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02-基于万矿计算因子数据
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from WindPy import *
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
w.start()
## 获取股票池
s_date = '2014-01-01'
e_date = '2016-01-01'
date = w.tdays(s_date, e_date, "preiod = D").Data[0] # 日期函数
stocks = w.wset("sectorconstituent", "date="+e_date+";windcode=000906.SH").Data[1] # 中证800股票池
## 获取日频量价原始数据
close = w.wsd(stocks,'close',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
# 改日期索引为数字，考虑后文rolling函数应用的便捷性
returns = w.wsd(stocks,'pct_chg',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
Open = w.wsd(stocks,'open',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
low = w.wsd(stocks,'low',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
vwap = w.wsd(stocks,'vwap',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
high = w.wsd(stocks,'high',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
cap = w.wsd(stocks,'mkt_cap_ashare',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
volume = w.wsd(stocks,'volume',s_date,e_date,usedf = True)[1].reset_index().drop(columns = ['index'])
ind = w.wss(stocks, "industry_citic","unit=1;tradeDate="+date[0].strftime("%Y%m%d")+";industryType=1")
## 计算因子值
start_1 = datetime.now() # 记录计算用时
alpha_1 = alpha1(close,returns)
alpha_2 = alpha2(Open,close,volume)
alpha_3 = alpha3(Open,volume)
alpha_4 = alpha4(low)
alpha_5 = alpha5(Open,vwap,close)
alpha_6 = alpha6(Open, volume)
alpha_7 = alpha7(volume,close)
alpha_8 = alpha8(Open,returns)
alpha_9 = alpha9(close)
alpha_10 = alpha10(close)
alpha_11 = alpha11(vwap,close,volume)
alpha_12 = alpha12(volume,close)
alpha_13 = alpha13(volume,close)
alpha_14 = alpha14(Open,volume,returns)
alpha_15 = alpha15(high,volume)
alpha_16 = alpha16(high,volume)
alpha_17 = alpha17(volume,close)
alpha_18 = alpha18(close,Open)
alpha_19 = alpha19(close,returns)
alpha_20 = alpha20(Open,high,close,low)
alpha_21 = alpha21(volume,close)
alpha_22 = alpha22(high,volume,close)
alpha_23 = alpha23(high,close)
alpha_24 = alpha24(close)
alpha_25 = alpha25(volume,returns,vwap,high,close)
alpha_26 = alpha26(volume,high)
alpha_27 = alpha27(volume,vwap)
alpha_28 = alpha28(volume,high,low,close)
alpha_29 = alpha29(close,returns)
alpha_30 = alpha30(close,volume)
alpha_31 = alpha31(close,low,volume)
alpha_32 = alpha32(close,vwap)
alpha_33 = alpha33(Open,close)
alpha_34 = alpha34(close,returns)
alpha_35 = alpha35(volume,close,high,low,returns)
alpha_36 = alpha36(Open,close,volume,returns,vwap)
alpha_37 = alpha37(Open,close)
alpha_38 = alpha38(close,Open)
alpha_39 = alpha39(volume,close,returns)
alpha_40 = alpha40(high,volume)
alpha_41 = alpha41(high,low,vwap)
alpha_42 = alpha42(vwap,close)
alpha_43 = alpha43(volume,close)
alpha_44 = alpha44(high,volume)
alpha_45 = alpha45(close,volume)
alpha_46 = alpha46(close)
alpha_47 = alpha47(volume,close,high,vwap)
alpha_49 = alpha49(close)
alpha_50 = alpha50(volume,vwap)
alpha_51 = alpha51(close)
alpha_52 = alpha52(returns,volume,low)
alpha_53 = alpha53(close,high,low)
alpha_54 = alpha54(Open,close,high,low)
alpha_55 = alpha55(high,low,close,volume)
alpha_56 = alpha56(returns,cap)
alpha_57 = alpha57(close,vwap)
alpha_60 = alpha60(close,high,low,volume)
alpha_61 = alpha61(volume,vwap)
alpha_62 = alpha62(volume,high,low,Open,vwap)
alpha_64 = alpha64(high,low,Open,volume,vwap)
alpha_65 = alpha65(volume,vwap,Open)
alpha_66 = alpha66(vwap,low,Open,high)
alpha_68 = alpha41(high,low,vwap)
alpha_71 = alpha71(volume,close,low,Open,vwap)
alpha_72 = alpha72(volume,high,low,vwap)
alpha_73 = alpha73(vwap,Open,low)
alpha_74 = alpha74(volume,close,high,vwap)
alpha_75 = alpha75(volume,vwap,low)
alpha_77 = alpha77(volume,high,low,vwap)
alpha_78 = alpha78(volume,low,vwap)
