bgithub1 / da_rnn Goto Github PK

hello, your code in Class da_rnn init(), the code of getting data is below, why do you use companies' stock price to predict NASDAQ-100 Index?
especially ticker='NDX' in function‘s brackets and
self.X = df_dat.loc[:, self.x_columns].as_matrix()
self.y = np.array(df_dat[ticker])

    def __init__(self, df_dat, logger, encoder_hidden_size = 64, decoder_hidden_size = 64, T = 10,
                 learning_rate = 0.01, batch_size = 128, parallel = True, debug = False,ticker='NDX'):
        self.df_dat = df_dat
        self.T = T
        self.logger = logger
        self.logger.info("Shape of data: %s.\nMissing in data: %s." %(str(df_dat.shape), str(df_dat.isnull().sum().sum())))
        self.x_columns = [x for x in df_dat.columns.tolist() if x != ticker]
        self.X = df_dat.loc[:, self.x_columns].as_matrix()
        self.y = np.array(df_dat[ticker])
        self.batch_size = batch_size

NDX should be calculated by these stock prices, isn’t it? why u have to learn the calculation formula by RNN?
The DA-RNN paper gives a time series predicting model, right? But where is your time series predicting? I am confusion.

That's what I found when I read the code repeatedly, If I got wrong or missed something, please tell me.
Thank you.

How to solve?