mirror of
https://github.com/freqtrade/freqtrade.git
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79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
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import logging
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from typing import Tuple
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from pandas import DataFrame
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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logger = logging.getLogger(__name__)
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class BaseTensorFlowModel(IFreqaiModel):
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"""
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Base class for TensorFlow type models.
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User *must* inherit from this class and set fit() and predict().
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"""
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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User uses this function to add any additional return values to the dataframe.
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return dataframe
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> Tuple[DataFrame, DataFrame]:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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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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logger.info("--------------------Starting training " f"{pair} --------------------")
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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if not self.freqai_info.get('fit_live_predictions', 0):
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dk.fit_labels()
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# normalize all data based on train_dataset only
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dk)
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logger.info(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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model = self.fit(data_dictionary)
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if pair not in self.dd.historic_predictions:
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self.set_initial_historic_predictions(
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data_dictionary['train_features'], model, dk, pair)
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elif self.freqai_info.get('fit_live_predictions_candles', 0):
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dk.fit_live_predictions()
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self.dd.save_historic_predictions_to_disk()
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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