fix issue with different backtesting prediction size
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09e0a8d4df
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@ -1312,14 +1312,16 @@ class FreqaiDataKitchen:
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append_df = pd.read_hdf(self.backtesting_results_path)
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append_df = pd.read_hdf(self.backtesting_results_path)
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return append_df
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return append_df
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def check_if_backtest_prediction_exists(
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def check_if_backtest_prediction_is_valid(
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self
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self,
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length_backtesting_dataframe: int
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) -> bool:
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) -> bool:
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"""
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"""
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Check if a backtesting prediction already exists
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Check if a backtesting prediction already exists and if the predictions
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:param dk: FreqaiDataKitchen
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to append has the same sime of backtesting dataframe slice
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:param length_backtesting_dataframe: Length of backtesting dataframe slice
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:return:
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:return:
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:boolean: whether the prediction file exists or not.
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:boolean: whether the prediction file is valid.
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"""
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"""
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path_to_predictionfile = Path(self.full_path /
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path_to_predictionfile = Path(self.full_path /
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self.backtest_predictions_folder /
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self.backtest_predictions_folder /
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@ -1327,10 +1329,18 @@ class FreqaiDataKitchen:
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self.backtesting_results_path = path_to_predictionfile
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self.backtesting_results_path = path_to_predictionfile
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file_exists = path_to_predictionfile.is_file()
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file_exists = path_to_predictionfile.is_file()
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if file_exists:
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if file_exists:
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logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
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append_df = self.get_backtesting_prediction()
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if len(append_df) == length_backtesting_dataframe:
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logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
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return True
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else:
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logger.info("A new backtesting prediction file is required. "
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"(Number of predictions is different of dataframe length).")
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return False
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else:
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else:
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logger.info(
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logger.info(
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f"Could not find backtesting prediction file at {path_to_predictionfile}"
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f"Could not find backtesting prediction file at {path_to_predictionfile}"
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)
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)
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return file_exists
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return False
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@ -275,7 +275,7 @@ class IFreqaiModel(ABC):
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dk.set_new_model_names(pair, trained_timestamp)
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dk.set_new_model_names(pair, trained_timestamp)
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if dk.check_if_backtest_prediction_exists():
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if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
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self.dd.load_metadata(dk)
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self.dd.load_metadata(dk)
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dk.find_features(dataframe_train)
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dk.find_features(dataframe_train)
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self.check_if_feature_list_matches_strategy(dk)
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self.check_if_feature_list_matches_strategy(dk)
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