Fix for #1467 (Forecast weight bug with auto-grouping)

Construct an empty DataFrame with a Datetime index instead of using concat.

syscore/pandas/frequency.py

@@ -215,6 +215,9 @@ def interpolate_for_a_single_day(
 
 
 def reindex_last_monthly_include_first_date(df: pd.DataFrame) -> pd.DataFrame:
+    if df.empty:
+        return df
+
     df_monthly_index = list(df.resample("1M").last().index)  ## last day in month
     df_first_date_in_index = df.index[0]
     df_monthly_index = [df_first_date_in_index] + df_monthly_index

systems/forecast_combine.py

@@ -251,6 +251,9 @@ def get_unsmoothed_forecast_weights(self, instrument_code: str):
             monthly_forecast_weights=monthly_forecast_weights,
         )
 
+        if forecast_weights_fixed_to_forecasts.empty:
+            return forecast_weights_fixed_to_forecasts
+
         # Remap to business day frequency so the smoothing makes sense also space saver
         daily_forecast_weights_fixed_to_forecasts_unsmoothed = (
             forecast_weights_fixed_to_forecasts.resample("1B").mean()
@@ -445,6 +448,9 @@ def get_forecasts_given_rule_list(
             for rule_variation_name in rule_variation_list
         ]
 
+        if not forecasts:
+            return pd.DataFrame(index=pd.DatetimeIndex([]))
+
         forecasts = pd.concat(forecasts, axis=1)
 
         forecasts.columns = rule_variation_list