修复250

app/service/portfolio_diagnose.py

@@ -715,17 +715,6 @@ class PortfolioDiagnose(object):
         propose_fund_return_limit_data = propose_fund_return[propose_fund_return.index >= group_order_start_date]
         start_return = propose_fund_return_limit_data['return'].values[0]
         propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return)/(1+start_return)
-        # 指数收益
-        index_return = index_return[index_return.index >= group_order_start_date]
-        start_index_return = index_return[" close"].values[0]
-        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
-        index_return_ratio = index_return["new_index_return"].values[-1]
-        index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1], index_return["new_index_return"], 250)
-        index_volatility = volatility(index_return["new_index_return"]+1, 250)
-        index_drawdown = max_drawdown(index_return["new_index_return"]+1)
-        index_sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
-        index_exc = excess_return(index_sim, BANK_RATE, 250)
-        index_sharpe = sharpe_ratio(index_exc, index_sim, 250)
 
         # 新组合累积收益
         new_return_ratio = propose_fund_return_limit_data["new_return"].values[-1]
@@ -745,6 +734,17 @@ class PortfolioDiagnose(object):
         exc = excess_return(sim, BANK_RATE, n_freq)
         new_sharpe = sharpe_ratio(exc, sim, n_freq)
 
+        # 指数收益
+        index_return = index_return[index_return.index >= group_order_start_date]
+        start_index_return = index_return[" close"].values[0]
+        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
+        index_return_ratio = index_return["new_index_return"].values[-1]
+        index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1], index_return["new_index_return"], n_freq)
+        index_volatility = volatility(index_return["new_index_return"]+1, n_freq)
+        index_drawdown = max_drawdown(index_return["new_index_return"]+1)
+        index_sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
+        index_exc = excess_return(index_sim, BANK_RATE, n_freq)
+        index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
 
         # 收益对比数据
         return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]], right_index=True,