298 lines
22 KiB
MQL5
298 lines
22 KiB
MQL5
//+------------------------------------------------------------------+
|
|
//| Study.mq5 |
|
|
//| Copyright DNG® |
|
|
//| https://www.mql5.com/ru/users/dng |
|
|
//+------------------------------------------------------------------+
|
|
#property copyright "Copyright DNG®"
|
|
#property link "https://www.mql5.com/ru/users/dng"
|
|
#property version "1.00"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#define Study
|
|
#include "Trajectory.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| Input parameters |
|
|
//+------------------------------------------------------------------+
|
|
input int Iterations = 1e4;
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
STrajectory Buffer[];
|
|
CNet Autobot;
|
|
//---
|
|
float dError;
|
|
datetime dtStudied;
|
|
//---
|
|
CBufferFloat State;
|
|
CBufferFloat *Result;
|
|
CBufferFloat Ones;
|
|
CBufferFloat Gradient;
|
|
vector<float> STE;
|
|
vector<float> STE_Noise;
|
|
//---
|
|
COpenCLMy *OpenCL;
|
|
//+------------------------------------------------------------------+
|
|
//| Expert initialization function |
|
|
//+------------------------------------------------------------------+
|
|
int OnInit()
|
|
{
|
|
//---
|
|
ResetLastError();
|
|
if(!LoadTotalBase())
|
|
{
|
|
PrintFormat("Error of load study data: %d", GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
//--- load models
|
|
float temp;
|
|
if(!Autobot.Load(FileName + "Traj.nnw", temp, temp, temp, dtStudied, true))
|
|
{
|
|
Print("Init new models");
|
|
CArrayObj *autobot = new CArrayObj();
|
|
if(!CreateTrajNetDescriptions(autobot))
|
|
{
|
|
delete autobot;
|
|
return INIT_FAILED;
|
|
}
|
|
if(!Autobot.Create(autobot))
|
|
{
|
|
delete autobot;
|
|
return INIT_FAILED;
|
|
}
|
|
delete autobot;
|
|
//---
|
|
}
|
|
//---
|
|
Autobot.getResults(Result);
|
|
if(Result.Total() != PrecoderBars * 3)
|
|
{
|
|
PrintFormat("The scope of the Autobot does not match the precoder bars (%d <> %d)", PrecoderBars * 3, Result.Total());
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
Autobot.GetLayerOutput(0, Result);
|
|
if(Result.Total() != (HistoryBars * BarDescr))
|
|
{
|
|
PrintFormat("Input size of Autobot doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
OpenCL = Autobot.GetOpenCL();
|
|
if(!Ones.BufferInit(EmbeddingSize, 1) ||
|
|
!Gradient.BufferInit(EmbeddingSize, 0) ||
|
|
!Ones.BufferCreate(OpenCL) ||
|
|
!Gradient.BufferCreate(OpenCL))
|
|
{
|
|
PrintFormat("Error of create buffers: %d", GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
State.BufferInit(HistoryBars * BarDescr, 0);
|
|
//---
|
|
if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
|
|
{
|
|
PrintFormat("Error of create study event: %d", GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
return(INIT_SUCCEEDED);
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Expert deinitialization function |
|
|
//+------------------------------------------------------------------+
|
|
void OnDeinit(const int reason)
|
|
{
|
|
//---
|
|
if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
|
|
Autobot.Save(FileName + "Traj.nnw", 0, 0, 0, TimeCurrent(), true);
|
|
delete Result;
|
|
delete OpenCL;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| ChartEvent function |
|
|
//+------------------------------------------------------------------+
|
|
void OnChartEvent(const int id,
|
|
const long &lparam,
|
|
const double &dparam,
|
|
const string &sparam)
|
|
{
|
|
//---
|
|
if(id == 1001)
|
|
Train();
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Train function |
|
|
//+------------------------------------------------------------------+
|
|
void Train(void)
|
|
{
|
|
//---
|
|
vector<float> probability = GetProbTrajectories(Buffer, 0.9);
|
|
//---
|
|
vector<float> result, target, inp;
|
|
matrix<float> targets;
|
|
matrix<float> delta;
|
|
STE = vector<float>::Zeros(PrecoderBars * 3);
|
|
int std_count = 0;
|
|
int batch = GPTBars + 50;
|
|
bool Stop = false;
|
|
uint ticks = GetTickCount();
|
|
ulong size = HistoryBars * BarDescr;
|
|
//---
|
|
for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter ++)
|
|
{
|
|
int tr = SampleTrajectory(probability);
|
|
int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
|
|
if(state < 0)
|
|
{
|
|
iter--;
|
|
continue;
|
|
}
|
|
int end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
|
|
Autobot.Clear();
|
|
