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#!/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, get_trade_cal
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("2020-12-31"), index_id="IN0000007M"):
self.user_id = user_id
self.customer_id = customer_id
self.compare_index_id = index_id
self.valueSex = ''
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("2020-12-25")
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("2020-12-01")
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):
try:
df = get_trade_cal()
df = df[df["cal_date"] >= start_date]
df.drop(['end_date'], 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
except:
pass
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,f3.valueSex,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_diagnose_app.customer_view f3 where f2.id=f1.user_id and f3.id=f1.customer_id and f1.delete_tag=0 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', 'valueSex', '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()
user_customer_order_df["confirm_share_date"] = user_customer_order_df["confirm_share_date"].apply(lambda x: pd.to_datetime(x.date()))
self.customer_real_name = user_customer_order_df["customer_name"].values[0]
self.ifa_real_name = user_customer_order_df["username"].values[0]
self.valueSex = user_customer_order_df["valueSex"].values[0]
user_customer_order_df = user_customer_order_df[user_customer_order_df["confirm_share_date"] <= self.end_date]
user_customer_order_df["confirm_amount"] = user_customer_order_df["confirm_amount"] - user_customer_order_df["subscription_fee"]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
for index, row in user_customer_order_df.iterrows():
if row["order_type"] == 2:
need_less_share = row["confirm_share"]
for index_ori, row_ori in user_customer_order_df.iterrows():
if index_ori >= index:
break
if row["fund_id"] == row_ori["fund_id"] and row_ori["order_type"] == 1:
ori_share = row_ori["confirm_share"]
if need_less_share >= ori_share:
need_less_share -= ori_share
user_customer_order_df.loc[index_ori, "confirm_share"] = 0
else:
need_less_share = 0
user_customer_order_df.loc[index_ori, "confirm_share"] = row_ori[
"confirm_share"] - need_less_share
if need_less_share <= 0:
break
user_customer_order_df = user_customer_order_df[(user_customer_order_df["order_type"] == 1)&(user_customer_order_df["confirm_share"] > 0)]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
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):
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'])
cur_fund_performance_df = cur_fund_performance_df[
cur_fund_performance_df["price_date"] <= self.end_date.date()]
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().head(1)
last_cnav_data = p_cnav_df[p_cnav_df.index > confirm_share_date][cur_fund_id].dropna().head(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().head(1)
last_cnav_data = p_cnav_df[p_cnav_df.index > confirm_share_date][cur_fund_id].dropna().head(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