Fix exceptions when training fails
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		| @@ -104,6 +104,7 @@ class IFreqaiModel(ABC): | ||||
|         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 | ||||
|  | ||||
|         record_params(config, self.full_path) | ||||
|  | ||||
| @@ -338,13 +339,14 @@ class IFreqaiModel(ABC): | ||||
|                     except Exception as msg: | ||||
|                         logger.warning( | ||||
|                             f"Training {pair} raised exception {msg.__class__.__name__}. " | ||||
|                             f"Message: {msg}, skipping.") | ||||
|                             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: | ||||
|                     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: | ||||
|                     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: | ||||
|   | ||||
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