diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index d2abd0ad2..005005368 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -775,12 +775,22 @@ class FreqaiDataKitchen:
 
     def compute_inlier_metric(self, set_='train') -> None:
         """
-
         Compute inlier metric from backwards distance distributions.
         This metric defines how well features from a timepoint fit
         into previous timepoints.
         """
 
+        def normalise(dataframe: DataFrame, key: str) -> DataFrame:
+            if set_ == 'train':
+                min_value = dataframe.min()
+                max_value = dataframe.max()
+                self.data[f'{key}_min'] = min_value
+                self.data[f'{key}_max'] = max_value
+            else:
+                min_value = self.data[f'{key}_min']
+                max_value = self.data[f'{key}_max']
+            return (dataframe - min_value) / (max_value - min_value)
+
         no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
 
         if set_ == 'train':
@@ -825,7 +835,12 @@ class FreqaiDataKitchen:
         inliers = pd.DataFrame(index=distances.index)
         for key in distances.keys():
             current_distances = distances[key].dropna()
-            fit_params = stats.weibull_min.fit(current_distances)
+            current_distances = normalise(current_distances, key)
+            if set_ == 'train':
+                fit_params = stats.weibull_min.fit(current_distances)
+                self.data[f'{key}_fit_params'] = fit_params
+            else:
+                fit_params = self.data[f'{key}_fit_params']
             quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
 
             df_inlier = pd.DataFrame(