the strategy.
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User models should inherit from this class as shown in
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templates/ExamplePredictionModel.py where the user overrides
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train(), predict(), fit(), and make_labels().
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Author: Robert Caulk, rob.caulk@gmail.com
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.freqai_info = config["freqai"]
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self.data_split_parameters = config["freqai"]["data_split_parameters"]
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self.model_training_parameters = config["freqai"]["model_training_parameters"]
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self.feature_parameters = config["freqai"]["feature_parameters"]
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# self.backtest_timerange = config["timerange"]
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self.time_last_trained = None
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self.current_time = None
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self.model = None
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self.predictions = None
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def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
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"""
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Entry point to the FreqaiModel, it will train a new model if
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necesssary before making the prediction.
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The backtesting and training paradigm is a sliding training window
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with a following backtest window. Both windows slide according to the
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length of the backtest window. This function is not intended to be
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overridden by children of IFreqaiModel, but technically, it can be
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if the user wishes to make deeper changes to the sliding window
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logic.
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:params:
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:dataframe: Full dataframe coming from strategy - it contains entire
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backtesting timerange + additional historical data necessary to train
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the model.
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:metadata: pair metadataa coming from strategy.
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"""
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live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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self.pair = metadata["pair"]
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self.dh = FreqaiDataKitchen(self.config, dataframe, live)
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if live:
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# logger.info('testing live')
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self.start_live(dataframe, metadata, strategy)
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
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# Loop enforcing the sliding window training/backtesting paragigm
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# tr_train is the training time range e.g. 1 historical month
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# tr_backtest is the backtesting time range e.g. the week directly
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# following tr_train. Both of these windows slide through the
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# entire backtest
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for tr_train, tr_backtest in zip(
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self.dh.training_timeranges, self.dh.backtesting_timeranges
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):
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gc.collect()
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# self.config['timerange'] = tr_train
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self.dh.data = {} # clean the pair specific data between models
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self.training_timerange = tr_train
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dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
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logger.info("training %s for %s", self.pair, tr_train)
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self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train)))
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if not self.model_exists(self.pair, training_timerange=tr_train):
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self.model = self.train(dataframe_train, metadata)
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self.dh.save_data(self.model)
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else:
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self.model = self.dh.load_data()
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preds, do_preds = self.predict(dataframe_backtest, metadata)
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self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
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print('predictions', len(self.dh.full_predictions),
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'do_predict', len(self.dh.full_do_predict))
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self.dh.fill_predictions(len(dataframe))
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None:
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"""
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The main broad execution for dry/live. This function will check if a retraining should be
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performed, and if so, retrain and reset the model.
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"""
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self.dh.set_paths()
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file_exists = self.model_exists(metadata['pair'],
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training_timerange=self.freqai_info[
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'live_trained_timerange'])
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(retrain,
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new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
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'live_trained_timerange'],
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metadata)
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if retrain or not file_exists:
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self.dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = self.dh.load_pairs_histories(new_trained_timerange,
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metadata)
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unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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self.model = self.train(unfiltered_dataframe, metadata)
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self.dh.save_data(self.model)
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self.model = self.dh.load_data()
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preds, do_preds = self.predict(dataframe, metadata)
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self.dh.append_predictions(preds, do_preds, len(dataframe))
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return
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def make_labels(self, dataframe: DataFrame) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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return
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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for storing, saving, loading, and analyzing the data.
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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@abstractmethod
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def fit(self) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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return
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@abstractmethod
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def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:predictions: np.array of predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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def model_exists(self, pair: str, training_timerange: str) -> bool:
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"""
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Given a pair and path, check if a model already exists
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:param pair: pair e.g. BTC/USD
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:param path: path to model
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"""
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coin, _ = pair.split("/")
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self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange
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path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
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file_exists = path_to_modelfile.is_file()
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if file_exists:
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logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename)
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else:
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logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename)
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return file_exists
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