diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 2cde31441..5c9eef03f 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -112,7 +112,7 @@ class FreqaiDataKitchen:
         self.unique_class_list: list = []
         self.backtest_live_models_data: Dict[str, Any] = {}
         self.normalizer: Normalization = normalization_factory(config, self.data, self.pkl_data,
-                                                self.unique_class_list)
+                                                               self.unique_class_list)
 
     def set_paths(
         self,
diff --git a/freqtrade/freqai/normalization.py b/freqtrade/freqai/normalization.py
index 641bfcdaa..ae395008a 100644
--- a/freqtrade/freqai/normalization.py
+++ b/freqtrade/freqai/normalization.py
@@ -16,20 +16,19 @@ def normalization_factory(
             config: Config,
             meta_data: Dict[str, Any],
             pickle_meta_data: Dict[str, Any],
-            unique_class_list: list
-    ):
-        freqai_config: Dict[str, Any] = config["freqai"]
-        norm_config_id = freqai_config["feature_parameters"].get("data_normalization", "legacy")
-        if norm_config_id.lower() == "legacy":
-            return LegacyNormalization(config, meta_data, pickle_meta_data, unique_class_list)
-        elif norm_config_id.lower() == "standard":
-            return StandardNormalization(config, meta_data, pickle_meta_data, unique_class_list)
-        elif norm_config_id.lower() == "minmax":
-            return MinMaxNormalization(config, meta_data, pickle_meta_data, unique_class_list)
-        elif norm_config_id.lower() == "quantile":
-            return QuantileNormalization(config, meta_data, pickle_meta_data, unique_class_list)
-        else:
-            raise OperationalException(f"Invalid data normalization identifier '{norm_config_id}'")
+            unique_class_list: list):
+    freqai_config: Dict[str, Any] = config["freqai"]
+    norm_config_id = freqai_config["feature_parameters"].get("data_normalization", "legacy")
+    if norm_config_id.lower() == "legacy":
+        return LegacyNormalization(config, meta_data, pickle_meta_data, unique_class_list)
+    elif norm_config_id.lower() == "standard":
+        return StandardNormalization(config, meta_data, pickle_meta_data, unique_class_list)
+    elif norm_config_id.lower() == "minmax":
+        return MinMaxNormalization(config, meta_data, pickle_meta_data, unique_class_list)
+    elif norm_config_id.lower() == "quantile":
+        return QuantileNormalization(config, meta_data, pickle_meta_data, unique_class_list)
+    else:
+        raise OperationalException(f"Invalid data normalization identifier '{norm_config_id}'")
 
 
 class Normalization(ABC):
@@ -268,5 +267,3 @@ class QuantileNormalization(SKLearnNormalization):
                  unique_class_list: list):
         super().__init__(config, meta_data, pickle_meta_data, unique_class_list,
                          QuantileTransformer)
-
-
diff --git a/tests/freqai/test_normalization.py b/tests/freqai/test_normalization.py
index 36294215e..375f6c477 100644
--- a/tests/freqai/test_normalization.py
+++ b/tests/freqai/test_normalization.py
@@ -84,6 +84,7 @@ def test_assertion_invalid_normalization_id(mocker, freqai_conf):
         assert str(e_info).startswith("Invalid data normalization identifier"), \
             "unexpected exception string"
 
+
 @pytest.mark.parametrize(
     "config_id",
     [