新版持仓报告修复bug

app/service/data_service_v2_1.py

@@ -106,11 +106,32 @@ class UserCustomerDataAdaptor:
 
             user_customer_order_df = order_df.set_index('fund_id').join(product_df.set_index('fund_id')).reset_index()
             user_customer_order_df["confirm_share_date"] = user_customer_order_df["confirm_share_date"].apply(lambda x: pd.to_datetime(x.date()))
-            self.start_date = user_customer_order_df["confirm_share_date"].min()
             self.customer_real_name = user_customer_order_df["customer_name"].values[0]
             self.ifa_real_name = user_customer_order_df["username"].values[0]
             user_customer_order_df = user_customer_order_df[user_customer_order_df["confirm_share_date"] <= self.end_date]
+            user_customer_order_df["confirm_amount"] = user_customer_order_df["confirm_amount"] - user_customer_order_df["subscription_fee"]
             user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
+
+            for index, row in user_customer_order_df.iterrows():
+                if row["order_type"] == 2:
+                    need_less_share = row["confirm_share"]
+                    for index_ori, row_ori in user_customer_order_df.iterrows():
+                        if index_ori >= index:
+                            break
+                        if row["fund_id"] == row_ori["fund_id"] and row_ori["order_type"] == 1:
+                            ori_share = row_ori["confirm_share"]
+                            if need_less_share >= ori_share:
+                                need_less_share -= ori_share
+                                user_customer_order_df.loc[index_ori, "confirm_share"] = 0
+                            else:
+                                need_less_share = 0
+                                user_customer_order_df.loc[index_ori, "confirm_share"] = row_ori[
+                                                                                           "confirm_share"] - need_less_share
+                            if need_less_share <= 0:
+                                break
+            user_customer_order_df = user_customer_order_df[(user_customer_order_df["order_type"] == 1)&(user_customer_order_df["confirm_share"] > 0)]
+            user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
+            self.start_date = user_customer_order_df["confirm_share_date"].min()
             return user_customer_order_df
 
     # 获取客户持有的基金净值数据

app/service/result_service_v2.py

@@ -28,7 +28,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             folio_report_data = {}
 
             cur_folio_result_cnav_data = self.group_data[folio]["result_cnav_data"]
-            cur_folio_order_data = self.group_data[folio]["order_df"]
+            cur_folio_order_data = self.group_data[folio]["order_df"].copy()
+
             freq_max = cur_folio_order_data["freq"].max()
             first_trade_date = cur_folio_order_data["confirm_share_date"].min()
 
@@ -62,8 +63,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                                                                "cumulative": cumulative_data}
 
             # 总成本
-            total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
-                         cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
+            total_cost = round(float((cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_share"]*cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["nav"]).sum()), 2)
             folio_report_data["total_cost"] = total_cost
 
             # 累积盈利
@@ -103,7 +103,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 本月累积收益率
             last_profit_ratio = return_ratio_df.loc[:self.month_start_date, "cum_return_ratio"].values
-            cur_profit_ratio = return_ratio_df.loc[self.month_start_date:, "cum_return_ratio"].values
+            cur_profit_ratio = return_ratio_df.loc[self.month_start_date - datetime.timedelta(days=1):, "cum_return_ratio"].values
             if len(last_profit_ratio) <= 0:
                 cur_month_profit_ratio = cur_profit_ratio[-1] - 1
             else:
@@ -182,7 +182,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         report_data = {}
 
         cur_folio_result_cnav_data = self.total_customer_order_cnav_df.copy()
-        cur_folio_order_data = self.user_customer_order_df
+        cur_folio_order_data = self.user_customer_order_df.copy()
         freq_max = cur_folio_order_data["freq"].max()
         #
         fund_id_list = list(cur_folio_order_data["fund_id"].unique())
@@ -207,8 +207,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         resample_df = resample_df[resample_df.index <= self.end_date]
 
         # 总成本
-        total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
-                           cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
+        total_cost = round(float((cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_share"]*cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["nav"]).sum()), 2)
         report_data["total_cost"] = total_cost
 
