implemented a parameter for skipping errors during model training in the backtest process

freqtrade/freqai/freqai_interface.py

@@ -69,7 +69,7 @@ def __init__(self, config: Config) -> None:
  self.retrain = False
  self.first = True
  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:
  logger.info('Backtesting module configured to save all models.')
 
@@ -108,7 +108,6 @@ def __init__(self, config: Config) -> None:
  self.data_provider: Optional[DataProvider] = None
  self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
  self.can_short = True # overridden in start() with strategy.can_short
- self.model: Any = None
  if self.ft_params.get('principal_component_analysis', False) and self.continual_learning:
  self.ft_params.update({'principal_component_analysis': False})
  logger.warning('User tried to use PCA with continual learning. Deactivating PCA.')
@@ -257,6 +256,20 @@ def _start_scanning(self, strategy: IStrategy) -> None:
  if self.freqai_info.get('write_metrics_to_disk', False):
  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(
  self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
  ) -> FreqaiDataKitchen:
@@ -344,35 +357,26 @@ def start_backtesting(
  if not self.model_exists(dk):
  dk.find_features(dataframe_train)
  dk.find_labels(dataframe_train)
-
- try:
- self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path,
- self.activate_tensorboard)
- self.model = self.train(dataframe_train, pair, dk)
- self.tb_logger.close()
- except Exception as msg:
- logger.warning(
- f"Training {pair} raised exception {msg.__class__.__name__}. "
- f"Message: {msg}, skipping.", 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)
+ self.model = self._train_model(dataframe_train, pair, dk, tr_backtest)
+
+ if self.model:
+ 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:
  self.model = self.dd.load_data(pair, dk)
 
- pred_df, do_preds = self.predict(dataframe_backtest, dk)
- append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
- dk.append_predictions(append_df)
- dk.save_backtesting_prediction(append_df)
+ if self.model and len(dataframe_backtest):
+ pred_df, do_preds = self.predict(dataframe_backtest, dk)
+ append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
+ dk.append_predictions(append_df)
+ dk.save_backtesting_prediction(append_df)
 
  self.backtesting_fit_live_predictions(dk)
  dk.fill_predictions(dataframe)