@antinomy thanks!

@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

@magenta-grimer Hello, you can have at most 50 running submissions in your user area. You can stop any of them any moment and replace it with another one.

Before the end of the Q17 submission phase, you should select at most 15 of them. These will take part to the live contest.

@sun-73 Yes, we will, sorry for the issue.

@support, thank you for the clarifications. Regards.

Hi @support,
Thanks for getting back. No worries, I was able to get 6 strategies into the Q16 competition so far.
qnt3.PNG

@alexeigor Hello. Version 0.0.501 of the qnt library works correctly in Colab. Python version support has been extended from 3.10 to 3.13. The basic functionality of the library should work without issues.

To install, use the following command:

Note: Installing ta-lib in Colab is not working for me at the moment.

Hello.

I checked this problem. The script which cut "###DEBUG###" cells was incorrect. I fixed this and resent your strategies (filtered by time out) to checking.

Regards.

@vyacheslav_b Thank you so much

@support
oh I see now what you mean.
15 strategies PER USER are selected.
At first, I thought you were only going to select 15 strategies total for all users.
Thanks.

@xiaolan That is correct, but the logic can be easily re-used. The only novel element will be the introduction of the liquidity filter at intermediate stages/at the final stage for the selection of the weights.

@vyacheslav_b perfect. It wasnt obvious to me that single pass was meant by that. Thank you

@vyacheslav_b Thank you so much

@xiaolan Ok, but please note that you can work all the time with xarray, the documentation is very good:

http://xarray.pydata.org/en/stable/

@omohyoid Dear omohyoid,

Yes, that's right. After submitting your strategy shouldn't override environment variables.

Regards

@omohyoid Hi, we do not have such calls, sorry

@sheikh It is fine, please just submit, check the result and let us know if you see any issue. It should work fine.

@support Thanks for your respond. Now I understand the cause and fixed it

@eddiee

Hello.

This code looks to the future.
It is needed to train the model.
Pay attention to the name of the variable.

@buyers_are_back We reprocessed the submission, it is formally correct and passes all the filters. Sorry for the issue, evidently on our side.