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114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
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 BaseRegressionModel(IFreqaiModel):
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"""
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Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
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User *must* inherit from this class and set fit() and predict(). See example scripts
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such as prediction_models/CatboostPredictionModel.py for guidance.
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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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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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) -> Tuple[DataFrame, DataFrame]:
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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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:pred_df: dataframe containing the 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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dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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for label in dk.label_list:
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pred_df[label] = (
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(pred_df[label] + 1)
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* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
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/ 2
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) + dk.data["labels_min"][label]
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return (pred_df, dk.do_predict)
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