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    • D

      Calculation of trading strategies
      Strategy help • • dark.pidgeot

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      D

      @jeppe_and Thanks for the reply, i'll check my code

    • S

      Submission failure
      Support • • Sun-73

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      support

      @antinomy great! Sorry, there were many submissions on the last day.

    • S

      Please advise on p settings. Thanks.
      Strategy help • • spancham

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      Hi @support
      Thank you, I'll try that.

    • magenta.grimer

      More color on contest rules
      General Discussion • • magenta.grimer

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      support

      @magenta-grimer Hello, the 34 M USD have been allocated to the winning strategies according to the contest rules.

      Other strategies have been funded, and agreements are in place between quantiacs, investors and quants. We cannot disclose more details now, sorry.

      5M USD is a reasonable capacity a strategy could handle, yes.

    • M

      Backtesting
      Strategy help • • magenta.kabuto

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      227
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      support

      @stefanm Thank you!

    • S

      Strategy Funding
      General Discussion • • spancham

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      support

      @sheikh Hi,

      it simply means that your system should make a new global high before you are entitled for a payment. If your system makes 1000 at the end of January, 800 at the end of Februray, 900 at the end of March and 1100 at the end of April, your profit will be generated at the end of April and they will amount to 1100-100=100;

      no, once the system starts being traded, it will be traded in the form it was at submission time, i.e. the quant will not be allowed to update parameters/change details. Of course a submitted system can have an adaptive logic, by changing parameters according to the value of some meta-indicator. If the quant believes there is some big change to be made, it is ok to re-submit the changed system, but it will need again to accumulate a track record before being traded.

    • S

      Machine Learning Strategy
      Strategy help • • spancham

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      S

      @vyacheslav_b
      Thank you! 🎉 🎉

    • M

      Different Sharpe Ratios for Multipass-Backtest and Quantiacs Mulipass Backtest
      Support • • magenta.kabuto

      13
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      130
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      support

      @vyacheslav_b thank you!

    • support

      Share the state between iterations
      Request New Features • • support

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      S

      @support
      ok I did what @antinomy said below and the 'state' strategy worked.
      https://quantiacs.com/community/topic/46/macroeconomic-data-with-quantiacs/3?_=1619554449031

      However, it broke my old strategies. So he further suggested to:
      https://quantiacs.com/community/topic/46/macroeconomic-data-with-quantiacs/5?_=1619556376479
      And my old strategies are running again.

      Ok, so looks like for now for all strategies without a state I have to output and pass None to weights for the state & pass a state variable to the strategy.
      Will keep you updated. Thanks for looking into the issue.

    • news-quantiacs

      The Q20 Contest Started
      News and Feature Releases • • news-quantiacs

      13
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      282
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      support

      @magenta-grimer hi, you can find one very simple example here:

      https://quantiacs.com/documentation/en/data/fundamental.html

      best regards

    • S

      Files disappeared from online env
      Support • • Sheikh

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      support

      @antinomy Hi, sorry for the late answer. The problem is the size of files, we are working on a solution for avoiding this issue in the future.

    • S

      Suggestions for the Q17 contest.
      News and Feature Releases • • Sun-73

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      support

      @pillocktailor Thank you for the interesting proposal.

    • A

      Why are my Q17 Strategies not running?
      Support • • Algotime

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      support

      @mwalimudan Yes, we are really sorry. We did not think about these corner cases, but with cryptos, they can take place.

    • A

      Submission Issue
      Support • • antinomy

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      A

      Just out of curiousity I did some testing and it looks like the class actually was the culprit.
      I submitted a simple strategy in 2 versions, one with a class and the other with a dictionary as state. The class version was rejected (exaclty like the one from my 1st post) and the dictionary version got accepted.

    • A

      Different Sharpe ratios in backtest and competition filter
      Support • • antinomy

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      C

      @support Thank you very much for the clarification, and once again congratulations for the great job you are doing 😉

    • news-quantiacs

      The Q16 Contest is open!
      News and Feature Releases • • news-quantiacs

      12
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      support

      @antinomy In the end we followed your advise and changed a little bit the algorithm for adding data, once a cryptocurrency is in the top10 we include it with its past history and go on with the update (now DASH and XMR are being updated). Of course once the crypto is not among the top 10, the liquidity tag for the filter is "zero". Thank you!

