Neural networks programming with prorealtime

• 08/24/2018 at 6:12 PM #79003
  Nicolas

  Discussion about the trading strategy made by GraHal from the classifier code can be found by following this link: Long only trading with trend confirmation on 5 minutes timeframe

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  Inertia

  

  Nicolas

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  08/26/2018 at 7:15 AM #79072
  Leo

  Hi all here is the complete classifier.

  Now that I completed it,  I realised that the clasiffier can be whatever you want for example using fractals and a loop for store the positions of the points. Or whatever method to detects the exactly change of tendency.

  //Variables: //candlesback=7 //ProfitRiskRatio=2 //spread=1.5 myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT IF classifierlong=1 then DRAWSEGMENT(candleentrylong,close[barindex-candleentrylong],barindex,close[barindex-candleentrylong]+LongTradeLength) COLOURED(0,150,0) DRAWELLIPSE(candleentrylong-1,low[barindex-candleentrylong]-ExtraStopLoss,barindex+1,high+ExtraStopLoss) COLOURED(0,150,0) ENDIF //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT IF classifiershort=1 then DRAWSEGMENT(candleentryshort,close[barindex-candleentryshort],barindex,close[barindex-candleentryshort]-ShortTradeLength) COLOURED(150,0,0) DRAWELLIPSE(candleentryshort-1,high[barindex-candleentryshort]+ExtraStopLoss,barindex+1,low-ExtraStopLoss) COLOURED(150,0,0) ENDIF return

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  //Variables: //candlesback=7 //ProfitRiskRatio=2 //spread=1.5     myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize   //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT IF classifierlong=1 then DRAWSEGMENT(candleentrylong,close[barindex-candleentrylong],barindex,close[barindex-candleentrylong]+LongTradeLength) COLOURED(0,150,0) DRAWELLIPSE(candleentrylong-1,low[barindex-candleentrylong]-ExtraStopLoss,barindex+1,high+ExtraStopLoss) COLOURED(0,150,0) ENDIF   //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT   IF classifiershort=1 then DRAWSEGMENT(candleentryshort,close[barindex-candleentryshort],barindex,close[barindex-candleentryshort]-ShortTradeLength) COLOURED(150,0,0) DRAWELLIPSE(candleentryshort-1,high[barindex-candleentryshort]+ExtraStopLoss,barindex+1,low-ExtraStopLoss) COLOURED(150,0,0) ENDIF     return

   

   

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  Nicolas, GraHal

  

  Leo

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  08/26/2018 at 8:19 AM #79073
  GraHal

  Nicolas wrote:

  can be found by following this link:

  The link comes up with 404 not found for me yesterday and today.

  Hey thank you so much for sharing your latest @Leo … this is new exciting work!

  Cheers

   

  

  GraHal

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  08/26/2018 at 10:12 PM #79135
  Leo

  Hi all,

  I write the main structure of the neural network.

  Still a lot work in process… for example to figure out the initial values for the weights and bias. I add a photo with the scheme

  ///////////////// CLASSIFIER ///////////// myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT ///////////////////////// NEURONAL NETWORK /////////////////// //variable1= // to be defined //variable2= // to be defined //variable3= // to be defined //variable4= // to be defined IF classifierlong=1 or classifiershort=1 THEN candleentry=max(candleentrylong,candleentryshort) // >>> INPUT NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2))

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  /////////////////    CLASSIFIER    /////////////   myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize   //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT   //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT   /////////////////////////  NEURONAL NETWORK  ///////////////////   //variable1=     // to be defined //variable2=     // to be defined //variable3=     // to be defined //variable4=     // to be defined   IF classifierlong=1 or classifiershort=1 THEN candleentry=max(candleentrylong,candleentryshort)   //    >>> INPUT NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2))

   

  

  Leo

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  08/27/2018 at 8:11 AM #79140
  Nicolas

  For the weights, I would suggest using the percentage of a similarity, as a coefficient. Like the way I did in this previous version: https://www.prorealcode.com/topic/neural-networks-programming-with-prorealtime/#post-78789

  

  Nicolas

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  08/27/2018 at 9:36 AM #79150
  Leo

  Nicolas wrote:

  For the weights, I would suggest using the percentage of a similarity, as a coefficient. Like the way I did in this previous version: https://www.prorealcode.com/topic/neural-networks-programming-with-prorealtime/#post-78789

  but we need 4*6+6+2*6+2 = 44 initial values   :s

   

  I still reading and learning about this topic

  

  Leo

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  08/27/2018 at 9:57 AM #79152
  Nicolas

  You are referring to this kind of line: F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 , right?

  I assume that a11 … a14 are different weights to give more or less importance to each of your input. That’s how weights are used in neural network, such as perceptron for instance. There is no one way to determine how weights are calculated or initiated.

  Like I said in my previous post you can set them as a statistical percentage of good value. So, let’s assume that all your inputs (from input1 to input4) are boolean variables (0=false, 1=true). For example, if your input1 is a value that gives better accuracy in your previous trades results (and you’ll have to make a study for this.. like the way I did in my example  ), you can weight the input like this:

  input1 was true for 53% when a classifierlong occurred so  a11=0.53

  F1 = 0.53*1+a12*input2+a13*input3+a14*input4+Fbias1

  This is a rough idea of how you can temper an input.

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  Leo

  

  Nicolas

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  08/27/2018 at 3:46 PM #79179
  Leo

  Thanks Nicolas,

  Yes indeed, so far I read is a lot of try and error for the initial values, some others said with experience, one can get a good initial values. the actual values will are achieve during the learning process of the neural network.

  Talking about the learning process…

  Imagine that we build our Neural Network with success.

  How do we do the learning process?

  Is the fuction: DEFPARAM PreLoadBars = 50000 ?

  it means that 50k candles are loaded and the code is run through all of those candles? that will be great for the learning process.

  

  Leo

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  08/27/2018 at 4:59 PM #79185
  Nicolas

  Preloadbars is only used in ProBacktest and the maximum value you can preload is 10.000 bars.

  While it is not the case for indicator, maximum bars loaded is the value shown in the ‘units’ dropdown list.

   

  

  Nicolas

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  08/27/2018 at 8:16 PM #79194
  Leo

  Sorry Nicolas, I could not fully understand the concept of PreLoadBars = 10000.

  At the moment of activating the strategy , it means that the program is run 10000 units of time before barindex=1?

  I would like to understand correct because the Neuronal Network should learn before taking decisions for trading, otherwise we have to “wait” many “barindex” before actually buy or sell… that can be a pin in the neck using this methodology of trading.

  another way is to back test using the function Graph and “try” to export-import all the 44 values as initial values for a quicker learning process…

  puff… what if are eager to create a bigger Neural Network with hundreds for weights !!!?

  I put a lot of hope in that “preloadbar ” fuction. Am I right? or naive?

  

  Leo

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  08/27/2018 at 8:38 PM #79195
  Leo

  Hi all,

  I complete the calculus the partial derivates for the learning process of the neuronal network. I will really appreciate if someone can confirm or correct them if I miss something.

