@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.

@sun-73 @RoyPalo, Hi,

Q23 Leaderboard was updated several days ago, all eligible submissions are there now, sorry for late notice. Please let us know if you find any submission that is missing.

@roypalo Hi,

can you please send the submission id?

Dear @sun-73,

All strategies that are sent before contest deadline will be processed and eligible for participating in the contest, even if they finish with processing later.

The limit of 15 strategies per contest remains the same, but that doesn't mean one user can't submit more than 50 strategies. He just has to pick which 15 of them to send to contest, and if he doesn't, then the 15 best sorted by Sharpe ratio will be considered as participating in the contest.

The strategies of other users, or their number, will not make your strategies exceed limits and filter them out. If you suspect that some strategy shouldn't be filtered out by time limit constraints, you are free to submit the same strategy again. Maybe it will work the next time, and if not, then there is probably some issue with the strategy itself.

Regards

Dear @gjhernandezp,

That strategy is filtered out because it exceeds limits (memory or time) that a strategy has. Your strategy is using machine learning, so that is risky section and could lead to something like this. The success status is irrelevant, it is probably some edge case. The only thing that matters is the status of the strategy which is filtered.

Best regards

Dear @antinomy,

There has been some lag in processing, thanks for pointing it out.

Regards

Dear @antinomy,

We didn't want to completely reprocess your strategy, but since it was filtered out before we had to manually return it to eligible candidates list.

Regarding your second question, we have updated our correlation service, and you could try submitting again and see if it will be filtered out.

Best regards

Dear @antinomy,

This issue has been resolved, and your strategy is now eligible to participate in the future contest. Thank you for bringing this up.

Best regards

@antinomy Hi,

sorry for late notice, we are checking what is the reason of such behavior, we will let you know soon.

Best regards,

@sun-73 Hi,

sorry for late answer, this has been fixed, please use the latest qnt library version. Thanks, that's correct, there is a typo in check() function, it will be changed in next update - cut_big_positions() is function from qnt.exposure module.

Best regards

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