综合数据，分组数据隔离重构

app/service/data_service.py

@@ -39,6 +39,7 @@ class UserCustomerDataAdaptor:
         self.user_customer_order_df = self.get_user_customer_order_data()
         self.fund_nav_total, self.fund_cnav_total = self.get_customer_fund_nav_data()
         self.get_customer_index_nav_data()
+        self.total_customer_order_cnav_df = self.total_combine_data()
         self.group_operate()
 
     @staticmethod
@@ -133,7 +134,6 @@ class UserCustomerDataAdaptor:
 
     # 分组合计算
     def group_operate(self):
-
         for folio in self.user_customer_order_df["folio_name"].unique():
             cur_folio_order_df = self.user_customer_order_df[self.user_customer_order_df["folio_name"] == folio]
             fund_id_list = list(self.user_customer_order_df["fund_id"].unique())
@@ -216,5 +216,64 @@ class UserCustomerDataAdaptor:
         self.group_data[p_folio] = {"result_cnav_data": cnav_df, "order_df": p_order_df}
         return cnav_df
 
+    # 所有的数据操作
     def total_combine_data(self):
-        pass
+
+        p_order_df = self.user_customer_order_df.copy()
+        p_nav_df = self.fund_nav_total.copy()
+        p_cnav_df = self.fund_cnav_total.copy()
+
+        start_date = pd.to_datetime(p_order_df["confirm_share_date"].min())
+        cnav_df = p_cnav_df[p_cnav_df.index >= start_date].copy()
+
+        p_fund_id_list = list(p_order_df["fund_id"].unique())
+        for p_fund_id in p_fund_id_list:
+            order_min_date = p_order_df[p_order_df["fund_id"] == p_fund_id]["confirm_share_date"].min()
+            if pd.to_datetime(order_min_date) > start_date:
+                cnav_df.loc[:order_min_date - datetime.timedelta(days=1), p_fund_id] = np.nan
+
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+
+            # 根据确认净值日查看是否含有累积净值的数据，如果没有按照前后差值推算当天累积净值
+            if pd.isnull(cnav_df.loc[confirm_share_date, cur_fund_id]):
+                last_nav_data = p_nav_df[p_nav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                last_cnav_data = p_cnav_df[p_cnav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                # 判断上个净值日和当前确认日之中是否存在分红日
+                """need add judge"""
+
+                if len(last_nav_data) < 1:
+                    cnav_df.loc[confirm_share_date, cur_fund_id] = row["nav"]
+                else:
+                    diff_nav = row["nav"] - last_nav_data.values[0]
+                    cur_cnav = last_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')
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+            if cur_fund_id + "_amount" not in cnav_df:
+                profit = cnav_df[cur_fund_id].dropna() - cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_profit"] = profit
+                cnav_df[cur_fund_id + "_profit_ratio"] = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_amount"] = 0
+                cnav_df[cur_fund_id + "_earn"] = 0
+                # cnav_df[cur_fund_id + "_cum_earn"] = 0
+                cnav_df[cur_fund_id + "_share"] = 0
+
+            # buy
+            if row['order_type'] == 1:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] += row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] += row["confirm_share"]
+            # sell
+            elif row['order_type'] == 2:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] -= row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] -= row["confirm_share"]
+
+            cnav_df[cur_fund_id + "_earn"] = cnav_df[cur_fund_id + "_profit"] * cnav_df[cur_fund_id + "_share"]
+            cnav_df[cur_fund_id + "_earn"] = cnav_df[cur_fund_id + "_earn"].apply(lambda x: float(x))
+            cnav_df[cur_fund_id + "_cum_earn"] = cnav_df[cur_fund_id + "_earn"].cumsum()
+
+        return cnav_df

app/service/result_service.py

@@ -60,17 +60,16 @@ def resample(df, trading_cal, freq):
 
 class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     total_result_data = {}
-    group_result_data = []
+    group_result_data = {}
 
     def __init__(self, user_id, customer_id, end_date=str(datetime.date.today())):
         # super().__init__()
         super().__init__(user_id, customer_id, end_date)
 
