更改持仓报告不经过建议计算

app/service/result_service_v2.py

@@ -12,6 +12,7 @@ import datetime
 from decimal import Decimal
 from app.service.data_service_v2_1 import UserCustomerDataAdaptor
 from app.utils.fund_rank import get_frequency
+from app.service.portfolio_diagnose import cal_correlation, get_fund_name, get_frequency
 from app.utils.week_evaluation import *
 
 
@@ -194,6 +195,13 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                                                           "index")
             folio_report_data["index_result"] = index_result
             folio_report_data["return_df"] = resample_df
+
+            # 对应组合相关性
+            min_date = cur_folio_order_data["confirm_share_date"].min()
+            df_ = self.total_customer_order_cnav_df[fund_id_list]
+            df_ = df_[df_.index >= min_date]
+            correlation = self.old_correlation(df_)
+            folio_report_data["correlation"] = correlation
             self.group_result_data[folio] = folio_report_data
 
         return self.group_result_data
@@ -623,12 +631,11 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     def get_group_data(self):
         return self.group_result_data
 
-    # def __month_return(self, folio_cnav_data):
-    #     # 月度回报
-    #     def year_month(x):
-    #         a = x.year
-    #         b = x.month
-    #         return str(a) + "-" + str(b)
-    #     p_folio_cnav_data = folio_cnav_data.copy()
-    #     p_folio_cnav_data["date"] = p_folio_cnav_data.index
-    #     grouped_data = p_folio_cnav_data.groupby(p_folio_cnav_data["date"].apply(year_month))
+    def old_correlation(self, cnav_data):
+        folio_cnav_data = cnav_data.copy()
+        folio_cnav_data = folio_cnav_data.fillna(method="bfill")
+        old_correlation = cal_correlation(folio_cnav_data)
+        old_correlation = old_correlation.fillna(1).round(2)
+        old_correlation.columns = old_correlation.columns.map(lambda x: get_fund_name(x, self.all_fund_type_dict[x]).values[0][0])
+        old_correlation.index = old_correlation.index.map(lambda x: get_fund_name(x, self.all_fund_type_dict[x]).values[0][0])
+        return old_correlation

app/utils/jinjia2html_v2.py

@@ -56,7 +56,8 @@ class DataIntegrate:
                 'suggestions_result_asset': {},
                 'return_compare_pic': [],
                 'indicator_compare': [],
-                'new_group_evaluation': []
+                'new_group_evaluation': [],
+                "correlation": group_result["correlation"]
             }
 
             # 旧持仓组合点评
@@ -84,10 +85,14 @@ class DataIntegrate:
     def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
         if invest_amount < 10000000:
             invest_amount = 10000000
-        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=self.user_customer.all_fund_type_dict,
+        folio_fund_dict = {}
+        for fd in portfolio:
+            folio_fund_dict[fd] = self.user_customer.all_fund_type_dict[fd]
+        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=folio_fund_dict,
                                                invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
-        portfolio_diagnose.optimize()
+        if self.type == 2:
+            portfolio_diagnose.optimize()
         return portfolio_diagnose
 
     # 全部数据综述结果
@@ -169,7 +174,7 @@ class DataIntegrate:
 
     def get_old_correlation(self, portfolio_diagnose, cur_group_portfolio_result):
         """旧相关性分析."""
-        old_correlation = portfolio_diagnose.old_correlation
+        old_correlation = cur_group_portfolio_result["correlation"]
         old_correlation_columns = old_correlation.columns.tolist()
         old_correlation_values = old_correlation.values.tolist()
         cur_group_portfolio_result["old_correlation"] = list(zip(range(1, len(old_correlation_columns)+1), old_correlation_columns, old_correlation_values))