评价修改

app/service/portfolio_diagnose.py

@@ -373,7 +373,7 @@ class PortfolioDiagnose(object):
 
             if proposal in fund_rank['fund_id'].to_list():
                 proposal_z_score = search_rank(fund_rank, proposal, metric='z_score')
-                proposal_strategy = fund_rank[fund_rank['fund_id'] == proposal]['substrategy'].values[0]
+                # proposal_strategy = fund_rank[fund_rank['fund_id'] == proposal]['substrategy'].values[0]
             else:
                 continue
 
@@ -430,24 +430,32 @@ class PortfolioDiagnose(object):
         mu = expected_returns.mean_historical_return(propose_portfolio, frequency=min(self.freq_list))
         S = risk_models.sample_cov(propose_portfolio, frequency=min(self.freq_list))
         dd = expected_returns.drawdown_from_prices(propose_portfolio)
+
         # if self.client_type == 1:
-        # proposal_risk = [[x, get_risk_level(search_rank(fund_rank, x, metric='substrategy'))] for x in
-        #                  self.proposal_fund]
-        # self.proposal_fund = list(filter(lambda x: x[1] != 3, proposal_risk))
-        # proposal_portfolio = list((set(self.portfolio) - set(self.no_data_fund) - set(self.replace_pair.keys())) | \
-        #                           (set(self.proposal_fund) | set(self.replace_pair.values())))
+            # proposal_risk = [[x, get_risk_level(search_rank(fund_rank, x, metric='substrategy'))] for x in
+            #                  propose_portfolio.columns]
+            # self.proposal_fund = list(filter(lambda x: x[1] != 'H', proposal_risk))
+
+            # drop_fund_list = list(filter(lambda x: x[1] = 'H', proposal_risk))
+            # proposal_portfolio = list((set(self.portfolio) - set(self.no_data_fund) - set(self.replace_pair.keys())) | \
+            #                           (set(self.proposal_fund) | set(self.replace_pair.values())))
+            # propose_portfolio.drop()
+
+
         propose_risk_mapper = dict()
         for fund in propose_portfolio.columns:
             propose_risk_mapper[fund] = str(get_risk_level(search_rank(fund_rank, fund, metric='substrategy')))
-        risk_upper = {"H": 0.0}
-        risk_lower = {"L": 0.6, "M": 0.4}
 
+        # risk_upper = {"H": 0.0}
+        # risk_lower = {"L": 0.6, "M": 0.4}
+
+        w_low = 1e6 / self.invest_amount
         ef = EfficientFrontier(mu, S, expected_drawdown=dd)
-        ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
+        # ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
         # weights = ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)
-        ef.efficient_drawdown(drawdown_limit=0.5)
+        ef.efficient_return(0.3)
         clean_weights = ef.clean_weights()
-        # ef.portfolio_performance(verbose=True)
+        ef.portfolio_performance(verbose=True)
         self.new_weights = np.array(list(clean_weights.values()))
         print(clean_weights)
         end4 = time.time()
@@ -607,10 +615,10 @@ class PortfolioDiagnose(object):
         else:
             evaluation = choose_bad_evaluation(data)
 
-        ret = ""
+        ret = []
         for k, v in evaluation.items():
             # print(translate_single(content[k], v))
-            ret = ret + sentence[k] % translate_single(content[k], v)
+            ret.append(sentence[k] % translate_single(content[k], v))
         fund_name = get_fund_name(fund_id).values[0][0]
         return {'name': fund_name, 'data': ret}