-    # 综述数据
-    def get_total_data(self):
+    # 组合结果数据
+    def get_group_result_data(self):
 
         for folio in self.group_data.keys():
-            # self.group_result_data.append({folio: {}})
             folio_report_data = {}
 
             cur_folio_result_cnav_data = self.group_data[folio]["result_cnav_data"]
@@ -79,7 +78,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             fund_id_list = list(cur_folio_order_data["fund_id"].unique())
             fund_id_list_earn = [i + "_earn" for i in fund_id_list]
-            fund_id_list_amount = [i + "_amount" for i in fund_id_list]
+            # fund_id_list_amount = [i + "_amount" for i in fund_id_list]
             profit_df = cur_folio_result_cnav_data[fund_id_list_earn]
 
             # 组合收益率数组
@@ -106,7 +105,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 最大回撤
             drawdown = max_drawdown(resample_df["cum_return_ratio"])
-            folio_report_data["max_drawdown"] = float(drawdown)
+            folio_report_data["max_drawdown"] = drawdown
 
             # 夏普比率
             sim = simple_return(resample_df["cum_return_ratio"])
@@ -147,25 +146,25 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
             folio_report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)
 
-            # 月度回报
-            def year_month(x):
-                a = x.year
-                b = x.month
-                return str(a) + "/" + str(b)
-            profit_df_cp = profit_df.copy()
-            profit_df_cp["date"] = profit_df_cp.index
-            grouped = profit_df_cp.groupby(profit_df_cp["date"].apply(year_month))
-            sum_group = grouped.agg(np.sum)
-            month_sum = sum_group.sum(axis=1)
-            folio_report_data["month_return"] = month_sum
-
-            # 贡献分解
-            month_earn = sum_group.div(month_sum, axis='rows')
-            folio_report_data["contribution_decomposition"] = month_earn
-
-            # 累积收益率
-            cumulative_return= return_ratio_df["cum_return_ratio"].values[-1]
-            folio_report_data["contribution_decomposition"] = float(cumulative_return)
+            # # 月度回报
+            # def year_month(x):
+            #     a = x.year
+            #     b = x.month
+            #     return str(a) + "/" + str(b)
+            # profit_df_cp = profit_df.copy()
+            # profit_df_cp["date"] = profit_df_cp.index
+            # grouped = profit_df_cp.groupby(profit_df_cp["date"].apply(year_month))
+            # sum_group = grouped.agg(np.sum)
+            # month_sum = sum_group.sum(axis=1)
+            # folio_report_data["month_return"] = month_sum
+            #
+            # # 贡献分解
+            # month_earn = sum_group.div(month_sum, axis='rows')
+            # folio_report_data["contribution_decomposition"] = month_earn
+            #
+            # # 累积收益率
+            # cumulative_return= return_ratio_df["cum_return_ratio"].values[-1]
+            # folio_report_data["contribution_decomposition"] = float(cumulative_return)
 
             # 组合内单个基金净值数据  组合内基金持仓数据
             result_fund_nav_info, result_fund_hoding_info = self.group_fund_basic_info_data(fund_id_list, cur_folio_order_data, cur_folio_result_cnav_data, cumulative_profit, total_cost)
@@ -174,10 +173,119 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             folio_report_data["group_nav_info"] = result_fund_nav_info
             folio_report_data["group_hoding_info"] = result_fund_hoding_info
 
