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Merge d7b86ee436 into ae41ab101a

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Alxy Savin 2024-09-15 14:02:47 +02:00 committed by GitHub
commit 3a806ef2d9
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1 changed files with 35 additions and 29 deletions
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freqtrade/freqai/freqai_interface.py
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@ -70,7 +70,7 @@ class IFreqaiModel(ABC):
self.retrain = False self.retrain = False
self.first = True self.first = True
self.set_full_path() self.set_full_path()
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True) self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
if self.save_backtest_models: if self.save_backtest_models:
logger.info("Backtesting module configured to save all models.") logger.info("Backtesting module configured to save all models.")
@ -258,6 +258,23 @@ class IFreqaiModel(ABC):
if self.freqai_info.get("write_metrics_to_disk", False): if self.freqai_info.get("write_metrics_to_disk", False):
self.dd.save_metric_tracker_to_disk() self.dd.save_metric_tracker_to_disk()
def _train_model(self, dataframe_train, pair, dk, tr_backtest):
try:
self.tb_logger = get_tb_logger(
self.dd.model_type, dk.data_path, self.activate_tensorboard
)
model = self.train(dataframe_train, pair, dk)
self.tb_logger.close()
return model
except Exception as msg:
logger.warning(
f"Training {pair} raised exception {msg.__class__.__name__}. "
f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}."
f"Message: {msg}, skipping.",
exc_info=True,
)
return None
def start_backtesting( def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
) -> FreqaiDataKitchen: ) -> FreqaiDataKitchen:
@ -352,37 +369,26 @@ class IFreqaiModel(ABC):
if not self.model_exists(dk): if not self.model_exists(dk):
dk.find_features(dataframe_train) dk.find_features(dataframe_train)
dk.find_labels(dataframe_train) dk.find_labels(dataframe_train)
self.model = self._train_model(dataframe_train, pair, dk, tr_backtest)
try: if self.model:
self.tb_logger = get_tb_logger( self.dd.pair_dict[pair]["trained_timestamp"] = int(tr_train.stopts)
self.dd.model_type, dk.data_path, self.activate_tensorboard if self.plot_features and self.model is not None:
) plot_feature_importance(self.model, pair, dk, self.plot_features)
self.model = self.train(dataframe_train, pair, dk) if self.save_backtest_models and self.model is not None:
self.tb_logger.close() logger.info("Saving backtest model to disk.")
except Exception as msg: self.dd.save_data(self.model, pair, dk)
logger.warning( else:
f"Training {pair} raised exception {msg.__class__.__name__}. " logger.info("Saving metadata to disk.")
f"Message: {msg}, skipping.", self.dd.save_metadata(dk)
exc_info=True,
)
self.model = None
self.dd.pair_dict[pair]["trained_timestamp"] = int(tr_train.stopts)
if self.plot_features and self.model is not None:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models and self.model is not None:
logger.info("Saving backtest model to disk.")
self.dd.save_data(self.model, pair, dk)
else:
logger.info("Saving metadata to disk.")
self.dd.save_metadata(dk)
else: else:
self.model = self.dd.load_data(pair, dk) self.model = self.dd.load_data(pair, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk) if self.model and len(dataframe_backtest):
append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest) pred_df, do_preds = self.predict(dataframe_backtest, dk)
dk.append_predictions(append_df) append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
dk.save_backtesting_prediction(append_df) dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
self.backtesting_fit_live_predictions(dk) self.backtesting_fit_live_predictions(dk)
dk.fill_predictions(dataframe) dk.fill_predictions(dataframe)
@ -829,7 +835,7 @@ class IFreqaiModel(ABC):
:param pair: current pair :param pair: current pair
:return: if the data exists or not :return: if the data exists or not
""" """
if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0: if len_dataframe_backtest == 0:
logger.info( logger.info(
f"No data found for pair {pair} from " f"No data found for pair {pair} from "
f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. " f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "