修复名称显示，用户买入万元以下数据

app/service/statement_service.py

@@ -12,7 +12,7 @@ from datetime import datetime, date
 from decimal import Decimal
 from app.api.engine import tamp_order_engine, tamp_user_engine, tamp_diagnose_app_engine, TAMP_SQL
 # from app.service.data_service_v2_1 import *
-from app.service.customer import get_customer_list
+# from app.service.customer import get_customer_list
 from app.service.portfolio_diagnose import get_fund_name
 
 
@@ -51,7 +51,7 @@ def get_provision_info(user_id):
         cur = tamp_order_session.execute(sql)
         data = cur.fetchall()
         provision_df = pd.DataFrame(list(data),
-                                columns=['id', 'ifa_id', 'customer_id', 'fund_id', 'order_id', 'fund_nav', 'fund_nav_date',
+                                columns=['id', 'ifa_id', 'customer_id', 'order_id', 'fund_id', 'fund_nav', 'fund_nav_date',
                                          'predict_date', 'pro_date','pro_type',
                                          'manage_predict_fee', 'permc_exc_predict_fee', 'permc_rem_predict_fee',
                                          'manage_fee', 'permc_exc_fee','permc_rem_fee', 'recommend_ifa_id',
@@ -171,9 +171,7 @@ class Statement(object):
         provision_quarterly = self.ifa_provision[
             (self.ifa_provision['predict_date'] >= start_date) &
             (self.ifa_provision['predict_date'] < end_date.date())]
-        processed_provision = list(provision_quarterly.groupby([provision_quarterly.customer_id,
-                                   provision_quarterly.fund_id]))
-        return processed_provision
+        return provision_quarterly
 
     def trade_record(self):
         ifa_cus_order_info = list(self.ifa_order.groupby(self.ifa_order.customer_id))
@@ -181,11 +179,12 @@ class Statement(object):
         trade_record_data = []
         for customer in ifa_cus_order_info:
             customer_df = customer[1]
+            customer_df = customer_df[customer_df['order_type']!=4]
             df_length = customer_df['fund_id'].count()
             detail = []
 
             for i in range(df_length):
-                detail.append({'fund': customer_df['fund_id'].values[i],
+                detail.append({'fund': get_fund_name(customer_df['fund_id'].values[i]).values[0][0],
                                'pay_date': np.datetime_as_string(customer_df['pay_date'].values[i], unit='D'),
                                'confirm_share_date': np.datetime_as_string(customer_df['confirm_share_date'].values[i],
                                                                            unit='D'),
@@ -197,7 +196,7 @@ class Statement(object):
 
             total_amount = cal_total_amount(customer_df)
             trade_record_data.append({'customer': customer_df['customer_name'].values[0],
-                                      'total_amount': Decimal(round(float(total_amount) / 10000.0, 2)),
+                                      'total_amount': Decimal(round(float(total_amount) / 10000.0, 6)),
                                       'detail': detail})
 
         return trade_record_data
@@ -235,11 +234,11 @@ class Statement(object):
         month_remain_data['bonus_policy'] = bonus_policy
 
         bonus_image = []
-        bonus_image.append({'goal_value': float(quarter_amount)/500000.0, 'goal': '5000万目标'})
-        bonus_image.append({'goal_value': float(quarter_amount)/400000.0, 'goal': '4000万目标'})
-        bonus_image.append({'goal_value': float(quarter_amount)/300000.0, 'goal': '3000万目标'})
-        bonus_image.append({'goal_value': float(quarter_amount)/200000.0, 'goal': '2000万目标'})
-        bonus_image.append({'goal_value': float(quarter_amount)/100000.0, 'goal': '1000万目标'})
+        bonus_image.append({'goal_value': round(float(quarter_amount)/3.0/500000.0,2), 'goal': '5000万目标'})
+        bonus_image.append({'goal_value': round(float(quarter_amount)/3.0/400000.0,2), 'goal': '4000万目标'})
+        bonus_image.append({'goal_value': round(float(quarter_amount)/3.0/300000.0,2), 'goal': '3000万目标'})
+        bonus_image.append({'goal_value': round(float(quarter_amount)/3.0/200000.0,2), 'goal': '2000万目标'})
+        bonus_image.append({'goal_value': round(float(quarter_amount)/3.0/100000.0,2), 'goal': '1000万目标'})
         month_remain_data['bonus_image'] = bonus_image
         return month_remain_data
 