-            continue
+            self.group_result_data[folio] = folio_report_data
+
+        return self.group_result_data
+
+    # 综述数据
+    def get_total_data(self):
+        report_data = {}
+
+        cur_folio_result_cnav_data = self.total_customer_order_cnav_df
+        cur_folio_order_data = self.user_customer_order_df
+        freq_max = cur_folio_order_data["freq"].max()
+        #
+        fund_id_list = list(cur_folio_order_data["fund_id"].unique())
+        fund_id_list_earn = [i + "_earn" for i in fund_id_list]
+        profit_df = cur_folio_result_cnav_data[fund_id_list_earn]
+
+        # 持仓周期
+        first_trade_date = cur_folio_order_data["confirm_share_date"].min()
+        hold_days = self.end_date - pd.to_datetime(first_trade_date).days
+        report_data["hold_days"] = hold_days
+
+        # 组合收益率数组
+        return_ratio_df = self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
+        resample_df = resample(return_ratio_df, self.trade_cal, freq_max)
+
+        # 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())
+        report_data["total_cost"] = total_cost
+        #
+        # # 累积盈利
+        # cumulative_profit = profit_df.sum().sum()
+        # report_data["cumulative_profit"] = float(cumulative_profit)
+        #
+        # # 区间年化收益
+        # n_freq = freq_days(int(freq_max))
+        # return_ratio_year = annual_return((resample_df["cum_return_ratio"].values[-1] - 1), resample_df, n_freq)
+        # report_data["return_ratio_year"] = float(return_ratio_year)
+        #
+        # # 波动率
+        # volatility_ = volatility(resample_df["cum_return_ratio"], n_freq)
+        # report_data["volatility"] = float(volatility_)
+
+        # 最大回撤
+        drawdown = max_drawdown(resample_df["cum_return_ratio"])
+        report_data["max_drawdown"] = drawdown
+        #
+        # # 夏普比率
+        # sim = simple_return(resample_df["cum_return_ratio"])
+        # exc = excess_return(sim, BANK_RATE, n_freq)
+        # sharpe = sharpe_ratio(exc, sim, n_freq)
+        # report_data["sharpe"] = float(sharpe)
+        #
+        # # 期末资产
+        # ending_assets = cumulative_profit + total_cost
+        # report_data["ending_assets"] = float(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]
+        # cur_month_profit = cur_month_profit_df.sum().sum()
+        # report_data["cur_month_profit"] = float(cur_month_profit)
+        #
+        # # 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
+        # if len(last_profit_ratio) <= 0:
+        #     cur_month_profit_ratio = cur_profit_ratio[-1] - 1
+        # else:
+        #     cur_month_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
+        # report_data["cur_month_profit_ratio"] = float(cur_month_profit_ratio)
+        #
+        # # 今年累积收益
+        # cur_year_date = pd.to_datetime(str(datetime.date(year=self.end_date.year, month=1, day=1)))
+        # cur_year_profit_df = profit_df.loc[cur_year_date:self.end_date + datetime.timedelta(days=1), fund_id_list_earn]
+        # cur_year_profit = cur_year_profit_df.sum().sum()
+        # report_data["cur_year_profit"] = float(cur_year_profit)
+        #
+        # # 今年累积收益率
+        # last_profit_ratio = return_ratio_df.loc[:cur_year_date, "cum_return_ratio"].values
+        # cur_profit_ratio = return_ratio_df.loc[cur_year_date:, "cum_return_ratio"].values
+        # if len(last_profit_ratio) <= 0:
+        #     cur_year_profit_ratio = cur_profit_ratio[-1] - 1
+        # else:
+        #     cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
+        # report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)
+
+        # 月度回报
+        def year_month(x):
+            a = x.year
+            b = x.month
+            return str(a) + "/" + str(b)
+
+        profit_df_cp = profit_df.copy()
+        profit_df_cp["date"] = profit_df_cp.index
+        grouped = profit_df_cp.groupby(profit_df_cp["date"].apply(year_month))
+        sum_group = grouped.agg(np.sum)
+        month_sum = sum_group.sum(axis=1)
+        report_data["month_return"] = month_sum
+
+        # # 贡献分解
+        # month_earn = sum_group.div(month_sum, axis='rows')
+        # report_data["contribution_decomposition"] = month_earn
+
+        # 累积收益率
+        cumulative_return = return_ratio_df["cum_return_ratio"].values[-1]
+        report_data["contribution_decomposition"] = float(cumulative_return)
+
+        self.total_result_data = report_data
+        return report_data
 
     # 组合基金净值数据
-    def group_fund_basic_info_data(self, p_fund_id_list, p_order_df, p_result_cnav_data, p_sum_profit, p_total_amount):
+    def group_fund_basic_info_data(self, p_order_df, p_result_cnav_data, p_sum_profit, p_total_amount):
         group_fund_basic_info = []
         group_fund_hoding_info = []
         for index, row in p_order_df.iterrows():
@@ -261,5 +369,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         cum_return_ratio_df.index = return_ratio.index
         return cum_return_ratio_df
 
-
+    @staticmethod
+    def signal_fund_profit_result():
+        pass