Self Learning Indicators

Guys, I am sharing an indicator here with self learning capabilities.

The basic working objective was to forecast the trend moves atleast 1 hour before it happens.

I am using this on 1 hour charts, and is still in testing stage, and please I need expert comments on better newural network based systems and indicators so i could sharpen my own learning curve.

Only in trading room can i expect to get quality feedback, since i consider the members here as hardcore professionals.

Thanks for posting. I am going to download it and test it on a few pairs to see how it does.

I've just downloaded it and already posted it to my charts. Two things:

1) I've put mine on a daily chart since that's the lowest timeframe I use. So that's another realm of learning I suppose.

2) Could we ask what this is built on? I'm just curious what I'm seeing here on my charts. I have a few solid guesses what the indicator's tracking, but I'm more curious to know the premise. I understand that you don't want to give away everything, and that's more than fine with me.

Forexlion it would be great if you could give an idea of how this indicator works. Since only the compiled version is available, its not easy to see logic of the code.

I am particularly interested , without giving away your code, how this indicator "learns" and how it decides a trend is about to occur.

Otherwise, it looks essentially like a stochastic crossing indicator, with perhaps .8 and .2 as overbought/ oversold regions.

Having said that, one of the more complex self learning scripts I have seen is CyberiaTrader. I find its method of calculating probabilities in order to determine a profitable trade very intriguing. Perhaps looking at its logic may help in fine tuning your own neural indicator. Good luck.

Stochastics as you have given in the example above suffers from 1 major drawback and that is you are fixing the range between 0.8 and 0.2 as you say.

Now suppose the market goes flat and the total range movement is just 10 pips between high and low, the stoch will still base on this 0.8 to 0.2 of this 10 pip range and give wide swings which is something to be avoided,.

In NN we use normalize on condition which means that when the market goes flat then stop the wave modulation and give a horizontal line or no signal concept thus preventing losses.

Now here the stoch is not going to think whether the market is flat or not, it will just consider the range and give the % accordingly.

We need to avoid this, and trade only the major trends to be profitable and avoid getting caught in spreads.

The chart below should explain the concept better, when the market is flat the wave is also flat, till there is a breakout possibility.

A very good read on this topic is here
http://cortex.snowcron.com/forex_nn.htm

Great indicator!

Since there is no source code available... is there a way to get this to work on different time-frames?

Like M15 or H4 and D1?

Is it something you would need to optimize it for?

Thanks in advance!

Great work!

the source code is given here

//+------------------------------------------------------------------+
//| anchorfx_nn.mq4 |
//| Copyright 2008, forexlion |
//| [email protected] |
//+------------------------------------------------------------------+
#property copyright "Copyright 2008, forexlion"
#property link "[email protected]"

#property indicator_separate_window
#property indicator_buffers 2
#property indicator_color1 Red
#property indicator_color2 Yellow
#property indicator_minimum 0
#property indicator_maximum 1

// indicator parameters
// To do: read parameters, including weights, from a description file

int nNocInterval = 12;
double dNocRange = 0.012;
int nNocMa = 5;
int nOutLag = 2;

int nLayers = 3;
int arrNeurons[3] = { 17, 5, 1 };
int nNumOfLags = 17;
int arrLags[17] =
{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 };

double arrWeights_0[305];
double arrWeights_1[90];
double arrWeights_2[6];

//

double arrOutput_0[17];
double arrOutput_1[5];
double dNnOutput;

double arrPattern[18];

int nRemoveFirst = 200;
double dE = 2.7182818;

// indicator buffers
double arrNocBuffer[];
double arrNnBuffer[];

int nExtCountedBars = 0;

////////////////////////
int init()
{
// Layer: 0
arrWeights_0[0] = 0.376436;
arrWeights_0[1] = 0.690657;
arrWeights_0[2] = 0.512335;
arrWeights_0[3] = 0.786179;
arrWeights_0[4] = 0.671377;
arrWeights_0[5] = 0.614279;
arrWeights_0[6] = 0.539750;
arrWeights_0[7] = 0.820380;
arrWeights_0[8] = 0.750566;
arrWeights_0[9] = 0.707890;
arrWeights_0[10] = 0.396094;
arrWeights_0[11] = 0.725720;
arrWeights_0[12] = 0.555349;
arrWeights_0[13] = 0.395257;
arrWeights_0[14] = 0.227280;
arrWeights_0[15] = 0.128274;
arrWeights_0[16] = 0.289072;
arrWeights_0[17] = 0.387067;
arrWeights_0[18] = 0.662450;
arrWeights_0[19] = 1.019812;
arrWeights_0[20] = 0.761206;
arrWeights_0[21] = 0.984280;
arrWeights_0[22] = 0.878235;
arrWeights_0[23] = 0.829384;
arrWeights_0[24] = 0.786234;
arrWeights_0[25] = 1.189799;
arrWeights_0[26] = 1.112219;
arrWeights_0[27] = 1.069424;
arrWeights_0[28] = 0.640762;
arrWeights_0[29] = 0.730670;
arrWeights_0[30] = 0.067006;
arrWeights_0[31] = -0.320953;
arrWeights_0[32] = -0.600880;
arrWeights_0[33] = -0.371686;
arrWeights_0[34] = 0.261142;
arrWeights_0[35] = 0.063323;
arrWeights_0[36] = 0.413053;
arrWeights_0[37] = 0.737687;
arrWeights_0[38] = 0.555759;
arrWeights_0[39] = 0.815068;
arrWeights_0[40] = 0.692078;
arrWeights_0[41] = 0.630406;
arrWeights_0[42] = 0.556191;
arrWeights_0[43] = 0.846380;
arrWeights_0[44] = 0.763236;
arrWeights_0[45] = 0.715549;
arrWeights_0[46] = 0.390297;
arrWeights_0[47] = 0.705948;
arrWeights_0[48] = 0.482014;
arrWeights_0[49] = 0.291501;
arrWeights_0[50] = 0.101741;
arrWeights_0[51] = 0.048724;
arrWeights_0[52] = 0.280508;
arrWeights_0[53] = 0.428389;
arrWeights_0[54] = -6.065296;
arrWeights_0[55] = 0.559039;
arrWeights_0[56] = 1.732407;
arrWeights_0[57] = 2.446417;
arrWeights_0[58] = 2.226664;
arrWeights_0[59] = 1.043498;
arrWeights_0[60] = -0.892194;
arrWeights_0[61] = 0.538670;
arrWeights_0[62] = 1.696599;
arrWeights_0[63] = 2.895421;
arrWeights_0[64] = 1.936742;
arrWeights_0[65] = 0.857415;
arrWeights_0[66] = 1.777173;
arrWeights_0[67] = 1.489486;
arrWeights_0[68] = 0.990452;
arrWeights_0[69] = -0.312611;
arrWeights_0[70] = -2.485575;
arrWeights_0[71] = 5.784152;
arrWeights_0[72] = 0.691233;
arrWeights_0[73] = 1.003065;
arrWeights_0[74] = 0.746760;
arrWeights_0[75] = 0.973191;
arrWeights_0[76] = 0.868018;
arrWeights_0[77] = 0.821039;
arrWeights_0[78] = 0.770604;
arrWeights_0[79] = 1.139881;
arrWeights_0[80] = 1.057454;
arrWeights_0[81] = 1.004390;
arrWeights_0[82] = 0.595049;
arrWeights_0[83] = 0.733458;
arrWeights_0[84] = 0.177670;
arrWeights_0[85] = -0.170966;
arrWeights_0[86] = -0.443584;
arrWeights_0[87] = -0.345470;
arrWeights_0[88] = 0.135814;
arrWeights_0[89] = -0.010206;
arrWeights_0[90] = 0.585687;
arrWeights_0[91] = 1.040471;
arrWeights_0[92] = 0.778059;
arrWeights_0[93] = 0.994592;
arrWeights_0[94] = 0.882501;
arrWeights_0[95] = 0.819930;
arrWeights_0[96] = 0.783313;
arrWeights_0[97] = 1.225092;
arrWeights_0[98] = 1.138965;
arrWeights_0[99] = 1.112052;
arrWeights_0[100] = 0.667895;
arrWeights_0[101] = 0.725504;
arrWeights_0[102] = -0.042205;
arrWeights_0[103] = -0.470107;
