Indicators Used
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IndiTest
//+------------------------------------------------------------------+
//| expert.mq4 |
//| Denis Janulis |
//| denis.j@mits.lv |
//+------------------------------------------------------------------+
#property copyright "Denis Janulis"
#property link "denis.j@mits.lv"
#property indicator_chart_window
#import "dpnn.dll"
extern int lastBar=0;
/*
extern int Start=0;
extern int TargetUp=60;
extern int TargetDown=60;
extern int Totalsignals=1000;
extern bool UPDirection=true;
*/ // DPNN.DLL - ïîäêëþ÷àåì ñåòü
//--------------------------------------------------------------------
extern double MinW = 0.00100200; // êðèòåðèé ñëàáîñòè íåéðîíîâ. Åñëè 0, òî æèâóò âå÷íî
extern double LifeTime = 500; // âðåìÿ æèçíè íåéðîíîâ (â áàðàõ). Åñëè 0, òî æèâóò âå÷íî
extern double MinTV = 0.2; // Ïîðîã êîíôëèêòîâ
extern double MaxTV = 0.95;
// Ïîðîã èíäèâèäóàëüíîñòè
//--------------------------------------------------------------------
// Îñíîâíûå íåîáõîäèìûå ôóíêöèè ðàáîòû ñ ñåòüþ
double CreateNet(string NN, int InputVectorSize, // Ñîçäàåò ñåòü ñ êîëè÷åñòâîì íåéðîíîâ âî âõîäíîì âåêòîðå <i>.
double MinTV, double MaxTV, // è âîçâðàùàåò íîìåð ñåòè, ïîä êîòîðûì ñîçäàëàñü
double MW , int NL);
int DestroyNet(string NN); // Óíè÷òîæàåò ñåòü <NetName>, âîçâðàùàåò êîëè÷åñòâî îñòàâøèõñÿ ñåòåé
double TrainNet(string NetName,double& iX[],int T,datetime sT); // Îáó÷àåò ñåòü íà ïðèìåðå Õ[InputVectorSize] c îòâåòîì Ò
double GetPredict(string NetName,double& iX[],datetime sT); // Âîçâðàùàåò ïðåäñêàçàíèå ñåòè 0<=Out<=1
void Shrink(string NetName); // ÏÎÊÀ ÓÏÐÀÂËßÅÒÑß ÂÐÓ×ÍÓÞ!!! - íàäî çàïóñêàòü ïîñëå îáó÷åíèÿ
void WReset(string NetName); // (A) - ÏÎÊÀ ÓÏÐÀÂËßÅÒÑß ÂÐÓ×ÍÓÞ!!! - íàäî çàïóñêàòü ïîñëå îáó÷åíèÿ
void AEqual(string NetName); // (A) - ÏÎÊÀ ÓÏÐÀÂËßÅÒÑß ÂÐÓ×ÍÓÞ!!! - íàäî çàïóñêàòü ïîñëå îáó÷åíèÿ
void CW(string NetName); // (w) - ÏÎÊÀ ÓÏÐÀÂËßÅÒÑß ÂÐÓ×ÍÓÞ!!! - íàäî çàïóñêàòü ïîñëå îáó÷åíèÿ
int SizeOfNet(string NetName); // Âîçâðàùàåò êîëè÷åñâî Ãàóññîâñêèõ íåéðîíîâ
void KillWeaks(string NetName);
void KillOlds(string NetName,datetime sT);
//-------------------------------------------------------------------
int InputVectorSize = 24; // Ðàçìåðíîñòü âõîäíîãî âåêòîðà
double X[24]; // Âõîäíîé âåêòîð ñåòè
//-------------------------------------------------------------------
extern int n = 10; // Ïàðàìåòðû äëÿ "ó÷èòåëÿ" ñì. èíäèêàòîð djama
extern int p = 2;
extern int q = 2;
extern bool allowTrailingStop = true; // Ðàçðåøàåì òðàëû
extern bool allowPostTraining = true; // Ðàçðåøàåì äîîáó÷åíèå
extern int StopLoss = 50;
extern int TakeProfit = 400;
extern int TrailingStop = 150;
extern int HistoryBars = 500; // Íà ñêîëüêèõ áàðàõ ïðîâîäèòü ïðåäâàðèòåëüíîå îáó÷åíèå
extern int Repetitions = 5; // Ñêîëüêî ðàç ïðîãíÿòü îáó÷åíèå ñåòè ????
