preprocess.py 文件源码

def get_today_data_for_MLP(code):
    '''
    :param code:????
    :return: ?????X
    '''
    import numpy as np
    data_path = "./data/stock_data/"
    oneDayLine, date = load_data_from_tushare(data_path + str(code) + '.csv')
    volumn, volumn_dates = load_volume_from_tushare(data_path + str(code) + '.csv')
    daynum = 5
    X = []
    ef = Extract_Features()
    for i in range(daynum, len(date)):
        X_delta = [oneDayLine[k] - oneDayLine[k - 1] for k in range(i - daynum, i)] + \
                  [volumn[k] for k in range(i - daynum, i)] + \
                  [float(ef.parse_weekday(date[i]))] + \
                  [float(ef.lunar_month(date[i]))] + \
                  [ef.rrr(date[i])] + \
                  [ef.MoneySupply(date[i])]
        X.append(X_delta)

    X = preprocessing.MinMaxScaler().fit_transform(X)
    return np.array(X[-1])