new_data_process.py 文件源码

def one2two(file_in=PATH_FILE_OUT, file_out=PATH_FILE_FINAL):
    data = pd.read_pickle(file_in)['close']
    data = np.array([data[i:i + 576] for i in range(data.shape[0] - 576 + 1)])
    data = data.reshape(-1, 576)
    data_s = {
        'open_price': np.array([data[i][0]
                                for i in range(data.shape[0] - 576)]),
        'close_price': np.array([data[i][575]
                                 for i in range(data.shape[0] - 576)]),
        'max_price': np.array([data[i].max()
                               for i in range(data.shape[0] - 576)]),
        'min_price': np.array([data[i].min()
                               for i in range(data.shape[0] - 576)]),
        'mean_price': np.array([data[i].mean()
                                for i in range(data.shape[0] - 576)]),
        'median_price': np.array([np.median(data[i])
                                  for i in range(data.shape[0] - 576)]),
        'buy_or_sell': np.array(
            [int(data[i + 576][575] > data[i + 576][0])
             for i in range(data.shape[0] - 576)]),
        'change': np.array(
            [(data[i + 576][575] - data[i + 576][0]) /
             data[i + 576][575] * 100
             for i in range(data.shape[0] - 576)])}
    data_s = pd.DataFrame(data_s)
    bins = [-100, -5, -4, -3, -2, -1.5, -1, -
            0.5, 0, 0.5, 1, 1.5, 2, 3, 4, 5, 100]
    bins = [0.01 * x for x in bins]
    labels = [-8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8]
    data_s['change_D_16'] = pd.cut(data_s['change'], bins, labels=labels)
    bins = [-100, -5, -2, 0, 2, 5, 100]
    bins = [0.01 * x for x in bins]
    labels = [-3, -2, -1, 1, 2, 3]
    data_s['change_D'] = pd.cut(data_s['change'], bins, labels=labels)
    np.save(file_out[0], data[:len(data) - 576])
    data_s.to_pickle(file_out[1])