Dear @angusslq,

Once the competition ends its live period (currently 4 months for Q22), the prizes are given. That means that at the end of those 4 months we sort all strategies and only the top 7 by sharpe ratio are eligible for prize and get allocation: 1st place 1M, second place 500k etc. and this cannot be changed afterwards. The prizes are not given on the daily basis and certainly not during the contest live period. You can find more info in the contest rules page on our website.

For your second question, we assume risk-free rate to be zero. You can find additional information about how we use sharpe ratio here.

Regards

Dear @angusslq,

Allocations are set at the end of the contest, and they take into account the strategies ranking at that time. We update the rankings as we process the participating strategies with the new available data, which happens 5 times a week for 5 working days. The allocations that you refer are probably in the systems page and they take into account the current ranking in the contest but that doesn't mean anything until the contest ends and prizes are given.

Regards

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

@stefanm Thank you for the details

@support Thanks.