diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 83bd3550b..224b9dbe1 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Tuple
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 import datasieve.transforms as ds
 import numpy as np
@@ -108,7 +108,7 @@ 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
+        self.model: Union[Any, None] = 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 +257,21 @@ 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) -> Union[Any, None]:
+        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 +359,26 @@ class IFreqaiModel(ABC):
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
+                    self.model = self._train_model(dataframe_train, pair, dk, tr_backtest)
 
-                    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)
-
+                    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:
+                    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)