Global Moderators

@theflyingdutchman Hi, sorry for the delay, yes, all fine, more details by e-mail

@antinomy Hi,

You are absolutely correct, we will fix check() function ASAP. It should only cut weights which exceed 0.1 allocation by asset, and normalize the sum of allocation to maximum 1, on every timestamp. If sum was < 1, and weight of an asset < 0.1, the output remains the same.
Thanks a lot for pointing this out with examples.

@angusslq Hi,
yes, it is expected and will be updated soon. Since it is the end of Q22 contest period, this update will show preliminary results of Q22. Thanks for your patience.

@sun-73 Hello,

Thank you for pointing out the bug — we really appreciate it. The issue with the "Show only my results" checkbox on the Q23 Global Leaderboard has been fixed. You can now check that everything is working correctly on the site.

Please note that you might need to clear your browser cache for the changes to take effect.

If you have any suggestions regarding the interface or other features, we’d be happy to hear them!

Best regards,
Support Team

@illustrious-felice Hi,

sorry for late answer, please check the correctness of dataset used for defining weights in strategy. Be sure that 'stocks_s&p500' dataset is used and not 'stocks_nasdaq100' for current competition. To ensure strategy trades only liquid assets in certain time period, multiply the output from your strategy function with 'is_liquid' field from correct dataset, or simply use clean() function from qnt.output:

import qnt.data as qndata
import qnt.output as qnout

def strategy(data):
    .....
    # liquid = data.sel(field='is_liquid')
    # weights = weights * liquid
    return weights


data = qndata.stocks_load_spx_data(min_date='2005-01-01')
weights = strategy(data)
weights = qnout.clean(weights, data, kind='stocks_s&p500')

qnout.write(weights)

Also, keep in mind that submission will not be eligible for contest if stocks universe (in this case "top 7 magnificent") is hand picked (manually defined).

Best regards,

@magenta-grimer Hi, the Q22 basic template is a good starting point.

@nosaai Hi,

which version of qnt library is used? We introduced that function about a year ago, with S&P500 stocks dataset, maybe try with the most recent qnt version. If the issue persists, please let us know.

Regards

@alexeigor Hi,

sorry, it should work now.

Best regards

Hi,
thanks for pointing this out, we are working on refactoring the code, qnt is reverted to previous version. Sorry for late answer.

Dear @omohyoid,

That would not be a quantitative approach, hence it is not allowed based on the current contest rules.

Best regards