@buyers_are_back Hi,

You can use is_liquid field from spx dataset to filter out weights allocated to not members of S&P500 at given point in time:

from qnt.data import stocks_load_spx_data spx_data = stocks_load_spx_data(min_date="2006-01-01") is_liquid = spx_data.sel(field="is_liquid") final_weights = your_weights * is_liquid ### weights for given date (weights_spec) date = "2026-02-24" weights_spec = final_weights.sel(time=date)

The number 842 represents total number of assets that have been members of S&P500 index at some point in time (from given dataset), and 658 shows number of assets that are still active on market (have OHLC prices), but only ~500 are index constituents in that point in time.

To get the list of index members on certain date, just filter is_liquid field:

### liquidity field values for given date is_liquid_spec = is_liquid.sel(time=date) members_spec = is_liquid_spec.coords["asset"][is_liquid_spec == 1.0].asset.values

@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.

@lookman The answer we sent to your e-mail address bounced back.

@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.

@support, thank you for the clarifications. 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

@rrgiyer Hi.

I'm not sure I understood your question correctly, but most likely the answer is no. You have access to today's data, and today the algorithm allocates portions of capital to be invested in assets tomorrow (entry at the opening price), but tomorrow's opening price is unknown today. You can't look into the future.

It's better to define your own functions directly within your strategy. If you need to use existing functions but want to modify them, just copy the code and change it inside your strategy. Do not modify the library's standard files — they will be reset to default values.

If you’ve noticed, there’s now an AI assistant in the documentation where you can ask similar questions: https://quantiacs.com/documentation/en/
The answers may vary slightly depending on whether you are logged in to the platform or not. I’ve found that it works quite well for me.