基础数据脚本更新

app/service/data_service.py

@@ -9,20 +9,39 @@
 import pandas as pd
 import numpy as np
 from sqlalchemy import and_
+import tushare as ts
+import datetime
 from app.api.engine import tamp_user_session, tamp_product_session
 from app.model.tamp_user_models import t_customer_order, t_customer_info
 from app.model.tamp_product_models import t_fund_info
 
 
 class UserCustomerDataAdaptor:
-    fund_nav_total = pd.DataFrame()
     user_id = ""
     customer_id = ""
+    start_date = ""
+    group_data = {}
 
     def __init__(self, user_id, customer_id):
         self.user_id = user_id
         self.customer_id = customer_id
         self.user_customer_order_df = self.get_user_customer_order_data()
+        self.fund_nav_total = self.get_customer_fund_nav_data()
+        self.get_customer_index_nav_data()
+        self.group_operate()
+
+    @staticmethod
+    def get_trade_cal(start_date, end_date):
+        ts.set_token('ac1f734f8a25651aa07319ca35b1b0c0854e361e306fe85d85e092bc')
+        pro = ts.pro_api()
+        if end_date is not None:
+            df = pro.trade_cal(exchange='SSE', start_date=start_date, end_date=end_date, is_open='1')
+        else:
+            df = pro.trade_cal(exchange='SSE', start_date=start_date, is_open='1')
+        df.drop(['exchange', 'is_open'], axis=1, inplace=True)
+        df.rename(columns={'cal_date': 'end_date'}, inplace=True)
+        df["datetime"] = df["end_date"].apply(lambda x: datetime.datetime.strptime(x, "%Y%m%d"))
+        return df
 
     # 获取理财师下该用户所有订单列表
     def get_user_customer_order_data(self):
@@ -44,10 +63,15 @@ class UserCustomerDataAdaptor:
         product_df = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
 
         user_customer_order_df = order_df.set_index('fund_id').join(product_df.set_index('fund_id')).reset_index()
+        self.start_date = user_customer_order_df["confirm_share_date"].min()
         return user_customer_order_df
 
     # 获取客户持有的基金净值数据
     def get_customer_fund_nav_data(self):
+        now_date = datetime.datetime.now().strftime("%Y%m%d")
+        # trade_date_df = self.get_trade_cal(str(self.start_date).replace("-", ""), now_date)
+        trade_date_df = self.get_trade_cal("20000101", now_date)
+        all_fund_nav = pd.DataFrame(index=trade_date_df["datetime"])
 
         for cur_fund_id in self.user_customer_order_df["fund_id"].unique():
             # 对应基金净值
@@ -56,20 +80,40 @@ class UserCustomerDataAdaptor:
             data = cur.fetchall()
             cur_fund_nav_df = pd.DataFrame(list(data), columns=['price_date', 'nav', 'cnav'])
 
-            # 对应基金分红
-            sql = """select distinct `distribute_date`, `distribution` from `fund_distribution` where `fund_id`='{}' and `distribute_type`='1' order by `distribute_date` ASC""".format(
-                cur_fund_id)
-            cur = tamp_product_session.execute(sql)
-            data = cur.fetchall()
-            cur_fund_distribution_df = pd.DataFrame(list(data), columns=['price_date', 'distribution'])
+            # # 对应基金分红
+            # sql = """select distinct `distribute_date`, `distribution` from `fund_distribution` where `fund_id`='{}' and `distribute_type`='1' order by `distribute_date` ASC""".format(
+            #     cur_fund_id)
+            # cur = tamp_product_session.execute(sql)
+            # data = cur.fetchall()
+            # cur_fund_distribution_df = pd.DataFrame(list(data), columns=['price_date', 'distribution'])
+
+            cur_fund_nav_df["price_date"] = pd.to_datetime(cur_fund_nav_df["price_date"])
+            cur_fund_nav_df.set_index("price_date", inplace=True)
+            all_fund_nav[cur_fund_id] = cur_fund_nav_df["cnav"]
+
+        return all_fund_nav
 
-            print(cur_fund_id)
-        return
     # 获取客户对比指数净值数据
-    def get_customer_index_nav_data(self, index_id):
-        pass
+    def get_customer_index_nav_data(self, index_id="IN0000007M"):
+
+        sql = "select distinct price_date,close from fund_market_indexes where index_id='{}'  order by price_date ASC".format(index_id)
+        cur = tamp_product_session.execute(sql)
+        data = cur.fetchall()
+        index_df = pd.DataFrame(list(data), columns=['price_date', 'index'])
+        index_df["price_date"] = pd.to_datetime(index_df["price_date"])
+        index_df.set_index("price_date", inplace=True)
+        self.fund_nav_total["index"] = index_df["index"]
+
+        return index_df
+
+    # 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())
+            fund_id_list.append("index")
+            cur_folio_nav_df = self.fund_nav_total[fund_id_list]
+            continue
 
-    # 组合收益
-    def combine_ratio(self):
-        pass