@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.

Dear @cortezkwan,

Users can use their own files, so yes, it is possible to use your pre-trained weights or a model by uploading it into strategy environment filesystem and later using it in your code. The only important thing is that the output weights should be in the correct format that matches our structure.

Regards

@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

@omohyoid Hi,

To prevent potential forward-looking behavior in the strategy, we use day-by-day submission processing. This means that when defining weights for a specific date, only data up to that date is available. Please ensure that data used by your model is handled correctly within the strategy.
Any use of future data for a specific date will produce different results than expected, and the strategy will not be eligible for competition.
Additionally, using external data sources or data not provided by Quantiacs is not allowed.
Regards

Dear @darwinps,

The difference is that you call filter method in your code where you see this warning. When that method is called, it calculates stats internally at that point in time, so that's when you actually see the warning, since at that time, the liquidity filter hasn't been applied.

The final stat calculation that you are doing in your code (after the liquidity filter) is not giving any warnings which is expected behavior.

Best regards

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.

Dear @darwinps,

This discrepancy is normal due to different ways how the stats are calculated at the beginning of the in sample period. Over a longer period, these statistics should become very similar or identical.
Thanks for bringing this up, and if you notice some big discrepancies in the future, please report that to us.

Best regards

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

@carogate Hi, please take a look at this example:

https://github.com/quantiacs/strategy-predict-NASDAQ100-use-SPX/blob/master/strategy.ipynb

@sun-73 Hi, there is no limit to the maximum limit to the number of stocks. The cap of 10% stays the same. Call documented in:

https://quantiacs.com/documentation/en/data/stocks.html#stocks-s-p500

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