• 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?

My strategy trades a subset of assets from the S&P 500 universe, selected with certain filter rules. Most are technical and one of them is that they also belong to the Nasdaq 100 index.
Which contest rule would be violated when a strategy selects stocks which are part of the contest universe and also part of another index?

Is selecting assets based on them being part of an index considered as "manual selection"? This would mean every strategy for all the Nasdaq 100 and S&P 500 contests is against the rules and the contest rules would contradict themselves.

WARNING! This strategy correlates with other strategies and will be rejected.
Modify the strategy to produce the different output.

The number of systems with a larger Sharpe ratio and correlation larger than 0.9: 2
The max correlation value (with systems with a larger Sharpe ratio): 0.9818594562862993

Then I wanted to know to which strategies it correlates and simply put a print(json.dumps(cr_list, indent=4)) inside qnt.stats.check_correlation and got this:

[
    {
        "id": 18701501,
        "name": null,
        "my": false,
        "template": false,
        "cofactor": 0.9818594562862993,
        "sharpe_ratio": 2.364183164325061
    },
    {
        "id": 18701502,
        "name": null,
        "my": false,
        "template": false,
        "cofactor": 0.9559821139736059,
        "sharpe_ratio": 2.49988419656187
    }
]

The first one is my q23_4 and the other one is my q23_4a (funny that it correlates with itself by 0.98 and not 1.0). However, no correlation with any template is showing up there.