@buyers_are_back Hi,

Regarding your first question, yes, that is correct. As we look more into the past, it is more difficult to get data for companies which have been index members but don't exist anymore, for example. This is also related to your second question - symbols with '~1' are in almost all cases, the same companies with the same ticker symbol, but with different ISIN (International Securities Identification Number). For instance, SanDisk company ("NAS:SNDK") was standalone public company until 2016, when Western Digital acquired SanDisk. In 2025 company spinoff, SanDisk re-emerged on the Nasdaq as an independent public company, with the same ticker as it was ('SNDK'), but with different ISIN (considered as different company).
Those symbol pairs, should not have an intersection in membership ("is_liquid" field should not be 1.0 for both at the same time), otherwise it could be mistake by provider.

Thanks for bringing this up. I had a similar question when looking through the example. From what I understand, it can seem forward-looking at first because of how the data is prepared and the model is trained. The important part is whether the prediction at each point is based only on information that would have been available at that time. If that's the case, then it may not be true look-ahead bias, just a backtesting implementation detail. That said, it's definitely worth double-checking because data leakage in machine learning strategies can be surprisingly easy to miss and can make results look much better than they would be in live trading.

@nongnat Hi,

Ranking metric is Sharpe Ratio of the Contest Period (specifically for Q24 Contest it is April 1, 2026 - July 31, 2026).

Unique user - each user can only win one prize .

The prize structure is the same as all earlier competitions - allocation to top 7 submissions with structure $1M / $750K / $500K / $100K / $50K × 3

The notebook example/template is made for Q24 Contest. Scale date in the template is chosen randomly just as simulation, to present how the scaling function works. Q24 dates are:

Last date for Q24 submissions -> March 31, 2026 Scale date -> July 31, 2026 Live payouts -> September 1, 2026 - September 1, 2027

'In Contest' - submissions selected to participate to certain contest, and contest is still in the Contest Period (like Q24 at the moment).
'After Contest' - submissions from all earlier completed contests.
Strategies in Candidates, cannot be selected to running contests in which Entry phase is over (Contest Period started).

SubmissionEquity <= 1.0 - no payouts. SubmissionEquity > 1.0 but underperforms the benchmark - no payouts. SubmissionEquity > 1.0 and overperforms the benchmark, there are payouts calculated by the formula.

Payouts are calculated at the end of each month of Payout phase, based on the profit generated relative to the benchmark (only new profits):
Prize * 0.1 * (PayoutScore – 1.0),
which means 10% of the profit generated.

You can also check the Official rules for Q24 https://quantiacs.com/contest/24.
Please feel free to ask if you have any further questions.

Best regards,

@copper-herring ok, thanks for reporting, we will

@rubber-metapod We are not running any futures contest right now.

Hi, the progress depends on many submissions are in the queue.

@black-magmar Yes.

@optical-turtle The Q24 entries will be updated soon.

@vg2001 It has been fixed, sorry

And what if a strategy uses the following rules to select assets to trade:

the primary exchange is NAS the sector is not finance has price data for at least the previous 3 months has an average daily trading volume of at least 200 k based on the previous 3 months belongs to the top 100 of the thus far selected assets in terms of market capitalization

Would you say any of these rules violate the contest rules?

Because these are the selection criteria for the N100 constituents. The only difference in my strategy is that I'm using qnt.data.stocks_load_ndx_data.sel(field='is_liquid') instead.
Sure, the first of the rules above manually selects the exchange and the second one manually excludes a sector. But still none of these manually select assets and neither does the filter is_liquid from another dataset.

Also, lets take a look why you prohibited manual asset selection in the first place. Wasn't this to avoid lookahead bias? And isn't this also the reason for the existence of the field is_liquid in any of your datasets? Are you saying that the exact field you introduced to avoid lookahead bias is now the reason you disqualify a strategy because of lookahead bias just because it's from a dataset other than the one for the contest?

@svyable Hi,
sorry for late answer, no we don't provide that option, but we will think about adding it in future.

@wool-hypno Which codes?

@vyacheslav_b Forgive me for the late response, I have not been on the platform. Thank you for your assistance. I can report that I have had no joy in using the strategy builder. I followed what you said and went on to download Chrome and still no joy. The strategy did not even appear under My Strategies. I guess I will just have develop the strategy on my own IDE and upload. Anyway, thanks for assistance, most appreciated.
Cheers

@granite-ditto Hi, the first source of information is the documentation at:

https://quantiacs.com/documentation/en/

In addition, we recommend you to check the template notebook available at:

https://github.com/quantiacs/Q24-crypto-guide/blob/master/strategy.ipynb

Quantiacs provides a built-in filter for selecting the top crypto assets according to the "liquid" field as follows:

is_liquid = data.sel(field='is_liquid')

@green-flareon Thanks. The live phase of the Q23 is running. Quants can join any contest during the submission phase. Q24 is on.

@support thanks!!!

@tim-hub Hi,

the template processing has not finished yet, it is on day by day processing (currently beginning of 2023). The reason of the error is that helper function tries to compare stat after 2022-08-31, which is not possible if processing date is 2020-01-01 for example. It doesn't affect the output (weights), and now you don't see this error because processing date is after 2022-08-31. If you had cloned the template and run it, the error would not have occurred.