diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py
index 9e96c497..7bf02254 100644
--- a/src/features/cr_features_helper_methods.py
+++ b/src/features/cr_features_helper_methods.py
@@ -21,7 +21,7 @@ def extract_second_order_features(intraday_features, so_features_names, prefix="
             so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1)
         
         if "sd" in so_features_names:
-            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().add_suffix("_SO_sd")], axis=1)
+            so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().fillna(0).add_suffix("_SO_sd")], axis=1)
         
         if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
             for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]: