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    Different Sharpe Ratios for Multipass-Backtest and Quantiacs Mulipass Backtest

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    • M
      magenta.kabuto @kvanvanvant_test_python last edited by

      @kvanvanvant_test_python hey, thx a lot, will precheck it now. Doesnt multi-pass Backtest avoid looking into the future? Anyways, should be clear after precheck.
      Regards

      M 1 Reply Last reply Reply Quote 0
      • M
        magenta.kabuto @magenta.kabuto last edited by

        @magenta-kabuto 😑 Bildschirmfoto 2023-03-31 um 15.50.56.png

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        • M
          magenta.kabuto @magenta.kabuto last edited by

          @magenta-kabuto Unfortunately, even the strategy that got rejected due to a negative sharpe ratio, has the same ones as in the screenshot for 3 passes 😕

          M 1 Reply Last reply Reply Quote 0
          • M
            magenta.kabuto @magenta.kabuto last edited by magenta.kabuto

            @magenta-kabuto Sry to post again. Now I changed to 20 passes and suddenly the sharpe ratio went up and this is the strategy that got rejected. I am totally confused!Bildschirmfoto 2023-03-31 um 17.50.37.png

            M 1 Reply Last reply Reply Quote 0
            • M
              magenta.kabuto @magenta.kabuto last edited by magenta.kabuto

              @magenta-kabuto 50 passes..... Bildschirmfoto 2023-03-31 um 19.04.43.png

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              • V
                Vyacheslav_B @magenta.kabuto last edited by

                @magenta-kabuto

                Hello

                I see that you are using precheck, this is the fastest way to check the strategy
                https://github.com/quantiacs/toolbox/blob/main/qnt/precheck.ipynb

                if the strategy statistics are different, your algorithm looks into the future.

                does your algorithm calculate the weights to trade every day?

                or do you calculate all the weights using all the data in one pass and then use them for a specific date? like cache

                M 1 Reply Last reply Reply Quote 0
                • M
                  magenta.kabuto @Vyacheslav_B last edited by

                  @vyacheslav_b hey thx for your reply. Yes the strategy Sharpe ratio differs from the multipass backtest.
                  But Quantiacs says that multiple pass Backtesting avoids looking into the future, so I am confused.
                  The algorithm identifies certain patterns and only if those occur over a period of time, does it generate signals.
                  So it does not calculate weights to trade everyday

                  M V 2 Replies Last reply Reply Quote 0
                  • M
                    magenta.kabuto @magenta.kabuto last edited by

                    @magenta-kabuto something like this, if this puts a bit lightBildschirmfoto 2023-03-31 um 21.09.30.png

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                    • V
                      Vyacheslav_B @magenta.kabuto last edited by

                      @magenta-kabuto

                      Compare the two versions of the code.

                      def regime_trade(series):
                          return ""
                      
                      
                      def strategy(data):
                          result = regime_trade(data)
                          return result
                      
                      
                      backtest(strategy=strategy)
                      

                      in the first case, the available data for a specific date are used.

                      def regime_trade(series):
                          return ""
                      
                      
                      result = regime_trade(data)
                      
                      def strategy(data):
                          return result
                      
                      
                      backtest(strategy=strategy)
                      

                      in the second case, all data is used.

                      I see that you have a very large Sharpe ratio, I think that you are using something similar to the second option.

                      M support 2 Replies Last reply Reply Quote 2
                      • M
                        magenta.kabuto @Vyacheslav_B last edited by

                        @vyacheslav_b Hello again, thank you very much. Yes you are right.
                        Regards
                        Furqan

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                        • support
                          support @Vyacheslav_B last edited by

                          @vyacheslav_b thank you!

                          1 Reply Last reply Reply Quote 0
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