@@ -266,7 +265,7 @@ class Statement(object):
             for i in range(df_length):
                 tax_rate = get_tax_rate()
 
-                detail.append({'fund': customer_df['fund_id'].values[i],
+                detail.append({'fund': get_fund_name(customer_df['fund_id'].values[i]).values[0][0],
                                'confirm_amount': customer_df['confirm_amount'].values[i],
                                'pay_date': np.datetime_as_string(customer_df['pay_date'].values[i], unit='D'),
                                'subscription_fee': customer_df['subscription_fee'].values[i],
@@ -296,16 +295,18 @@ class Statement(object):
         total_after_tax = 0
         customer_detail = []
 
-        processed_provision = self.process_provision()
+        provision_quarterly = self.process_provision()
+        processed_provision = list(provision_quarterly.groupby([provision_quarterly.customer_id,
+                                   provision_quarterly.fund_id]))
+
         for customer_fund in processed_provision:
             customer_id = customer_fund[0][0]
             customer_name = self.ifa_order[self.ifa_order['customer_id'] == customer_id]['customer_name'].values[0]
-            fund_id = customer_fund[0][1]
-            try:
-                fund_name = get_fund_name(fund_id).values[0][0]
-            except:
-                fund_name = fund_id
+
             customer_fund_df = customer_fund[1]
+            fund_id = customer_fund_df['fund_id'].values[0]
+            fund_name = get_fund_name(fund_id).values[0][0]
+
             df_length = customer_fund_df['predict_date'].count()
             before_tax = 0
             after_tax = 0
@@ -371,10 +372,9 @@ class Statement(object):
                 provision_carry = row['permc_exc_fee']
                 if provision_carry == 0:
                     continue
-                try:
+
                 tax_rate = get_tax_rate()
-                except:
-                    tax_rate = 0
+
                 customer_id = row['customer_id']
                 customer_name = self.ifa_order[self.ifa_order['customer_id'] == customer_id]['customer_name'].values[0]
                 order_id = row['order_id']
@@ -415,15 +415,16 @@ class Statement(object):
         total_after_tax = 0
         customer_detail = []
 
-        processed_provision = self.process_provision()
+        provision_quarterly = self.process_provision()
+        processed_provision = list(provision_quarterly.groupby([provision_quarterly.customer_id,
+                                   provision_quarterly.fund_id]))
+
         for customer_fund in processed_provision:
             customer_id = customer_fund[0][0]
             customer_name = self.ifa_order[self.ifa_order['customer_id'] == customer_id]['customer_name'].values[0]
             fund_id = customer_fund[0][1]
-            try:
             fund_name = get_fund_name(fund_id).values[0][0]
-            except:
-                fund_name = fund_id
+
             customer_fund_df = customer_fund[1]
             incentive_rate_sum = customer_fund_df['incentive_fee_ratio'].sum()
             if incentive_rate_sum == 0:
@@ -436,10 +437,8 @@ class Statement(object):
                 incentive_rate = customer_fund_df['incentive_fee_ratio'].values[i]
                 if incentive_rate == 0:
                     continue
-                try:
+
                 tax_rate = get_tax_rate()
-                except:
-                    tax_rate = 0
 
                 detail.append({'date': customer_fund_df['predict_date'].values[i].strftime('%Y-%m-%d'),
                                'hold_share': customer_fund_df['retention_shares'].values[i],