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change BT prediction files to feather format

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This commit is contained in:
Wagner Costa 2022-11-29 10:38:35 -03:00
parent df979ece33
commit 8ea58ab352
2 changed files with 14 additions and 27 deletions
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40
freqtrade/freqai/data_kitchen.py
View File

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

1
freqtrade/freqai/freqai_interface.py
View File

@ -275,7 +275,6 @@ class IFreqaiModel(ABC):
self.pair_it += 1 self.pair_it += 1
train_it = 0 train_it = 0
dk.load_prediction_pair_file()
# Loop enforcing the sliding window training/backtesting paradigm # Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month # tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly # tr_backtest is the backtesting time range e.g. the week directly