    • S

      Different Sharpe ratios in backtest and after submission
      Support • • Sun-73

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      Hi @support,

      I modified the retraining interval to 1 day and it worked. Thank you for the help.

      You guys rock!

    • W

      Q20 information?
      News and Feature Releases • • wireless.trout

      11
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      support

      @magenta-muskrat Yes, we would have said crypto and stocks otherwise.

    • D

      Optimizer not working locally
      Support • • dark.yellowjacket

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      Thanks @dark-yellowjacket for post your solution for the optimizer, it works fine for me but could you please go deeper explaining how do you implement the optimized obtained parameter values in the final strategy. I have tried several methods (even in several computers) , obtaining always errors.
      For example using this template provided by Quantiacs (where i call to "Strategy" I use the same previouslly optimized, and the config.json is the output asset by asset obtained after optimization):

      import json import xarray as xr import qnt.backtester as qnbk from Strategy import * def optmized_strategy(data, config): results = [] for c in config: results.append(strategy_long(data, **c)) # align and join results results = xr.align(*results, join='outer') results = [r.fillna(0) for r in results] output = sum(results) / len(results) return output config = json.load(open('config.json', 'r')) # multi-pass # It may look slow, but it is ok. The evaluator will run only one iteration per day. qnbk.backtest( competition_type='stocks_nasdaq100', lookback_period=365, strategy=lambda d: optmized_strategy(d, config), # strategy=strategy_long, # you can check the base strategy too start_date='2006-01-01')

      It rises the following error:

      Reloaded modules: Estrategia fetched chunk 1/5 0s fetched chunk 2/5 0s fetched chunk 3/5 0s fetched chunk 4/5 0s fetched chunk 5/5 0s Data loaded 1s Run last pass... Load data... fetched chunk 1/1 0s Data loaded 0s Run strategy... Traceback (most recent call last): File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec exec(code, globals, locals) File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 39, in <module> start_date='2006-01-01') File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\qnt\backtester.py", line 291, in backtest result = strategy_wrap(data, state) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\qnt\backtester.py", line 270, in <lambda> strategy_wrap = (lambda d, s: strategy(d)) if args_count < 2 else strategy File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 37, in <lambda> strategy=lambda d: optmized_strategy(d, config), File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 22, in optmized_strategy results.append(strategy_long(data, **c)) File "C:\Users\LuisPC\Desktop\quantiacs\Q18\Estrategia.py", line 16, in strategy_long data = data.sel(asset=[asset]) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\dataarray.py", line 1337, in sel **indexers_kwargs, File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\dataset.py", line 2505, in sel self, indexers=indexers, method=method, tolerance=tolerance File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\coordinates.py", line 422, in remap_label_indexers obj, v_indexers, method=method, tolerance=tolerance File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\indexing.py", line 120, in remap_label_indexers idxr, new_idx = index.query(labels, method=method, tolerance=tolerance) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\indexes.py", line 242, in query raise KeyError(f"not all values found in index {coord_name!r}") KeyError: "not all values found in index 'asset'"

      I tried to explain this problem about the implementation in the Backtester in another forum thread time ago, without getting a valid answer, therefore I would appreciate any idea you can give me on this matter.
      ...Otherwise I am afraid that I will not present algorithms for this contest (Sadly 😢 )
      Regards.
      Luis G.

    • T

      Error Q20 output missing when submitting
      Strategy help • • TheFlyingDutchman

      10
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      228
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      V

      @theflyingdutchman

      it can help you. See topic 3) Not enough bid information.

      https://github.com/quantiacs/strategy-q20-nasdaq100-quick-start/blob/master/strategy.ipynb

      def get_enough_bid_for(data_, weights_): time_traded = weights_.time[abs(weights_).fillna(0).sum('asset') > 0] is_strategy_traded = len(time_traded) if is_strategy_traded: return xr.where(weights_.time < time_traded.min(), data_.sel(field="is_liquid"), weights_) return weights_ weights_new = get_enough_bid_for(data, weights) weights_new = weights_new.sel(time=slice("2006-01-01",None))
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