Merge pull request #1468 from vishalg/bug_1467_forecast_weight_auto_g…

…rouping

Fix for - #1467 forecast weight auto grouping

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

sysproduction/strategy_code/report_system_classic.py

@@ -212,10 +212,16 @@ def get_forecast_matrix_over_code(
     for instrument_code in instrument_codes:
         stage = getattr(data_backtest.system, stage_name)
         method = getattr(stage, method_name)
-        value_row = method(instrument_code).ffill()[:datetime_cutoff].iloc[-1]
-        values_by_rule = [
-            value_row.get(rule_name, np.nan) for rule_name in trading_rule_names
-        ]
+        values = method(instrument_code).ffill()[:datetime_cutoff]
+
+        if not values.empty:
+            value_row = values.iloc[-1]
+            values_by_rule = [
+                value_row.get(rule_name, np.nan) for rule_name in trading_rule_names
+            ]
+        else:
+            values_by_rule = [np.nan] * len(trading_rule_names)
+
         value_dict[instrument_code] = values_by_rule
 
     value_df = pd.DataFrame(value_dict, index=trading_rule_names)

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()
@@ -432,7 +435,7 @@ def get_trading_rule_list_for_estimated_weights(self, instrument_code: str) -> l
     @diagnostic()
     def get_forecasts_given_rule_list(
         self, instrument_code: str, rule_variation_list: list
-    ) -> pd.Series:
+    ) -> pd.DataFrame:
         """
         Convenience function to get a list of forecasts
 
@@ -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