Merge remote-tracking branch 'origin/dev' into dev

# Conflicts:
#	logs/tamp_course_order.log

.idea/fund_report.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.6" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="TestRunnerService">

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file

app/init/__main__.py

@@ -7,11 +7,15 @@
 # @Software   : PyCharm
 # -----------------------------------------------------------------------------
 
-from app.api.engine import tamp_user_engine, tamp_pay_session
-from app.api.engine import tamp_pay_engine
+from app.api.engine import tamp_user_engine, tamp_product_engine, tamp_product_session
 from app.model.base import Base
 from app.model.order import OrderInfo
+from app.model.tamp_product_models import t_fund_info
 
 
 if __name__ == '__main__':
-    Base.metadata.create_all(bind=tamp_pay_engine)
+    # Base.metadata.create_all(bind=tamp_pay_engine)
+    sql = 'select `fund_name` from `fund_info`'
+    row = tamp_product_session.execute(sql)
+    print(row)
+    # tamp_product_session.query(t_fund_info).filter(t_fund_info)

app/service/data_service.py

@@ -11,6 +11,7 @@ import numpy as np
 from sqlalchemy import and_
 import tushare as ts
 import datetime
+from decimal import Decimal
 from app.api.engine import tamp_user_session, tamp_product_session
 # from app.model.tamp_user_models import CustomerOrder, CustomerInfo
 # from app.model.tamp_product_models import FundInfo
@@ -33,10 +34,10 @@ class UserCustomerDataAdaptor:
         p_end_date = pd.to_datetime(end_date).date()
         p_end_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1) - datetime.timedelta(days=1)
         self.end_date = pd.to_datetime(str(p_end_date))
-        # self.end_date = pd.to_datetime("2020-11-23")
+        self.end_date = pd.to_datetime("2020-11-23")
         p_start_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1)
         self.month_start_date = p_start_date
-        # self.month_start_date = pd.to_datetime("2020-11-16")
+        self.month_start_date = pd.to_datetime("2020-11-16")
         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.index_df = self.get_customer_index_nav_data()
@@ -195,12 +196,15 @@ class UserCustomerDataAdaptor:
             #         cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
 
             if cur_fund_id+"_amount" not in cnav_df:
-                profit = cnav_df[cur_fund_id].dropna() - cnav_df[cur_fund_id].dropna().shift(1)
+                price = cnav_df[cur_fund_id].dropna()
+                profit = price.diff().fillna(Decimal(0))
                 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 + "_profit"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                profit_ratio = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_profit_ratio"] = profit_ratio
+                cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
                 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
@@ -212,9 +216,16 @@ class UserCustomerDataAdaptor:
                 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()
+            # 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()
+
+        for p_fund_id_ in p_fund_id_list:
+            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_]
         self.group_data[p_folio] = {"result_cnav_data": cnav_df, "order_df": p_order_df}
         return cnav_df
 
@@ -257,14 +268,27 @@ class UserCustomerDataAdaptor:
             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)
+                price = cnav_df[cur_fund_id].dropna()
+                profit = price.diff().fillna(Decimal(0))
                 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 + "_profit"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                profit_ratio = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_profit_ratio"] = profit_ratio
+                cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
                 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
 
+                # 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"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                # cnav_df[cur_fund_id + "_profit_ratio"] = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                # cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
+                # 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"]
@@ -274,8 +298,10 @@ class UserCustomerDataAdaptor:
                 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()
-
+        for p_fund_id_ in p_fund_id_list:
+            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_]
         return cnav_df

app/service/result_service.py

@@ -38,9 +38,18 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             profit_df = cur_folio_result_cnav_data[fund_id_list_earn]
 
