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    • C

      Different dataset locally and in jupiterLab
      Support • • cross_platform.zebra

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      @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.

    • M

      training, predicting and backtesting Neural Network
      Support • • magenta.kabuto

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

    • B

      Accessing both market and index data in strategy()
      Support • • buyers_are_back

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      @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

    • M

      Printing training performance of neural network models
      Support • • multi_byte.wildebeest

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

    • O

      Can I use astronomical data as features for my machine learning model?
      Support • • omohyoid

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      @support Thx for ur reply

    • M

      Why we need to limit the time to process the strategy ?
      Support • • multi_byte.wildebeest

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      @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.

    • A

      Correlation fails although Sharpe ratio > 1
      Support • • agent.hitmonlee

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      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.

    • S

      Q22 submission, strategies excluded
      Support • • Sun-73

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

    • A

      BTC and Crypto contest
      Support • • anthony_m

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      @support Ok, I see, thanks

    • A

      Submission Logic Questions
      Support • • auxiliary.snail

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      @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.

    • nosaai

      AttributeError: module 'qnt.data' has no attribute 'stocks_load_spx_data'
      Support • • nosaai

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      nosaai

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

    • O

      Where can I get the OHLC data of Nasdaq100 index?
      Support • • omohyoid

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      @support Thanks for ur help

    • B

      How to get stocks in SP500 index at a given time
      Support • • buyers_are_back

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      @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.

    • magenta.grimer

      Q16 strategies submitted and still in checking phase
      Support • • magenta.grimer

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      magenta.grimer

      @support yes, they have

    • R

      Processing submissions
      Support • • rezhak21

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      @support ok, I see, all my systems look processed btw

    • A

      Issues with the Legacy Website
      Support • • antinomy

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      @jeppe_and Ok, thanks for the quick reply!

    • cespadilla

      Q17 ML Example not running on Local Development
      Support • • cespadilla

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      cespadilla

      @support thanks, I deleted the old environment, installed it again according to the documentation, and now it is working 👌

    • A

      Output the results in an excel or other format file
      Support • • anshul96go

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      @anshul96go
      To get the actual statistics you currently have to calculate them like so:

      import qnt.stats as qns data = qndata.cryptodaily_load_data(min_date="2014-01-01") # or whenever your backtest started stats = qns.calc_stat(data, weights)

      And if you really need them as xls file you can do:

      stats.to_pandas().to_excel('stats.xls') # I got a ModuleNotFoundError the first time - pip install did the trick.

      Allthough I can't recommend xls because at least LibreOffice becomes very slow / unresponsive when handling such a file.

      Getting the statistics after a backtest could be a little simpler, which brings me to a feature request:
      @support
      Do you think you could add a parameter to the backtester which makes it return the statistics? They get calculated anyway by default, but we only see a truncated printout or the plots and can't use them for further analysis.
      .
      In my local environment I did it like this in qnt.backtester.py:

      Add the parameter return_stats: bool = False to the parameters of the backtest function From line 353 onward my backtester now looks like this: qnout.write(result) qnstate.write(state) if return_stats: analyze = True out = [result] if analyze: log_info("---") stats = analyze_results(result, data, competition_type, build_plots, start_date) if return_stats: out.append(stats) if args_count > 1: out.append(state) if len(out) == 1: out = out[0] return out finally: qndc.set_max_datetime(None) And of course I made analyze_results return the statistics like so (line 458 in the original): if not build_plots: log_info(stat_global.to_pandas().tail()) return stat_global # here log_info("---") log_info("Calc stats per asset...") stat_per_asset = qnstat.calc_stat(data, output, per_asset=True) stat_per_asset = stat_per_asset.loc[output.time.values[0]:] if is_notebook(): build_plots_jupyter(output, stat_global, stat_per_asset) else: build_plots_dash(output, stat_global, stat_per_asset) return stat_global # and there

      This might not be the most elegant solution but you get the idea.
      Now I can get the statistics immediately after the backtest with

      weights, stats = backtest(...return_stats=True)

      and can do further analysis.
      For instance, I started to calculate the correlations between my strategies to avoid uploading more of the same to the contest.

      It would be nice to have this feature in a future version, so I don't have to mess with the backtester after each update 😉

      Best regards

    • cespadilla

      Leaderboard not updating again
      Support • • cespadilla

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      @cespadilla It is fine now. We announced the Q15 winners and changed some details, sorry for the problems.

    • A

      Erroneous Data?
      Support • • antinomy

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      @antinomy Hello, sorry for delay again. We found a problem with the data provider, sorry.

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