波动率

app/service/result_service_v2.py

@@ -91,10 +91,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             return_ratio_year = annual_return((resample_df["cum_return_ratio"].values[-1]-1), resample_df, n_freq)
             folio_report_data["return_ratio_year"] = float(return_ratio_year)
 
-            # 波动率
-            volatility_ = volatility(resample_df["cum_return_ratio"], n_freq)
-            folio_report_data["volatility"] = float(volatility_) if not math.isnan(volatility_) else 0.0
-
             # 最大回撤
             drawdown = max_drawdown(resample_df["cum_return_ratio"])
             folio_report_data["max_drawdown"] = drawdown
@@ -108,6 +104,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 sharpe = 0.0
             folio_report_data["sharpe"] = float(sharpe) if not math.isnan(sharpe) else 0.0
 
+            # 波动率
+            volatility_ = volatility(sim, n_freq)
+            folio_report_data["volatility"] = float(volatility_*100) if not math.isnan(volatility_) else 0.0
+
             # 期末资产
             ending_assets = round(float(cur_folio_result_cnav_data[[i + "_net_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
             folio_report_data["ending_assets"] = ending_assets
@@ -619,10 +619,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         return_ratio_year = annual_return((fund_nav_df["cum_return_ratio"].values[-1] - 1), fund_nav_df, n_freq)
         result["return_ratio_year"] = float(return_ratio_year)
 
-        # 波动率
-        volatility_ = volatility(fund_nav_df["cum_return_ratio"], n_freq)
-        result["volatility"] = float(volatility_) if not math.isnan(volatility_) else 0.0
-
         # 最大回撤
         drawdown = max_drawdown(fund_nav_df["cum_return_ratio"])
         result["max_drawdown"] = drawdown
@@ -636,6 +632,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             sharpe = 0.0
         result["sharpe"] = float(sharpe)
 
+        # 波动率
+        volatility_ = volatility(sim, n_freq)
+        result["volatility"] = float(volatility_*100) if not math.isnan(volatility_) else 0.0
+
         return result
 
     def get_month_return_chart(self):