diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 39310a15e..ed64fb162 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -210,7 +210,10 @@ class FreqaiDataKitchen:
             const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
             if const_cols:
                 filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
+                self.data['constant_features_list'] = const_cols
                 logger.warning(f"Removed features {const_cols} with constant values.")
+            else:
+                self.data['constant_features_list'] = []
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
@@ -241,7 +244,8 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
-            filtered_df = self.check_pred_labels(filtered_df)
+            if len(self.data['constant_features_list']):
+                filtered_df = self.check_pred_labels(filtered_df)
             # we are backtesting so we need to preserve row number to send back to strategy,
             # so now we use do_predict to avoid any prediction based on a NaN
             drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -467,15 +471,14 @@ class FreqaiDataKitchen:
         :params:
         :df_predictions: incoming predictions
         """
-        train_labels = self.data_dictionary["train_features"].columns
-        pred_labels = df_predictions.columns
-        num_diffs = len(pred_labels.difference(train_labels))
-        if num_diffs != 0:
-            df_predictions = df_predictions[train_labels]
-            logger.warning(
-                f"Removed {num_diffs} features from prediction features, "
-                f"these were likely considered constant values during most recent training."
-            )
+        constant_labels = self.data['constant_features_list']
+        df_predictions = df_predictions.filter(
+            df_predictions.columns.difference(constant_labels)
+        )
+        logger.warning(
+            f"Removed {len(constant_labels)} features from prediction features, "
+            f"these were considered constant values during most recent training."
+        )
 
         return df_predictions
 
diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py
index f60b29bf1..f1203877e 100644
--- a/tests/freqai/test_freqai_datakitchen.py
+++ b/tests/freqai/test_freqai_datakitchen.py
@@ -125,7 +125,8 @@ def test_normalize_data(mocker, freqai_conf):
     freqai = make_data_dictionary(mocker, freqai_conf)
     data_dict = freqai.dk.data_dictionary
     freqai.dk.normalize_data(data_dict)
-    assert len(freqai.dk.data) == 32
+    assert any('_max' in entry for entry in freqai.dk.data.keys())
+    assert any('_min' in entry for entry in freqai.dk.data.keys())
 
 
 def test_filter_features(mocker, freqai_conf):