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    • M

      best parameters for indicators
      Strategy help • • maxime

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      support

      @maxime Yes, of course, you can use our optimizer:

      https://quantiacs.com/community/topic/29/optimizing-and-monitoring-a-trading-system-with-quantiacs

      Be careful with optimization as it will lead to overfitting....

    • D

      WARNING: Strategy trades non-liquid assets.
      Support • • darwinps

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      D

      Hi @support ,

      That makes perfect sense

      Thank so much

      regards

    • M

      Cant load data locally
      Support • • magenta.kabuto

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      M

      @magenta-kabuto thx a lot bro for your support and pointing out the mistakesπŸ‘ πŸ™‚
      I will try the revised code now.
      Good luck for the competition πŸ‘

    • A

      Issues with the Legacy Website
      Support • • antinomy

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      A

      @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 πŸ‘Œ

    • A

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

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      A

      @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 πŸ˜‰

      Best regards

    • cespadilla

      Leaderboard not updating again
      Support • • cespadilla

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      392
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      support

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

    • O

      QNT failed to load data after 2006-01-01
      Support • • omohyoid

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      O

      @support I got it. Thanks for ur reply

    • A

      Submitting stratgy
      Strategy help • • aybber

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      1402
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      support

      @mwalimudan Sorry for the issue, but do not worry, we are extending the submission deadline to end of October as there were several issues. Please do not hesitate to report problems.

    • illustrious.felice

      Strategies deleted
      Support • • illustrious.felice

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      illustrious.felice

      @support Thank you very much. Please delete all my strategies in the deleted section.

    • A

      Erroneous Data?
      Support • • antinomy

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      773
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      support

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

    • A

      notebook for googlecolab not working
      Support • • alfredaita

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      A

      @support Thanks seems fine

    • magenta.grimer

      Trend following strategy BUG
      Strategy help • • magenta.grimer

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      support

      @magenta-grimer

      Hello.

      I confirm this bug.
      It is fixed now.
      If you clone this template again, it will work ok.

      Thank you very much for your report.

    • A

      Clarification regarding execution time
      Support • • anshul96go

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      support

      @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.

    • A

      Q22 seems paused at 22-may, is it expected?
      Support • • angusslq

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      A

      @support great! i can see the latest result. Do you know when Quantiacs will announce the Q22 result formally? Thanks.

    • news-quantiacs

      Processing Large Numeric Arrays in Python
      News and Feature Releases • • news-quantiacs

      3
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      2647
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      No one has replied

    • N

      Use of Technical indicators
      Support • • noka'sworld

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      325
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      N

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

    • A

      Saving and recalling a dictionary of trained models
      Support • • alfredaita

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      A

      @alfredaita
      In case you don't want to run init.py every time in order to install external libraries, I came up with a solution for this. You basically install the library in a folder in your home directory and let the strategy create symlinks to the module path at runtime. More details in this post.

    • J

      Local SSH development
      General Discussion • • Joshua408

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      436
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      support

      @joshua408 We allow development on our cloud or local development on user's machines. No need to open any port.

    • B

      Can I reuse strategy
      Support • • buyers_are_back

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      B

      @support

      Thank you, I noticed some changes on the rules so I have to reimplement the strategy anyway.

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