delta = matrix<float>::Zeros(end - state - 1, Buffer[tr].States[state].state.Size());
|
|
for(int i = state; i < end; i++)
|
|
{
|
|
inp.Assign(Buffer[tr].States[i].state);
|
|
State.AssignArray(inp);
|
|
int row = i - state;
|
|
if(i < (end - 1))
|
|
delta.Row(inp, row);
|
|
if(row > 0)
|
|
delta.Row(delta.Row(row - 1) - inp, row - 1);
|
|
//---
|
|
if(!Autobot.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(Ones)))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
Stop = true;
|
|
break;
|
|
}
|
|
//---
|
|
targets = matrix<float>::Zeros(PrecoderBars, 3);
|
|
for(int t = 0; t < PrecoderBars; t++)
|
|
{
|
|
target.Assign(Buffer[tr].States[i + 1 + t].state);
|
|
if(size > BarDescr)
|
|
{
|
|
matrix<float> temp(1, size);
|
|
temp.Row(target, 0);
|
|
temp.Reshape(size / BarDescr, BarDescr);
|
|
temp.Resize(size / BarDescr, 3);
|
|
target = temp.Row(temp.Rows() - 1);
|
|
}
|
|
targets.Row(target, t);
|
|
}
|
|
targets.Reshape(1, targets.Rows()*targets.Cols());
|
|
target = targets.Row(0);
|
|
Autobot.getResults(result);
|
|
vector<float> error = target - result;
|
|
std_count = MathMin(std_count, 999);
|
|
STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
|
|
std_count++;
|
|
vector<float> check = MathAbs(error) - STE * STE_Multiplier;
|
|
if(check.Max() > 0)
|
|
{
|
|
//---
|
|
Result.AssignArray(target);
|
|
if(!Autobot.backProp(Result, GetPointer(Ones), GetPointer(Gradient)))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
Stop = true;
|
|
break;
|
|
}
|
|
}
|
|
//---
|
|
if(GetTickCount() - ticks > 500)
|
|
{
|
|
double percent = (double(i - state) / (2 * (end - state)) + iter) * 100.0 / (Iterations);
|
|
string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Autobot", percent, Autobot.getRecentAverageError());
|
|
Comment(str);
|
|
ticks = GetTickCount();
|
|
}
|
|
}
|
|
//--- With noise
|
|
vector<float> std_delta = delta.Std(0) * STD_Delta_Multiplier;
|
|
vector<float> mean_delta = delta.Mean(0);
|
|
ulong inp_total = std_delta.Size();
|
|
vector<float> noise = vector<float>::Zeros(inp_total);
|
|
double ar_noise[];
|
|
//---
|
|
tr = SampleTrajectory(probability);
|
|
state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
|
|
if(state < 0)
|
|
{
|
|
iter--;
|
|
continue;
|
|
}
|
|
end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
|
|
Autobot.Clear();
|
|
for(int i = state; i < end; i++)
|
|
{
|
|
if(!MathRandomNormal(0, 1, (int)inp_total, ar_noise))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
Stop = true;
|
|
break;
|
|
}
|
|
noise.Assign(ar_noise);
|
|
noise = mean_delta + std_delta * noise;
|
|
inp.Assign(Buffer[tr].States[i].state);
|
|
inp = inp + noise;
|
|
State.AssignArray(inp);
|
|
//---
|
|
if(!Autobot.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(Ones)))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
Stop = true;
|
|
break;
|
|
}
|
|
//---
|
|
targets = matrix<float>::Zeros(PrecoderBars, 3);
|
|
for(int t = 0; t < PrecoderBars; t++)
|
|
{
|
|
target.Assign(Buffer[tr].States[i + 1 + t].state);
|
|
if(size > BarDescr)
|
|
{
|
|
matrix<float> temp(1, size);
|
|
temp.Row(target, 0);
|
|
temp.Reshape(size / BarDescr, BarDescr);
|
|
temp.Resize(size / BarDescr, 3);
|
|
target = temp.Row(temp.Rows() - 1);
|
|
}
|
|
targets.Row(target, t);
|
|
}
|
|
targets.Reshape(1, targets.Rows()*targets.Cols());
|
|
target = targets.Row(0);
|
|
Autobot.getResults(result);
|
|
vector<float> error = target - result;
|
|
std_count = MathMin(std_count, 999);
|
|
STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
|
|
std_count++;
|
|
vector<float> check = MathAbs(error) - STE * STE_Multiplier;
|
|
if(check.Max() > 0)
|
|
{
|
|
//---
|
|
Result.AssignArray(target);
|
|
if(!Autobot.backProp(Result, GetPointer(Ones), GetPointer(Gradient)))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
Stop = true;
|
|
break;
|
|
}
|
|
}
|
|
//---
|
|
if(GetTickCount() - ticks > 500)
|
|
{
|
|
double percent = (double(i - state) / (2 * (end - state)) + iter + 0.5) * 100.0 / (Iterations);
|
|
string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Autobot", percent, Autobot.getRecentAverageError());
|
|
Comment(str);
|
|
ticks = GetTickCount();
|
|
}
|
|
}
|
|
}
|
|
Comment("");
|
|
//---
|
|
PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Autobot", Autobot.getRecentAverageError());
|
|
ExpertRemove();
|
|
//---
|
|
}
|
|
//+------------------------------------------------------------------+
|