             # 组合收益率数组
-            return_ratio_df = self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
+            # return_ratio_df, contribution_decomposition= self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
+            # resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
+            resample_cur_folio_result_cnav_data = resample(cur_folio_result_cnav_data, self.trade_cal_date, freq_max)
+            return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data,
+                                                                                 fund_id_list)
             resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
+
+            # 收益分解df
+            contribution_decomposition_df = contribution_decomposition.fillna(0)*100
+            folio_report_data["contribution_decomposition"] = contribution_decomposition_df
+
             # 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())
@@ -104,7 +113,29 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 累积收益率
             cumulative_return= return_ratio_df["cum_return_ratio"].values[-1]
-            folio_report_data["contribution_decomposition"] = float(cumulative_return)
+            folio_report_data["cumulative_return"] = float(cumulative_return)
+
+            # 月度分组
+            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)
+
+            # 贡献分解
+            month_earn = sum_group.div(month_sum, axis='rows')
+            month_earn["datetime"] = pd.to_datetime(month_earn.index)
+            month_earn.sort_values(by="datetime", inplace=True)
+            del month_earn["datetime"]
+            col = list(month_earn.columns)
+            col_ = {x: x.replace('_earn', '') for x in list(col)}
+            month_earn.rename(columns=col_, inplace=True)
+            folio_report_data["contribution_decomposition"] = month_earn
 
             # 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(cur_folio_order_data, cur_folio_result_cnav_data, cumulative_profit, total_cost)
@@ -141,7 +172,11 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         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)
+        # return_ratio_df = self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
+        # resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
+
+        resample_cur_folio_result_cnav_data = resample(cur_folio_result_cnav_data, self.trade_cal_date, freq_max)
+        return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data, fund_id_list)
         resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
         # 总成本
@@ -233,7 +268,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
         # 累积收益率
         cumulative_return = return_ratio_df["cum_return_ratio"].values[-1]
-        report_data["contribution_decomposition"] = float(cumulative_return)
+        report_data["cumulative_return"] = float(cumulative_return)
 
         # 对应指数数据
         index_df = self.get_customer_index_nav_data()
@@ -294,40 +329,41 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
     @staticmethod
     def combination_yield(p_combina_df, fund_id_list):
-        fund_id_list_amount = [i + "_amount" for i in fund_id_list]
+        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_amount_df = p_combina_df[fund_id_list + fund_id_list_amount+fund_id_list_profit_ratio].copy()
 
-        nav_amount_df["sum_amount"] = nav_amount_df[fund_id_list_amount].sum(axis=1).apply(lambda x: Decimal.from_float(x))
+
+        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:
-            nav_amount_df[amount_name+"_amount_ratio"] = nav_amount_df[amount_name+"_amount"]/nav_amount_df["sum_amount"]
-            nav_amount_df[amount_name+"_profit_ratio_weight"] = nav_amount_df[amount_name+"_amount_ratio"] * nav_amount_df[amount_name+"_profit_ratio"]
+            nav_net_amount_df[amount_name+"_amount_ratio"] = nav_net_amount_df[amount_name+"_net_amount"]/(nav_net_amount_df["sum_net_amount"])
+            nav_net_amount_df[amount_name+"_profit_ratio_weight"] = nav_net_amount_df[amount_name+"_amount_ratio"].shift(1) * nav_net_amount_df[amount_name+"_profit_ratio"]
+            fund_profit_ratio = nav_net_amount_df[amount_name + "_profit_ratio"].dropna() + 1
+            nav_net_amount_df[amount_name + "_profit_cum_ratio_weight"] = (fund_profit_ratio.cumprod()-1)*nav_net_amount_df[amount_name+"_amount_ratio"].shift(1)
+            # enter_date = nav_net_amount_df[amount_name+"_profit_ratio"].dropna()
 
         fund_id_list_profit_ratio_weight = [i + "_profit_ratio_weight" for i in fund_id_list]
-        nav_profit_ratio_weight = nav_amount_df[fund_id_list_profit_ratio_weight].copy().fillna(method='ffill')
+        nav_profit_ratio_weight = nav_net_amount_df[fund_id_list_profit_ratio_weight].copy().fillna(method='ffill')
 
-        # 收益率
+        # 组合收益率
         return_ratio = nav_profit_ratio_weight.sum(axis=1)
 