alpha_81 = alpha81(volume,vwap)
alpha_83 = alpha83(high,low,close,volume)
alpha_84 = alpha84(vwap,close)
alpha_85 = alpha85(volume,high,close,low)
alpha_86 = alpha41(high,low,vwap)
alpha_88 = alpha88(volume,Open,low,high,close)
alpha_92 = alpha92(volume,high,low,close,Open)
alpha_94 = alpha94(volume,vwap)
alpha_95 = alpha95(volume,high,low,Open)
alpha_96 = alpha96(volume,vwap,close)
alpha_98 = alpha98(volume,Open,vwap)
alpha_99 = alpha99(volume,high,low)
alpha_100 = alpha41(high,low,vwap)
alpha_101 = alpha101(close,Open,high,low)
alpha_48 = alpha48(close,ind) # 这些因子涉及行业中性化，计算时间较长
alpha_58 = alpha58(vwap,volume,ind)
alpha_59 = alpha59(vwap,volume,ind)
alpha_63 = alpha63(volume,close,vwap,Open,ind)
alpha_67 = alpha67(volume,vwap,high,ind)
alpha_69 = alpha69(volume,vwap,ind,close)
alpha_70 = alpha70(close,ind,vwap)
alpha_76 = alpha76(volume,vwap,low,ind)
alpha_79 = alpha79(volume,close,Open,ind,vwap)
alpha_80 = alpha80(Open,high,ind)
alpha_82 = alpha82(Open,volume,ind)
alpha_87 = alpha87(volume,close,vwap)
alpha_89 = alpha89(low,vwap,ind)
alpha_90 = alpha90(volume,close,ind,low)
alpha_91 = alpha91(close,ind,volume,vwap)
alpha_93 = alpha93(vwap,ind,volume,close)
alpha_97 = alpha97(volume,low,vwap,ind)
alpha_100 = alpha100(volume,close,low,high,ind)
# 因子放入列表
data_alpha = [alpha_1,alpha_2,alpha_3,alpha_4,alpha_5,alpha_6,alpha_7,alpha_8,alpha_9,alpha_10,
alpha_11,alpha_12,alpha_13,alpha_14,alpha_15,alpha_16,alpha_17,alpha_18,alpha_19,alpha_20,
alpha_21,alpha_22,alpha_23,alpha_24,alpha_25,alpha_26,alpha_27,alpha_28,alpha_29,alpha_30,
alpha_31,alpha_32,alpha_33,alpha_34,alpha_35,alpha_36,alpha_37,alpha_38,alpha_39,alpha_40,
alpha_41,alpha_42,alpha_43,alpha_44,alpha_45,alpha_46,alpha_47,alpha_48,alpha_49,alpha_50,
alpha_51,alpha_52,alpha_53,alpha_54,alpha_55,alpha_56,alpha_57,alpha_58,alpha_59,alpha_60，
alpha_61,alpha_62,alpha_63,alpha_64,alpha_65,alpha_66,alpha_67,alpha_68,alpha_69,alpha_70，
alpha_71,alpha_72,alpha_73,alpha_74,alpha_75,alpha_76,alpha_77,alpha_78,alpha_79,alpha_80，
alpha_81,alpha_82,alpha_83,alpha_84,alpha_85,alpha_86,alpha_87,alpha_88,alpha_89,alpha_90,
alpha_91,alpha_92,alpha_93,alpha_94,alpha_95,alpha_96,alpha_97,alpha_98,alpha_99,alpha_100,alpha_101]
# 由于一些因子应用前几个月的数据进行计算，因此初始时期因子为错误值，所以取两年保留一年
for i in range(len(data_alpha)):
data_alpha[i] = data_alpha[i].iloc[245:] # 243,245,244,244,244/这些数字为某年交易日数量
df_101 = pd.concat(data_alpha) # 合并各因子的数据
df_101.to_csv('data/alpha.csv') # 储存数据至csv文件
## 提取数据并转换数据结构
# 因子数据转化为时间+股票的双重索引格式
def zhuanhuan(alpha_1,date,stocks,columns = ['alpha']):
alpha_year = alpha_1.fillna(value = 0)
alpha_year.set_index(date,inplace = True)
index = pd.MultiIndex.from_product([date,stocks],names = ['date', 'codes'])
df = pd.DataFrame(alpha_year.stack(),columns = columns)
alpha = pd.DataFrame(df.values,columns = columns,index = index)
return alpha
date_list = ['2014-01-01','2015-01-01','2016-01-01','2017-01-01','2018-01-01','2019-05-30'] # 测试区间，每年更换一次股票池
data_alpha = []
div = 101
# 从csv文件取出因子数据存进列表
for i in range(5):
data_ = pd.read_csv('data/data_alpha_re'+str(i+1)+'.csv')
data_.drop(data_.columns[0], axis=1, inplace=True)
data_alpha.append(data_)
# 将之前合并的因子进行分割，并转换数据结构为双重索引，存进二维列表
for i in range(5):
data = []
df = data_alpha[i]
n = int(len(df)/div)
date = w.wsd('000001.SZ','close',date_list[i],date_list[i+1],period = 'D',usedf = True)[1].index
stocks = w.wset("sectorconstituent", "date="+date_list[i+1]+";windcode=000906.SH").Data[1]
for j in range(div):
d = zhuanhuan(df.iloc[n*j:n*(j+1)],date,stocks,columns = ['alpha'+str(j+1)])
data.append(d)
data_alpha[i] = data
# 将各因子不同时期的数据进行合并，得到一维列表data存放各因子的双重索引可用数据
data = []
for j in range(div):
d = pd.concat([data_alpha[0][j],data_alpha[1][j],data_alpha[2][j],data_alpha[3][j],data_alpha[4][j]])
data.append(d)