iCustom returns 2147483647
Unable to get the value of iCustom.
Returning 2147483647 ????
double EncogExample1 = iCustom(Symbol_1, 60, "EncogExample_Trained", false, 0, 0);
The indicator draws correctly. There is a value, but I could only get the EMPTY_VALUE.
What's wrong or missing in the indicator codes?
From your image it appears that the indicator does not draw a line on bar[0] and [1]
so
that could be why you are getting EMPTY_VALUE
Check values for earlier bars.
Also make sure that you are calling the correct buffer index.
I was having the same problem on my EA. I solved it with a minimun bars before start the calculation of the EA.
void OnTick()
{
if(Bars<100)
{
Print("Less than 100 bars"); //number of bars according the minimum bars of the indicator that you are using, the 100 is only for the example, you need to use a variable with the number of periods for the indicator
return;
}
//..... Your logic calling the indicator
}
Use the SRC button to post your code.
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
You agree to website policy and terms of use
#property indicator_separate_window
#property indicator_buffers 1
#property indicator_color1 Silver
//--- input_1 parameters
extern bool Export=false;
//--- buffers
double ExtMapBuffer1[];
int iHandle = -1;
int iErrorCode;
// begin Encog main config
string EXPORT_FILENAME = "mt4.csv";
int _neuronCount = 103;
int _layerCount = 3;
int _contextTargetOffset[] = {0,0,0};
int _contextTargetSize[] = {0,0,0};
bool _hasContext = false;
int _inputCount = 40;
int _layerContextCount[] = {0,0,0};
int _layerCounts[] = {1,61,41};
int _layerFeedCounts[] = {1,60,40};
int _layerIndex[] = {0,1,62};
double _layerOutput[] = {0,0,1};
double _layerSums[] = {0,0,0};
int _outputCount = 1;
int _weightIndex[] = {0,61,2521};
double _weights[] = {0.2938570914,-0.069337041,0.3257095745};
int _activation[] = {1,1,0};
double _p[] = {1,1,1};
// end Encog main config
//+------------------------------------------------------------------+
//| Custom indicator initialization function |
//+------------------------------------------------------------------+
int init()
{
IndicatorBuffers(1);
SetIndexStyle(0,DRAW_LINE);
SetIndexBuffer(0,ExtMapBuffer1);
IndicatorShortName("Encog Generated Indicator" );
SetIndexLabel(0,"Line1");
if( Export )
{
iHandle = FileOpen(EXPORT_FILENAME,FILE_CSV|FILE_WRITE,',');
if(iHandle < 1)
{
iErrorCode = GetLastError();
Print("Error updating file: ",iErrorCode);
return(false);
}
FileWrite(iHandle,"time","close","stoch_k","stoch_d","williams_r","prediction");
}
else
{
iHandle = -1;
}
return(0);
}
void ActivationTANH(double& x[], int start, int size)
{
for (int i = start; i < start + size; i++)
{
x[i] = 2.0 / (1.0 + MathExp(-2.0 * x[i])) - 1.0;
}
}
void ActivationSigmoid(double& x[], int start, int size)
{
for (int i = start; i < start + size; i++)
{
x[i] = 1.0/(1.0 + MathExp(-1*x[i]));
}
}
void ActivationElliottSymmetric(double& x[], int start, int size)
{
for (int i = start; i < start + size; i++)
{
double s = _p[0];
x[i] = (x[i] * s) / (1 + MathAbs(x[i] * s));
}
}
void ActivationElliott(double& x[], int start, int size)
{
for (int i = start; i < start + size; i++)
{
double s = _p[0];
x[i] = ((x[i]*s)/2)/(1 + MathAbs(x[i]*s)) + 0.5;
}
}
void ComputeLayer(int currentLayer)
{
int x,y;
int inputIndex = _layerIndex[currentLayer];
int outputIndex = _layerIndex[currentLayer - 1];
int inputSize = _layerCounts[currentLayer];
int outputSize = _layerFeedCounts[currentLayer - 1];
int index = _weightIndex[currentLayer - 1];
int limitX = outputIndex + outputSize;
int limitY = inputIndex + inputSize;
// weight values
for (x = outputIndex; x < limitX; x++)
{
double sum = 0;
for (y = inputIndex; y < limitY; y++)
{
sum += _weights[index] *_layerOutput[y];
index++;
}
_layerOutput[x] = sum;
_layerSums[x] = sum;
}
switch(_activation[currentLayer - 1] )
{
case 0: // linear
break;
case 1:
ActivationTANH(_layerOutput, outputIndex, outputSize);
break;
case 2:
ActivationSigmoid(_layerOutput, outputIndex, outputSize);
break;
case 3:
ActivationElliottSymmetric(_layerOutput, outputIndex, outputSize);
break;
case 4:
ActivationElliott(_layerOutput, outputIndex, outputSize);
break;
}
// update context values
int offset = _contextTargetOffset[currentLayer];
for (x = 0; x < _contextTargetSize[currentLayer]; x++)
{
_layerOutput[offset + x] = _layerOutput[outputIndex + x];
}
}
void Compute(double &input_1[], double &output[])
{
int i,x;
int sourceIndex = _neuronCount
- _layerCounts[_layerCount - 1];
ArrayCopy(_layerOutput,input_1,sourceIndex,0,_inputCount);
for(i = _layerCount - 1; i > 0; i--)
{
ComputeLayer(i);
}
// update context values
int offset = _contextTargetOffset[0];
for(x = 0; x < _contextTargetSize[0]; x++)
{
_layerOutput[offset + x] = _layerOutput[x];
}
ArrayCopy(output,_layerOutput,0,0,_outputCount);
}
//+------------------------------------------------------------------+
//| Custom indicator deinitialization function |
//+------------------------------------------------------------------+
int deinit()
{
//----
if( iHandle>0 )
{
FileClose(iHandle);