  ///////////////// CLASSIFIER ///////////// myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT ///////////////////////// NEURONAL NETWORK /////////////////// //variable1= // to be defined //variable2= // to be defined //variable3= // to be defined //variable4= // to be defined IF classifierlong=1 or classifiershort=1 THEN candleentry=max(candleentrylong,candleentryshort) Y1=classifierlong Y2=classifiershort // >>> INPUT NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) // >>>PARTIAL DERIVATES OF COST FUNCTION <<< // COST = (1/2)* ( (Y1-output1)^2 + (Y2-output2)^2 ) DerObias1 = (Y1-output1) * output1*(1-output1) * 1 DerObias2 = (Y2-output2) * output2*(1-output2) * 1 Derb11 = (Y1-output1) * output1*(1-output1) * F1 Derb12 = (Y1-output1) * output1*(1-output1) * F2 Derb13 = (Y1-output1) * output1*(1-output1) * F3 Derb14 = (Y1-output1) * output1*(1-output1) * F4 Derb15 = (Y1-output1) * output1*(1-output1) * F5 Derb16 = (Y1-output1) * output1*(1-output1) * F6 Derb21 = (Y2-output2) * output2*(1-output2) * F1 Derb22 = (Y2-output2) * output2*(1-output2) * F2 Derb23 = (Y2-output2) * output2*(1-output2) * F3 Derb24 = (Y2-output2) * output2*(1-output2) * F4 Derb25 = (Y2-output2) * output2*(1-output2) * F5 Derb26 = (Y2-output2) * output2*(1-output2) * F6 DerFbias1 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * 1 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * 1 DerFbias2 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * 1 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * 1 DerFbias3 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * 1 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * 1 DerFbias4 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * 1 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * 1 DerFbias5 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * 1 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * 1 DerFbias6 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * 1 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * 1 Dera11 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input1 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input1 Dera12 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input2 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input2 Dera13 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input3 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input3 Dera14 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input4 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input4 Dera21 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input1 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input1 Dera22 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input2 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input2 Dera23 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input3 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input3 Dera24 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input4 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input4 Dera31 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input1 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input1 Dera32 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input2 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input2 Dera33 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input3 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input3 Dera34 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input4 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input4 Dera41 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input1 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input1 Dera42 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input2 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input2 Dera43 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input3 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input3 Dera44 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input4 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input4 Dera51 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input1 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input1 Dera52 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input2 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input2 Dera53 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input3 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input3 Dera54 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input4 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input4 Dera61 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input1 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input1 Dera62 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input2 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input2 Dera63 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input3 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input3 Dera64 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input4 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input4 GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64)

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133

  /////////////////    CLASSIFIER    /////////////   myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize   //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT   //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT   /////////////////////////  NEURONAL NETWORK  ///////////////////   //variable1=     // to be defined //variable2=     // to be defined //variable3=     // to be defined //variable4=     // to be defined   IF classifierlong=1 or classifiershort=1 THEN candleentry=max(candleentrylong,candleentryshort)   Y1=classifierlong Y2=classifiershort   //    >>> INPUT NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2))   // >>>PARTIAL DERIVATES OF COST FUNCTION <<< // COST = (1/2)* ( (Y1-output1)^2 + (Y2-output2)^2 )   DerObias1 = (Y1-output1) * output1*(1-output1) * 1 DerObias2 = (Y2-output2) * output2*(1-output2) * 1   Derb11    = (Y1-output1) * output1*(1-output1) * F1 Derb12    = (Y1-output1) * output1*(1-output1) * F2 Derb13    = (Y1-output1) * output1*(1-output1) * F3 Derb14    = (Y1-output1) * output1*(1-output1) * F4 Derb15    = (Y1-output1) * output1*(1-output1) * F5 Derb16    = (Y1-output1) * output1*(1-output1) * F6   Derb21    = (Y2-output2) * output2*(1-output2) * F1 Derb22    = (Y2-output2) * output2*(1-output2) * F2 Derb23    = (Y2-output2) * output2*(1-output2) * F3 Derb24    = (Y2-output2) * output2*(1-output2) * F4 Derb25    = (Y2-output2) * output2*(1-output2) * F5 Derb26    = (Y2-output2) * output2*(1-output2) * F6   DerFbias1 = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * 1 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * 1 DerFbias2 = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * 1 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * 1 DerFbias3 = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * 1 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * 1 DerFbias4 = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * 1 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * 1 DerFbias5 = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * 1 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * 1 DerFbias6 = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * 1 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * 1   Dera11    = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input1 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input1 Dera12    = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input2 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input2 Dera13    = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input3 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input3 Dera14    = (Y1-output1) * output1*(1-output1) * b11 * F1*(1-F1) * input4 + (Y2-output2) * output2*(1-output2) * b21 * F1*(1-F1) * input4   Dera21    = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input1 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input1 Dera22    = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input2 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input2 Dera23    = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input3 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input3 Dera24    = (Y1-output1) * output1*(1-output1) * b12 * F2*(1-F2) * input4 + (Y2-output2) * output2*(1-output2) * b22 * F2*(1-F2) * input4   Dera31    = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input1 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input1 Dera32    = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input2 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input2 Dera33    = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input3 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input3 Dera34    = (Y1-output1) * output1*(1-output1) * b13 * F3*(1-F3) * input4 + (Y2-output2) * output2*(1-output2) * b23 * F3*(1-F3) * input4   Dera41    = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input1 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input1 Dera42    = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input2 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input2 Dera43    = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input3 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input3 Dera44    = (Y1-output1) * output1*(1-output1) * b14 * F4*(1-F4) * input4 + (Y2-output2) * output2*(1-output2) * b24 * F4*(1-F4) * input4   Dera51    = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input1 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input1 Dera52    = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input2 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input2 Dera53    = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input3 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input3 Dera54    = (Y1-output1) * output1*(1-output1) * b15 * F5*(1-F5) * input4 + (Y2-output2) * output2*(1-output2) * b25 * F5*(1-F5) * input4   Dera61    = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input1 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input1 Dera62    = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input2 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input2 Dera63    = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input3 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input3 Dera64    = (Y1-output1) * output1*(1-output1) * b16 * F6*(1-F6) * input4 + (Y2-output2) * output2*(1-output2) * b26 * F6*(1-F6) * input4   GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64)

