支持新表的版本

app/service/data_service_v2.py

+#!/usr/bin/python3.6
+# -*- coding: utf-8 -*-
+# @Time    : 2020/11/18 19:12
+# @Author  : Jie. Z
+# @Email   : zhaojiestudy@163.com
+# @File    : data_service.py
+# @Software: PyCharm
+
+import pandas as pd
+import numpy as np
+from sqlalchemy import and_
+import tushare as ts
+import datetime
+import math
+from decimal import Decimal
+from app.api.engine import tamp_user_engine, tamp_product_engine, TAMP_SQL, tamp_fund_engine, tamp_diagnose_app_engine
+
+
+# from app.model.tamp_user_models import CustomerOrder, CustomerInfo
+# from app.model.tamp_product_models import FundInfo
+from app.utils.fund_rank import get_frequency
+
+
+class UserCustomerDataAdaptor:
+    user_id = ""
+    customer_id = ""
+    customer_real_name = ""
+    month_date = ""
+    end_date = ""
+    group_data = {}
+    trade_cal_date = None
+    all_fund_distribution = {}
+    all_fund_performance = {}
+
+    def __init__(self, user_id, customer_id, end_date=str(datetime.date.today()), index_id="IN0000007M"):
+        self.user_id = user_id
+        self.customer_id = customer_id
+        self.compare_index_id = index_id
+        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)
+        self.end_date = pd.to_datetime(str(p_end_date))
+        self.end_date = pd.to_datetime("2020-12-11")
+        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("2020-11-01")
+        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()
+        self.total_customer_order_cnav_df = self.total_combine_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):
+
+        with TAMP_SQL(tamp_fund_engine) as tamp_fund, TAMP_SQL(tamp_product_engine) as tamp_product, TAMP_SQL(tamp_diagnose_app_engine) as tamp_diagnose_app:
+            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_diagnose_app.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 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',
+                                                         'subscription_fee', 'confirm_share_date', 'confirm_share',
+                                                         'confirm_amount', 'nav', 'folio_name'])
+            cur_fund_id = list(order_df["fund_id"].unique())
+            fund_list_str = str(cur_fund_id).replace("[", "(").replace("]", ")")
+
+            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'])
+
+            if len(cur_fund_id) > 0:
+                sql_fund = "select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info` where  `id` in {}".format(fund_list_str)
+                cur = tamp_fund_session.execute(sql_fund)
+                data = cur.fetchall()
+                fund_df = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
+
+                if len(fund_df) > 0:
+                    product_df = product_df.append(fund_df)
+                    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()
+            self.start_date = user_customer_order_df["confirm_share_date"].min()
+            self.customer_real_name = user_customer_order_df["customer_name"].values[0]
+            self.ifa_real_name = user_customer_order_df["username"].values[0]
+            user_customer_order_df = user_customer_order_df[user_customer_order_df["confirm_share_date"] <= self.end_date]
+            user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
+            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):
+        with TAMP_SQL(tamp_product_engine) as tamp_product, TAMP_SQL(tamp_fund_engine) as tamp_fund:
+            tamp_product_session = tamp_product.session
+            tamp_fund_session = tamp_fund.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"])
+
+            temp_fund_id = []
+            # for cur_fund_id in self.user_customer_order_df["fund_id"].unique():
+            for index, row in self.user_customer_order_df.iterrows():
+                cur_fund_id = row["fund_id"]
+                if cur_fund_id in temp_fund_id:
+                    continue
+                else:
+                    temp_fund_id.append(cur_fund_id)
+                fund_type = row["type"]
+                # 对应基金净值
+                if fund_type == 0:
+                    sql = """select distinct `end_date`, `unit_nav`,`accum_nav` from `public_fund_nav` where `id`='{}'  order by `end_date` ASC""".format(cur_fund_id)
+                    cur = tamp_fund_session.execute(sql)
+                elif fund_type == 1:
+                    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_fund_session.execute(sql)
+                elif fund_type == 2:
+                    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.drop_duplicates(subset="price_date", keep='first', inplace=True)
+                cur_fund_nav_df.set_index("price_date", inplace=True)
+                cur_fund_nav_df = cur_fund_nav_df[cur_fund_nav_df.index.isin(all_fund_nav.index)]
+
+                if math.isnan(row["freq"]):
+                    try:
+                        freq = get_frequency(cur_fund_nav_df)
+                    except:
+                        freq = 250
+                    timeoffset_dict = {250: 1, 52: 5, 12: 30, 24: 15, 3: 120}
+                    self.user_customer_order_df.loc[index, "freq"] = timeoffset_dict[freq]
+
+                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]
+            # for cur_fund_id in self.user_customer_order_df["fund_id"].unique():
+            #     all_fund_nav[cur_fund_id][all_fund_nav[cur_fund_id].apply(lambda x: math.isnan(x))]=np.nan
+            #     all_fund_cnav[cur_fund_id][all_fund_cnav[cur_fund_id].apply(lambda x: math.isnan(x))] = np.nan
+            self.last_nav_date = str(all_fund_cnav.dropna(how="all").index.values[-1])[:10]
+            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"):
+        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()
+            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
+
+    # 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())
+            cur_folio_nav_df = self.fund_nav_total[fund_id_list]
+            # fund_id_list.append("index")
+            cur_folio_cnav_df = self.fund_cnav_total[fund_id_list]
+            self.signal_folio_operate(folio, cur_folio_order_df, cur_folio_nav_df, cur_folio_cnav_df)
+            continue
+
+    # 单个组合数据操作
+    def signal_folio_operate(self, p_folio, p_order_df, p_nav_df, p_cnav_df):
+        start_date = pd.to_datetime(p_order_df["confirm_share_date"].min())
+
+        cnav_df = p_cnav_df[p_cnav_df.index >= start_date].copy()
+
+        p_fund_id_list = list(p_order_df["fund_id"].unique())
+        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:
+                cnav_df.loc[:order_min_date - datetime.timedelta(days=1), p_fund_id] = np.nan
+
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+
+            # 根据确认净值日查看是否含有累积净值的数据，如果没有按照前后差值推算当天累积净值
+            if pd.isnull(p_nav_df.loc[confirm_share_date, cur_fund_id]):
+                last_nav_data = p_nav_df[p_nav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                last_cnav_data = p_cnav_df[p_cnav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                # 判断上个净值日和当前确认日之中是否存在分红日
+                """need add judge"""
+
+                if len(last_nav_data) < 1:
+                    cnav_df.loc[confirm_share_date, cur_fund_id] = row["nav"]
+                else:
+                    diff_nav = Decimal(row["nav"]) - Decimal(last_nav_data.values[0])
+                    cur_cnav = last_cnav_data.values[0] + diff_nav
+                    cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+            else:
+                confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
+                confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
+                diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
+                cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+
+        cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+
+            # # 根据确认净值日查看是否含有累积净值的数据，如果没有按照前后差值推算当天累积净值
+            # if pd.isnull(cnav_df.loc[confirm_share_date, cur_fund_id]):
+            #     last_nav_data = p_nav_df[p_nav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+            #     last_cnav_data = p_cnav_df[p_cnav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+            #     # 判断上个净值日和当前确认日之中是否存在分红日
+            #     """need add judge"""
+            #
+            #     if len(last_nav_data) < 1:
+            #         cnav_df.loc[confirm_share_date, cur_fund_id] = row["nav"]
+            #     else:
+            #         diff_nav = row["nav"] - last_nav_data.values[0]
+            #         cur_cnav = last_cnav_data.values[0] + diff_nav
+            #         cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+
+            if cur_fund_id+"_amount" not in cnav_df:
+                price = cnav_df[cur_fund_id].dropna()
+                profit = price.diff().fillna(Decimal(0))
+                cnav_df[cur_fund_id + "_profit"] = profit
+                cnav_df[cur_fund_id + "_profit"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                profit_ratio = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_profit_ratio"] = profit_ratio
+                cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
+                cnav_df[cur_fund_id+"_amount"] = 0
+                cnav_df[cur_fund_id + "_earn"] = 0
+                cnav_df[cur_fund_id + "_share"] = 0
+
+            # buy
+            if row['order_type'] == 1:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] += row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] += row["confirm_share"]
+            # sell
+            elif row['order_type'] == 2:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] -= row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] -= row["confirm_share"]
+
+            # cnav_df[cur_fund_id + "_earn"] = cnav_df[cur_fund_id + "_profit"] * cnav_df[cur_fund_id + "_share"]
+            # cnav_df[cur_fund_id + "_earn"] = cnav_df[cur_fund_id + "_earn"].apply(lambda x: float(x))
+            # cnav_df[cur_fund_id + "_cum_earn"] = cnav_df[cur_fund_id + "_earn"].cumsum()
+
+        for p_fund_id_ in p_fund_id_list:
+            cnav_df[p_fund_id_ + "_earn"] = (cnav_df[p_fund_id_ + "_profit"] * cnav_df[p_fund_id_ + "_share"]).apply(lambda x: float(x)).fillna(0)
+            # cnav_df[p_fund_id_ + "_earn"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: float(x))
+            cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
+            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
+            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
+            # cnav_df[p_fund_id_ + "_profit_ratio"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: Decimal(x)) / cnav_df[
+            #     p_fund_id_ + "_net_amount"].shift()
+        self.group_data[p_folio] = {"result_cnav_data": cnav_df, "order_df": p_order_df}
+        return cnav_df
+
+    # 所有的数据操作
+    def total_combine_data(self):
+
+        p_order_df = self.user_customer_order_df.copy()
+        p_nav_df = self.fund_nav_total.copy()
+        p_cnav_df = self.fund_cnav_total.copy()
+
+        start_date = pd.to_datetime(p_order_df["confirm_share_date"].min())
+        cnav_df = p_cnav_df[p_cnav_df.index >= start_date].copy()
+
+        p_fund_id_list = list(p_order_df["fund_id"].unique())
+        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:
+                cnav_df.loc[:order_min_date - datetime.timedelta(days=1), p_fund_id] = np.nan
+
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+
+            # 根据确认净值日查看是否含有累积净值的数据，如果没有按照前后差值推算当天累积净值
+            if pd.isnull(p_nav_df.loc[confirm_share_date, cur_fund_id]):
+                last_nav_data = p_nav_df[p_nav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                last_cnav_data = p_cnav_df[p_cnav_df.index < confirm_share_date][cur_fund_id].dropna().tail(1)
+                # 判断上个净值日和当前确认日之中是否存在分红日
+                """need add judge"""
+
+                if len(last_nav_data) < 1:
+                    cnav_df.loc[confirm_share_date, cur_fund_id] = row["nav"]
+                else:
+                    diff_nav = Decimal(row["nav"]) - Decimal(last_nav_data.values[0])
+                    cur_cnav = last_cnav_data.values[0] + diff_nav
+                    cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+            else:
+                confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
+                confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
+                diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
+                cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+
+        cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')
+        for index, row in p_order_df.iterrows():
+            cur_fund_id = str(row["fund_id"])
+            confirm_share_date = pd.to_datetime(row["confirm_share_date"])
+            if cur_fund_id + "_amount" not in cnav_df:
+                price = cnav_df[cur_fund_id].dropna()
+                profit = price.diff().fillna(Decimal(0))
+                cnav_df[cur_fund_id + "_profit"] = profit
+                cnav_df[cur_fund_id + "_profit"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                profit_ratio = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                cnav_df[cur_fund_id + "_profit_ratio"] = profit_ratio
+                cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
+                cnav_df[cur_fund_id + "_amount"] = 0
+                cnav_df[cur_fund_id + "_earn"] = 0
+                cnav_df[cur_fund_id + "_share"] = 0
+
+                # profit = cnav_df[cur_fund_id].dropna() - cnav_df[cur_fund_id].dropna().shift(1)
+                # cnav_df[cur_fund_id + "_profit"] = profit
+                # cnav_df[cur_fund_id + "_profit"] = cnav_df[cur_fund_id + "_profit"].fillna(Decimal(0))
+                # cnav_df[cur_fund_id + "_profit_ratio"] = profit / cnav_df[cur_fund_id].dropna().shift(1)
+                # cnav_df[cur_fund_id + "_profit_ratio"] = cnav_df[cur_fund_id + "_profit_ratio"].fillna(Decimal(0))
+                # cnav_df[cur_fund_id + "_amount"] = 0
+                # cnav_df[cur_fund_id + "_earn"] = 0
+                # cnav_df[cur_fund_id + "_cum_earn"] = 0
+                # cnav_df[cur_fund_id + "_share"] = 0
+
+            # buy
+            if row['order_type'] == 1:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] += row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] += row["confirm_share"]
+            # sell
+            elif row['order_type'] == 2:
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_amount"] -= row["confirm_amount"]
+                cnav_df.loc[confirm_share_date:, cur_fund_id + "_share"] -= row["confirm_share"]
+
+        for p_fund_id_ in p_fund_id_list:
+            cnav_df[p_fund_id_ + "_earn"] = (cnav_df[p_fund_id_ + "_profit"] * cnav_df[p_fund_id_ + "_share"]).apply(lambda x: float(x)).fillna(0)
+            # cnav_df[p_fund_id_ + "_earn"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: float(x))
+            cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
+            cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
+
+            # cnav_df[p_fund_id_ + "_profit_ratio"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: Decimal(x)) / cnav_df[
+            #     p_fund_id_ + "_net_amount"].shift()
+            # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
+
+        return cnav_df