Step 4 Final Strategy V1 with 4 factors.py 文件源码

def GetResiduals(stock,enddate):
    Xinput = [EquityOCFP(stock,enddate), EquitySize(stock,enddate), RSIIndividual(stock,enddate), Min130Day(stock,enddate)]
    X = pd.concat(Xinput, axis=1)
    date = enddate
    tempprice = get_price(list(stock), date, "{:%Y-%m-%d}".format(datetime.datetime.strptime(date, '%Y-%m-%d') + datetime.timedelta(days=30)), frequency='1d', fields=None)['OpeningPx']
    y = np.log(tempprice.iloc[-1]/tempprice.iloc[0])
    DataAll = pd.concat([X,y],axis = 1)
    DataAll = DataAll.dropna()
    regr = linear_model.LinearRegression()
    regr.fit(np.matrix(DataAll.ix[:,0:4]), np.transpose(np.matrix(DataAll.ix[:,4])))
    residuals = regr.predict(np.matrix(DataAll.ix[:,0:4])) - np.transpose(np.matrix(DataAll.ix[:,4]))
    residuals = pd.DataFrame(data = residuals, index = np.transpose(np.matrix(DataAll.index.values)))
    residuals.index = DataAll.index.values
    residuals.columns = [enddate]
    return residuals

#This function is used in the later function