         # 累积盈利
@@ -339,6 +338,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         n_freq = freq_days(int(freq_max))
         resample_df = resample(p_result_cnav_data, self.trade_cal_date, freq_max)
         for index, row in p_order_df.iterrows():
+            if row['order_type'] == 2 or row["confirm_share"] <= 0:
+                continue
             cur_fund_id = str(row["fund_id"])
             cur_fund_performance = self.all_fund_performance[cur_fund_id]
             if len(cur_fund_performance) <=0:
@@ -399,24 +400,37 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             fund_hoding_info["confirm_date"] = row["confirm_share_date"].strftime("%Y-%m-%d")
             fund_hoding_info["hold_year"] = "%.2f" % round((self.end_date - pd.to_datetime(row["confirm_share_date"])).days/365.0, 2)    # 存续年数
             fund_hoding_info["market_values"] = round((float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"]))/10000, 2)
+            temp_market_values = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"])
             fund_hoding_info["weight"] = "%.2f" % round(float(fund_hoding_info["market_values"]) / total_market_values * 10000.0 * 100, 2)  # 月末占比
             fund_hoding_info["cost"] = "%.2f" % 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()
+            if row['confirm_share_date'] > self.month_start_date:
+                cal_month_start_date = row['confirm_share_date']
+                last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index == pd.to_datetime(cal_month_start_date)][
+                    row["fund_id"]].dropna()
+            else:
+                cal_month_start_date = self.month_start_date - datetime.timedelta(days=1)
+                last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index<pd.to_datetime(cal_month_start_date)][row["fund_id"]].dropna()
             if len(last_month_cnav_serise) == 0:
                 fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) / 10000, 2)
+                temp_profit = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav)
+                temp_profit_ratio = (fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav
             else:
                 last_month_cnav = float(last_month_cnav_serise.values[-1])
                 fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - last_month_cnav)/10000, 2)
+                temp_profit = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - last_month_cnav)
+                temp_profit_ratio = (fund_basic_info["cur_cnav"] - last_month_cnav) / last_month_cnav
             # 当月收益率
-            fund_hoding_info["month_return_ratio"] = "%.2f" % round(float(fund_hoding_info["profit"]) / float(fund_hoding_info["market_values"])*100, 2)
+            # fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit / temp_market_values*100, 2)
+            fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit_ratio * 100, 2)
             # 累积收益
             fund_hoding_info["cum_profit"] = "%.2f" % round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) / 10000, 2)
             # 累积收益率
             fund_hoding_info["cum_profit_ratio"] = "%.2f" % round((fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav*100, 2)
+            cum_profit_ratio_temp = (fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav
             # 累积年化收益率
             cur_resample_df = resample_df[[row["fund_id"]]].dropna()
-            return_ratio_year = annual_return(float(fund_hoding_info["cum_profit_ratio"])/100.0, cur_resample_df, n_freq)
+            return_ratio_year = annual_return(float(cum_profit_ratio_temp), cur_resample_df, n_freq)
             fund_hoding_info["return_ratio_year"] = "%.2f" % round(float(return_ratio_year)*100, 2)
             group_fund_hoding_info.append(fund_hoding_info)
         return group_fund_basic_info, group_fund_hoding_info

app/templates/v2/monthReportV2.1.html

@@ -1046,7 +1046,7 @@
                 {%endif%}
               </td>
               <td>
-                {%if (now_month_income| float) >=0%}
+                {%if (now_month_income_temp| float) >=0%}
                 本月收益：<span class="red">{{now_month_income}}元</span>
                 {%else%}
                 本月收益：<span class="green">{{now_month_income}}元</span>
@@ -1060,7 +1060,7 @@
                 {%endif%}
               </td>
               <td>
-                {%if (now_year_income|float)>=0%}
+                {%if (now_year_income_temp|float)>=0%}
                 今年累计收益：<span class="red">{{now_year_income}}元</span>
                 {%else%}
                 今年累计收益：<span class="green">{{now_year_income}}元</span>
@@ -1113,7 +1113,7 @@
               </td>
               <td>
 
-                {%if (total_profit|float)>=0%}
+                {%if (total_profit_temp|float)>=0%}
                 累计盈利：<span class="red">{{total_profit}}元</span>
                 {%else%}
                 累计盈利：<span class="green">{{total_profit}}元</span>

app/utils/jinjia2html_v2.py

@@ -263,6 +263,8 @@ class DataIntegrate:
                 'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal, 'expected_withdrawal': 20,
                 'now_year_income': '{:,}'.format(self.now_year_income), 'now_month_income': '{:,}'.format(self.now_month_income),
                 'final_balance': '{:,}'.format(self.final_balance), 'total_profit': '{:,}'.format(self.total_profit),
+                'total_profit_temp': self.total_profit,
+                'now_year_income_temp': self.now_year_income, 'now_month_income_temp': self.now_month_income,
 
                 'monthly_return_performance_pic': self.monthly_return_performance_pic,
                 'month_rise': self.month_rise, 'year_totoal_rate_of_return': self.year_totoal_rate_of_return,