Peter

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  • #10963
    Participant

      Hello

      The network Deep Q which is based on re-enforcement learning , the algorithm would continue learn on-line until results cannot be improved,

      The result of the network learning would be transferred  to MT,

      The idea using a Node.js app,  is to use data from MT (OHLC and indicator data ) to feed  Deep Q Nework as input

      The deep Q network would perform its calculation then the output can be feed back to MT for our use.

      This information can be displayed as an indicator or used as part of a trading system

      At the moment the demo uses 5 columns which is simply (Date Open High Low and Close) plans in  to expand columns so that indicator data can be used,  this can be intermarket close information or simple indicators such as moving average rsi, or bands

      The network can work with any Data EURUSD , GBPUSD, etc I have ran network using 66000 rows

      The you-tube demo  https://youtu.be/QJHpaIZ7o_c uses 5 min data,  The network goal is the maximise points/pips over time, for more robust network I can focus the goal on average profit per trade, of which is the single best measure  of network performance 

      My suggestion is to simply cut and paste your desired data set into the Deep Q Demo website ai.marketcheck.co.uk/Forex then monitor results

       

      Hope this helps

       

      Peter

      #10952
      Participant

        I have chosen to implement version shown in http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html

        Although  you research and write your own version by studying white paper at http://arxiv.org/pdf/1312.5602v1.pdf

        The code is written in JavaScript so it can be ran in your browser, my suggestion if you want to run it locally then simply copy file from ai.marketcheck.co.uk/Forex  transfer to your  locally on your computer, I will make source code files aviable at https://github.com/AIForex

        Hope this helps

        Thanks

         

        Peter

         

         

         

         

        #10939
        Participant

          Hello

          If you would like further information on the source code and operation, theory  you can check http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html

          1)The first step has already been taken into add raw Forex data  to network

          2) Add further inputs such as simply indicators

          3) Build Node.js App  to integrate to metatrader 4

          All source code plans and notes  would be available  at https://github.com/AIForex

          The current results is interesting, and would expand with new findings and information

          With regards to the Atari demo I see that the demo can be expanded to the Forex markets provided that the environment is adjusted to suit trading styles, the method, that I have incorporated is my style of trading …

          I feel the Algo would benefit using good quality leading indicators and perhaps inter-market Forex data

          I believe no Algo can would work on the markets without applying human trading experience,  the main point about this Deep Q it feedback Algorithm that is always learning and improving this is key feature as markets continually change

           

          Regards

           

          Peter

          #10934
          Participant

            Hello Group

            The bottom line is find an edge, in future I’ll add basic indicators such as moving average, bands or RSI as an input then monitor results

            Also it possible to add close prices inter-market related data

            The demo at https://youtu.be/QJHpaIZ7o_c  is based on 5 minutes raw price data nothing else , and it attempting to place 1)Buy Exit open, 2)Sell exit open  or 3)Do Nothing

            If we have any  Javascript programmers in the group we can accelerate progress, source code will be posted at https://github.com/AIForex

            Note anyone with CSV data can now cut and paste data window at ai.marketcheck.co.uk/Forex  and update group with results

            Thanks

             

            Peter

            ai.marketcheck.co.uk/Forex

             

             

            #10919
            Participant

              Interesting vid! The ‘Theory of Everything’, and physical Unified Field Theory being only a part of it ? Wow – this guy has really got a vision! And all this uncovered by AI algorithms. Forgive me if I’m a bit doubtful about this.

              <b><i><u>Google brought his company for $400 Million, maybe the saw something of interest.</u></i></b>


              Back to our ‘simple’ FX business. After having learnt a bit more about the general approach, I might see those 52% success rate in a different light now, and I wonder what would happen if we modified what the speaker called the ‘Environment’ in his slides:
              a) offer Deep Q a longer data history to learn from

              <b><i><u>Yes you can feed the network more data anytime frame any market, I feed it with 66,000 rows of data, but you can add more if you wish</u></i></b>


              b) offer it a broader data universe, say all OHLC+V of all crosses of EUR, CHF, GBP, USD, CAD, AUD, NZD, JPY to learn from

              <b><i>Yes </i></b>


              c) offer it a dynamic model of pair specific trading cost
              and then let it learn.

              <i><u>Yes, </u></i><i><u>this also fine and good idea</u></i>
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              Learn for how long?

              <i><u>Data depend few minutes to a few hours </u></i><i><u>
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              <!–[endif]–>
              </u></i>

              Which computing resources (CPU, RAM) provided?

              <b><i><u>The higher the better, large network models can from the use of super computer this can be hired on a time basis from Amazon, </u></i></b><b><i><u>
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              </u></i></b>

              Question above all: What exactly is the ‘Goal’ we’re setting up for the learning process?

               

              <b style=”mso-bidi-font-weight: normal;”><i style=”mso-bidi-font-style: normal;”><u>Goal is simply a reward I choose maximum pips per trade an example you can use percentage correct as a Goal/Reward if you wish </u></i></b><b style=”mso-bidi-font-weight: normal;”><i style=”mso-bidi-font-style: normal;”><u><br style=”mso-special-character: line-break;” /> <!– [if !supportLineBreakNewLine]–><br style=”mso-special-character: line-break;” /> <!–[endif]–></u></i></b>

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