@rezhak21 Hi, the lookback period used in the builtin backtest function is expressed in calendar days, while indicators are computed in trading days. As a rule of thumb, add 2 more days every 5 trading days to take weekends into account.

@anshul96go Just to give you more context: when you submit a strategy to the contest, the strategy is queued. Depending on the current load, the processing can take some time. If you submit when many other users are submitting, then processing will take more time.

Moreover, when you develop on the notebook, processing is instantaneous because no particular checks are done (well, it depends on the complexity of the strategy). You just run your code and get the result.

If you code it using a single-pass approach the processing will be very fast. If you use our backtesting function (which prevents forward looking), processing will be a bit slower.

When you submit the strategy, however, several sanity checks are run on the strategy. The system checks if allocations are defined for all datapoints, if your strategy is performing looking forward operations, if it stops producing the output before current day, it computes the Sharpe ratio and other statistical indicators, and moreover it computes the correlation of your strategy with all examples we provide, as we do not allow an example to win a contest...so the processing is slower.

For quick development, please refer to the results you get in the notebook, submit the strategy and then it will be ok.

@spancham Hi, yes, understood. The 10% rule applies to the contest entries and to the prizes (1M USD invested, 500k USD invested, etc, see https://quantiacs.com/contest).

However, strategies can get funded by investors even if they do not win contests. In this case 2 schemes are possible:

1. a fixed management fee, i.e. the quant earns a monthly fixed fee.

2. a performance fee, i.e. the quant earns a monthly performance fee.

It is already happening with quants who submitted systems which developed a long track record. In both cases, there is a mutual agreement between Quantiacs, the investor and the quant on the level of the fees.

Hi.

When you submit the strategy, the evaluator checks the strategy using data isolation and runs the notebook each day during the in-sample period.

This is necessary because there is a very common issue - looking forward.

The evaluator can parallelize this process, but anyway it takes more time.

@spancham Hello, the management fee is order of magnitude of a performance fee. Imagine that you get 1m USD allocated, the system has 10% volatility, a Sharpe ratio of 1 for 1 year, and you make 10% in performance fees per year. Instead of choosing a performance fee (which could be zero in bad months) you could choose at the very beginning a fixed fee. In this case, it would be order of 1k USD per month.

The length of the track record depends on the strategy and many factors, let us say no less than some months, longer time for a larger capacity.

@sheikh Hi Sheikh, we are busy preparing the new contest.

The algorithm you mention actually has a lot of flat phases also in sample, why do you think the problem is an external library?

If you run the system in a notebook and plot the equity chart, do you get the same result or not?

Thank you

@spancham The performance fee is a yearly performance fee as per industry standard. If system makes 100k USD in profits per year (1 Sharpe, volatility 10%, 1M USD invested), then the quant will receive 10k USD per year.

@spancham Currently there is no investor tab, it is part of our roadmap and we are working on that. At the moment we are focusing on improving the software and the data, the new version of Quantiacs is up and running since 3 months only. As soon as the fund is up, we will announce it.

We published a summary of the past 14 contests on the home page, with quant names and allocations made by Quantiacs (own money). Quantiacs has private agreements with investors allocating their money to selected systems, and with quants who developed systems and are getting fees.

If you/your family have enough capital and want to invest in your own algo and bear the risk of downside losses, well, you will be able to do it with Quantiacs (once we start the fund) if you want.

@magenta-grimer yes, correct, and it does not have to win a contest, we monitor all submitted systems and will contact the quants who wrote interesting systems.

But if you implemented a stateful strategy, the evaluator can't parallelize the checking. It will take much more time. You can see the time of the one-day evaluation in the log and estimate how long it will take.

@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