@sun-73 it should be ok

@multi_byte-wildebeest Hi. Without an example, it's unclear what the problem might be.

If you use a state and a function that returns the prediction for one day, you will not get correct results with precheck.

This was discussed here: https://quantiacs.com/community/topic/555/access-previous-weights/18

@alphastar Sorry for the issue, it has been fixed.

@captain-nidoran Hello, please try to add in a cell at the beginning:

pip install 'sklearn==0.0.post1'

or in the init file:

! apt update && apt install -y libgomp1 && rm -rf /var/lib/apt/lists/*

Best regards

@commanderangle Dear commanderangle,

If you use ML in your strategy but not select that option we can't guarantee for how your strategy will be evaluated and it could be filtered out.

Regards

Hi @stefanm ,

How reckless of me, "data" should have been f_es_data. They are perfectly synced now.
Thank you so much. I really appreciate the help.

sincerely

@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

@support Yes, I can load now the stocks data. Thank you once again!

@magenta-kabuto We support only Python 3.7 right now. But it can coexist with Python 3.9:

https://quantiacs.com/documentation/en/user_guide/local_development.html

Basically you can use Python 3.7 inside a conda environment.

@antinomy

I got it !
Thanks a lot !!

@antinomy thanks!

@vyacheslav_b Thank you so much

@vyacheslav_b Thank you so much

@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.

@iron-tentacruel Sorry for the delay in the answer. We recommend conda as we can better track dependencies. With conda you can create locally an environment which mirrors the one on the Quantiacs server and you can work locally as you would on the server. If you need a specific version of a package, please let us know.

@support I have done that twice before my post, but now F_RY looks fine. There are several directories with scripts and notebooks I use with qnt, so maybe I deleted the wrong data-cache before...
Thanks for fixing the data!

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.

@antinomy Ty

@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.