Merge branch 'dev' of http://47.100.44.39:10001/pengxiong/fund_report into dev

app/service/portfolio_diagnose.py

@@ -602,14 +602,18 @@ class PortfolioDiagnose(object):
             raise ValueError
 
         w_low = 1000000 / self.invest_amount
-        ef = EfficientFrontier(mu, S, weight_bounds=[w_low, 1], expected_drawdown=dd)
-        # ef = EfficientFrontier(mu, S, weight_bounds=[0, 1], expected_drawdown=dd)
-        ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
-        ef.efficient_return(target_return=self.expect_return, target_drawdown=self.expect_drawdown)
-        clean_weights = ef.clean_weights()
-        ef.portfolio_performance(verbose=True)
-        self.new_weights = np.array(list(clean_weights.values()))
-        print(clean_weights)
+        try:
+            ef = EfficientFrontier(mu, S, weight_bounds=[w_low, 1], expected_drawdown=dd)
+            # ef = EfficientFrontier(mu, S, weight_bounds=[0, 1], expected_drawdown=dd)
+            ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
+            ef.efficient_return(target_return=self.expect_return, target_drawdown=self.expect_drawdown)
+            clean_weights = ef.clean_weights()
+            ef.portfolio_performance(verbose=True)
+            self.new_weights = np.array(list(clean_weights.values()))
+        except:
+            self.new_weights = np.asarray([1/len(self.propose_portfolio.columns)] * len(self.propose_portfolio.columns))
+
+        print(self.new_weights)
         end4 = time.time()
         print("模型计算一次时间：", end4 - end3)
         # S = np.asmatrix(S)
@@ -1069,7 +1073,7 @@ class PortfolioDiagnose(object):
             radar_data.append(get_radar_data(fund))
         return radar_data
 
-#
+
 # portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
 # portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
 # portfolio_diagnose.optimize()