修改数据库为with形式调用

app/api/engine.py

@@ -57,9 +57,9 @@ tamp_user_engine = create_engine(
     ),
     echo=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))()
+# 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

@@ -12,7 +12,7 @@ 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.api.engine import tamp_user_engine, tamp_product_engine, TAMP_SQL
 # from app.model.tamp_user_models import CustomerOrder, CustomerInfo
 # from app.model.tamp_product_models import FundInfo
 
@@ -64,76 +64,82 @@ class UserCustomerDataAdaptor:
         #         #     .filter(user_id = self.user_id).all()
         # data2 = tamp_user_session.query(t_customer_info).all()
         # data3 = tamp_product_session.query(t_fund_info).all()
+        with TAMP_SQL(tamp_user_engine) as tamp_user, TAMP_SQL(tamp_product_engine) as tamp_product:
+            tamp_user_session = tamp_user.session
+            tamp_product_session = tamp_product.session
+            sql_user = """select f2.realname,f3.customer_name,fund_id,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 customer_order f1, user_info f2,customer_info f3   where f2.id=f1.user_id and f3.id=f1.customer_id and f1.user_id='{}' and f1.customer_id='{}'""".format(self.user_id, self.customer_id)
+            cur = tamp_user_session.execute(sql_user)
+            data = cur.fetchall()
+            order_df = pd.DataFrame(list(data), columns=['username', 'customer_name', 'fund_id', 'order_type', 'pay_date',
+                                                         'subscription_fee', 'confirm_share_date', 'confirm_share',
+                                                         'confirm_amount', 'nav', 'folio_name'])
 
-        sql_user = """select f2.realname,f3.customer_name,fund_id,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 customer_order f1, user_info f2,customer_info f3   where f2.id=f1.user_id and f3.id=f1.customer_id and f1.user_id='{}' and f1.customer_id='{}'""".format(self.user_id, self.customer_id)
-        cur = tamp_user_session.execute(sql_user)
-        data = cur.fetchall()
-        order_df = pd.DataFrame(list(data), columns=['username', 'customer_name', 'fund_id', 'order_type', 'pay_date',
-                                                     'subscription_fee', 'confirm_share_date', 'confirm_share',
-                                                     'confirm_amount', 'nav', 'folio_name'])
-
-        sql_product = "select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info`"
-        cur = tamp_product_session.execute(sql_product)
-        data = cur.fetchall()
-        product_df = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
+            sql_product = "select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info`"
+            cur = tamp_product_session.execute(sql_product)
+            data = cur.fetchall()
+            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
+            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("20000101", now_date)
-        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"])
-
-        for cur_fund_id in self.user_customer_order_df["fund_id"].unique():
-            # 对应基金净值
-            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)
-            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'])
-            self.all_fund_distribution[cur_fund_id] = cur_fund_distribution_df
+        with TAMP_SQL(tamp_product_engine) as tamp_product:
+            tamp_product_session = tamp_product.session
+            now_date = datetime.datetime.now().strftime("%Y%m%d")
+            trade_date_df = self.get_trade_cal("20000101", now_date)
+            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"])
+
+            for cur_fund_id in self.user_customer_order_df["fund_id"].unique():
+                # 对应基金净值
+                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)
+                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'])
+                self.all_fund_distribution[cur_fund_id] = cur_fund_distribution_df
+
+                # 对应基金performance数据
+                sql = """select distinct `price_date`, `ret_1w`, `ret_cum_1m`, `ret_cum_6m`, `ret_cum_1y`, `ret_cum_ytd`, `ret_cum_incep` from `fund_performance` where `fund_id`='{}' order by `price_date` ASC""".format(
+                    cur_fund_id)
+                cur = tamp_product_session.execute(sql)
+                data = cur.fetchall()
+                cur_fund_performance_df = pd.DataFrame(list(data),
+                columns=['price_date', 'ret_1w', 'ret_cum_1m', 'ret_cum_6m', 'ret_cum_1y', 'ret_cum_ytd', 'ret_cum_incep'])
+                self.all_fund_performance[cur_fund_id] = cur_fund_performance_df
+
+                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["nav"]
+                all_fund_cnav[cur_fund_id] = cur_fund_nav_df["cnav"]
+
+            all_fund_nav = all_fund_nav[all_fund_nav.index <= self.end_date]
+            all_fund_cnav = all_fund_cnav[all_fund_cnav.index <= self.end_date]
+            return all_fund_nav, all_fund_cnav
 
-            # 对应基金performance数据
-            sql = """select distinct `price_date`, `ret_1w`, `ret_cum_1m`, `ret_cum_6m`, `ret_cum_1y`, `ret_cum_ytd`, `ret_cum_incep` from `fund_performance` where `fund_id`='{}' order by `price_date` ASC""".format(
-                cur_fund_id)
+    # 获取客户对比指数净值数据
+    def get_customer_index_nav_data(self, index_id="IN0000007M"):
+        with TAMP_SQL(tamp_product_engine) as tamp_product:
+            tamp_product_session = tamp_product.session
+            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()
-            cur_fund_performance_df = pd.DataFrame(list(data),
-            columns=['price_date', 'ret_1w', 'ret_cum_1m', 'ret_cum_6m', 'ret_cum_1y', 'ret_cum_ytd', 'ret_cum_incep'])
-            self.all_fund_performance[cur_fund_id] = cur_fund_performance_df
-
-            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["nav"]
-            all_fund_cnav[cur_fund_id] = cur_fund_nav_df["cnav"]
+            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_cnav_total["index"] = index_df["index"]
+            self.index_df = index_df
 
-        all_fund_nav = all_fund_nav[all_fund_nav.index <= self.end_date]
-        all_fund_cnav = all_fund_cnav[all_fund_cnav.index <= self.end_date]
-        return all_fund_nav, all_fund_cnav
-
-    # 获取客户对比指数净值数据
-    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_cnav_total["index"] = index_df["index"]
-        self.index_df = index_df
-
-        return index_df
+            return index_df
 
     # 分组合计算
     def group_operate(self):