修复同一日期多笔分红的核减

app/service/data_service_v2_1.py

@@ -173,6 +173,29 @@ class UserCustomerDataAdaptor:
         inter_order_df = order_df.copy()
         inter_order_df.sort_values(by="confirm_share_date", inplace=True)
 
+        def operate_type4(df):
+            """
+            处理同一天同一个基金的多笔分红记录（不同银行账号付款导致的作业流订单分红也会拆开成多笔）
+            """
+            df_row = []
+            fund_dis_his = {}
+            for index, row in df.iterrows():
+                fund_id = row["fund_id"]
+                if row["order_type"] != 4:
+                    df_row.append(row)
+                    continue
+                dis_his = fund_dis_his.get(fund_id, None)
+                if dis_his is None:
+                    fund_dis_his[fund_id] = row
+                    continue
+                if row["confirm_share_date"] == dis_his["confirm_share_date"]:
+                    dis_his["confirm_share"] += row["confirm_share"]
+                    dis_his["original_confirm_share"] += row["original_confirm_share"]
+                    dis_his["divident_share"] += row["divident_share"]
+                    dis_his["reduce_share"] += row["reduce_share"]
+            final_df = pd.DataFrame(data=df_row + list(fund_dis_his.values()))
+            return final_df
+
         def reduce_order(fund_order, d_fund_id, d_date, d_nav):
             """
             再平衡分红后单子的份额数量，对分红前的单子进行拆分
@@ -191,6 +214,11 @@ class UserCustomerDataAdaptor:
 
             return fund_order
 
+        if inter_order_df[inter_order_df["order_type"] == 4].groupby(["fund_id", "confirm_share_date"]).count()[
+            "order_id"].max() > 1:
+            inter_order_df = operate_type4(inter_order_df)
+            inter_order_df.sort_values(by="confirm_share_date", inplace=True)
+
         for index, row in inter_order_df.iterrows():
             if row["order_type"] == 4:
                 dividend_date = row["confirm_share_date"]