旧组合收益比较剥离

app/service/data_service.py

@@ -190,7 +190,7 @@ class UserCustomerDataAdaptor:
                 confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
-                cur_cnav = confirm_date_cnav_data.values[0] + diff_nav
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
                 cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
 
         cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')
@@ -285,7 +285,7 @@ class UserCustomerDataAdaptor:
                 confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
-                cur_cnav = confirm_date_cnav_data.values[0] + diff_nav
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
                 cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
 
         cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')

app/service/portfolio_diagnose.py

@@ -749,7 +749,41 @@ class PortfolioDiagnose(object):
         ret = []
         for k, v in data.items():
             ret.append(sentence[k].format(*data[k]).replace("，；", "；"))
-        return ret
+
+        # 旧组合累积收益df
+        group_result_data = group_result[group_name]
+        hold_info = group_result_data["group_hoding_info"]
+        group_order_df = data_adaptor.user_customer_order_df[
+            data_adaptor.user_customer_order_df["folio_name"] == group_name]
+        group_order_start_date = pd.to_datetime(group_order_df["confirm_share_date"].min())
+        old_return_df = group_result_data["return_df"]
+        old_return_df["cum_return_ratio"] = old_return_df["cum_return_ratio"] - 1
+
+        # 指数收益
+        index_data = get_index_daily(self.index_id, self.start_date)
+        index_data = pd.merge(index_data, self.propose_portfolio, how='inner', left_index=True, right_index=True)
+        index_return = index_data.iloc[:, :] / index_data.iloc[0, :] - 1
+
+        # 指数收益
+        index_return = index_return[index_return.index >= group_order_start_date]
+        start_index_return = index_return[" close"].values[0]
+        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
+
+        # 收益对比数据
+        return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]],
+                                     right_index=True,
+                                     left_index=True)
+        return_compare_df["date"] = return_compare_df.index
+        return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
+        return_compare_df.iloc[1:-1, :]["date"] = ""
+        old_return_compare_result = {
+
+            "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values},
+            "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values},
+            "xlabels": return_compare_df["date"].values
+        }
+
+        return ret, old_return_compare_result
 
     def new_evaluation(self, group_name, group_result, data_adaptor):
         try:

app/service/result_service_v2.py

@@ -369,8 +369,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             fund_hoding_info = {"fund_strategy_name": dict_substrategy[int(row["substrategy"])], "fund_name": row["fund_name"]}
             fund_hoding_info["confirm_date"] = row["confirm_share_date"]
             fund_hoding_info["hold_year"] = round((self.end_date - pd.to_datetime(row["confirm_share_date"])).days/365.0, 2)    # 存续年数
-            fund_hoding_info["weight"] = round(float(row["confirm_amount"]) / total_market_values * 100, 2)      # 月末占比
             fund_hoding_info["market_values"] = round((float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"]))/10000, 2)
+            fund_hoding_info["weight"] = round(fund_hoding_info["market_values"] / total_market_values * 10000.0 * 100, 2)  # 月末占比
             fund_hoding_info["cost"] = round(float(row["confirm_amount"])/10000, 2)     # 投资本金
             # 当月收益
             last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index<pd.to_datetime(self.month_start_date)][row["fund_id"]].dropna()

app/utils/jinjia2html.py

@@ -82,8 +82,8 @@ class DataIntegrate:
         self.monthly_table_return = self.df["month_return_data_dict"]
 
     def comments_on_position_portfolio(self):
-        """旧持仓组合点评."""
-        self.old_evaluation = self.portfolio_diagnose.old_evaluation('default', self.d, self.user_customer)
+        """旧持仓组合点评. 旧贡献分解数据"""
+        self.old_evaluation, self.old_return_compare_data = self.portfolio_diagnose.old_evaluation('default', self.d, self.user_customer)
 
     def contribution_deco(self):
         """贡献分解."""
@@ -116,12 +116,14 @@ class DataIntegrate:
 
     def get_old_compare_pic(self):
         """旧收益比较"""
-        self.suggestions_result, self.suggestions_result_asset, self.return_compare_data,\
-        self.indicator_compare, self.new_group_evaluation = self.portfolio_diagnose.new_evaluation("default", self.d, self.user_customer)
-        self.old_return_compare_pic = draw_old_combination_chart(self.return_compare_data["xlabels"], self.return_compare_data["origin_combination"], self.return_compare_data["index"])
+        self.old_return_compare_pic = draw_old_combination_chart(self.old_return_compare_data["xlabels"], self.old_return_compare_data["origin_combination"], self.old_return_compare_data["index"])
 
     def get_transfer_suggestions(self):
         """新收益比较，调仓建议"""
+        self.suggestions_result, self.suggestions_result_asset, self.return_compare_data, \
+        self.indicator_compare, self.new_group_evaluation = self.portfolio_diagnose.new_evaluation("default", self.d,
+                                                                                                   self.user_customer)
+
         self.return_compare_pic = draw_combination_chart(self.return_compare_data["xlabels"], self.return_compare_data["new_combination"],
                                                          self.return_compare_data["origin_combination"], self.return_compare_data["index"])