Best posts made by illustrious.felice

@vyacheslav_b Thank you for responding.

I realize that even now I have a strategy that is not forward-looking

close = data.sel(field="close")
price_yesterday = qnta.shift(close, 1)
target_price_up = xr.where((price_yesterday < close), 1, 0)

Why is Sharpe worse after the strategy goes through training-testing by ML/DL models compared to when using a single backtest?

I have tried many different strategies and changed different feature trains, but they all showed that after going through a certain model, the sharpness decreased compared to when using a single backtest. Please answer. Thank you so much.

@support Thank you for your feedback. I also hope Quantiacs updates new strategy examples on how to use technical analysis (besides sma, trix_ema, atr_lwma,...), and strategies on using ML/DL models effectively (not an example that strategy forward-looking),...

Hopefully in the future Quantiacs will release new data sets such as news, sentiment, macro, options,... Create new contests that allow merging strategies to build portfolios,...

Hopefully, Quantiacs will continue to grow. Sincere thanks to Quantiacs for creating extremely high-quality contests.

Hi,
I encountered the problem of running time too long when running backtest_ml. Below is my code:


Running time can take up to hours. I would like to ask what parameters can I edit to make the run time faster? Or is there any other method that makes the runtime faster?

Thank you so much.

Hi, I noticed that the time to enter Quantiacs was very long (from entering the platform to successfully logging in took about a few minutes, then accessing the notebook repo, accessing the init and strategy files also took a few more minutes). Hopefully Quantiacs can speed up access to files on the platform as well as when logging in.

Thank you very much.

@support @Vyacheslav_B

@vyacheslav_b Hello, I was trying the code you gave and realized that using state for train ml_backtest only works when the get feature function is a feature like ohlc or log of ohlc (open, high, low, close).

I added some other features (eg: trend = qnta.roc(qnta.lwma(data.sel(field='close'), 40), 1),...) and noticed that after passing ml_backtest, every The indexes are all nan. Looking forward to your help. Thank you.

@Vyacheslav_B

Hello, I would like to ask what is the difference between relative_return & mean_return and what information do these two parameters indicate?

Thank you