Merge remote-tracking branch 'origin/app_v3' into app_v3

app/service/data_service_v2_1.py

@@ -29,6 +29,8 @@ class UserCustomerDataAdaptor:
     end_date = ""
     group_data = {}
     trade_cal_date = None
+    all_fund_id_list = None
+    all_fund_type_dict = None
     all_fund_distribution = {}
     all_fund_performance = {}
 
@@ -36,13 +38,14 @@ class UserCustomerDataAdaptor:
         self.user_id = user_id
         self.customer_id = customer_id
         self.compare_index_id = index_id
+        self.valueSex = ''
         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)
+        # 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("2020-12-18")
+        # self.end_date = pd.to_datetime("2020-12-25")
         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("2020-12-01")
+        # self.month_start_date = pd.to_datetime("2020-12-01")
         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()
@@ -80,10 +83,10 @@ class UserCustomerDataAdaptor:
             tamp_diagnose_app_session = tamp_diagnose_app.session
             tamp_product_session = tamp_product.session
             tamp_fund_session = tamp_fund.session
-            sql_user = """select f1.fund_id, f2.realname,f3.customer_name,f1.type,f1.order_type,f1.pay_date,f1.subscription_fee,f1.confirm_share_date,f1.confirm_share,f1.confirm_amount,f1.nav,f1.folio_name from tamp_order.customer_order_view f1, tamp_user.user_info f2,tamp_user.customer_info f3   where f2.id=f1.user_id and f3.id=f1.customer_id  and f1.delete_tag=0 and user_id='{}' and customer_id='{}'""".format(self.user_id, self.customer_id)
+            sql_user = """select f1.fund_id, f2.realname,f3.customer_name,f3.valueSex,f1.type,f1.order_type,f1.pay_date,f1.subscription_fee,f1.confirm_share_date,f1.confirm_share,f1.confirm_amount,f1.nav,f1.folio_name from tamp_diagnose_app.customer_order_view f1, tamp_user.user_info f2,tamp_diagnose_app.customer_view f3   where f2.id=f1.user_id and f3.id=f1.customer_id  and f1.delete_tag=0 and user_id='{}' and customer_id='{}'""".format(self.user_id, self.customer_id)
             cur = tamp_diagnose_app_session.execute(sql_user)
             data = cur.fetchall()
-            order_df = pd.DataFrame(list(data), columns=['fund_id', 'username', 'customer_name', 'type', 'order_type', 'pay_date',
+            order_df = pd.DataFrame(list(data), columns=['fund_id', 'username', 'customer_name', 'valueSex', 'type', 'order_type', 'pay_date',
                                                          'subscription_fee', 'confirm_share_date', 'confirm_share',
                                                          'confirm_amount', 'nav', 'folio_name'])
             cur_fund_id = list(order_df["fund_id"].unique())
@@ -103,11 +106,26 @@ class UserCustomerDataAdaptor:
                 if len(fund_df) > 0:
                     product_df = product_df.append(fund_df)
                     product_df = product_df.drop_duplicates("fund_id")
+            fund_type3 = order_df[order_df["type"] == 3]
+            if len(fund_type3) > 0:
+                type3_fund_id = list(fund_type3["fund_id"].unique())
+                fund_list_str_3 = str(type3_fund_id).replace("[", "(").replace("]", ")")
+                sql_fund_3 = "select distinct `id`, `fund_name`, `substrategy` from `ifa_imported_fund_info` where  `id` in {}".format(
+                    fund_list_str_3)
+                cur = tamp_fund_session.execute(sql_fund_3)
+                data = cur.fetchall()
+                fund_df_3 = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'substrategy'])
+                fund_df_3["freq"] = 1
+
+                if len(fund_df_3) > 0:
+                    product_df = product_df.append(fund_df_3)
+                    product_df = product_df.drop_duplicates("fund_id")
 
             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.customer_real_name = user_customer_order_df["customer_name"].values[0]
             self.ifa_real_name = user_customer_order_df["username"].values[0]
+            self.valueSex = user_customer_order_df["valueSex"].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)))
@@ -166,6 +184,10 @@ class UserCustomerDataAdaptor:
                     sql = """select distinct `price_date`, `nav`,`cumulative_nav` from `fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
                         cur_fund_id)
                     cur = tamp_product_session.execute(sql)
+                elif fund_type == 3:
+                    sql = """select distinct `price_date`, `nav`,`cumulative_nav` from `ifa_imported_fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
+                        cur_fund_id)
+                    cur = tamp_fund_session.execute(sql)
                 data = cur.fetchall()
                 cur_fund_nav_df = pd.DataFrame(list(data), columns=['price_date', 'nav', 'cnav'])
 
@@ -340,6 +362,8 @@ class UserCustomerDataAdaptor:
         cnav_df = p_cnav_df[p_cnav_df.index >= start_date].copy()
 
         p_fund_id_list = list(p_order_df["fund_id"].unique())
+        self.all_fund_id_list = p_fund_id_list
+        self.all_fund_type_dict = {values["fund_id"]: values["type"] for values in p_order_df[["fund_id", "type"]].drop_duplicates().to_dict(orient='records')}
         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:

app/service/result_service_v2.py

@@ -10,7 +10,7 @@ import pandas as pd
 import numpy as np
 import datetime
 from decimal import Decimal
-from app.service.data_service_v2 import UserCustomerDataAdaptor
+from app.service.data_service_v2_1 import UserCustomerDataAdaptor
 from app.service.portfolio_diagnose import cal_correlation, get_fund_name, get_frequency
 from app.utils.week_evaluation import *
 
@@ -188,7 +188,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                  "return_ratio_year": "%.2f" % round(return_ratio_year*100, 2)}
 
             # 对应指数数据
-            index_df = self.get_customer_index_nav_data()
+            index_df = self.get_customer_index_nav_data().dropna()
             index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)],
                                                           "index")
             folio_report_data["index_result"] = index_result
@@ -209,13 +209,13 @@ 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
-        freq_max = cur_folio_order_data["freq"].max()
-        # freq_list = [get_frequency(cur_folio_result_cnav_data[[p_nav]]) for p_nav in
-        #              cur_folio_result_cnav_data.columns]
-        # freq_dict = {250: 1, 52: 2, 24: 4, 12: 3, 4: 5}
-        # freq_max = freq_dict[min(freq_list)]
-
+        cur_folio_order_data = self.user_customer_order_df.copy()
+        # freq_max = cur_folio_order_data["freq"].max()
+        freq_list = [get_frequency(cur_folio_result_cnav_data[[p_nav]]) for p_nav in
+                     cur_folio_result_cnav_data.columns]
+        freq_dict = {250: 1, 52: 2, 24: 4, 12: 3, 4: 5}
+        freq_max = freq_dict[min(freq_list)]
+        #
         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 + "_net_amount" for i in fund_id_list]
@@ -371,7 +371,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         report_data["cumulative_return"] = float(cumulative_return)
 
         # 对应指数数据
-        index_df = self.get_customer_index_nav_data()
+        index_df = self.get_customer_index_nav_data().dropna()
         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
 
@@ -406,8 +406,8 @@ 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"] = str(round(
-                    (fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav * 100, 2)) + "%"
+                fund_basic_info["ret_after_confirm"] = 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:
@@ -634,6 +634,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         folio_cnav_data = folio_cnav_data.fillna(method="bfill")
         old_correlation = cal_correlation(folio_cnav_data)
         old_correlation = old_correlation.fillna(1).round(2)
-        old_correlation.columns = old_correlation.columns.map(lambda x: get_fund_name(x).values[0][0])
-        old_correlation.index = old_correlation.index.map(lambda x: get_fund_name(x).values[0][0])
+        old_correlation.columns = old_correlation.columns.map(lambda x: get_fund_name(x, self.all_fund_type_dict[x]).values[0][0])
+        old_correlation.index = old_correlation.index.map(lambda x: get_fund_name(x, self.all_fund_type_dict[x]).values[0][0])
         return old_correlation

app/utils/jinjia2html_v2.py

@@ -57,7 +57,8 @@ class DataIntegrate:
                 'suggestions_result_asset': {},
                 'return_compare_pic': [],
                 'indicator_compare': [],
-                'new_group_evaluation': []
+                'new_group_evaluation': [],
+                "correlation": group_result["correlation"]
             }
 
             # 旧持仓组合点评
@@ -83,8 +84,15 @@ class DataIntegrate:
             self.all_folio_result[group_name] = cur_group_portfolio_result
 
     def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
-        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=portfolio, invest_amount=float(invest_amount),
+        if invest_amount < 10000000:
+            invest_amount = 10000000
+        folio_fund_dict = {}
+        for fd in portfolio:
+            folio_fund_dict[fd] = self.user_customer.all_fund_type_dict[fd]
+        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=folio_fund_dict,
+                                               invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
+        if self.type == 2:
             portfolio_diagnose.optimize()
         return portfolio_diagnose
 
@@ -167,10 +175,11 @@ class DataIntegrate:
 
     def get_old_correlation(self, portfolio_diagnose, cur_group_portfolio_result):
         """旧相关性分析."""
-        old_correlation = portfolio_diagnose.old_correlation
+        old_correlation = cur_group_portfolio_result["correlation"]
         old_correlation_columns = old_correlation.columns.tolist()
         old_correlation_values = old_correlation.values.tolist()
         cur_group_portfolio_result["old_correlation"] = list(zip(range(1, len(old_correlation_columns)+1), old_correlation_columns, old_correlation_values))
+        del cur_group_portfolio_result["correlation"]
 
     def get_new_correlation(self, portfolio_diagnose, cur_group_portfolio_result):
         """新相关性分析."""
@@ -258,7 +267,7 @@ class DataIntegrate:
             data = {
                 # 全局数据
                 'customer_name': self.customer_name,
-                'year_month': self.user_customer.month_start_date.strftime("%Y-%m-%d"),
+                'year_month': self.user_customer.end_date.strftime("%Y-%m-%d"),
                 'valueSex': self.user_customer.valueSex,
                 'month': self.user_customer.month_start_date.strftime("%m"),
                 'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),