   

  

  Leo

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  08/27/2018 at 9:10 PM #79197
  Leo

  Now every  time there is a new input generated by the Classifier, every weight or bias can be improve ( the neural network learns) by using the equation I attached (epsilon is a small value that can be a variable to be optimised in the walk forward testing) .

  It will be great if some of you can help me with this project.

  In fact, now that I finished the back-propagation algorithm,   it is very near already for create an indicator for prediction using Neural Network

  All I do, is to apply what I learn in here:

  http://neuralnetworksanddeeplearning.com/

  

  Leo

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  08/27/2018 at 9:41 PM #79200
  Nicolas

  Leo wrote:

  At the moment of activating the strategy , it means that the program is run 10000 units of time before barindex=1?

  Just try if a variable had incremented on the first bar:

  defparam preloadbars=10000 a = a + 1 if a = 0 then buy at market endif graph a

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  defparam preloadbars=10000   a = a + 1   if a = 0 then buy at market endif   graph a

  if a >=10.000 on the first bar of the backtest, then the code been read and executed 10 thousands times (I have a very bad feeling about this! 🙂 )

   

  

  Nicolas

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  08/27/2018 at 10:45 PM #79206
  GraHal

  Leo wrote:

  It will be great if some of you can help me with this project.

  I only wish I could Leo and I feel sad that only Nicolas is helping you, but you also need folks with more time than Nicolas can spare.

  Your work is ground breaking as far as most of us are concerned. I did read the reference you posted and broadly understood, but it’s the coding I struggle with.

  Tomorrow I will see if I can contact @Maz as he is a capable member who sticks in my mind as one who similarly tried to move forward with complex collaborative projects on here.

   

  

  GraHal

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  08/28/2018 at 3:19 AM #79209
  Leo

  it works!

  defparam preloadbars=10000 a = a + 1 if a = 10001 then buy at market endif graph a

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  defparam preloadbars=10000 a = a + 1 if a = 10001 then buy at market endif graph a

  that means it will learn 10000 bars that is a very good news, cause we do not have to wait until it learns

  

  Leo

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