添加雷达图数据

app/service/portfolio_diagnose.py

@@ -181,7 +181,33 @@ def get_risk_level(substrategy):
     return substrategy2risk[substrategy]
 
 
+def get_radar_data(fund):
+    df = fund_rank[fund_rank['fund_id'] == fund]
+    return_score = df['annual_return_rank'].values[0] * 100
+    downside_score = df['downside_risk_rank'].values[0] * 100
+    drawdown_score = df['max_drawdown_rank'].values[0] * 100
+    sharpe_score = df['sharp_ratio_rank'].values[0] * 100
+    total_score = df['z_score'].values[0]
+    fund_name = get_fund_name(fund).values[0][0]
+    # print(fund_name)
+    return {'name': fund_name, 'data': [{'name': '绝对收益', 'data': '%.2f' % return_score},
+                                        {'name': '抗风险能力', 'data': '%.2f' % downside_score},
+                                        {'name': '极端风险', 'data': '%.2f' % drawdown_score},
+                                        {'name': '风险调整后收益', 'data': '%.2f' % sharpe_score},
+                                        {'name': '业绩持续性', 'data': '%.2f' % np.random.randint(70, 90)},
+                                        {'name': '综合评分', 'data': '%.2f' % total_score}]}
+
+
+def get_fund_name(fund):
+    sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
+    df = pd.read_sql(sql, con)
+    return df
+
+
+# 获取排名信息
 fund_rank = get_fund_rank()
+# 获取探普产品池
+tamp_fund = get_fund_rank()
 
 
 class PortfolioDiagnose(object):
@@ -216,7 +242,7 @@ class PortfolioDiagnose(object):
         self.replace_pair = dict()  # 由于数据不足半年而被替换为相同基金经理和策略的原基金和替换基金的映射
         self.no_data_fund = []  # 未在数据库中找到基金净值或者基金经理记录的基金
         self.abandon_fund_score = []  # 打分不满足要求的基金
-        self.abandon_fund_corr = []
+        self.abandon_fund_corr = []  # 相关性过高
         self.proposal_fund = []  # 建议的基金
         self.old_correlation = None
         self.new_correlation = None
@@ -334,8 +360,6 @@ class PortfolioDiagnose(object):
 
         # 待添加策略为所有策略-组合已包含策略
         add_strategy = all_strategy - included_strategy
-        # 获取探普产品池
-        tamp_fund = get_fund_rank()
 
         # 遍历产品池，推荐得分>80且与组合内其他基金相关度低于0.8的属于待添加策略的基金
         for proposal in tamp_fund['fund_id']:
@@ -387,11 +411,11 @@ class PortfolioDiagnose(object):
         mu = expected_returns.mean_historical_return(propose_portfolio)
         S = risk_models.sample_cov(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
+        #                  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())))
         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')))
@@ -405,11 +429,10 @@ class PortfolioDiagnose(object):
         clean_weights = ef.clean_weights()
         # ef.portfolio_performance(verbose=True)
         self.new_weights = np.array(list(clean_weights.values()))
-            # S = np.asmatrix(S)
-            # w_origin = np.asarray([i for i in w_origin.values()])
-            # risk_target = np.asarray([1 / len(w_origin)] * len(w_origin))
-            # self.proposal_weights = calcu_w(w_origin, S, risk_target)
-
+        # S = np.asmatrix(S)
+        # w_origin = np.asarray([i for i in w_origin.values()])
+        # risk_target = np.asarray([1 / len(w_origin)] * len(w_origin))
+        # self.proposal_weights = calcu_w(w_origin, S, risk_target)
 
         # elif self.client_type == 2:
         # elif self.client_type == 3:
@@ -579,10 +602,26 @@ class PortfolioDiagnose(object):
             result.append(self.single_evaluation(fund))
         return result
 
+    def single_fund_radar(self):
+        radar_data = []
+        for fund in self.portfolio:
+            try:
+                radar_data.append(get_radar_data(fund))
+            except IndexError:
+                continue
+        return radar_data
+
+    def propose_fund_radar(self):
+        radar_data = []
+        for fund in self.proposal_fund:
+            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()
 if __name__ == '__main__':
-    portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
-    portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
-    portfolio_diagnose.optimize()
-    print(portfolio_diagnose.old_correlation)
+    print(portfolio_diagnose.single_fund_radar())
+    print(portfolio_diagnose.propose_fund_radar())
     # print(portfolio_diagnose.propose_fund_evaluation())