修改排名数据

app/service/portfolio_diagnose.py

@@ -109,18 +109,18 @@ def choose_good_evaluation(evaluation):
     v2 = evaluation[2]
     v3 = evaluation[3]
     v4 = evaluation[4]
-    v5 = evaluation[5]
+    v5 = evaluation.get(5)
 
     if v1[0] > 1:
         del evaluation[1]
-    # if v2[0] > 1:
-    if v2:
+    if v2[0] > 1:
         del evaluation[2]
     if v3[0] > 1:
         del evaluation[3]
     if v4[0] != 0 or v4[1] != 0:
         del evaluation[4]
-    if v5[0] < 3 or v5[2] > 1:  # 基金经理的基金管理年限小于三年或平均业绩处于中下水平
+    # if v5[0] < 3 or v5[2] > 1:  # 基金经理的基金管理年限小于三年或平均业绩处于中下水平
+    if v5:
         del evaluation[5]
 
     return evaluation
@@ -134,8 +134,7 @@ def choose_bad_evaluation(evaluation):
 
     if v1[0] < 2:
         del evaluation[1]
-    # if v2[0] < 2:
-    if v2:
+    if v2[0] < 2:
         del evaluation[2]
     if v3[0] < 2:
         del evaluation[3]
@@ -152,7 +151,7 @@ def get_fund_rank():
     """
     with TAMP_SQL(tamp_fund_engine) as tamp_fund:
         tamp_fund_session = tamp_fund.session
-        sql = "SELECT * FROM fund_rank"
+        sql = "SELECT * FROM new_fund_rank"
 
         # df = pd.read_sql(sql, con)
         # df = pd.read_csv('fund_rank.csv', encoding='gbk')
@@ -455,8 +454,8 @@ class PortfolioDiagnose(object):
 
         """
         self.old_correlation = cal_correlation(prod)
-
         for fund in prod.columns:
+            print(fund)
             z_score = search_rank(fund_rank, fund, metric='z_score')
             # 建议替换得分为60或与其他基金相关度大于0.8的基金
             if z_score < 60:
@@ -1061,14 +1060,14 @@ class PortfolioDiagnose(object):
         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__':
-#     print(portfolio_diagnose.single_fund_radar())
-#     print(portfolio_diagnose.propose_fund_radar())
-#     print(portfolio_diagnose.old_portfolio_evaluation())
-#     print('旧组合相关性：', portfolio_diagnose.old_correlation)
-#     print('新组合相关性：', portfolio_diagnose.new_correlation)
-#     print('旧组合个基评价：', portfolio_diagnose.old_portfolio_evaluation())
-#     print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())
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+portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
+portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
+portfolio_diagnose.optimize()
+if __name__ == '__main__':
+    print(portfolio_diagnose.single_fund_radar())
+    print(portfolio_diagnose.propose_fund_radar())
+    print(portfolio_diagnose.old_portfolio_evaluation())
+    print('旧组合相关性：', portfolio_diagnose.old_correlation)
+    print('新组合相关性：', portfolio_diagnose.new_correlation)
+    print('旧组合个基评价：', portfolio_diagnose.old_portfolio_evaluation())
+    print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())
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