}
//----
return(0);
}
string PadInt(int num, int digits)
{
string result = num;
while( StringLen(result)<digits )
{
result="0" + result;
}
return (result);
}
void WriteExportLine(int pos)
{
datetime dt = Time[pos];
string when =
PadInt(TimeYear(dt),4) +
PadInt(TimeMonth(dt),2) +
PadInt(TimeDay(dt),2) +
PadInt(TimeHour(dt),2) +
PadInt(TimeMinute(dt),2) +
PadInt(TimeSeconds(dt),2);
FileWrite(iHandle, when,
Close[pos],
iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos),
iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos),
iWPR(Symbol(), Period(), 21, 0)
);
}
double Norm(double x,double normalizedHigh, double normalizedLow, double dataHigh, double dataLow)
{
return (((x - dataLow)
/ (dataHigh - dataLow))
* (normalizedHigh - normalizedLow) + normalizedLow);
}
double DeNorm(double x,double normalizedHigh, double normalizedLow, double dataHigh, double dataLow) {
return (((dataLow - dataHigh) * x - normalizedHigh
* dataLow + dataHigh * normalizedLow)
/ (normalizedLow - normalizedHigh));
}
//+------------------------------------------------------------------+
//| Custom indicator iteration function |
//+------------------------------------------------------------------+
int start()
{
int countedBars = IndicatorCounted();
//---- check for possible errors
if (countedBars<0) return(-1);
//---- last counted bar will be recounted
if (countedBars>0) countedBars--;
int pos=Bars-countedBars-1;
static datetime Close_Time;
// ArraySetAsSeries(ExtMapBuffer1,false);
// only do this on a new bar
if ( Close_Time != Time[0])
{
Close_Time = Time[0];
while(pos>1)
{
if( _inputCount>0 && Bars>=9 )
{
double input_1[40];
double output[1];
input_1[0]=Norm(Close[pos+0],1.0,-1.0,1.15926,1.03555);
input_1[1]=Norm(Close[pos+1],1.0,-1.0,1.15926,1.03555);
input_1[2]=Norm(Close[pos+2],1.0,-1.0,1.15926,1.03555);
input_1[3]=Norm(Close[pos+3],1.0,-1.0,1.15926,1.03555);
input_1[4]=Norm(Close[pos+4],1.0,-1.0,1.15926,1.03555);
input_1[5]=Norm(Close[pos+5],1.0,-1.0,1.15926,1.03555);
input_1[6]=Norm(Close[pos+6],1.0,-1.0,1.15926,1.03555);
input_1[7]=Norm(Close[pos+7],1.0,-1.0,1.15926,1.03555);
input_1[8]=Norm(Close[pos+8],1.0,-1.0,1.15926,1.03555);
input_1[9]=Norm(Close[pos+9],1.0,-1.0,1.15926,1.03555);
input_1[10]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+0),1.0,-1.0,96.55705996,0.0);
input_1[11]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+1),1.0,-1.0,96.55705996,0.0);
input_1[12]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+2),1.0,-1.0,96.55705996,0.0);
input_1[13]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+3),1.0,-1.0,96.55705996,0.0);
input_1[14]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+4),1.0,-1.0,96.55705996,0.0);
input_1[15]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+5),1.0,-1.0,96.55705996,0.0);
input_1[16]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+6),1.0,-1.0,96.55705996,0.0);
input_1[17]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+7),1.0,-1.0,96.55705996,0.0);
input_1[18]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+8),1.0,-1.0,96.55705996,0.0);
input_1[19]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 0, pos+9),1.0,-1.0,96.55705996,0.0);
input_1[20]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+0),1.0,-1.0,93.56165112,0.0);
input_1[21]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+1),1.0,-1.0,93.56165112,0.0);
input_1[22]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+2),1.0,-1.0,93.56165112,0.0);
input_1[23]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+3),1.0,-1.0,93.56165112,0.0);
input_1[24]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+4),1.0,-1.0,93.56165112,0.0);
input_1[25]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+5),1.0,-1.0,93.56165112,0.0);
input_1[26]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+6),1.0,-1.0,93.56165112,0.0);
input_1[27]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+7),1.0,-1.0,93.56165112,0.0);
input_1[28]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+8),1.0,-1.0,93.56165112,0.0);
input_1[29]=Norm(iStochastic(Symbol(), Period(), 8, 5, 5, MODE_EMA, 0, 1, pos+9),1.0,-1.0,93.56165112,0.0);
input_1[30]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[31]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[32]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[33]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[34]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[35]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[36]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[37]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[38]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
input_1[39]=Norm(iWPR(Symbol(), Period(), 21, 0),1.0,-1.0,0.0,-11.39430285);
Compute(input_1,output);
ExtMapBuffer1[pos] = DeNorm(output[0],1.0,-1.0,430.0,-113.0);
}
if( Export )
{
WriteExportLine(pos);
}
pos--;
}
}
//----
//----
return(0);
}
//+------------------------------------------------------------------+
What's wrong with this indicator?
Unable to get the value of iCustom.
Returning 2147483647 ????