@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.

@yonasbo Hi, sorry for delay, we will start soon a new contest, in the next 2 weeks

@cross_platform-zebra Hi, there is no other limitation regarding local development. It is already configured to be exactly the same datasets for Nasdaq100 stocks, and returns the same statistics for trading system running locally or online.

Hello @cross_platform-zebra
Thanks, this is just experimental additional dataset we don't provide for local development.
For trading systems and competitions, please use Nasdaq100 stocks, which can be obtained by calling qndata.stocks.load_ndx_list(min_date="2006-01-01").

Best regards,

@scalpingaf Correct, all trades (buy or sell) are taken at the open of the next day you take the decision.

@scalpingaf Also for Bitcoin, the exposure is the fractional allocation for the capital allocated to Bitcoin.

@scalpingaf Hello, thanks for asking.

1. As I understood, when calculating "weights" I should operate by positive and negative numbers as an order of position size. Everytime in your examples you use 1 or -1, and as I understood, it means we will buy 1 contract or sell 1 contract, am I right? If I'd like to buy 10 contracts, I should mark "10"?

-> No, the weights are the allocations to a given asset. In other words, a weight of 0.1 means a 10% allocation to a given asset. In case the sum over absolute weights is larger than 1, we normalize it to 1. No leverage is allowed.

If I need to close a current open position, should I mark 0 in a "weights"?

-> Yes, correct.

3. How do you calculate close position price? Can I send to backtester my close price? As I understood, you just use a candle close price?

-> We provide a pre-defined close.

4. How do you calculate open position price? Is it an open candle price? Can I mark my open price? For example, it can be the price inside the current candle - breaking through the price level.

-> Good question. No, we just provide a single value for the open price.

5. In my strategies I use a stop loss. To calculate it I need to know the balance. How can I get info about balance using your framework or how should I correctly init it? It's also needed to calculate an order position size.

-> That is possible, as the backtester can be used with a "stateful" version which knows the balance, but not intraday, that can be checked at the end of the trading session only.

@algotime Dear Algotime, all three strategies will participate in the contest. We will update the contest leaderboard once all eligible strategies have finished processing. Thank you for your patience.

Dear magenta.kabuto, it will be done at the open price in t+1 time.

@eddiee Yes, exactly, the submission date matters, not the date when processing is completed. Although we have to improve definitely in the processing....

@captain-nidoran Dear captain-nidoran, only 15 submission can take part to the contest per user, but users are submitting more, like in your case 20 as you say. In the end only the top 15 will take part to the contest, or the 15 you select. But they are creating a long queue. In addition to your 20, there are many other users who submit many systems just shortly before the submission window closes. So the processing of all submissions takes longer.