Dear @svyable,

If you use our backtest function, the liquidity filter will applied automatically, you can just ignore that warning.
On the other hand, if you decide to use single-pass strategy option you should apply that filter manually or just use clean function that has that option included.

Regards

@angusslq Hi. Try cloning the template or restarting JupyterLab. The error was fixed in the new version of the qnt library.

Dear @angusslq

Yes, there is a large queue currently as we are nearing the end of the submission period for the following contest. All submissions submitted before the deadline will be eligible for participating in the contest, including yours.

Regards

@lookman Hello. Try cloning your strategy and running it again. It should work correctly with the new version of the qnt library.

import qnt.data as qndata import qnt.data.secgov_fundamental as fundamental market_data = qndata.stocks.load_spx_data(min_date="2005-01-01") indicators_data = fundamental.load_indicators_for(market_data, indicator_names=['roe']) display(indicators_data.sel(field="roe").to_pandas().tail(2)) display(indicators_data.sel(asset='NAS:AAPL').to_pandas().tail(2)) display(indicators_data.sel(asset=['NAS:AAPL']).sel(field="roe").to_pandas().tail(2))

https://quantiacs.com/documentation/en/data/fundamental.html

@auxiliary-snail Hi,

unfortunately, this is not allowed and in accordance with the rules. Using hard-coded time periods in which trading algorithm will work differently, is not a quantitative method (just like manual asset selection, e.g. "trade only Apple or Microsoft"). We still haven't implemented a mechanism for automatic recognition of such behaviors in trading strategies, and even though a strategy could be successfully submitted, it will not be eligible for prize winning.
What we are searching for, is well performing strategy over entire in_sample period (SR>0.7), robust to all market movements 2006-2025, so we can expect it will perform well in future, too.

Hi @support, everything is all right now. Thank you!

Dear @angusslq,

That line is recommendation for the users to not create a strategy that exceeds the timeout limits. What that means is that after submitting, the strategy should ideally compute weights for one trading day in less than 10 minutes. Depending on our available resources, we can also increase that limit somewhat but it is not recommended to create strategies that exceed this limit.

Regards

@sun-73 it should be ok

@stefanm Thank you for the details

@support Thanks.

@support I got it. Thanks for ur reply

@kairos Hi,

thanks for pointing this out, we are working on it, it will be fixed as soon as possible.
The problem is, that for some dates prior to 2014 we don't get OHLC prices at all for most assets (all NaNs, so is_liquid is NaN too). We have already reported it to our data provider. Anyway, the clean() function should work properly.

Best regards

Hi @support ,

That makes perfect sense

Thank so much

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

Dear @omohyoid,

Currently we are facing some data issues, the error is not caused by changes in qnt library. At the moment, only current index members of S&P500 index are available, but Nasdaq100 stocks are temporarily unavailable. Sorry for the inconvenience, we are working on a solution.

@carogate Hi, at the moment unfortunately not, sorry...

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

@support Thank you!

@dark-pidgeot Hi! After the release of version qnt “0.0.402” the issue with data loading in the local environment has been resolved. The library now uses newer dependencies, including pandas version 2.2.2.