综述数据接口返回

app/api/engine.py

@@ -14,8 +14,7 @@ import os
 import sys
 import yaml
 from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
-
+from sqlalchemy.orm import sessionmaker, scoped_session
 
 env = sys.argv[-1]
 work_dir = os.getcwd()
@@ -56,9 +55,9 @@ tamp_user_engine = create_engine(
     ),
     echo=True
 )
-tamp_product_session = sessionmaker(bind=tamp_product_engine, autocommit=True)()
-tamp_order_session = sessionmaker(bind=tamp_order_engine, autocommit=True)()
-tamp_user_session = sessionmaker(bind=tamp_user_engine, autocommit=True)()
+tamp_product_session = scoped_session(sessionmaker(bind=tamp_product_engine))()
+tamp_order_session = scoped_session(sessionmaker(bind=tamp_order_engine))()
+tamp_user_session = scoped_session(sessionmaker(bind=tamp_user_engine))()
 
 # redis = redis.StrictRedis(
 #     host=config[env]['redis']['host'],

app/service/data_service.py

@@ -22,7 +22,7 @@ class UserCustomerDataAdaptor:
     month_date = ""
     end_date = ""
     group_data = {}
-    trade_cal = None
+    trade_cal_date = None
     all_fund_distribution = {}
     all_fund_performance = {}
 
@@ -83,7 +83,7 @@ class UserCustomerDataAdaptor:
     def get_customer_fund_nav_data(self):
         now_date = datetime.datetime.now().strftime("%Y%m%d")
         trade_date_df = self.get_trade_cal("20000101", now_date)
-        self.trade_cal = trade_date_df
+        self.trade_cal_date = trade_date_df
         all_fund_nav = pd.DataFrame(index=trade_date_df["datetime"])
         all_fund_cnav = pd.DataFrame(index=trade_date_df["datetime"])
 

app/service/result_service.py

@@ -67,7 +67,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         super().__init__(user_id, customer_id, end_date)
 
     # 组合结果数据
-    def get_group_result_data(self):
+    def calculate_group_result_data(self):
 
         for folio in self.group_data.keys():
             folio_report_data = {}
@@ -83,7 +83,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 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)
+            resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
             # 总成本
             total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
@@ -94,7 +94,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             cumulative_profit = profit_df.sum().sum()
             folio_report_data["cumulative_profit"] = float(cumulative_profit)
 
-            # 区间年化收益
+            # 区间年化收益率
             n_freq = freq_days(int(freq_max))
             return_ratio_year = annual_return((resample_df["cum_return_ratio"].values[-1]-1), resample_df, n_freq)
             folio_report_data["return_ratio_year"] = float(return_ratio_year)
@@ -146,28 +146,12 @@ 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)
+            # 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)
+            result_fund_nav_info, result_fund_hoding_info = self.group_fund_basic_info_data(cur_folio_order_data, cur_folio_result_cnav_data, cumulative_profit, total_cost)
 
             # 拼接组合以及综合结果数据
             folio_report_data["group_nav_info"] = result_fund_nav_info
@@ -178,7 +162,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         return self.group_result_data
 
     # 综述数据
-    def get_total_data(self):
+    def calculate_total_data(self):
         report_data = {}
 
         cur_folio_result_cnav_data = self.total_customer_order_cnav_df
@@ -191,12 +175,12 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
         # 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
+        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)
+        resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
         # 总成本
         total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
@@ -271,7 +255,15 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         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
+
+        return_ratio_df["date"] = return_ratio_df.index
+        return_group = return_ratio_df.groupby(return_ratio_df["date"].apply(year_month))
+        month_last_return_ratio = return_group.last()["cum_return_ratio"]
+        month_result = pd.DataFrame({"date": month_sum.index, "profit": month_sum.values, "ratio": month_last_return_ratio.values})
+        month_result["datetime"] = pd.to_datetime(month_result["date"])
+        month_result.sort_values(by="datetime", inplace=True)
+        report_data["month_return"] = month_result
+
 
         # # 贡献分解
         # month_earn = sum_group.div(month_sum, axis='rows')
@@ -284,7 +276,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         self.total_result_data = report_data
         return report_data
 
-    # 组合基金净值数据
+    # 基金净值数据，持仓数据
     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 = []
@@ -373,3 +365,16 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     def signal_fund_profit_result():
         pass
 
+    def get_month_return_chart(self):
+        res = self.total_result_data["month_return"]
+        xlabels = res["date"].values
+        res["profit"] = res["profit"].apply(lambda x: round(x/100.0, 2))
+        res["ratio"] = res["ratio"].apply(lambda x: round((x-1)*100, 2))
+        product_list = {'name': '月度回报', 'data': res["profit"].values}
+        cumulative = {'name': '累积收益', 'data': res["ratio"].values}
+
+        return xlabels, [product_list], cumulative
+
+    def get_total_basic_data(self):
+        return self.total_result_data
+