//+------------------------------------------------------------------+
//| 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 Green
#property indicator_color2 Blue
#property indicator_color3 Red
//---- input parameters
extern int BeginBar=300; // íà÷àëüíûé áàð â îáó÷àþùåé âûáîðêå
extern int EndBar=201; // êîíå÷íûé áàð â îáó÷àþùåé âûáîðêå
extern int StudyNumber=100; // êîëè÷åñòâî îáó÷àþùèõ öèêëîâ
extern double Betta=0.5; // êðóòèçíà ñèãìîèäàëüíîé ôóíêöèè
extern double StudyCoeff=0.1; // êîýôôèöèåíò îáó÷åíèÿ
extern double MaxRandom=0.66;//2.0/MathSqrt(9);
//---- buffers
double NeuroExitBuffer[]; // âûõîäíûå çíà÷åíèÿ íåéðîñåòè
double TeacherBuffer[]; // ýòàëîííûå çíà÷åíèÿ íåéðîñåòè (îáó÷àþùèå ñèãíàëû)
double ErrorBuffer[]; // îòêëîíåíèÿ íà êàæäîì áàðå
//----
double deviation; // îòêëîíåíèå íà êàæäîì øàãå îáó÷åíèÿ
double GradientFirstLay[9][9];// äëÿ ðàñ÷åòà ãðàäèåíòà 1-ãî ñëîÿ
double GradientSecondLay[9]; // äëÿ ðàñ÷åòà ãðàäèåíòà 2-ãî ñëîÿ
double OutputFirstLay[9]; // ñóììà âûõîäà êàæäîãî íåéðîíà
double SecondSum;// ñóììà âûõîäíîãî ñëîÿ - àðãóìåíò ñèãìîèäàëüíîé ôóíêöèè
double FirstLayWeights[9][9]; // âåñà íåéðîíîâ ïåðâîãî (ñêðûòîãî ñëîÿ)
double OutputSecondWeights[9]; // âåñà âûõîäíîãî íåéðîíà
double InputNormalX[100][9]; // íîðìàëèçîâàííàÿ âûáîðêà âõîäíûõ ñèãíàëîâ
double InputX[9]; // òåêóùèé âõîäíîé âåêòîð (íåíîðìàëèçîâàííûé)
double Normà;// íîðìà òåêóùåãî âõîäíîãî âåêòîðà
double U_First[9];// çíà÷åíèÿ íà âûõîäå ñëîÿ ñêðûòûõ (âõîäíûõ) íåéðîíîâ
double U_Second;// çíà÷åíèå íà âûõîäå âûõîäíîãî íåéðîíà (àðãóìåíò ñèãìîèäàëüíîé ôóíêöèè)
double SigmaCurr;
double Differ;
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
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];// èçìåíåíèå âåñîâ âûõîäíîãî íåéðîíà
}
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
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 - èíäåêñ âåñîâ âûõîäíîãî íåéðîíà, p - ñ÷åò÷èê öèêëà îáó÷åíèÿ
for(i=BeginBar;i>=EndBar;i--)
{
Normà=0.0;
for(k=0;k<=8;k++)
{InputX[k]=Close[i]-Close[i+k+1];
Normà=Normà+InputX[k]*InputX[k];
}
Normà=MathSqrt(Normà);
for(k=0;k<=8;k++)
{
InputNormalX[BeginBar-i,k]=InputX[k]/Normà;
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("Èíèöèàëèçàöèÿ çàêîí÷åíà");
//----
for(p=1;p<=StudyNumber;p++) // öèêë îáó÷åíèÿ - p-íîìåð îáó÷åíèÿ
{
for(n=0;n<100;n++) // ïðîõîä ïî âûáîðêå âõîäíûõ ñèãíàëîâ - n-íîìåð îáó÷àþùåãî ñèãíàëà
{
for(int z=0;z<9;z++) InputX[z]=InputNormalX[n,z]; // çàïîëíèì âõîäíîé âåêòîð èç ìàññèâà ïîäãîòîâëåííûõ íîðìàëèçîâàííûõ âåêòîðîâ
GetFirstLayOutput();
U_Second=GetArgument();
Print("p=",p," n=",n," U_Second=",U_Second);
PrintWeights();
SigmaCurr=Sigma(Betta*U_Second);
Differ=Betta*(1-SigmaCurr*SigmaCurr); // ïðîèçâîäíàÿ îò òàíãåíñà ãèïåðáîëè÷åñêîãî
deviation=TeacherBuffer[BeginBar-n]-SigmaCurr; //îøèáêà ïîëó÷åíà, òåïåðü íàäî ïîäêîððåêòèðîâàòü âåñà
SetGradientWeights();
ChangeWeigts();
}// ïðîõîä ïî âûáîðêå âõîäíûõ ñèãíàëîâ
}// öèêë îáó÷åíèÿ
//----
PrintWeights();
Comment("Îáó÷åíèå çàêîí÷åíî");
//----
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;
Normà=0.0;
for(m=0;m<9;m++)
{
InputX[m]=Close[i]-Close[i+m+1];
Normà=Normà+InputX[m]*InputX[m];
}
Normà=MathSqrt(Normà);
Print("Normà=",Normà);
for(m=0;m<9;m++)
{
InputX[m]=InputX[m]/Normà;
}
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);
}
//+------------------------------------------------------------------+
Sample
Analysis
Market Information Used:
Series array that contains close prices for each bar
Indicator Curves created:
Implements a curve of type DRAW_LINE
Indicators Used:
Custom Indicators Used:
Kaufman2
Order Management characteristics:
Other Features: