波动率，最大回撤

app/service/portfolio_diagnose.py

@@ -735,7 +735,7 @@ class PortfolioDiagnose(object):
             self.proposal_fund.append(proposal)
 
         prod = prod[(prod.index >= self.start_date) & (prod.index <= self.end_date)]
-        prod = resample(prod, get_trade_cal(), min(self.freq_list))
+        prod = resample(prod, get_trade_cal(), max(self.freq_list))
         if 'cal_date' in prod.columns:
             prod.drop(labels='cal_date', inplace=True, axis=1)
         if 'end_date' in prod.columns:
@@ -1199,13 +1199,10 @@ class PortfolioDiagnose(object):
             # 新组合累积收益
             new_return_ratio = propose_fund_return_limit_data["new_return"].values[-1]
             # 新组合区间年化收益率
-            freq_max = group_order_df["freq"].max()
+            freq_max = group_order_df["freq"].min()
             n_freq = freq_days(int(freq_max))
             new_return_ratio_year = annual_return(propose_fund_return_limit_data["new_return"].values[-1], propose_fund_return_limit_data, n_freq)
 
-            # 新组合波动率
-            new_volatility = volatility(propose_fund_return_limit_data["new_return"]+1, n_freq)
-
             # 新组合最大回撤
             new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"]+1)
 
@@ -1218,6 +1215,8 @@ class PortfolioDiagnose(object):
                     new_sharpe = 0
             except:
                 new_sharpe = 0
+            # 新组合波动率
+            new_volatility = volatility(sim, n_freq)
 
             # 指数收益
             index_return = index_return[index_return.index >= group_order_start_date]