@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.

@support
Thanks a lot !

@magenta-grimer thank you @support 🙇 🙇 🙇 🙇 🙇 🙇
My compliments four your exceptional python skills

@support said in Local development import error:

conda install -y "quantiacs-source::qnt >= 0.0.225"

Now it works. Thanks!

@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.

@damnedlies Hello:

we are taking relative daily returns, in other words the ratio: [price(t)-price(t-1)]/price(t-1)

we are using no benchmark.

Note also that we apply reinvesting when computing the numerator of the Sharpe ratio (geometric mean)

@antinomy wow, thank you so much, this is awesome!

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