data_handler.py 文件源码

def ma_ribbon(df, ma_series):
    ma_array = np.zeros([len(df)])
    for idx, ma_len in enumerate(ma_series):
        key = 'EMA_CLOSE_' + str(ma_len)
        ema(df, ma_len, field = 'close')
        ma_array[idx] = df[key][-1]
    corr, pval = stats.spearmanr(ma_array, range(len(ma_series), 0, -1))
    dist = max(ma_array) - min(ma_array)
    df["MARIBBON_CORR"][-1] = corr * 100
    df["MARIBBON_PVAL"][-1] = pval * 100
    df["MARIBBON_DIST"][-1] = dist