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poc_micro_sta.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Sep 14 15:42:12 2017
@author: wangm
"""
import numpy as np
import robolib as rl
from pylab import *
import pandas as pd
import iolib as il
import time as time
import poc_statistics as pocs
zs_funds_fee = il.getZS_Funds_Fee()
zs_funds_discount = il.getZS_Funds_discount()
zs_funds_tdays = il.getZS_Funds_tdays()
users = il.getZS_users()
funds_net = il.getFunds_Net()
funds_profit = il.getFunds_Profit()
startday_str = "2017-07-01"
endday_str = "2017-10-29"
format = "%Y-%m-%d"
strptime, strftime = datetime.datetime.strptime, datetime.datetime.strftime
datelist = rl.dateRange(startday_str, endday_str)
datelist_possible = list(set(funds_net["date"].values.tolist()))
datelist_possible.sort(key=funds_net["date"].values.tolist().index)
datelist_possible_moneyfund = list(set(funds_profit["date"].values.tolist()))
datelist_possible_moneyfund.sort(key=funds_profit["date"].values.tolist().index)
def poc_detail_corr(company_file_names_poc, poctype, users_inside):
'''
计算不同厂家给出的配置相关系数，并输出为文件
'''
funds_net_df = il.getZS_funds_net()
company_detail = pd.DataFrame()
user_corr_df = pd.DataFrame()
for company_file in company_file_names_poc:
# 对每一个公司给出的配置情况循环
company_df = il.getZS_Company_combination(il.cwd + r"\history_data\\" + poctype + "_" + company_file + ".csv")
time_cost = 0.0
count = 0
user_corr_dic = {}
for index, row in users_inside.iterrows():
count += 1
start = time.clock()
print("计算第" + str(count) + "/" + str(len(users_inside)) + "个用户.")
# 对每一个用户循环
userid = row["userid"]
user_combination = company_df[company_df['userid'] == userid]
user_corr = {}
user_corr_avg = 0.0
for date in datelist:
# print("当前回测日期为" + str(date) + ".")
# 对回测时间段内的每一个日期循环
funds_dic = pocs.getUserCombinationByDate(date, user_combination)
if bool(funds_dic):
fund_net_combination = pd.DataFrame(index=funds_net_df.index.tolist())
for fund, percent in funds_dic.items():
if fund in funds_net_df.columns.values.tolist():
fund_net_combination[fund] = funds_net_df[fund]
if not fund_net_combination.empty:
funds_raito = np.log(fund_net_combination / fund_net_combination.shift(1))
funds_combination_corr = funds_raito.corr()
funds_combination_corr_abs_avg = (abs(funds_combination_corr).mean()).mean()
user_corr[date] = funds_combination_corr_abs_avg
if user_corr_avg == 0.0:
user_corr_avg = funds_combination_corr_abs_avg
else:
user_corr_avg = (user_corr_avg + funds_combination_corr_abs_avg) / 2
elapsed = (time.clock() - start)
time_cost += elapsed
print("Time used:", elapsed)
print("Time Left Estimated:", (time_cost / (int(count))) * len(users_inside) - time_cost)
user_corr_dic[userid] = user_corr_avg
company_detail = company_detail.append(user_corr, ignore_index=True)
company_detail.to_csv(il.cwd + r"\result\\" + company_file + "_corrdetail_combine_" + poctype + ".csv")
print("File saved:", il.cwd + r"\result\\" + company_file + "_corrdetail_combine_" + poctype + ".csv")
user_corr_df = user_corr_df.append(user_corr_dic, ignore_index=True)
user_corr_df = user_corr_df.T
user_corr_df.columns = company_file_names_poc
user_corr_df.to_csv(il.cwd + r"\result\\all_corr_combine_" + poctype + ".csv")
print("File saved:", il.cwd + r"\result\\all_corr_combine_" + poctype + ".csv")
if __name__ == '__main__':
poctype_out_list = ["bs"]
for poctype_out in poctype_out_list:
# company_file_names_poc = ["sz"]
company_file_names_poc = ["kmrd", "betago", "sz"]
poc_detail_corr(company_file_names_poc, poctype_out, users)