-        # 累积收益率
-        return_ratio_list = list(return_ratio.values)
-        cum_return_ratio = []
-        last_ratio = 0
-        for i in range(len(return_ratio_list)):
-            if i == 0:
-                last_ratio = 1 + return_ratio_list[i]
-                cum_return_ratio.append(last_ratio)
-                continue
-
-            cur_ratio = (1 + return_ratio_list[i]) * last_ratio
-            cum_return_ratio.append(cur_ratio)
-            last_ratio = cur_ratio
+        # 组合累积收益率
+        # return_ratio_list = list(return_ratio.values)
+        cum_return_ratio = (return_ratio + 1).fillna(0).cumprod()
 
-        # 收益率
+        # 收益率df
         cum_return_ratio_df = pd.DataFrame(return_ratio.values, columns=["return_ratio"])
-        cum_return_ratio_df["cum_return_ratio"] = cum_return_ratio
+        cum_return_ratio_df["cum_return_ratio"] = cum_return_ratio.values
         cum_return_ratio_df.index = return_ratio.index
-        return cum_return_ratio_df
+
+        # 单个基金累计收益分解df
+        weight_name_list = [i + "_profit_cum_ratio_weight"  for i in fund_id_list]
+        signal_fund_cum_weight = nav_net_amount_df[weight_name_list]
+        re_name = {x: x.replace("_profit_cum_ratio_weight", "") for x in weight_name_list}
+        signal_fund_cum_weight.rename(columns=re_name, inplace=True)
+        return cum_return_ratio_df, signal_fund_cum_weight
 
     @staticmethod
     def signal_fund_profit_result(p_fund_nav_df, cur_fund_id):

app/utils/draw.py

@@ -6,10 +6,6 @@
 # @Email      : acepengxiong@163.com
 # @Software   : PyCharm
 # -----------------------------------------------------------------------------
-import base64
-from urllib import parse
-
-from io import BytesIO
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -18,11 +14,11 @@ from matplotlib.ticker import FuncFormatter
 from matplotlib.font_manager import FontProperties
 
 # 中文字体初始化
-plt.rcParams['font.sans-serif']=['Heiti TC']
+plt.rcParams['font.sans-serif']=['simsunb.ttf']
 
 
 def to_percent(temp, position):
-    return '%.2f' % temp + '%'
+    return '%1.0f' % temp + '%'
 
 
 def draw_month_return_chart(xlabels, product_list, cumulative):
@@ -32,7 +28,7 @@ def draw_month_return_chart(xlabels, product_list, cumulative):
     # plt.ylabel('Scores')
     figsize = (24, 12)
     # 标签文字大小
-    fontsize = 15
+    fontsize = 20
     # 初始化
     fig = plt.figure(figsize=figsize)
     ax1 = fig.add_subplot(111)
@@ -45,17 +41,16 @@ def draw_month_return_chart(xlabels, product_list, cumulative):
     # 坐标轴
     ax1.tick_params(labelsize=fontsize)
     ax2.tick_params(labelsize=fontsize)
-
     # 坐标轴颜色
     ax2.tick_params(axis='y', colors='#C6A774')
     ax1.set_xticks(loc)
     ax1.set_xticklabels(xlabels)
     # ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
     ax2.yaxis.set_major_formatter(FuncFormatter(to_percent))
-    # temp_rate = np.zeros(max_x_count)
-    # for i in range(len(product_list)):
-    #     temp_rate += product_list[i]['data']
-    # max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
+    temp_rate = np.zeros(max_x_count)
+    for i in range(len(product_list)):
+        temp_rate += product_list[i]['data']
+    max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
     # ax2.set_ylim(0, max_rate + 15)
 
     # 柱状图
@@ -87,12 +82,8 @@ def draw_month_return_chart(xlabels, product_list, cumulative):
         ax2.text(a, b + 0.1, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize)
     ax2.legend(loc='upper center', fontsize=fontsize)
 
-    # plt.show()
-    imgdata = BytesIO()
-    fig.savefig(imgdata, format='png')
-    imgdata.seek(0)  # rewind the data
-    month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
-    return month_return_img
+    plt.show()
+    plt.savefig("")
 
 
 def draw_contribution_chart(xlabels, product_list, cumulative):