arrWeights_0[104] = -0.758346;
arrWeights_0[105] = -0.356139;
arrWeights_0[106] = 0.477461;
arrWeights_0[107] = 0.159831;
arrWeights_0[108] = 0.465873;
arrWeights_0[109] = 0.827723;
arrWeights_0[110] = 0.635881;
arrWeights_0[111] = 0.876557;
arrWeights_0[112] = 0.751904;
arrWeights_0[113] = 0.689548;
arrWeights_0[114] = 0.624036;
arrWeights_0[115] = 0.943642;
arrWeights_0[116] = 0.844483;
arrWeights_0[117] = 0.793347;
arrWeights_0[118] = 0.439269;
arrWeights_0[119] = 0.710473;
arrWeights_0[120] = 0.362673;
arrWeights_0[121] = 0.107347;
arrWeights_0[122] = -0.123129;
arrWeights_0[123] = -0.079149;
arrWeights_0[124] = 0.300953;
arrWeights_0[125] = 0.333110;
arrWeights_0[126] = 36.878339;
arrWeights_0[127] = -34.157785;
arrWeights_0[128] = 13.570499;
arrWeights_0[129] = -9.445193;
arrWeights_0[130] = 5.906855;
arrWeights_0[131] = -2.631383;
arrWeights_0[132] = -3.466850;
arrWeights_0[133] = 1.387212;
arrWeights_0[134] = 1.168750;
arrWeights_0[135] = -1.252440;
arrWeights_0[136] = -4.167107;
arrWeights_0[137] = 7.462264;
arrWeights_0[138] = 4.218665;
arrWeights_0[139] = -10.616881;
arrWeights_0[140] = 2.816792;
arrWeights_0[141] = -0.854816;
arrWeights_0[142] = 0.465124;
arrWeights_0[143] = 3.805423;
arrWeights_0[144] = 17.371809;
arrWeights_0[145] = -10.758329;
arrWeights_0[146] = 4.288758;
arrWeights_0[147] = -3.300900;
arrWeights_0[148] = -1.387066;
arrWeights_0[149] = -0.545407;
arrWeights_0[150] = -0.627846;
arrWeights_0[151] = 0.359405;
arrWeights_0[152] = -0.910465;
arrWeights_0[153] = 1.672222;
arrWeights_0[154] = -1.087077;
arrWeights_0[155] = 1.711690;
arrWeights_0[156] = 1.892743;
arrWeights_0[157] = 1.370835;
arrWeights_0[158] = -1.074922;
arrWeights_0[159] = -2.069598;
arrWeights_0[160] = 2.132245;
arrWeights_0[161] = 3.323435;
arrWeights_0[162] = 3.192245;
arrWeights_0[163] = 0.722216;
arrWeights_0[164] = 0.595036;
arrWeights_0[165] = 2.562026;
arrWeights_0[166] = 4.118245;
arrWeights_0[167] = 2.533825;
arrWeights_0[168] = 0.661547;
arrWeights_0[169] = -0.522384;
arrWeights_0[170] = -0.923482;
arrWeights_0[171] = -0.744284;
arrWeights_0[172] = -0.531758;
arrWeights_0[173] = -1.737114;
arrWeights_0[174] = -0.894963;
arrWeights_0[175] = 0.335197;
arrWeights_0[176] = 2.767129;
arrWeights_0[177] = 0.169424;
arrWeights_0[178] = -1.868230;
arrWeights_0[179] = 2.057215;
arrWeights_0[180] = 0.408584;
arrWeights_0[181] = 0.730536;
arrWeights_0[182] = 0.549013;
arrWeights_0[183] = 0.810092;
arrWeights_0[184] = 0.687577;
arrWeights_0[185] = 0.626130;
arrWeights_0[186] = 0.551439;
arrWeights_0[187] = 0.839836;
arrWeights_0[188] = 0.758454;
arrWeights_0[189] = 0.711269;
arrWeights_0[190] = 0.388133;
arrWeights_0[191] = 0.706349;
arrWeights_0[192] = 0.491137;
arrWeights_0[193] = 0.305519;
arrWeights_0[194] = 0.119262;
arrWeights_0[195] = 0.059367;
arrWeights_0[196] = 0.280111;
arrWeights_0[197] = 0.432421;
arrWeights_0[198] = 11.382900;
arrWeights_0[199] = -2.355699;
arrWeights_0[200] = 0.800231;
arrWeights_0[201] = -0.812782;
arrWeights_0[202] = -1.725548;
arrWeights_0[203] = 0.506914;
arrWeights_0[204] = 3.685745;
arrWeights_0[205] = -1.570360;
arrWeights_0[206] = -0.678070;
arrWeights_0[207] = -1.385545;
arrWeights_0[208] = 0.665419;
arrWeights_0[209] = -2.403472;
arrWeights_0[210] = 0.348713;
arrWeights_0[211] = 0.948801;
arrWeights_0[212] = 3.943966;
arrWeights_0[213] = 0.609010;
arrWeights_0[214] = -2.954932;
arrWeights_0[215] = 6.570737;
arrWeights_0[216] = 1.098680;
arrWeights_0[217] = 0.457162;
arrWeights_0[218] = 0.047123;
arrWeights_0[219] = 0.314413;
arrWeights_0[220] = 0.270546;
arrWeights_0[221] = 0.271241;
arrWeights_0[222] = 0.444340;
arrWeights_0[223] = 0.685449;
arrWeights_0[224] = 0.881795;
arrWeights_0[225] = 0.904727;
arrWeights_0[226] = 0.538997;
arrWeights_0[227] = 0.274688;
arrWeights_0[228] = -0.107260;
arrWeights_0[229] = -0.182735;
arrWeights_0[230] = -0.297917;
arrWeights_0[231] = -0.722563;
arrWeights_0[232] = -1.004438;
arrWeights_0[233] = 2.114029;
arrWeights_0[234] = 0.625319;
arrWeights_0[235] = 1.073737;
arrWeights_0[236] = 0.794572;
arrWeights_0[237] = 1.012257;
arrWeights_0[238] = 0.909403;
arrWeights_0[239] = 0.851104;
arrWeights_0[240] = 0.819041;
arrWeights_0[241] = 1.284499;
arrWeights_0[242] = 1.213157;
arrWeights_0[243] = 1.195116;
arrWeights_0[244] = 0.731964;
arrWeights_0[245] = 0.733472;
arrWeights_0[246] = -0.119051;
arrWeights_0[247] = -0.574390;
arrWeights_0[248] = -0.865588;
arrWeights_0[249] = -0.405175;
arrWeights_0[250] = 0.494158;
arrWeights_0[251] = 0.142718;
arrWeights_0[252] = 0.443620;
arrWeights_0[253] = 0.790221;
arrWeights_0[254] = 0.604280;
arrWeights_0[255] = 0.852407;
arrWeights_0[256] = 0.728354;
arrWeights_0[257] = 0.666281;
arrWeights_0[258] = 0.596990;
arrWeights_0[259] = 0.903026;
arrWeights_0[260] = 0.809120;
arrWeights_0[261] = 0.758782;
arrWeights_0[262] = 0.416953;
arrWeights_0[263] = 0.708992;
arrWeights_0[264] = 0.415040;
arrWeights_0[265] = 0.186673;
arrWeights_0[266] = -0.028387;
arrWeights_0[267] = -0.027435;
arrWeights_0[268] = 0.289143;
arrWeights_0[269] = 0.376176;
arrWeights_0[270] = 0.377669;
arrWeights_0[271] = 0.691528;
arrWeights_0[272] = 0.513124;
arrWeights_0[273] = 0.786421;
arrWeights_0[274] = 0.670892;
arrWeights_0[275] = 0.613345;
arrWeights_0[276] = 0.538605;
arrWeights_0[277] = 0.819386;
arrWeights_0[278] = 0.749119;
arrWeights_0[279] = 0.706133;
arrWeights_0[280] = 0.394004;
arrWeights_0[281] = 0.723393;
arrWeights_0[282] = 0.551766;
arrWeights_0[283] = 0.390955;
arrWeights_0[284] = 0.222564;
arrWeights_0[285] = 0.125193;
arrWeights_0[286] = 0.287931;
arrWeights_0[287] = 0.397175;
arrWeights_0[288] = 0.390177;
arrWeights_0[289] = 0.704718;
arrWeights_0[290] = 0.524964;
arrWeights_0[291] = 0.793337;
arrWeights_0[292] = 0.673926;
arrWeights_0[293] = 0.614109;
arrWeights_0[294] = 0.538623;
arrWeights_0[295] = 0.821537;
arrWeights_0[296] = 0.747044;
arrWeights_0[297] = 0.702102;
arrWeights_0[298] = 0.386249;
arrWeights_0[299] = 0.712341;
arrWeights_0[300] = 0.526445;
arrWeights_0[301] = 0.357474;
arrWeights_0[302] = 0.183363;
arrWeights_0[303] = 0.099539;
arrWeights_0[304] = 0.282065;
arrWeights_0[305] = 0.431269;

// Layer: 1
arrWeights_1[0] = 0.313367;
arrWeights_1[1] = 0.600233;
arrWeights_1[2] = 0.413307;
arrWeights_1[3] = 0.723853;
arrWeights_1[4] = 0.604417;
arrWeights_1[5] = 0.564724;
arrWeights_1[6] = 0.494233;
arrWeights_1[7] = 0.776964;
arrWeights_1[8] = 0.713700;
arrWeights_1[9] = 0.689037;
arrWeights_1[10] = 0.403412;
arrWeights_1[11] = 0.743660;
arrWeights_1[12] = 0.681661;
arrWeights_1[13] = 0.582135;
arrWeights_1[14] = 0.470205;
arrWeights_1[15] = 0.320594;
arrWeights_1[16] = 0.360557;
arrWeights_1[17] = 0.505358;
arrWeights_1[18] = 0.207009;
arrWeights_1[19] = -0.600315;
arrWeights_1[20] = 0.292799;
arrWeights_1[21] = -0.486397;
arrWeights_1[22] = -0.698483;
arrWeights_1[23] = -0.466675;
arrWeights_1[24] = 0.161680;
arrWeights_1[25] = 1.267072;
arrWeights_1[26] = 0.942115;
arrWeights_1[27] = 1.601429;
arrWeights_1[28] = 0.294829;
arrWeights_1[29] = 2.066898;
arrWeights_1[30] = 1.149577;
arrWeights_1[31] = -0.608109;
arrWeights_1[32] = 0.236174;
arrWeights_1[33] = 0.223691;
arrWeights_1[34] = 0.281142;
arrWeights_1[35] = 2.639872;
arrWeights_1[36] = 0.298441;
arrWeights_1[37] = 0.585156;
arrWeights_1[38] = 0.397331;
arrWeights_1[39] = 0.714002;
arrWeights_1[40] = 0.591642;
arrWeights_1[41] = 0.547087;
arrWeights_1[42] = 0.477829;
arrWeights_1[43] = 0.768086;
arrWeights_1[44] = 0.709662;
arrWeights_1[45] = 0.679375;
arrWeights_1[46] = 0.387499;
arrWeights_1[47] = 0.736123;
arrWeights_1[48] = 0.665259;
arrWeights_1[49] = 0.564510;
arrWeights_1[50] = 0.453956;
arrWeights_1[51] = 0.305558;
arrWeights_1[52] = 0.345016;
arrWeights_1[53] = 0.508310;
arrWeights_1[54] = -0.677255;
arrWeights_1[55] = -1.032676;
arrWeights_1[56] = -0.744642;
arrWeights_1[57] = -0.925155;
arrWeights_1[58] = -1.197128;
arrWeights_1[59] = -0.992372;
arrWeights_1[60] = -0.855172;
arrWeights_1[61] = 7.016197;
arrWeights_1[62] = 5.226784;
arrWeights_1[63] = 2.411577;
arrWeights_1[64] = -0.733721;
arrWeights_1[65] = 3.402664;
arrWeights_1[66] = 1.729731;
arrWeights_1[67] = -0.991801;
arrWeights_1[68] = -0.815011;
arrWeights_1[69] = -0.677321;
arrWeights_1[70] = -0.693829;
arrWeights_1[71] = 9.138765;
arrWeights_1[72] = -0.246490;
arrWeights_1[73] = -0.391846;
arrWeights_1[74] = -0.347720;
arrWeights_1[75] = -1.096698;
arrWeights_1[76] = -0.001496;
arrWeights_1[77] = -0.693606;
arrWeights_1[78] = -0.468658;
arrWeights_1[79] = 6.178259;
arrWeights_1[80] = 4.306803;
arrWeights_1[81] = 2.409460;
arrWeights_1[82] = -0.336563;
arrWeights_1[83] = 2.374341;
arrWeights_1[84] = 1.504236;
arrWeights_1[85] = -0.726152;
arrWeights_1[86] = -0.426187;
arrWeights_1[87] = -0.254840;
arrWeights_1[88] = -0.293797;
arrWeights_1[89] = -1.892730;

// Layer: 2
arrWeights_2[0] = -0.626160;
arrWeights_2[1] = 1.717378;
arrWeights_2[2] = -0.485501;
arrWeights_2[3] = 2.751261;
arrWeights_2[4] = 2.323994;
arrWeights_2[5] = 2.047616;


// drawing settings
SetIndexStyle(0, DRAW_LINE);
SetIndexShift(0, 0);
SetIndexEmptyValue(0, 0.0);

SetIndexStyle(1, DRAW_LINE);
SetIndexShift(1, 0);
SetIndexEmptyValue(1, 0.0);

IndicatorDigits(4);

string strIndicatorShortName = "anchorfx_nn";
IndicatorShortName(strIndicatorShortName);

// indicator buffers mapping
SetIndexBuffer(0, arrNocBuffer);
SetIndexBuffer(1, arrNnBuffer);

return(0);
}
////////////////////
int start()
{
nExtCountedBars = IndicatorCounted();
if(nExtCountedBars < 0)
return(-1);

// last counted bar will be recounted
if(nExtCountedBars > 0)
nExtCountedBars--;

Noc();
ApplyNn();

return(0);
}
///////////////////
// For the selected period:
// CLV = ((Close - Low) - (High - Close)) / (High - Low)
void Noc()
{
int nPos = Bars - nExtCountedBars;

while(nPos > 0)
{
if(nPos > Bars - nRemoveFirst)
{
arrNocBuffer[nPos] = 0.5;
nPos--;
continue;
}

double dClose = Close[nPos];
double dLow = Low[ArrayMinimum(Low, nNocInterval, nPos)];
double dHigh = High[ArrayMaximum(High, nNocInterval, nPos)];

arrNocBuffer[nPos] = 0.1;

if(dHigh - dLow > dNocRange)
arrNocBuffer[nPos] = (((dClose - dLow) - (dHigh - dClose))
/ (dHigh - dLow)) / 2 + 0.5;
else
arrNocBuffer[nPos] = (((dClose - dLow) - (dHigh - dClose))
/ dNocRange) / 2 + 0.5;

nPos--;
}

if(nNocMa > 1)
Ema(arrNocBuffer);
}
///////////////////
void Ema(double& arr[])
{
double dPr = 2.0 / (nNocMa + 1);

