修改综合数据和组合数据的错乱bug

app/service/data_service.py

@@ -233,8 +233,8 @@ class UserCustomerDataAdaptor:
             cnav_df[p_fund_id_ + "_earn"] = (cnav_df[p_fund_id_ + "_profit"] * cnav_df[p_fund_id_ + "_share"]).apply(lambda x: float(x)).fillna(0)
             # cnav_df[p_fund_id_ + "_earn"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: float(x))
             cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
-            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"] + cnav_df[p_fund_id_ + "_amount"].apply(lambda x: float(x))
-            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
+            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
+            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
         self.group_data[p_folio] = {"result_cnav_data": cnav_df, "order_df": p_order_df}
         return cnav_df
 
@@ -311,6 +311,6 @@ class UserCustomerDataAdaptor:
             cnav_df[p_fund_id_ + "_earn"] = (cnav_df[p_fund_id_ + "_profit"] * cnav_df[p_fund_id_ + "_share"]).apply(lambda x: float(x)).fillna(0)
             # cnav_df[p_fund_id_ + "_earn"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: float(x))
             cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
-            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"] + cnav_df[p_fund_id_ + "_amount"].apply(lambda x: float(x))
-            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
+            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
+            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
         return cnav_df

app/templates/v2/monthReportV2.html

@@ -1054,7 +1054,7 @@
                 成立以来累计收益率：<span class="red">{{totoal_rate_of_return}}%</span>
               </td>
               <td>
-                年化收益率：<span class="green">{{annualised_return}}%</span>
+                年化收益率：<span class="green">{{now_annualised_return}}%</span>
               </td>
               <td>
                 最大回撤：<span class="red">{{now_withdrawal}}%</span>
@@ -1073,7 +1073,7 @@
           <table class="tss1_table">
             <tr>
               <td>
-                投资成本：<span class="red">{{cost_of_investment}}元</span>
+                投资成本：<span class="red">{{now_allocation_amount}}元</span>
               </td>
               <td>
                 期末资产：<span class="green">{{final_balance}}元</span>

app/utils/jinjia2html.py

@@ -24,7 +24,7 @@ class DataIntegrate:
         # 组合数据
         self.group_result = self.d["default"]
         self.get_portfolio_diagnose(self.group_result["fund_id_list"])
-        # 几月综述部分
+        # 投资总览
         self.get_summarize()
         # 月度回报
         self.get_month_return()
@@ -56,12 +56,19 @@ class DataIntegrate:
         self.portfolio_diagnose.optimize()
 
     def get_summarize(self):
-        """几月综述部分."""
-        self.total_cost = self.df["total_cost"]/10000
-        self.now_yield = round((self.df['cumulative_return']-1)*100,2)
-        self.index_yield = round((self.df["index_result"]["return_ratio"]-1)*100, 2)
-        self.now_withdrawal = round(self.df["max_drawdown"][0]*100,2)
-        self.index_withdrawal = round(self.df["index_result"]["max_drawdown"][0]*100, 2)
+        """投资总览."""
+        self.total_cost = round(self.df["total_cost"], 2)   # 投资成本
+        self.now_yield = round((self.df['cumulative_return']-1)*100, 2)     # 成立以来累计收益率
+        self.now_annualised_return = round(self.df["return_ratio_year"] * 100, 2)  # 年化收益率
+        self.index_yield = round((self.df["index_result"]["return_ratio"]-1)*100, 2)    # 指数收益率
+        self.now_withdrawal = round(self.df["max_drawdown"][0]*100, 2)  # 最大回撤
+        self.index_withdrawal = round(self.df["index_result"]["max_drawdown"][0]*100, 2)    # 指数最大回撤
+        self.now_month_income = int(self.df["cur_month_profit"])  # 本月收益
+        self.month_rise = round(self.df["cur_month_profit_ratio"] * 100, 2)  # 本月涨幅
+        self.year_totoal_rate_of_return = round(self.df["cur_year_profit_ratio"] * 100, 2)  # 今年累计收益率
+        self.now_year_income = int(self.df["cur_year_profit"])  # 今年累计收益
+        self.final_balance = int(self.df["total_cost"] + self.df["cumulative_profit"])  # 期末资产
+        self.total_profit = int(self.df["cumulative_profit"])  # 累计盈利
 
     def get_month_return(self):
         """月度回报."""
@@ -112,7 +119,6 @@ class DataIntegrate:
         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"])
 
-
     def get_old_correlation(self):
         """旧相关性分析."""
         old_correlation = self.portfolio_diagnose.old_correlation
@@ -120,7 +126,6 @@ class DataIntegrate:
         old_correlation_values = old_correlation.values.tolist()
         self.old_correlation = list(zip(range(1, len(old_correlation_columns)+1), old_correlation_columns, old_correlation_values))
 
-
     def get_new_correlation(self):
         """新相关性分析."""
         new_correlation = self.portfolio_diagnose.new_correlation
@@ -128,7 +133,6 @@ class DataIntegrate:
         new_correlation_values = new_correlation.values.tolist()
         self.new_correlation = list(zip(range(1, len(new_correlation_columns)+1), new_correlation_columns, new_correlation_values))
 
-
     def propose_fund(self):
         """新增基金"""
         # 优化组合建议1 -- 新增基金
@@ -145,19 +149,13 @@ class DataIntegrate:
 
     def objectives_performance(self, group_result):
         """目标与业绩"""
-        self.now_month_income = int(group_result["cur_month_profit"])   # 本月收益
-        self.now_year_income = int(group_result["cur_year_profit"])   # 今年累计收益
+
         self.totoal_rate_of_return = round((group_result['cumulative_return']-1)*100, 2)       # 成立以来累计收益率
-        self.month_rise = round(group_result["cur_month_profit_ratio"]*100, 2)       # 本月涨幅s
-        self.year_totoal_rate_of_return = round(group_result["cur_year_profit_ratio"]*100, 2)        # 今年累计收益率
         self.annualised_return = round(group_result["return_ratio_year"]*100, 2)     # 年化收益率
         self.volatility = round(group_result["volatility"]*100, 2)
         self.max_withdrawal = round(group_result["max_drawdown"][0]*100, 2)
         self.sharpe_ratio = round(group_result["sharpe"], 2)
         self.cost_of_investment = round(group_result["total_cost"]/10000.0, 2)    # 投资成本
-        self.final_balance = int(group_result["total_cost"] + group_result["cumulative_profit"])    # 期末资产
-        self.total_profit = int(group_result["cumulative_profit"])  # 累计盈利
-
         self.index_section_return = round((group_result["index_result"]["return_ratio"]-1)*100, 2)
         self.index_annualised_return = round(group_result["index_result"]["return_ratio_year"]*100, 2)     # 年化收益率
         self.index_volatility = round(group_result["index_result"]["volatility"]*100, 2)
@@ -205,7 +203,7 @@ class DataIntegrate:
                 # 'new_evaluation': self.new_evaluation,
                 'position_years': '5年', 'planned_allocation_amount': 2000.00,
                 'now_allocation_amount': self.total_cost, 'now_yield': self.now_yield, 'index_yield': self.index_yield,
-                'expected_yield': 20,
+                'expected_yield': 20, "now_annualised_return ": self.now_annualised_return,
                 'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal, 'expected_withdrawal': 20,
                 'now_year_income': self.now_year_income, 'now_month_income': self.now_month_income,
                 'totoal_rate_of_return': self.totoal_rate_of_return,