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Group: Metatrader MQ4 Indicators
Title: NeuroProba
Description:
NeuroProba
//+------------------------------------------------------------------+ //| NeuroProba.mq4 | //| Copyright © 2005, MetaQuotes Software Corp. | //| http://www.metaquotes.net | //+------------------------------------------------------------------+ #property copyright "Copyright © 2005, MetaQuotes Software Corp." #property link "http://www.metaquotes.net" #property indicator_separate_window #property indicator_buffers 3 #property indicator_color1 Yellow #property indicator_color2 Blue #property indicator_color3 Red //---- input parameters extern int BeginBar=300; // ????????? ??? ? ????????? ??????? extern int EndBar=201; // ???????? ??? ? ????????? ??????? extern int StudyNumber=100; // ?????????? ????????? ?????? extern double Betta=0.5; // ???????? ????????????? ??????? extern double StudyCoeff=0.1; // ??????????? ???????? extern double MaxRandom=0.66;//2.0/MathSqrt(9); //---- buffers double NeuroExitBuffer[]; // ???????? ???????? ????????? double TeacherBuffer[]; // ????????? ???????? ????????? (????????? ???????) double ErrorBuffer[]; // ?????????? ?? ?????? ???? //---- double deviation; // ?????????? ?? ?????? ???? ???????? double GradientFirstLay[9][9];// ??? ??????? ????????? 1-?? ???? double GradientSecondLay[9]; // ??? ??????? ????????? 2-?? ???? double OutputFirstLay[9]; // ????? ?????? ??????? ??????? double SecondSum;// ????? ????????? ???? - ???????? ????????????? ??????? double FirstLayWeights[9][9]; // ???? ???????? ??????? (???????? ????) double OutputSecondWeights[9]; // ???? ????????? ??????? double InputNormalX[100][9]; // ??????????????? ??????? ??????? ???????? double InputX[9]; // ??????? ??????? ?????? (?????????????????) double Norm?;// ????? ???????? ???????? ??????? double U_First[9];// ???????? ?? ?????? ???? ??????? (???????) ???????? double U_Second;// ???????? ?? ?????? ????????? ??????? (???????? ????????????? ???????) double SigmaCurr; double Differ; //+---------------------------------------------- // ??????????????? ????????? ??????? ?? ????????? (0,MaxValue), ???? ???????? ???????? - ?? (0,1) //+---------------------------------------------- double NormalRand(double MaxValue=1.0) { double answer; answer=MathRand()/32767.0; answer=MaxValue*answer; return(answer); } //+---------------------------------------------- // ??????????????? ????????? ??????? //+---------------------------------------------- //------------------------------------------ // ??????????? ????????????? ??????? (??????? ???????????????) //------------------------------------------ double Sigma(double argument) { double help; help=(MathExp(argument)-MathExp(-argument))/(MathExp(argument)+MathExp(-argument)); return(help); } //------------------------------------------ // ??????????? ????????????? ??????? //------------------------------------------ //------------------------------------------ // ?????????? ????????? ????????????? ??????? //------------------------------------------ double GetArgument() { int j; double answer=0.0; for (j=0;j<9;j++) { answer=answer+OutputSecondWeights[j]*OutputFirstLay[j]; } return(answer); } //------------------------------------------ // ?????????? ????????? ????????????? ??????? //------------------------------------------ //------------------------------------------ // ?????????? ??????? ???????? ??????? ???? //------------------------------------------ void GetFirstLayOutput() { int j,t; for (j=0;j<9;j++) { OutputFirstLay[j]=0.0; for (t=0;t<9;t++) { OutputFirstLay[j]=OutputFirstLay[j]+FirstLayWeights[j,t]*InputX[t]; } } return(0); } //------------------------------------------ // ?????????? ??????? ???????? ??????? ???? //------------------------------------------ //------------------------------------------ // ?????????? ????????? ????? ???????? //------------------------------------------ void SetGradientWeights() { int j,t; for (j=0;j<9;j++) { //GradientFirstLay[j,t]=0.0; for (t=0;t<9;t++) { GradientFirstLay[j,t]=OutputSecondWeights[j]*InputX[t]*deviation*Differ; //GradientFirstLay[j,t]=GradientFirstLay[j,t]+OutputSecondWeights[j]*InputX[t]; } //GradientFirstLay[j,t]=GradientFirstLay[j,t]*deviation*Differ; GradientSecondLay[j]=OutputFirstLay[j]*deviation*Differ; } } //------------------------------------------ // ?????????? ????????? ????? ???????? //------------------------------------------ //------------------------------------------ // ????????? ????? ???????? //------------------------------------------ void ChangeWeigts() { int i,k; for (i=0;i<9;i++) { for (k=0;k<9;k++) { FirstLayWeights[i,k]=FirstLayWeights[i,k]-StudyCoeff*GradientFirstLay[i,k]; } OutputSecondWeights [i]=OutputSecondWeights [i]-StudyCoeff*GradientSecondLay[i];// ????????? ????? ????????? ??????? } } //------------------------------------------ // ????????? ????? ???????? //------------------------------------------ //------------------------------------------ // ????? ? ?????? ????? ???????? //------------------------------------------ void PrintWeights() { int i,k; for (i=0;i<9;i++) { for (k=0;k<9;k++) { // Print("W1["+i+","+k+"]=",FirstLayWeights[i,k]); } // Print("W2["+i+"]=",OutputSecondWeights[i]); } } //------------------------------------------ // ????? ? ?????? ????? ???????? //------------------------------------------ //+------------------------------------------------------------------+ //| Custom indicator initialization function | //+------------------------------------------------------------------+ int init() { //---- indicators SetIndexStyle(0,DRAW_LINE); SetIndexBuffer(0,NeuroExitBuffer); SetIndexStyle(1,DRAW_LINE); SetIndexBuffer(1,TeacherBuffer); SetIndexStyle(2,DRAW_LINE); SetIndexBuffer(2,ErrorBuffer); SetIndexLabel(0,"Answer"); SetIndexLabel(1,"Teacher"); SetIndexLabel(2,"Error"); ArrayInitialize(NeuroExitBuffer,0.0); ArrayInitialize(TeacherBuffer,0.0); ArrayInitialize(ErrorBuffer,0.0); //------------------------------------------ // ?????????? ??????? ??????? ???????? //------------------------------------------ int i,k,m,n,p; // m - ?????? ????? ????????? ???????, p - ??????? ????? ???????? for (i=BeginBar;i>=EndBar;i--) { Norm?=0.0; for (k=0;k<=8;k++) {InputX[k]=Close[i]-Close[i+k+1]; Norm?=Norm?+InputX[k]*InputX[k]; } Norm?=MathSqrt(Norm?); for (k=0;k<=8;k++) { InputNormalX[BeginBar-i,k]=InputX[k]/Norm?; TeacherBuffer[BeginBar-i]=0.0; if (iCustom(NULL,0,"Kaufman2",1,i)>0) TeacherBuffer[BeginBar-i]=1; if (iCustom(NULL,0,"Kaufman2",2,i)>0) TeacherBuffer[BeginBar-i]=-1; //TeacherBuffer[BeginBar-i]=iCustom(NULL,0,"KaufmanTrend",0,BeginBar); } } //------------------------------------------ // ?????????? ??????? ??????? ???????? //------------------------------------------ MathSrand(LocalTime()); //------------------------------------------ // ?????????? ????? ???????? ?????????? ?????????? //------------------------------------------ for (i=0;i<9;i++) { for (k=0;k<9;k++) { FirstLayWeights[i,k]=NormalRand(MaxRandom); // Print("FirstLayWeights["+i+","+k+"]=",FirstLayWeights[i,k]); } OutputSecondWeights[i]=NormalRand(MaxRandom); } //------------------------------------------ // ?????????? ????? ???????? ?????????? ?????????? //------------------------------------------ PrintWeights(); // Print("Sigma=",Sigma(NormalRand())); Print("????????????? ?????????"); //------------------------------------------ // ???????? //------------------------------------------ for (p=1;p<=StudyNumber;p++) // ???? ???????? - p-????? ???????? { for (n=0;n<100;n++) // ?????? ?? ??????? ??????? ???????? - n-????? ?????????? ??????? { for (int z=0;z<9;z++) InputX[z]=InputNormalX[n,z]; // ???????? ??????? ?????? ?? ??????? ?????????????? ??????????????? ???????? GetFirstLayOutput(); U_Second=GetArgument(); Print("p=",p," n=",n," U_Second=",U_Second); PrintWeights(); SigmaCurr=Sigma(Betta*U_Second); Differ=Betta*(1-SigmaCurr*SigmaCurr); // ??????????? ?? ???????? ???????????????? deviation=TeacherBuffer[BeginBar-n]-SigmaCurr; //?????? ????????, ?????? ???? ????????????????? ???? SetGradientWeights(); ChangeWeigts(); }// ?????? ?? ??????? ??????? ???????? }// ???? ???????? //------------------------------------------ // ???????? //------------------------------------------ PrintWeights(); Comment("???????? ?????????"); //---- return(0); } //+------------------------------------------------------------------+ //| Custor indicator deinitialization function | //+------------------------------------------------------------------+ int deinit() { //---- TODO: add your code here //---- return(0); } //+------------------------------------------------------------------+ //| Custom indicator iteration function | //+------------------------------------------------------------------+ int start() { int i,m; int counted_bars=IndicatorCounted(); if (counted_bars<0) return(-1); int limit; //limit=Bars-10-counted_bars; limit=300; for (i=limit;i>=0;i--) { if (iCustom(NULL,0,"Kaufman2",1,i)>0) TeacherBuffer[i]=1; if (iCustom(NULL,0,"Kaufman2",2,i)>0) TeacherBuffer[i]=-1; Norm?=0.0; for (m=0;m<9;m++) { InputX[m]=Close[i]-Close[i+m+1]; Norm?=Norm?+InputX[m]*InputX[m]; } Norm?=MathSqrt(Norm?); Print("Norm?=",Norm?); for (m=0;m<9;m++) { InputX[m]=InputX[m]/Norm?; } GetFirstLayOutput(); U_Second=GetArgument(); Print("U_Second=",U_Second); NeuroExitBuffer[i]=Sigma(Betta*U_Second); //Print("NeuroExitBuffer["+i+"]=",NeuroExitBuffer[i]); ErrorBuffer[i]=TeacherBuffer[i]-NeuroExitBuffer[i]; } //---- TODO: add your code here //---- return(0); } //+------------------------------------------------------------------+
Author/Source:
Rosh
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