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As asked in the title, in base/env/market.py, when generating sequences from orignal dataset, the label of training set is at time of t+2(data_index + 1) compared to x(from data_index - seq_len to data_index, a semi-closed interval), while when choosing label for the validate set, it is at t+1 instead.
Note that there is a tiny bug in master in the statement of instruments_y assiginment and I fixed in according to the code in dev branch.
`
if date_index < self.bound_index:
# Get y, y is not at date index, but plus 1. (Training Set)
instruments_y = scaled_frame.iloc[date_index + 1]['close']
else:
# Get y, y is at date index. (Test Set)
instruments_y = scaled_frame.iloc[date_index]['close'] # data_index + 1 --> data_index here
`
Thanks for your answer :)
The text was updated successfully, but these errors were encountered:
In addition, I had post an email to @Ceruleanacg before, while it may be a better approach to send the question here (to make a record). It could help owners to collect information about my question, too.
Another question in the mail is posted in the next issue seperately.
xtyangjie
changed the title
Two questions: In the RL
In the RL algorithms, why label in training set is at t+2 while which in validate set is at time t+1 ?
Jun 29, 2018
As asked in the title, in base/env/market.py, when generating sequences from orignal dataset, the label of training set is at time of t+2(data_index + 1) compared to x(from data_index - seq_len to data_index, a semi-closed interval), while when choosing label for the validate set, it is at t+1 instead.
Note that there is a tiny bug in master in the statement of instruments_y assiginment and I fixed in according to the code in dev branch.
`
if date_index < self.bound_index:
`
Thanks for your answer :)
The text was updated successfully, but these errors were encountered: