@wool-dewgong Hello! We added one template which should address your issue and allow you to perform a rolling fast ML training with retraining. It is available in your user space in the Examples section and you can read it here also in the public docs:

https://quantiacs.com/documentation/en/examples/machine_learning_with_a_voting_classifier.html

@vyacheslav_b
Thank you! 🎉 🎉

@magenta-grimer

Hello.

I confirm this bug.
It is fixed now.
If you clone this template again, it will work ok.

Thank you very much for your report.

@anshul96go Hi Anshul, the IS period starts on January 1st 2014. You can use a strategy with the following logic:

if Volume/OI is zero, then take this decision:

Otherwise, take this decision:

But yous algorithm should produce results also in the period when Volume/OI data were not available.

@support
Thank you! I'll try that and let you know.

Hi @support
Thank you, I'll try that.

@magenta-grimer

Hello.

Remove .isel(time=-1).

ma_slow = close.rolling(time=parameter1).mean() #.isel(time=-1) ma_fast = close.rolling(time=parameter2).mean()#.isel(time=-1)

It selects the last day, you need an entire series.

Regards.

@support Thank you very much!

I hope to don't disturb you so often in the future @support , but before that I need help to "get going".

@magenta-grimer Hi, the lookback period in the backtester function is expressed in calendar days, and the indicators are expressed in trading days. So as far as the lookback period is long enough to include all indicator needed periods, all choices are fine.

If the longest period needed for an indicator is 10 trading days (2 weeks), a lookback period of (for example) 20, 50 or 100 for the backtester function will deliver the same results. The shorter the better for efficiency.