-
Notifications
You must be signed in to change notification settings - Fork 45
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How to reproduce the results on more recent data? #6
Comments
To generate Data.db about the latest data, please refer to the code contributed by ZhengyaoJiang(https://github.com/zhengyaujiang/PGPortfolio). |
Hi, thanks a lot for the reply. About this step described in the paper: "we normalize the price series of each asset by element-wise dividing the prices regarding the last period in the price series." Is that done by the code deposited here, or that is a previous step that I have to do before processing the dataset? |
You did mention to generate the .db file from the code contributed by ZhengyaoJiang. But is this .db file compatible with the code from RAT? I managed to normalize the data with the pandas library however when I save it as .db file I am not able to preserve the information fully and that results in the error: ValueError: the length of selected coins 0 is not equal to expected 10. So the algorithm is not able to see the coin data as before which results in no coins to be found. I was not able to find a way to resolve this issue in particular with pandas. |
The link above is no longer working ZhengyaoJiang(https://github.com/zhengyaujiang/PGPortfolio) |
It's compatible. You only need to use ZhengyaoJiang's code to generate Data.db file. The normalization is done in RAT. |
@Ivsxk The link you mentioned above is no longer available
|
Hi,
I am trying to replicate the results of this algo but everytime I try I get very, very poor results. I am wondering how the data was preprocessed. Could you clarify that? Any help is very welcome.
Thanks!
The text was updated successfully, but these errors were encountered: