Merge 46d5c6cf5d into 22c0af9bb1

config_examples/config_freqai.example.json

@@ -54,6 +54,7 @@
         "backtest_period_days": 7,
         "live_retrain_hours": 0,
         "identifier": "unique-id",
+        "warn_exceptions_on_backtest_only": true,
         "feature_parameters": {
             "include_timeframes": [
                 "3m",

freqtrade/freqai/freqai_interface.py

@@ -70,7 +70,7 @@ class IFreqaiModel(ABC):
         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.")
 
@@ -270,6 +270,26 @@ class IFreqaiModel(ABC):
                 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:
+            if self.freqai_info.get("warn_exceptions_on_backtest_only", False):
+                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,
+                )
+            else:
+                raise
+            return None
+
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
     ) -> FreqaiDataKitchen:
@@ -366,37 +386,26 @@ class IFreqaiModel(ABC):
                 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)
@@ -840,7 +849,7 @@ class IFreqaiModel(ABC):
         :param pair: current pair
         :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(
                 f"No data found for pair {pair} from "
                 f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "