You are here

Question with output * Very Newbish*

Hello,

I am using java workbench 3.2 with MT4. I am trying to generate a indicator that gives best guess movement based on the input.

Just for easy examples sake I won't list all the indicators.
Source fields I am using:
Time Source: Time
Change Source: Close[##] - Open[##] //Was trying to get pip movement
slope Source: (iMA(NULL,0,13,8,MODE_SMMA,PRICE_MEDIAN,##+1)-iMA(NULL,0,13,8,MODE_SMMA,PRICE_MEDIAN,##))/Point

Thats not everything but some of them.

I choose for
Field to predict = Change.
Prediction Type = Max Pips
Forward Window = 3
Backward Window = 7

The issue I am having is the indicator once built gives output between 70 - 77.

This is nowhere close to the predict "Change" value I was expecting? +- 0.0008

Am I doing something horribly wrong? Or am I just not looking at the inputs and outputs correctly?

Any help is appreciated :D

Regards

Maxss280

Neural Network Forums: 
maxss280's picture

If I need to rephrase let me know lol. Question makes sense in my head. I decided to use slope for determination sense it already looks at a majority of the data. However under predict I thought it would give the output of what was predicted for change.

I am not sure how to use the out or what it is telling me.

Is it supposed to give a number similar to the input for change or some strange value like the 70.

maxss280's picture

Strange I have tried a few different setting even just one iMA with Time and the Close inputs. Still get off the wall numbers like 90000+ or 50000- to ranging in between 50 - 60???
This is my EGA file.

Am I doing something wrong or is it a bug?
//+------------------------------------------------------------------+
//| EncogExample.mq4 |
//| Heaton Research |
//| http://www.heatonresearch.com/encog |
//+------------------------------------------------------------------+
#property copyright "Heaton Research"
#property link "http://www.heatonresearch.com/encog"

#property indicator_separate_window
#property indicator_buffers 1
#property indicator_color1 Silver

//--- input parameters
extern bool Export=false;

//--- buffers
double ExtMapBuffer1[];

int iHandle = -1;
int iErrorCode;

// begin Encog main config
string EXPORT_FILENAME = "mt4.csv";
int _neuronCount = 53;
int _layerCount = 3;
int _contextTargetOffset[] = {0,0,0};
int _contextTargetSize[] = {0,0,0};
bool _hasContext = false;
int _inputCount = 20;
int _layerContextCount[] = {0,0,0};
int _layerCounts[] = {1,31,21};
int _layerFeedCounts[] = {1,30,20};
int _layerIndex[] = {0,1,32};
double _layerOutput[] = {0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,1,
0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,1};
double _layerSums[] = {0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0};
int _outputCount = 1;
int _weightIndex[] = {0,31,661};
double _weights[] = {0.0514676135,-0.0717669148,-0.1962972763,-0.1637181143,0.0660339813,-0.0006581141,0.0163045651,-0.0817895722,-0.041628885,-0.0877052069,
-0.0864229519,0.0706388686,-0.2066460563,-0.1661198943,0.0625976949,0.2283204576,-0.0641856278,0.0855571571,-0.196846956,0.0082296992,0.0045093895,
0.0502393783,-0.0507166307,-0.0123810837,-0.007785889,-0.0102499881,0.1353211782,-0.0671048087,-0.2217205189,0.0155836184,0.1119899732,0.066394676,
0.2058385257,0.0294648911,0.0696184441,0.1005831666,0.2552678015,0.1029326999,0.0114907453,0.262742024,0.217582419,0.1450869027,-0.0919623073,
0.1097605853,0.0801878529,-0.0240169649,0.1434138781,0.2224544731,0.0437741938,0.172939633,0.2474555894,-0.1425594472,0.3009143616,0.2955014599,
0.0688754074,0.2910295777,0.1380504682,0.0629409985,0.3526899534,0.2099451239,0.4063822833,0.2341533571,0.2097492243,0.2386735482,0.3619055354,
0.213104213,0.2426669361,0.0418502221,0.2980927721,0.2609900197,0.084024886,0.0420797098,-0.2040742111,0.0340982682,-0.1708687679,0.1366732667,
0.1097874423,0.084126451,0.1288059703,-0.034768622,0.1269711791,0.1200559108,0.051709331,-0.1731670264,0.1080838374,0.0210099276,0.1169362337,
0.110619784,-0.1118232362,-0.0523657405,-0.0331561848,-0.0335385599,0.0804471164,0.2174335018,-0.0651330849,-0.0749073179,0.1784875783,0.1505002039,
0.0435198965,-0.0110166452,0.1818963359,0.0708439653,0.2279561537,0.0998096593,-0.0679003607,-0.0214319909,0.161809278,0.155628443,0.1799717093,
0.2126592104,-0.0985469629,-0.056433314,-0.0899940615,0.0350096261,-0.0041975898,0.0532769032,-0.0584087444,0.3076408706,0.191677897,-0.0161732762,
0.0998049899,0.0261352698,0.202259026,-0.0551912926,0.0507640588,0.0298777292,-0.065524524,0.2956996608,0.2669614961,0.0867238198,0.0394894158,
-0.0415060052,0.2015323417,0.1129112647,0.2225238337,-0.3747476943,0.1027517935,-0.0909876238,0.1897076901,0.0728393309,0.1506288232,0.2962395708,
0.2411089196,0.2065599868,0.0134252099,0.3111943525,-0.0318785689,0.1754464121,0.1876623237,0.2795810248,0.0832176017,0.268178581,0.1041360864,
0.0926685125,0.0197362158,0.0329232473,-0.2819769652,0.1538154861,-0.0790141736,0.0682903336,-0.0707368641,0.1473153669,0.0200388508,0.1352528254,
-0.0153606323,0.1634120512,-0.0868872143,-0.1223936715,0.2031638042,0.1207347915,0.1581101786,-0.0957151731,-0.0427598456,0.1343989106,-0.1012433778,
0.0768815836,0.1727905111,0.4644268118,0.1005832179,0.3058006997,0.0578807266,0.3755065912,0.2317665356,0.4124151636,0.1851432043,0.3849094079,
0.2193586397,0.3622722591,0.0764515718,0.0779285467,0.1566815314,0.1830692271,0.2670181954,0.0408489617,0.2972895479,0.2703756282,0.1616511896,
0.0508235968,-0.3905311977,0.3760201078,0.051627045,0.2797611567,0.0108588437,0.1364923555,0.3768207755,0.3811294366,0.1548253535,0.2877669326,
0.2250245424,0.2283233322,0.3757913169,0.2190694187,0.0652295658,0.2521340373,0.0881987378,0.0818531475,0.1022035707,0.2265428709,0.1544999641,
-0.1291944679,0.3969748769,0.2711826842,0.2824807179,0.18518873,0.2431965239,0.4265327042,0.1078662128,0.3292632858,0.3073830148,0.1029620659,
0.1476197446,0.2346462265,0.1280741876,0.0731662838,0.1865374115,0.4126595161,0.0447912688,0.2002844744,0.2348789152,0.1262975711,-0.2311508919,
-0.0175910189,0.0213929583,0.1142451821,0.0796301284,0.1750174768,-0.0381336418,0.037064044,0.2702138121,0.1570684015,0.007486089,0.3217754903,
-0.0219115573,-0.0254109891,0.0346553851,0.2541794747,0.0501836577,-0.0284568349,0.0021074078,0.1484283789,0.0402196682,0.0723374119,-0.0515778599,
0.0164689518,0.0243840004,0.2685946063,-0.0803219957,0.2874961419,-0.0659702504,0.3087515788,0.1874843245,-0.0355452209,0.2907164405,0.2613080174,
0.0563219091,0.1062964999,0.2620163881,0.3093033569,-0.0613907598,0.0827464479,0.0098995689,0.3020363973,0.09505717,-0.1020923055,-0.0223591991,
-0.0396182313,0.179178687,0.131956504,0.1317836074,0.0938555505,-0.1127928467,0.139712889,-0.0759496865,0.2175725232,0.1518229033,0.1888550971,
0.1827218354,0.0088304758,0.0746481327,-0.0489501609,-0.0798416735,0.1802674458,-0.0028698853,0.3120210483,0.0445684883,0.1206672038,0.1948925654,
0.2062195212,-0.0025408354,0.0839424289,0.1168663324,-0.0057737374,0.0203977015,0.0613862812,0.074770163,-0.1182632497,-0.0715334201,0.1751935661,
-0.0104042383,-0.0121911489,0.0677973309,0.0253742911,-0.1628969059,0.1975555784,0.2595641059,0.1340375644,0.2345496122,-0.0891117056,0.1545482885,
0.1693118929,0.0109167386,-0.0375112149,0.1651281972,0.0675317566,0.1963912074,0.2330572161,0.0510500116,-0.1012243534,-0.0815701238,-0.0950712988,
0.1660629191,0.0639113495,0.0327445247,0.0477958555,-0.1080194861,0.2277809237,0.0054990837,0.1465296088,0.0394765036,-0.0994968355,0.146337972,
0.066727982,-0.0241260238,0.2006779027,0.1005850081,-0.1211126076,0.1261351542,0.1239880755,-0.028796563,0.1767391267,-0.0289810909,-0.1301214354,
0.0490004405,-0.0535396828,0.2187360645,-0.0875109587,-0.2130558768,0.0401918604,0.3681158751,0.3219729337,0.0083826051,0.2930384981,0.0920045414,
0.1601023566,0.2310970231,0.3780104664,0.2540787808,0.2261635042,0.0611304628,0.2899587911,0.0428893393,0.1175668196,0.3976529384,0.0779305297,
0.4089116921,0.4073795241,0.0198345293,0.3816681409,0.0585840569,0.1701551443,0.0477900508,0.1160063244,0.0638151299,0.2494632918,-0.0024481782,
-0.0113038636,-0.0103761033,0.126094036,0.1440762192,0.2660741495,0.016183931,0.2642514162,0.244341782,0.2711425249,0.0497524524,0.077341041,
0.2663882514,0.0838235133,0.3893760775,0.2211748206,-0.0221696393,0.0723307301,0.0376180102,0.0107545829,-0.0785481345,0.0396535947,-0.0252027489,
0.2225233337,0.0647636424,0.1927106298,-0.1472238292,0.1846368509,0.2127078167,-0.0160907891,-0.0069228636,-0.0051007255,0.2214834375,0.0089561752,
-0.0617377796,0.2580117997,-0.0674401333,-0.1417024506,0.0562872291,0.152775862,-0.15619451,0.0655360233,-0.1659538316,-0.0250201478,-0.1796946641,
-0.1336500603,0.1832440427,0.1478365993,0.0452519844,0.0008638391,-0.1808207448,-0.1598406759,-0.1322673671,-0.0145263569,0.1152999908,-0.0299695219,
0.0701872091,0.1546182776,0.0517949423,-0.059331121,0.0804179207,0.1660573426,-0.171981422,0.1605137745,-0.0306862481,0.1271284727,0.1886814748,
0.0136612522,0.0270598426,-0.0661532873,-0.1574334886,0.207269623,0.2036780349,-0.0073134878,-0.0386269284,0.0250550805,-0.0384223257,-0.0334696501,
0.2140236846,-0.133667873,-0.102285892,0.0852130666,0.1147836027,-0.088955458,-0.1616843699,0.2460426148,0.08834456,0.1791546746,-0.1054225242,
0.2394795206,0.1964352406,-0.1061936927,-0.0052374616,0.2428683368,-0.0723367079,-0.0800238079,-0.0910234638,-0.1556677472,-0.3065232441,-0.0093916756,
0.0076888006,-0.013429242,0.3767262646,0.3879042411,0.2228786594,0.10244479,-0.0236296936,-0.0204603836,0.2660768132,0.1984345615,0.3351097462,
0.2216801519,0.1045998658,0.1475701855,-0.0204588308,0.2681049553,0.0300891243,0.3413475311,0.1030824613,0.2375844091,0.1561533065,0.0973356264,
-0.0815453044,0.2169735187,0.1061511505,0.0017875467,0.1800946674,0.0742895022,-0.0266724347,0.0015862493,-0.1258482431,-0.1286168626,-0.1286248528,
-0.0961999898,0.195424951,-0.0708904798,-0.0766170683,0.2369393757,-0.0719192397,0.247923788,-0.1281959262,0.2929088717,0.0262888175,0.055826575,
0.158068314,0.2640107034,0.132255457,0.1060920005,0.0404489124,-0.0713756294,0.2873948375,0.1591574433,0.1312353699,-0.0888223866,0.0898267723,
0.306397473,-0.0803697342,0.0906281403,0.1274700062,-0.0223210217,0.1658240538,-0.1848140557,-0.1807862421,-0.1394732561,0.0810997997,0.083649229,
0.1452717831,-0.0473515638,0.0975430501,-0.1503163311,-0.0281042628,-0.144161884,-0.0478865961,0.1700643354,-0.0297523824,-0.0714650919,-0.1494875533,
0.0318902977,0.1386884189,0.1553044146,-0.1571600796,-0.1540339544,0.2626821851,0.1908151384,-0.0116963784,0.3811396935,0.1004805557,0.2243691809,
0.1946627437,0.0440423879,0.1980999227,0.1342429382,0.3988613326,0.0020105913,0.3998132705,0.318482889,0.1173163776,0.0545043443,0.0219023506,
0.019178802,0.042710031,0.0608004012,0.133207133,0.2965603666,0.4011203807,0.2234903367,0.3369417514,0.106800254,0.3140007433,-0.0114859801,
0.2353873358,0.165365277,0.0093996244,0.3286248063,0.2896594154,0.1594050182,0.025979977,0.2952624249,0.1904777421,0.1434561935,0.357069136,
0.119785515,0.1102157785,0.3067653101,0.3700728676,-0.0098681182,-0.0189822027,0.092918362,-0.036980493,0.02815725,0.2171100232,0.1027520513,
-0.1109699071,-0.066618093,0.1005369363,-0.0821559671,-0.0824103563,0.1460813535,0.0392110759,-0.0596086924,0.2226236571,-0.1477633774,-0.0240570862,
-0.150465101,0.0006773613,-0.1477077846,-0.1245125382,0.1202756385,0.1237126349,-0.1141022611,0.0255561924,0.1828971317,0.0370846594,0.2081093277,
0.1572083165,0.0921178858,0.0581854695,-0.1160555733,-0.0842614394,0.0145145219,0.1151796705,0.1495440831,0.2328177172,-0.1529659354,0.2089630385,
-0.0112708592,0.4462405031};
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","slope13","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[], double& output[])
{
int i,x;
int sourceIndex = _neuronCount
- _layerCounts[_layerCount - 1];

ArrayCopy(_layerOutput,input,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)0) countedBars--;

int pos=Bars-countedBars-1;

static datetime Close_Time;

// 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[20];
double output[1];
input[0]=Norm(Close[pos+0],1.0,-1.0,2.0103,1.35909);
input[1]=Norm(Close[pos+1],1.0,-1.0,2.0103,1.35909);
input[2]=Norm(Close[pos+2],1.0,-1.0,2.0103,1.35909);
input[3]=Norm(Close[pos+3],1.0,-1.0,2.0103,1.35909);
input[4]=Norm(Close[pos+4],1.0,-1.0,2.0103,1.35909);
input[5]=Norm(Close[pos+5],1.0,-1.0,2.0103,1.35909);
input[6]=Norm(Close[pos+6],1.0,-1.0,2.0103,1.35909);
input[7]=Norm(Close[pos+7],1.0,-1.0,2.0103,1.35909);
input[8]=Norm(Close[pos+8],1.0,-1.0,2.0103,1.35909);
input[9]=Norm(Close[pos+9],1.0,-1.0,2.0103,1.35909);
input[10]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+0+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+0))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[11]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+1+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+1))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[12]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+2+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+2))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[13]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+3+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+3))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[14]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+4+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+4))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[15]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+5+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+5))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[16]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+6+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+6))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[17]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+7+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+7))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[18]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+8+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+8))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
input[19]=Norm(((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+9+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,pos+9))/Point),1.0,-1.0,3660.30324762,-198514.61538462);
Compute(input,output);
ExtMapBuffer1[pos] = DeNorm(output[0],1.0,-1.0,581.0,-290.0);
}
if( Export )
{
WriteExportLine(pos);
}
pos--;
}
}
//----

//----
return(0);
}
//+------------------------------------------------------------------+

maxss280's picture

Oppps meant to paste
[HEADER]
[HEADER:DATASOURCE]
rawFile=FILE_PROCESSED
sourceFile=
sourceFormat=
sourceHeaders=t
[SETUP]
[SETUP:CONFIG]
allowedClasses=integer,string
csvFormat=decpnt|comma
inputHeaders=t
maxClassCount=50
[SETUP:FILENAMES]
FILE_CODE=mt4_code.mql4
FILE_RAW=mt4.csv
FILE_NORMALIZE=mt4_norm.csv
FILE_PROCESSED=mt4_process.csv
FILE_EVAL_NORM=mt4_eval_norm.csv
FILE_EVAL=mt4_eval.csv
FILE_ML=mt4_train.eg
FILE_OUTPUT=mt4_output.csv
FILE_TRAINSET=mt4_train.egb
FILE_TRAIN=mt4_train.csv
[DATA]
[DATA:CONFIG]
goal=regression
[DATA:STATS]
"name","isclass","iscomplete","isint","isreal","amax","amin","mean","sdev","source"
"time",0,1,0,1,20121128160000,20080627080000,20102682428111,12976701462.83919,"time"
"close",0,1,0,1,2.0103,1.35909,1.5913374732,0.0970159003,"Close[##]"
"slope13",0,1,0,1,3660.30324762,-198514.61538462,-22.8270487527,2373.3938207824,"((iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,##+1)-iMA(NULL,0,13,0,MODE_SMMA,PRICE_MEDIAN,##))/Point)"
"prediction",0,1,1,1,581,-290,44.0289627622,74.6316021116,"prediction"
[DATA:CLASSES]
"field","code","name","count"
[NORMALIZE]
[NORMALIZE:CONFIG]
missingValues=DiscardMissing
sourceFile=FILE_TRAIN
targetFile=FILE_NORMALIZE
[NORMALIZE:RANGE]
"name","io","timeSlice","action","high","low"
"time","input",0,"ignore",1,-1
"close","input",0,"range",1,-1
"close","input",-1,"range",1,-1
"close","input",-2,"range",1,-1
"close","input",-3,"range",1,-1
"close","input",-4,"range",1,-1
"close","input",-5,"range",1,-1
"close","input",-6,"range",1,-1
"close","input",-7,"range",1,-1
"close","input",-8,"range",1,-1
"close","input",-9,"range",1,-1
"slope13","input",0,"range",1,-1
"slope13","input",-1,"range",1,-1
"slope13","input",-2,"range",1,-1
"slope13","input",-3,"range",1,-1
"slope13","input",-4,"range",1,-1
"slope13","input",-5,"range",1,-1
"slope13","input",-6,"range",1,-1
"slope13","input",-7,"range",1,-1
"slope13","input",-8,"range",1,-1
"slope13","input",-9,"range",1,-1
"prediction","output",1,"range",1,-1
"time","input",0,"ignore",1,-1
[PROCESS]
[PROCESS:CONFIG]
backwardSize=10
forwardSize=5
sourceFile=FILE_RAW
targetFile=FILE_PROCESSED
[PROCESS:FIELDS]
"name","command"
"time","cint(field(""time"",0))"
"close","cfloat(field(""close"",0))"
"slope13","cfloat(field(""slope13"",0))"
"prediction","fieldmaxpip(""close"",-5,-1)"
[RANDOMIZE]
[RANDOMIZE:CONFIG]
sourceFile=
targetFile=
[CLUSTER]
[CLUSTER:CONFIG]
clusters=
sourceFile=
targetFile=
type=
[BALANCE]
[BALANCE:CONFIG]
balanceField=
countPer=
sourceFile=
targetFile=
[CODE]
[CODE:CONFIG]
embedData=t
targetFile=FILE_CODE
targetLanguage=MQL4
[SEGREGATE]
[SEGREGATE:CONFIG]
sourceFile=FILE_PROCESSED
[SEGREGATE:FILES]
"file","percent"
"FILE_TRAIN",75
"FILE_EVAL",25
[GENERATE]
[GENERATE:CONFIG]
sourceFile=FILE_NORMALIZE
targetFile=FILE_TRAINSET
[ML]
[ML:CONFIG]
architecture=?:B->TANH->30:B->TANH->?
evalFile=FILE_EVAL
machineLearningFile=FILE_ML
outputFile=FILE_OUTPUT
query=
trainingFile=FILE_TRAINSET
type=feedforward
[ML:TRAIN]
arguments=
cross=
targetError=0.05
type=rprop
[TASKS]
[TASKS:task-cluster]
cluster
[TASKS:task-code]
code
[TASKS:task-create]
create
[TASKS:task-evaluate]
evaluate
[TASKS:task-evaluate-raw]
set ML.CONFIG.evalFile="FILE_EVAL_NORM"
set NORMALIZE.CONFIG.sourceFile="FILE_EVAL"
set NORMALIZE.CONFIG.targetFile="FILE_EVAL_NORM"
normalize
evaluate-raw
[TASKS:task-full]
process
segregate
normalize
generate
create
train
evaluate
code
[TASKS:task-generate]
segregate
normalize
generate
[TASKS:task-preprocess]
process
[TASKS:task-train]
train

jeffheaton's picture

Okay, been off of the forums for a few days. :)

But I will take a look. I agree that you should be getting output very similar to the range that you trained on, that seems unusual. Thanks for posting the EGA file, that is quite useful.

The code generation is very new, so it could well be a bug.

oritemis's picture

I am using the workbench with Ninjatrader and getting results way of scale too.

Is this a bug?

Thank you.

vise_guy's picture

Also, there seems to be an issue when generating the norm.csv file. Instead of t, t-1, t-2.. for multiple indicators, it seems to be repeating the values used for "Close".

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer