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poc_zsmk_online.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Sep 14 15:42:12 2017
@author: wangm
"""
import os as os
import numpy as np
import robolib as rl
from pylab import *
import pandas as pd
import iolib as il
import time as time
import zsmk_util as zsmk
import funds_selection as fs
import mpt as mpt
def get_return_by_combination(funds_weight_dic_inside, start_date, end_date, funds_net_compute_return):
"""
计算给定的组合从start_date到end_date时间内的收益率
:param funds_weight_dic_inside：组合相对应的基金编号和对应比例的字典，dic
:param start_date: 初始日期，格式为2017-01-01，str
:param end_date: 结束日期，格式为2017-01-01，str
:param funds_net_compute_return:基金净值数据，dataframe
:return: 在该段时间内的收益率，float
"""
start_value = 0
end_value = 0
daterange = rl.dateRange(start_date, end_date, step=1, format="%Y-%m-%d")
for fund_ticker, fund_percent in funds_weight_dic_inside.items():
fund_net_start = rl.getFundsNetBefore_byTickerDate_MartrixFundsDf(fund_ticker, start_date.replace("-", ""),
funds_net_compute_return, "%Y%m%d")
if fund_net_start == 0:
fund_net_start = rl.getFundsNetNext_byTickerDate_MartrixFundsDf(fund_ticker, start_date.replace("-", ""),
funds_net_compute_return,
"%Y%m%d")
fund_net_end = rl.getFundsNetBefore_byTickerDate_MartrixFundsDf(fund_ticker, end_date.replace("-", ""),
funds_net_compute_return, "%Y%m%d")
if fund_net_end == 0:
fund_net_end = rl.getFundsNetNext_byTickerDate_MartrixFundsDf(fund_ticker, end_date.replace("-", ""),
funds_net_compute_return,
"%Y%m%d")
start_value += fund_net_start * fund_percent
end_value += fund_net_end * fund_percent
return ((end_value - start_value) / start_value) / (len(daterange) / 365)
def get_ZScom_online(user_detail_df, company_combination, current_date, daysbefore, funds_net_inside,
funds_profit_inside, riskfree, minpercent, change_return_differ):
"""
增量在线的方式生成组合策略
:param user_detail_df：给定的用户列表，dataframe
:param company_combination:当前的基金组合列表，dataframe
:param current_date:当前日期，格式为2017-01-01，str
:param daysbefore:以当前日期之前多少天的历史数据作为计算依据
:param funds_net_inside:基金净值数据，*****此处的fund_net需要是原始未进行缺失值处理的dataframe*****，dataframe
:param funds_profit_inside:货币型基金日万份收益数据，dataframe
:param riskfree:无风险利率，float
:param minpercent: 某一个基金大类的所占比例的下限约束，float
:param var_goal:预期波动率，预期波动率为0时，表示没有约束，float
:return: None 但是会输出生成的文件
"""
datelist = rl.dateRange_daysbefore(current_date, daysbefore, 1)
end_date = current_date
start_date = datelist[0]
zs_combination_df = pd.DataFrame()
moneyfund_ticker_for_net = zsmk.get_best_moneyfundticker(start_date, daysbefore, funds_profit_inside,
method="maxmeanreturn")
funds_type_df, fund_type_list = il.get_funds_type()
funds_net_df_fill = funds_net_inside.copy()
funds_net_df_fill = funds_net_df_fill.fillna(method="pad")
funds_net_df_fill = funds_net_df_fill.fillna(method="bfill")
type_return_avg_df = fs.type_return_avg(funds_net_df_fill, fund_type_list, funds_type_df)
combination_change_flag = False
type_num = len(set(user_detail_df["risk_type"].values.tolist()))
for index, row in user_detail_df.iterrows():
userid = row["userid"]
usermoneyamount = row["moneyamount"]
userriskscore = row["risk_score"]
userrisktype = row["risk_type"]
user_combination = company_combination[company_combination["userid"] == userid]
if user_combination.empty:
continue
else:
user_combination_cp = user_combination.copy()
if "type" in user_combination.columns.tolist():
pass
else:
user_combination_cp.insert(len(user_combination.columns), "type", 1)
for index, row in user_combination.iterrows():
fund_type = funds_type_df[funds_type_df["ticker"] == row["ticker"]]["fund_type"].values.tolist()[0]
fund_name = funds_type_df[funds_type_df["ticker"] == row["ticker"]]["name"].values.tolist()[0]
user_combination_cp.loc[index, "name"] = fund_name
user_combination_cp.loc[index, "type"] = fund_type
zs_combination_df = zs_combination_df.append(user_combination_cp)
if userrisktype == "0保守型":
pass
else:
# 找到用户当前的组合配置
user_combination_sorted = user_combination.sort_values(by="date")
last_date_in_user_combination_sorted = user_combination_sorted.iloc[-1]["date"]
user_combination_last_date = user_combination[
user_combination["date"] == last_date_in_user_combination_sorted]
funds_weight_dic_old = {}
for index, row in user_combination_last_date.iterrows():
fund_ticker = row["ticker"]
fund_percent = float(row["percent"])
if fund_ticker in funds_net_df_fill.columns.tolist():
funds_weight_dic_old[fund_ticker] = fund_percent
original_return = get_return_by_combination(funds_weight_dic_old, start_date, end_date,
funds_net_inside)
type_return_avg_pass_df = type_return_avg_df.ix[
start_date.replace("-", ""):end_date.replace("-", "")]
log_return_df = np.log(type_return_avg_pass_df / type_return_avg_pass_df.shift(1))
type_weight_list, target_ret, target_var = zsmk.get_ZScom_by_var(type_return_avg_pass_df, riskfree,
type_num, minpercent)
type_fundticker_dic, selected_fund_list = fs.funds_select_for_type(funds_net_inside, fund_type_list,
funds_type_df,
type_return_avg_pass_df,
funds_each_type=2,
selectby="corr")
funds_weight_dic, total_net_percent = zsmk.get_user_fund_weight_by_risk(type_weight_list,
fund_type_list,
type_fundticker_dic,
userriskscore)
current_return = get_return_by_combination(funds_weight_dic, start_date, end_date,
funds_net_inside)
if current_return - original_return > change_return_differ:
combination_change_flag = True
combination_df_inside = pd.DataFrame(
columns=["userid", "date", "ticker", "name", "percent", "type"])
for fund, percent in funds_weight_dic.items():
change_dic = {}
change_dic["userid"] = userid
change_dic["date"] = end_date
change_dic["ticker"] = fund
change_dic["name"] = funds_type_df[funds_type_df["ticker"] == fund]["name"].values.tolist()[0]
change_dic["percent"] = float(percent) * total_net_percent
change_dic["type"] = \
funds_type_df[funds_type_df["ticker"] == fund]["fund_type"].values.tolist()[0]
combination_df_inside = combination_df_inside.append(change_dic, ignore_index=True)
change_dic = {}
change_dic["userid"] = userid
change_dic["date"] = end_date
change_dic["ticker"] = moneyfund_ticker_for_net
change_dic["name"] = \
funds_type_df[funds_type_df["ticker"] == moneyfund_ticker_for_net]["name"].values.tolist()[0]
change_dic["percent"] = 1 - total_net_percent
change_dic["type"] = \
funds_type_df[funds_type_df["ticker"] == moneyfund_ticker_for_net]["fund_type"].values.tolist()[
0]
combination_df_inside = combination_df_inside.append(change_dic, ignore_index=True)
zs_combination_df = zs_combination_df.append(combination_df_inside, ignore_index=True)
else:
pass
save_old_file_str = il.cwd + r"\result\\zs_combine_type_users__seg_before" + current_date + ".xls"
save_new_file_str = il.cwd + r"\result\\zs_combine_type_users_seg.xls"
if combination_change_flag:
company_combination.to_excel(save_old_file_str)
zs_combination_df.to_excel(save_new_file_str)
print("File saved:", save_new_file_str + ", change date: " + current_date)
else:
print("No combination changes,no new file outputs: " + current_date)
if __name__ == '__main__':
format = "%Y-%m-%d"
days_before = 30
userid = 1
riskfree = 0.03
combination_startdate = "2017-10-29"
combination_enddate = "2017-12-07"
datelist_out = rl.dateRange(combination_startdate, combination_enddate)
funds_net_df_out = il.getZS_funds_net(fill=False)
funds_profit_df = il.getZS_funds_Profit()
# user_detail_df = il.getZS_users_complete()
user_detail_df = il.getZS_users_complete(os.getcwd() + r"\history_data\zs_user_test.csv")
minpercent = 0.1
poctype = "zs"
company_file = "zsmk"
time_cost = 0
usercount = 0
change_return_differ_out = 0.01
date_count = 0
for current_date in datelist_out:
date_count += 1
company_df = il.getZS_Company_combination_by_excel(il.cwd + r"\result\\zs_combine_type_users_seg.xls")
usercount += 1
if date_count % 10 == 0:
start = time.clock()
print("计算第" + str(usercount) + "/" + str(len(datelist_out)) + "天.")
get_ZScom_online(user_detail_df, company_df, current_date, days_before, funds_net_df_out,
funds_profit_df, riskfree, minpercent, change_return_differ_out)
elapsed = (time.clock() - start)
time_cost += elapsed
print("Time used:", elapsed)
print("Time Left Estimated (min):", ((time_cost / (int(usercount))) * len(datelist_out) - time_cost) / 60)