内外部订单隔离计算分红订单收益与成本

app/service/result_service_v2.py

@@ -14,6 +14,7 @@ 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 *
+from app.service.substrategy_dict import get_substrategy_name
 
 
 class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
@@ -65,7 +66,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 resample_df.set_index("index_date", inplace=True)
             resample_df = resample_df[resample_df.index <= self.end_date]
 
-
             # 收益分解df
             contribution_decomposition_df = contribution_decomposition.fillna(0)*100
             p_plot_data = []
@@ -79,7 +79,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                                                                "cumulative": cumulative_data}
 
             # 总成本
-            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()), 0)
+            total_cost = round(float(round(float(cur_folio_result_cnav_data[[i + "_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)), 0)
             folio_report_data["total_cost"] = total_cost
 
             # 累积盈利
@@ -109,8 +109,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             folio_report_data["sharpe"] = float(sharpe) if not math.isnan(sharpe) else 0.0
 
             # 期末资产
-            ending_assets = cumulative_profit + total_cost
-            folio_report_data["ending_assets"] = float(ending_assets)
+            ending_assets = round(float(cur_folio_result_cnav_data[[i + "_net_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
+            folio_report_data["ending_assets"] = ending_assets
 
             # 本月收益
             cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date+datetime.timedelta(days=1), fund_id_list_earn]
@@ -250,7 +250,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         resample_df = resample_df[resample_df.index <= self.end_date]
 
         # 总成本
-        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()), 0)
+        total_cost = round(float(cur_folio_result_cnav_data[[i + "_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
         report_data["total_cost"] = total_cost
 
         # 累积盈利
@@ -277,8 +277,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         # report_data["sharpe"] = float(sharpe)
 
         # 期末资产
-        ending_assets = cumulative_profit + total_cost
-        report_data["ending_assets"] = float(ending_assets)
+        ending_assets = round(float(cur_folio_result_cnav_data[[i + "_net_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
+        report_data["ending_assets"] = ending_assets
 
         # 本月收益
         cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date + datetime.timedelta(days=1),
@@ -377,8 +377,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)], "index")
         report_data["index_result"] = index_result
 
-        # self.__month_return(cur_folio_result_cnav_data, fund_id_list)
-
         self.total_result_data = report_data
         return report_data
 
@@ -389,12 +387,14 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         freq_max = p_order_df["freq"].max()
         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
+        # for index, row in p_order_df.iterrows():
+        for hold_fund_id in p_order_df["fund_id"].unique():
+            order = p_order_df[(p_order_df["fund_id"] == hold_fund_id)]
+            row = order[order["order_type"] == 1].iloc[0]
+
             cur_fund_id = str(row["fund_id"])
             cur_fund_performance = self.all_fund_performance[cur_fund_id]
-            if len(cur_fund_performance) <=0:
+            if len(cur_fund_performance) <= 0:
                 fund_basic_info = {"fund_name": row["fund_name"], "confirm_nav": round(row["nav"], 4)}
                 fund_basic_info["cur_nav"] = round(float(self.fund_nav_total[cur_fund_id].dropna().values[-1]), 4)
                 fund_basic_info["cur_cnav"] = round(float(self.fund_cnav_total[cur_fund_id].dropna().values[-1]), 4)
@@ -408,8 +408,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 # 申购以来
                 confirm_date = pd.to_datetime(row["confirm_share_date"])
                 confirm_cnav = float(p_result_cnav_data.loc[confirm_date, cur_fund_id])
-                fund_basic_info["ret_after_confirm"] = round(
-                    (fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav * 100, 2)
+                fund_basic_info["ret_after_confirm"] = str(round(
+                    (fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav * 100, 2)) + "%"
                 # 分红
                 distribution_df = self.all_fund_distribution[cur_fund_id]
                 if distribution_df.empty:
@@ -448,23 +448,19 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 基金持仓数据
             total_market_values = p_sum_profit + p_total_amount #   月末总市值
-            fund_strategy_name = dict_substrategy[int(row["substrategy"])]
+            fund_strategy_name = get_substrategy_name(row["substrategy"])
             if "长富" in row["fund_name"] or "盈沛" in row["fund_name"]:
                 fund_strategy_name = "FOF"
             fund_hoding_info = {"fund_strategy_name": fund_strategy_name, "fund_name": row["fund_name"]}
             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
-            # fund_hoding_info["cost"] = "%.2f" % round(float(row["confirm_share"]*row["nav"])/10000, 2)
-            temp_market_values = float(row["confirm_share"]) * (
-                        fund_basic_info["cur_cnav"] - confirm_cnav + float(row["nav"]))
+            # 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(p_result_cnav_data[cur_fund_id + "_net_amount"].values[-1])
             fund_hoding_info["market_values"] = round(temp_market_values / 10000.0, 2)
             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_share"] * row["nav"]) / 10000, 2)  # 投资本金
+            temp_cost = float(p_result_cnav_data[cur_fund_id + "_amount"].values[-1])
+            fund_hoding_info["cost"] = "%.2f" % round( temp_cost / 10000, 2)  # 投资本金
 