extern int Activation_n = 100; // Ïàðàìåòðû äëÿ "àêòèâàöèîííîé ôóíêéèè" ñì. èíäèêàòîð djama
extern int Activation_p = 2;
extern int Activation_q = 30;
extern double ActivationMax = 0.95;
extern double ActivationMin = 0.6;
extern double ActivationK = 15000;
double ActivationBuy;
double ActivationSell;
//--------------------------------------------------------------------
double Lots = 0.1;
int MinMargin = 200; // Ìèíèìàëüíàÿ ìàðæà
datetime prevtime = 0;
double Out; // Ðåçóëüòàò ðàáîòû ñåòåé
bool existent = false;
//********************************************************************
void BuildX(int FinishBar) // Ìîäóëü ñîáèðàåò è ïîäãîòàâëèâàåò äàííûå äëÿ çàãðóçêè â ñåòü
{ // FinishBar - Ñ êàêîãî áàðà ñíèìàòü çíà÷åíèÿ.
int B = InputVectorSize;
double rD[24][24];
double Min[24];
double Max[24];
if (FinishBar > Bars)
{
Print("MaxBars Error = ",Bars);
return;
}
for (int i=B-1;i>=0;i--)
{
rD[i][0] = iMA(NULL,0,2,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,2,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][1] = iMA(NULL,0,4,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,4,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][2] = iMA(NULL,0,6,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,6,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][3] = iMA(NULL,0,8,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,8,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][4] = iMA(NULL,0,10,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,10,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][5] = iMA(NULL,0,12,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,12,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][6] = iMA(NULL,0,14,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,14,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][7] = iMA(NULL,0,16,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,16,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][8] = iMA(NULL,0,18,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,18,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][9] = iMA(NULL,0,20,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,20,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][10] = iMA(NULL,0,22,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,22,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][11] = iMA(NULL,0,24,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,24,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][12] = iMA(NULL,0,26,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,26,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][13] = iMA(NULL,0,28,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,28,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][14] = iMA(NULL,0,30,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,30,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][15] = iMA(NULL,0,32,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,32,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][16] = iMA(NULL,0,34,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,34,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][17] = iMA(NULL,0,36,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,36,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][18] = iMA(NULL,0,38,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,38,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][19] = iMA(NULL,0,40,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,40,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][20] = iMA(NULL,0,42,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,42,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][21] = iMA(NULL,0,44,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,44,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][22] = iMA(NULL,0,46,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,46,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
rD[i][23] = iMA(NULL,0,48,0,MODE_EMA,PRICE_WEIGHTED,i+FinishBar)-iMA(NULL,0,48,0,MODE_EMA,PRICE_WEIGHTED,i+1+FinishBar);
}
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
for (int n = 0; n <= B-1; n++) // Îïðåäåëÿåì ìèíèìàëüíûå è ìàêñèìàëüíûå âåëè÷èíû
{
Min[n] = rD[0][n];
Max[n] = rD[0][n];
for ( i = 0; i <= B-1; i++)
{
if (rD[i][n] > Max[n]) Max[n] = rD[i][n];
if (rD[i][n] < Min[n]) Min[n] = rD[i][n];
}
}
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
for ( n = 0; n <= B-1; n++) // Íîðìèðóåò âõîäíîé âåêòîð íà 1
{
if (Max[n]== Min[n])
{
for ( i = 0; i <= B-1; i++) X[i] = Max[n];
return;
}
X[n] = (rD[0][n] - Min[n])/(Max[n]-Min[n]); // Âõîäíîé âåêòîð, íîðìèðîâàííûé íà 1
}
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
return;
}
//-------------------------------------------------------------------------------------------------------------------
int BuildT(int i) // Òóò ïîëó÷àåì ïðàâèëüíûé îòâåò ïî ïåðåêóïëåííîñòè
{ // èëè ïåðåïðîäàííîñòè
int T= 0;
double Previous = djama(n,p,q,i+1);
double Current = djama(n,p,q,i);
double Next = djama(n,p,q,i-1);
if (Current <= Next) T = -1;
if (Current > Next) T = 1;
return (T);
}
//*******************************************************************************************************************
int Create(string NetName,int InputVectorSize) // Ñîçäàåò ñåòü ñ èìåíåì <NetName> è âõîäíûì âåêòîðîì ðàçìåðà
{ // InputVectorSize. Âûâîäèò íîìåð ñåòè (òóò îí æå îáùåå êîëè÷åñòâî
int NeuronLife = Period()*60*LifeTime; // ñåòåé) â áèáëèîòåêå.
int Out = CreateNet(NetName,InputVectorSize,MinTV,MaxTV,MinW,NeuronLife);
return(Out);
}
//-------------------------------------------------------------------------------------------------------------------
int Destroy(string NetName) // Óíè÷òîæàåò ñåòü ñ èìåíåì <NetName> è âîçâðàùàåò îáùåå
{ // êîëè÷åñòâî ñåòåé îñòàâøèõñÿ â áèáëèîòåêå
return(DestroyNet(NetName));
}
//-------------------------------------------------------------------------------------------------------------------
double Train(string NetName,double& iX[],int T) // Ïîäàåò â ñåòü <NetName> âåêòîð X è îòâåò T äëÿ îáó÷åíèÿ íà ïðèìåðå i
{ // Âîçâðàùàåò Out cåòè
double Out = TrainNet(NetName,iX,T,iTime(Symbol(),0,1)); // Out=1, ñåòü äîáàâèëà íîâûé îáðàç. Out =2 - ñåòü ðàñïîçíàëà îáðàç è ïîäïðàâèëàñü.
return(Out);
}
//-------------------------------------------------------------------------------------------------------------------
double Predict(string NetName) // Ïîäàåò â ñåòü <NetName> âåòîð X äëÿ ïðåäñêàçàíèÿ
{
BuildX(1+lastBar);
double Out = GetPredict(NetName,X,iTime(Symbol(),0,1)); // Âîçâðàùàåò Out cåòè
return(Out);
}
//--------------------------------------------------------------------------------------------------------------------
double PostTraining(string NetName,int ST)
{
int T = BuildT(2+lastBar); // Ñ÷èòûâàåì ïðàâèëüíûé îòâåò
if (T == ST) // Îáó÷àåì òîëüêî íà òîì ïðèìåðå, êîòîðûé ïîäõîäèò äëÿ îáó÷åíèÿ
{
BuildX(2+lastBar); // Ôîðìèðóåì âõîäíîé îáó÷àþùèé âåêòîð
Train(NetName,X,T); // Îáó÷àåì ñåòü íà êîíêðåòíîì ïðèìåðå
Print("New neurons amount: ",SizeOfNet("OverBought")," ; ",SizeOfNet("OverSold"));
}
return (0);
}
//-------------------------------------------------------------------------------------------------------------------
double InitialTraining(string NetName,int HistoryBars,int ST) // Ðóêîâîäèò çàãðóçêîé äàííûõ â ñåòü äëÿ ïåðâè÷íîãî îáó÷åíèÿ
{ // çà ïåðèîä HistoryBars
for ( int i = HistoryBars-1; i > 6; i--) // ïî áàðàì
{
int T = BuildT(i+lastBar); // Ñ÷èòûâàåì ïðàâèëüíûé îòâåò
if (T == ST) // Îáó÷àåì òîëüêî íà òîì ïðèìåðå, êîòîðûé ïîäõîäèò äëÿ îáó÷åíèÿ
{
BuildX(i+lastBar); // Ôîðìèðóåì âõîäíîé îáó÷àþùèé âåêòîð
Train(NetName,X,T); // Îáó÷àåì ñåòü íà êîíêðåòíîì ïðèìåðå
}
}
return (0);
}
//-------------------------------------------------------------------------------------------------------------------
int init()
{
NetInitialTraining();
}
//********************************************************************
int start()
{
//-------------------------------------------------------------------
if (allowPostTraining)
{
if (LifeTime > 0) KillOlds("OverBought",iTime(Symbol(),0,1)); // Óáèâàåì ñòàðûå íåéðîíû
PostTraining("OverBought",-1); // Äîîáó÷àåì ïî õîäó äåëà
Shrink("OverBought"); // Ìîäåðíèçèðóåì ñåòü
//AEqual("OverBought"); // Óñòàíàâëèâàåì âñå A =1
CW("OverBought"); // Ðàññ÷èòûâàåì âåñà
KillWeaks("OverBought"); // Óáèâàåì ñëàáûå íåéðîíû
CW("OverBought"); // Ðàññ÷èòûâàåì âåñà
if (LifeTime > 0) KillOlds("OverSold",iTime(Symbol(),0,1)); // Óáèâàåì ñòàðûå íåéðîíû
PostTraining("OverSold",1); // Äîîáó÷àåì ïî õîäó äåëà
//AEqual("OverSold"); // Óñòàíàâëèâàåì âñå A =1
Shrink("OverSold"); // Ìîäåðíèçèðóåì ñåòü
CW("OverSold"); // Ðàññ÷èòûâàåì âåñà
KillWeaks("OverSold"); // Óáèâàåì ñëàáûå íåéðîíû
CW("OverSold"); // Ðàññ÷èòûâàåì âåñà
}
//--------------------------------------------------------------------
double OverBought = Predict("OverBought");
double OverSold = Predict("OverSold");
Out = (OverBought - OverSold)/(OverBought+OverSold);
Alert("OverBought"+OverBought);
Alert("OverSold "+OverSold);
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Activation(1);
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if ( Out < (-1)*ActivationBuy)Alert("sell");
if (Out > ActivationSell)Alert("buy");
return(0);
}
//+------------------------------------------------------------------+
int deinit()
{
if (!IsOptimization())
{
Print("Destroying net=>",Destroy ("OverBought")); // Óáèâàåì, ÷òî ïîðîäèëè
Print("Destroying net=>",Destroy ("OverSold")); // Óáèâàåì, ÷òî ïîðîäèëè
}
}
//-------------------------------------------------------------------------------------
///////////////////////////////////////////////////////////////////////////////
//------------------------------------------
double Activation(int i )
{
double t = (djama(Activation_n,Activation_p,Activation_q,i)-djama(Activation_n,Activation_p,Activation_q,i+Activation_n))/Activation_n;
//~~~~ActivationBuy
double M = (ActivationMin-ActivationMax)/(-ActivationK);
if (t <= 0) ActivationBuy = ActivationMax;
if (t >= M) ActivationBuy = ActivationMin;
if ((t > 0) && (t < M)) ActivationBuy = -ActivationK*t+ActivationMax;
//~~~~ActivationSell
M = (ActivationMin-ActivationMax)/(-ActivationK);
if (t >= 0) ActivationSell = ActivationMax;
if (t <= -M) ActivationSell = ActivationMin;
if ((t < 0) && (t > -M)) ActivationSell = ActivationK*t+ActivationMax;
return(t);
}
//-------------------------------------------
void NetInitialTraining()
{
int Size0 = 0;
int Size1 = 0;
Print("Net Created with number: ",Create("OverBought",24)); // Ñîçäàåì ñåòü äëÿ ñèãíàëîâ Sell
Print("Net Created with number: ",Create("OverSold",24)); // Ñîçäàåì ñåòü äëÿ ñèãíàëîâ Buy
//~~
Print("Initial training started ...");
for (int R = 1; R <= Repetitions; R++) // Ïðîöåññ ïåðâè÷íîãî îáó÷åíèÿ
{ // íà èñòîðè÷åñêèõ äàííûõ
WReset("OverBought"); // Îáíóëÿåì çíà÷åíèÿ À
InitialTraining("OverBought",HistoryBars,-1); // Ïðîãîíÿåì îáó÷åíèå
Shrink("OverBought"); // Ìîäåðíèçèðóåì ñåòü
AEqual("OverBought"); // Óñòàíàâëèâàåì âñå A =1
CW("OverBought"); // Ðàññ÷èòûâàåì âåñà
KillWeaks("OverBought"); // Óáèâàåì ñëàáûå íåéðîíû
CW("OverBought"); // Ðàññ÷èòûâàåì âåñà
WReset("OverSold"); // Îáíóëÿåì çíà÷åíèÿ À
InitialTraining("OverSold",HistoryBars,1); // Ïðîãîíÿåì îáó÷åíèå
AEqual("OverSold"); // Óñòàíàâëèâàåì âñå A =1
Shrink("OverSold"); // Ìîäåðíèçèðóåì ñåòü
CW("OverSold"); // Ðàññ÷èòûâàåì âåñà
KillWeaks("OverSold"); // Óáèâàåì ñëàáûå íåéðîíû
CW("OverSold"); // Ðàññ÷èòûâàåì âåñà
Print("Cycle ",R," completed. Neurons: ",SizeOfNet("OverBought")," ; ",SizeOfNet("OverSold"));
if ((SizeOfNet("OverBought") == Size0) && (SizeOfNet("OverSold") == Size1)) break;
Size0 = SizeOfNet("OverBought");
Size1 = SizeOfNet("OverSold");
}
Print("Initial training completed.");
}
//-------------------------------------------------------------+
double djama(int n, int p, int q, int i)
{
return(iCustom(Symbol(),0,"djama",n,p,q,0,i+lastBar));
}
//-------------------------------------------------------------+
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