@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.

@magenta-kabuto The weights generated are simply the daily allocations to the various assets.

@buyers_are_back Hello.
Here is a new example of stock prediction using index data.
I recommend using the single-pass version.
https://quantiacs.com/documentation/en/data/indexes.html

@multi_byte-wildebeest Hello. I don't use machine learning models in trading.

@support Thx for ur reply

@multi_byte-wildebeest Hi, these limitations refer to the processing time per point in time, not for the full strategy.

If it takes 10 minutes per historical day, and the simulation has to take into account 250 days for let us say 10 years, the multi-pass simulation would process 6 days per hour, 144 days per real day, that means 2 weeks of processing time for the full submission, it is a lot of time.

Thanks for the answer!

I still think something is wrong with this correlation checker. I even used this function to randomize the weights a few times, and I got the same correlation error:

def add_random_noise(weights, noise_level=0.01): noise = np.random.uniform(-noise_level, noise_level, size=weights.shape) return weights + noise

I am pretty sure it's impossible to have 90% correlation in this case.

Hi @support, everything is all right now. Thank you!

@support Ok, I see, thanks

@auxiliary-snail Hi,

unfortunately, this is not allowed and in accordance with the rules. Using hard-coded time periods in which trading algorithm will work differently, is not a quantitative method (just like manual asset selection, e.g. "trade only Apple or Microsoft"). We still haven't implemented a mechanism for automatic recognition of such behaviors in trading strategies, and even though a strategy could be successfully submitted, it will not be eligible for prize winning.
What we are searching for, is well performing strategy over entire in_sample period (SR>0.7), robust to all market movements 2006-2025, so we can expect it will perform well in future, too.

@vyacheslav_b Apologies for the late response. Thanks for the assistance, all is now well. Cheers

@support Thanks for ur help

@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.

@anshul96go Dear Anshul, it means that the weights for each day have to be generated in less than 10 minutes of time per day.

Note that all submissions are processed on the server after submission using a muti-pass approach (not single-pass).

10 minutes per day, times 250 days, times 10 years, that is more than 400 hours of running time.

@support Thank you for your feedback. I also hope Quantiacs updates new strategy examples on how to use technical analysis (besides sma, trix_ema, atr_lwma,...), and strategies on using ML/DL models effectively (not an example that strategy forward-looking),...

Hopefully in the future Quantiacs will release new data sets such as news, sentiment, macro, options,... Create new contests that allow merging strategies to build portfolios,...

Hopefully, Quantiacs will continue to grow. Sincere thanks to Quantiacs for creating extremely high-quality contests.

@support The webpage is fine now. Thank you for the prompt response!

@magenta-kabuto Thank you very much for your advice. I will research to apply your suggestions to the algorithm

@antinomy Hello, sorry for the delay in the answer, the problem has been fixed in the meanwhile. If correlations are too high, you will get a warning. Note that correlations will be checked against template code and submissions to past contests of the same kind (which are not present currently as this is the 1st crypto long-only contest we run).

@support that is really useful! thank you very much!

@support. Thank you. So as I understand it, I will give higher weight to strategies with lower correlation and vice versa, right? According to your answer, I understand that I can also give high weight to low-volatility strategies and vice versa. So what about equal risk portfolio? In your opinion, is this an effective way to optimize your portfolio?