int nPos = Bars - nExtCountedBars;

while(nPos > 0)
{
if(nPos == Bars - 2)
arrNocBuffer[nPos + 1] = arr[nPos + 1];

arrNocBuffer[nPos] = arr[nPos] * dPr + arrNocBuffer[nPos + 1]
* (1 - dPr);
nPos--;
}
}
///////////////////
void ApplyNn()
{
int nPos = Bars - nExtCountedBars - 2;

while(nPos > 0)
{
if(nPos > Bars - nRemoveFirst)
{
arrNnBuffer[nPos] = 0.5;
nPos--;
continue;
}

arrNnBuffer[nPos] = 0.5;

for(int nLagNo = 0; nLagNo < nNumOfLags; nLagNo++)
arrPattern[nLagNo]
= arrNocBuffer[nPos + arrLags[nLagNo] + nOutLag];

for(int nLayer = 0; nLayer < nLayers; nLayer++)
{
// Absolute index of the weight in an array
int nWeightIdx = 0;

for(int nNeuron = 0; nNeuron < arrNeurons[nLayer];
nNeuron++)
{
double dLinearCombiner = 0;

int nInputs = arrNeurons[nLayer];
if(nLayer != 0)
nInputs = arrNeurons[nLayer - 1];

for(int nInput = 0; nInput < nInputs; nInput++)
{
double dInput;
double dWeight;
switch(nLayer)
{
case 0:
{
dInput = arrPattern[nInput];
dWeight = arrWeights_0[nWeightIdx];
}
break;

case 1:
{
dInput = arrOutput_0[nInput];
dWeight = arrWeights_1[nWeightIdx];
}
break;

default:
{
dInput = arrOutput_1[nInput];
dWeight = arrWeights_2[nWeightIdx];
}
break;
}

dLinearCombiner += dWeight * dInput;

nWeightIdx++;
}

switch(nLayer)
{
case 0:
dWeight = arrWeights_0[nWeightIdx]; break;
case 1:
dWeight = arrWeights_1[nWeightIdx]; break;
default:
dWeight = arrWeights_2[nWeightIdx]; break;
}

dLinearCombiner += (-1) * dWeight;
nWeightIdx++;

double dActivation = Activation(dLinearCombiner, 0);

switch(nLayer)
{
case 0:
arrOutput_0[nNeuron] = dActivation; break;
case 1:
arrOutput_1[nNeuron] = dActivation; break;
default:
dNnOutput = dActivation; break;
}

} // for all neurons

} // for all layers

arrNnBuffer[nPos] = dNnOutput;

nPos--;

} // for all patterns
}
///////////////////
double Activation(double u, int nType)
{
//double dEx = TANH(u / 2);

double dPow = MathPow(dE, 2 * u / 2);
double dEx = (dPow - 1) / (dPow + 1);
if(dEx == 1 || dEx == -1)
dEx = dEx * 0.999999999;

// 0 - sigmoid, 1 - tangent
if(nType == 0)
return((dEx + 1) / 2);

return(dEx);
}

Very nice of you to share! Thank you!

However I am unsure as to how to adapt this for different time frame as it seems the NN weights were probably optimized for H1 and we don't have whatever you used to generate them.

....Hmmmm, heh, you may be just on to something here, but not what I believe you had intended.

Very good indicator with fine tuning. At this point, the weight settings you have published, make it too erratic, no, let me rephrase that, too sensitive, to be very reliable in trading based on crossing pre-determined levels. Perhaps you have different settings? Anyway, since I have a system that works I'm too lazy to go and delve into research on which settings would make for better optimization.

BUT, I believe your indicator, at these settings, make a very good divergence indicator , when compared with say, the RSI.

Being what I consider a cobbler, just cutting and pasting pieces of mql codes, I am truly impressed with the logical thought processes that led to the coding of this indicator.

Please can you post here your versions of those 3 indicators (I prefer .mq4)that are you using in this screenshot? Thanks a lot!