@support Ohh, I understand. Thank you for your support.

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.

@alphastar Sorry for the very late answer, the issue has been fixed in the meantime....

@support It's working now, thanks!

@sun-73 it should be ok

@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

@black-magmar You are correct, but this kind of forward-looking is always present when you have all the data at your disposal. The important point is that there is no forward-looking in the live results, and that should not happen as the prediction will be done for a day for which data are not yet available.

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

@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.

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

@vyacheslav_b Thank you so much

@vyacheslav_b Thank you so much

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

@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.

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

@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!