diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index f320bdc2f..74d763e1b 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -675,12 +675,10 @@ class FreqaiDataKitchen:
         Set weights so that recent data is more heavily weighted during
         training than older data.
         """
-
+        wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
         weights = np.zeros(num_weights)
-        for i in range(1, len(weights)):
-            weights[len(weights) - i] = np.exp(
-                -i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights)
-            )
+        weights[1:] = np.exp(
+            - np.arange(1, len(weights)) / (wfactor * num_weights))[::-1]
         return weights
 
     def append_predictions(self, predictions, do_predict, len_dataframe):
diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
index f9a9bb69f..6f345ef67 100644
--- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
@@ -39,7 +39,10 @@ class BaseRegressionModel(IFreqaiModel):
         :model: Trained model which can be used to inference (self.predict)
         """
 
-        logger.info("--------------------Starting training " f"{pair} --------------------")
+        start_date = unfiltered_dataframe["date"].iloc[0]
+        end_date = unfiltered_dataframe["date"].iloc[-1]
+        logger.info("-------------------- Starting training " f"{pair} --------------------")
+        logger.info("-------------------- Using data " f"from {start_date} to {end_date}--------------------")
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(