             # 当月收益
             if row['confirm_share_date'] > self.month_start_date:
@@ -472,28 +468,29 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 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)
                 cal_month_start_date = self.month_start_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()
+            cal_month_cnav_result = p_result_cnav_data[p_result_cnav_data.index >= pd.to_datetime(cal_month_start_date)]
             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
+                temp_profit = 0
+                fund_hoding_info["profit"] = temp_profit
+                temp_profit_ratio = 0
             else:
                 last_month_cnav = float(last_month_cnav_serise.values[-1])
+                temp_profit = float(cal_month_cnav_result[cur_fund_id + "_earn"].sum())
                 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(temp_profit / temp_market_values*100, 2)
-            fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit_ratio * 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)
+            cum_profit = float(p_result_cnav_data[cur_fund_id + "_earn"].sum())
+            fund_hoding_info["cum_profit"] = "%.2f" % round(cum_profit / 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
+            fund_hoding_info["cum_profit_ratio"] = "%.2f" % round(cum_profit / temp_cost *100, 2)
+            cum_profit_ratio_temp = cum_profit / temp_cost
             # 累积年化收益率
-            cur_resample_df = resample_df[[row["fund_id"]]].dropna()
+            cur_resample_df = resample_df[[row["fund_id"]]]
             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)
@@ -504,23 +501,23 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         fund_id_list_amount = [i + "_net_amount" for i in fund_id_list]
         fund_id_list_profit_ratio = [i + "_profit_ratio" for i in fund_id_list]
 
-
         nav_net_amount_df = p_combina_df[fund_id_list + fund_id_list_amount+fund_id_list_profit_ratio].copy()
         # nav_net_amount_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
         nav_net_amount_df["sum_net_amount"] = nav_net_amount_df[fund_id_list_amount].sum(axis=1).apply(lambda x: Decimal.from_float(x))
         for amount_name in fund_id_list:
-            price = nav_net_amount_df[amount_name].dropna()
-            profit = price.diff().fillna(Decimal(0))
-            profit_ratio_new = profit / price.shift(1)
-            profit_ratio_old = nav_net_amount_df[amount_name+"_profit_ratio"]
-            nan_index = profit_ratio_new[pd.isna(profit_ratio_new)].index
-            profit_ratio_new[nan_index] = profit_ratio_old[nan_index]
-            nav_net_amount_df[amount_name + "_profit_ratio"] = profit_ratio_new
+            # price = nav_net_amount_df[amount_name].dropna()
+            # profit = price.diff().fillna(Decimal(0))
+            # profit_ratio_new = profit / price.shift(1)
+            # profit_ratio_old = nav_net_amount_df[amount_name+"_profit_ratio"]
+            # nan_index = profit_ratio_new[pd.isna(profit_ratio_new)].index
+            # profit_ratio_new[nan_index] = profit_ratio_old[nan_index]
+            #
+            # nav_net_amount_df[amount_name + "_profit_ratio"] = profit_ratio_new
             nav_net_amount_df[amount_name+"_amount_ratio"] = nav_net_amount_df[amount_name+"_net_amount"]/(nav_net_amount_df["sum_net_amount"])
 
             fund_profit_ratio = nav_net_amount_df[amount_name + "_profit_ratio"].dropna() + 1
             amount_ratio_shift = nav_net_amount_df[amount_name + "_amount_ratio"].shift(1)
-            num_va = len(amount_ratio_shift[amount_ratio_shift.values==0])
+            num_va = len(amount_ratio_shift[amount_ratio_shift.values == 0])
             if num_va+1 >= len(amount_ratio_shift):
                 amount_ratio_shift.iloc[num_va] = 0
             else: