@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

@support It's working now, thanks!

@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

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

@support Great! This route opens new possibilities in terms of model design. Thanks a lot!

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

@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

@sun-73 it should be ok

@antinomy

I got it !
Thanks a lot !!

@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.

@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.

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

@vyacheslav_b Thank you so much

@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

@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!

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

@vyacheslav_b Thank you so much

@vyacheslav_b Thank you so much