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

config_examples/config_freqai.example.json

@@ -48,6 +48,7 @@
  ],
  "freqai": {
  "enabled": true,
+ "warn_exceptions_on_backtest_only": false,
  "purge_old_models": 2,
  "train_period_days": 15,
  "backtest_period_days": 7,

freqtrade/freqai/freqai_interface.py

@@ -116,6 +116,9 @@
 
  record_params(config, self.full_path)
 
+ self.new_feature_selector = self.config.get('freqai', {}) \
+ .get('warn_exceptions_on_backtest_only', False)
+
  def __getstate__(self):
  """
  Return an empty state to be pickled in hyperopt
@@ -257,7 +260,7 @@
  if self.freqai_info.get('write_metrics_to_disk', False):
  self.dd.save_metric_tracker_to_disk()
 
  def start_backtesting(
  self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
  ) -> FreqaiDataKitchen:
  """
@@ -354,25 +357,43 @@
  logger.warning(
  f"Training {pair} raised exception {msg.__class__.__name__}. "
  f"Message: {msg}, skipping.", exc_info=True)
+
+ if self.new_feature_selector:
+ logger.warning(
+ "Train failed. Try to train on next pair." \
+ if self.new_feature_selector else
+ "Train failed. Raise error, fix data issue and try again."
+ )
+ self.tb_logger.close()
  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)
+ hard_check_model_valid = True
+ if self.new_feature_selector:
+ hard_check_model_valid = bool(False if self.model is None else True)
+ if hard_check_model_valid:
+ logger.info(
+ "Model is trained. Saving model and metadata to disk."
+ )
+
+ if hard_check_model_valid:
+ 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 hard_check_model_valid:
+ 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)