@support Thanks for the feedback. I would like to ask the following, for example if I want to filter out the 10 tickers with the best sharpe of the strategy, how should I do it, to avoid being filtered by hand?

Please provide an example code

def filter_sharpe_ratio(data, weights, top_assets):
stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True)
sharpe_ratio = stats_per_asset.sel(field="sharpe_ratio")
return qnfilter.rank_assets_by(data, sharpe_ratio, top_assets, ascending=False)

asset_filter = filter_sharpe_ratio(data, weights, 150)
weights = weights * asset_filter

I used this code, but the result is still nearly 300 tickers.

Hi @support, thank you! The box is now working fine!

I have a simple suggestion regarding the charts created for each strategy:

Besides the equity curve (strategy, SPX and Nasdaq100), Logarithmic scale, Long/Short, Underwater, and Bias, it would be nice to see an additonal chart (with two lines) showing the number of assets in each day that have a positive weight (long position), as well as the number of assets in each day that have a negative weight (short position).

This will help us developing the strategies, since we can better track the outcomes generated by different algorithms being constructed.

Also, this can help you identify asset hand-picking with buy-and-hold positions.

Thanks!

@support: Is the Q21 allocation actually submitted? I never heard anything about winning? Best Regards

@alexeigor Hello. Version 0.0.501 of the qnt library works correctly in Colab. Python version support has been extended from 3.10 to 3.13. The basic functionality of the library should work without issues.

To install, use the following command:

!pip install git+https://github.com/quantiacs/toolbox.git 2>/dev/null

Note: Installing ta-lib in Colab is not working for me at the moment.

Thanks 🙏

@vyacheslav_b Apologies for the late response. Thanks for the assistance, all is now well. Cheers

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

@alexeigor Hi,

sorry, it should work now.

Best regards

@support Thx for ur reply

Dear @angusslq,

The ranking is calculated based on contest sharpe ratio only. The situation that you saw at that time was only temporary because at that point the calculations for new day were ongoing, so some strategies were processed before others, and their contest sharpe was updated, but the ranking is only updated after every single strategy from that contest has been processed and updated.

Regards

Dear @silverstar1003,

There was a temporary problem with our servers which has been resolved, and your strategy should be calculated soon.

Regards

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