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change BT prediction files to feather format
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df979ece33
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@ -1317,41 +1317,24 @@ class FreqaiDataKitchen:
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self, append_df: DataFrame
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) -> None:
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"""
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Save prediction dataframe from backtesting to h5 file format
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Save prediction dataframe from backtesting to feather file format
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:param append_df: dataframe for backtesting period
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"""
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full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
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if not full_predictions_folder.is_dir():
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full_predictions_folder.mkdir(parents=True, exist_ok=True)
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append_df.to_hdf(self.backtesting_results_path, key=self.model_filename)
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append_df.to_feather(self.backtesting_results_path)
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def get_backtesting_prediction(
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self
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) -> DataFrame:
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"""
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Get prediction dataframe from h5 file format
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Get prediction dataframe from feather file format
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"""
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append_df = self.backtesting_h5_data[self.model_filename]
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append_df = pd.read_feather(self.backtesting_results_path)
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return append_df
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def load_prediction_pair_file(
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self
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) -> None:
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"""
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Load prediction file if it exists
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"""
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pair_file_name = self.pair.split(':')[0].replace('/', '_').lower()
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path_to_predictionfile = Path(self.full_path /
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self.backtest_predictions_folder /
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f"{pair_file_name}_prediction.h5")
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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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if file_exists:
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self.backtesting_h5_data = pd.HDFStore(path_to_predictionfile)
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else:
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self.backtesting_h5_data = {}
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def check_if_backtest_prediction_is_valid(
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self,
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len_backtest_df: int
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@ -1363,11 +1346,17 @@ class FreqaiDataKitchen:
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:return:
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:boolean: whether the prediction file is valid.
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"""
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if self.model_filename in self.backtesting_h5_data:
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path_to_predictionfile = Path(self.full_path /
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self.backtest_predictions_folder /
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f"{self.model_filename}_prediction.feather")
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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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if file_exists:
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append_df = self.get_backtesting_prediction()
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if len(append_df) == len_backtest_df and 'date' in append_df:
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logger.info("Found backtesting prediction file "
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f"at {self.backtesting_results_path.name}")
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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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@ -1376,8 +1365,7 @@ class FreqaiDataKitchen:
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return False
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else:
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logger.info(
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"Could not find backtesting prediction file "
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f"at {self.backtesting_results_path.name}"
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f"Could not find backtesting prediction file at {path_to_predictionfile}"
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)
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return False
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@ -275,7 +275,6 @@ class IFreqaiModel(ABC):
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self.pair_it += 1
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train_it = 0
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dk.load_prediction_pair_file()
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# Loop enforcing the sliding window training/backtesting paradigm
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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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