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archive config to the model folder, filter out features before returning dataframe to strategy (to alleviate frequi issues)`
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@ -2,7 +2,7 @@
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import datetime
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import gc
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import logging
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# import sys
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import shutil
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import threading
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from abc import ABC, abstractmethod
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from pathlib import Path
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@ -109,6 +109,7 @@ class IFreqaiModel(ABC):
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logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
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dh = self.start_backtesting(dataframe, metadata, self.dh)
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dataframe = self.remove_features_from_df(dataframe)
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return self.return_values(dataframe, dh)
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@threaded
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@ -413,6 +414,18 @@ class IFreqaiModel(ABC):
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self.full_path = Path(self.config['user_data_dir'] /
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"models" /
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str(self.freqai_info.get('identifier')))
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self.full_path.mkdir(parents=True, exist_ok=True)
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shutil.copy(self.config['config_files'][0], Path(self.full_path,
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self.config['config_files'][0]))
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def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
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"""
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Remove the features from the dataframe before returning it to strategy. This keeps it
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compact for Frequi purposes.
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"""
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to_keep = [col for col in dataframe.columns
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if not col.startswith('%') or col.startswith('%%')]
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return dataframe[to_keep]
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@threaded
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def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, pair: str,
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