简单的alpha策略，
选取某一时间点所有股票的相关信息ps、pb、pe等。用三因子回归获取alpha，分别用每只股票计算。选取排名靠前的n只股票计算组合净值计算结果和画图
注:代码运行需安装tushar pro 并获取TOKEN码， 这里获取token码
# coding=utf-8import mathimport tushare as tsimport pandas as pdimport matplotlibimport matplotlib.pyplot as pltimport numpy as npimport talibimport pandas as pdfrom datetime import datetime, datefrom sklearn import datasetsfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionmatplotlib.rcParams['axes.unicode_minus']=Falseplt.rcParams['font.sans-serif']=['SimHei']ts.set_token('”输入token码“')pro = ts.pro_api()############################读取数据类###################################class readData:def read_index_daily(self,code,star,end):#指数数据dsb = pro.index_daily(ts_code=code, start_date=star, end_date=end,fields='ts_code,trade_date,close,change')#默认读取三个数据return dsbdef read_daily(self,code,star,end):dsc1 = pro.daily(ts_code=code, start_date=star, end_date=end,fields='ts_code,trade_date,close')return dsc1def read_CPI(self,star,end):#时间格式start_cpi='09'dc=_cpi(start_m=star, end_m=end,fields='month,nt_yoy')return dcdef read_GDP(self,star,end):#时间格式star='Q4'df1 = _gdp(start_q=star, end_q=end,fields='quarter,gdp_yoy')return df1def read_bond(self,code,star,end):df=pro.cb_daily(ts_code=code,start_date=star,end_date=end)def read_base(self,code):df=pro.query('daily_basic', ts_code=code,fields='close,ts_code,pb,total_mv,trade_date')return df####################################################################################### start_time='0226'#发布GDP需要时间，我们延迟1个月，即第一季度的GDP4月份才发布。end_time="0521"dc=readData()dsc1=readData()dsb1=readData()###################计算alpha的值###########################################################def alpha_fun(code):dsb=dsb1.read_base(code) .fillna(0) dsc=dsc1.read_index_daily('000300.SH',start_time,end_time)dsc.set_index(['trade_date'],inplace=True)dsb.set_index(['trade_date'],inplace=True)df=pd.merge(dsc, dsb, on='trade_date').fillna(0)R=np.reshape( np.array([df.close_y]) , (-1,1) )R_f=np.reshape(np.array([ (df.change/(df.close_x.shift(-1))).fillna(0) ]) , (-1,1))#用0 填充nanHMI=np.reshape( np.array([ (1/df.pb).fillna(0) ]), (-1,1) )SMB=np.reshape( np.array([ df.total_mv]) , (-1,1) )X=np.concatenate(( R_f-4/252, HMI,SMB ),axis=1) y1=np.reshape(R,(1,-1)).TX_train, X_test, y_train, y_test = train_test_split(X, y1, test_size=0.3, random_state=0)linear = LinearRegression()linear.fit(X_train, y_train)alpha=linear.intercept_-4/252return alpha,linear.intercept_ ,linear.coef_,linear.score(X_test, y_test),df############################计算篮子里的每只股票净值###############################################def jinzhi_fun(code,start_time,end_time):#计算净值df=pro.query('daily', ts_code=str(code), start_date=start_time, end_date=end_time,fields='') df.index=pd.to_datetime(df.trade_date,format="%Y-%m-%d")jinzhi=(df.close/df.close[-1])return jinzhi######################选股条件####################################################co=pro.query('daily_basic', ts_code="",trade_date="0226",fields='ts_code')code_list=[]N=30#股票池k=0JZ=0#组合净值cum=0for i in co.ts_code.values[0:N]:try:if alpha_fun(i)[0]<0:k+=1JZ=jinzhi_fun( str(i) ,start_time,end_time) +JZJZ=JZ.fillna( method='bfill')#用前后数据特征填充except ValueError:passcontinue #########################计算权重收益净值#########################################JZ=JZ.sort_index(axis=0,ascending=True) JZ_avg=JZ/k#平均净值cum=np.cumprod(JZ_avg)# 累计收益 ####################计算收益率函数，如沪深300#####################################def JZ_function(code,star,end):df12 = pro.index_daily( ts_code=code, start_date=star, end_date=end)df12=df12.sort_index( )df12.index=pd.to_datetime(df12.trade_date,format='%Y-%m-%d')#设置日期索引ret12=df12.close/df12.close[-1]return ret12.dropna()#############################策略的年化统计######################################def Tongji(jz):NH=(jz.dropna()[-1]-1)*100*252/len(jz.index)BD=np.std(jz)*100*np.sqrt(252)SR=(NH-400/252)/BDreturn_list=jz.dropna()MHC=((np.maximum.accumulate(return_list) - return_list) / np.maximum.accumulate(return_list)).max()*100print("年化收益率：{:.2f}%:，年化夏普率：{:.2f},波动率为：{:.2f}%,最大回撤：{:.2f}%".format( NH,SR,BD,MHC))############################################################################if __name__=="__main__":cum12=JZ_function('000300.SH',start_time,end_time)Tongji(JZ_avg)plt.plot(cum12,label="沪深300",color='b')plt.plot(JZ_avg,label="股票组合",color='r')plt.title("alpha股+指期对冲策略")plt.legend()
结果：
年化收益率：44.75%:，夏普率：0.84,波动率为：51.64%,最大回撤：7.77%
1.python量化——alpha股票-指数期货对冲策略
2.多因子选股策略
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7.改进的美林时钟策略（二）
8.改进的美林时钟策略（三）
这里获取token码
