NN_in_Trading/Experts/SPOT/StudyCVAE.mq5

174 lines
13 KiB
MQL5
Raw Permalink Normal View History

2026-03-12 15:02:23 +02:00
<EFBFBD><EFBFBD>//+------------------------------------------------------------------+
//| Study.mq5 |
//| Copyright DNG<EFBFBD> |
//| https://www.mql5.com/ru/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG<00>"
#property link "https://www.mql5.com/ru/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
#define Study
#include "Trajectory.mqh"
//+------------------------------------------------------------------+
//| Input parameters |
//+------------------------------------------------------------------+
input int Iterations = 5e5;
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory Buffer[];
CNet Encoder;
CNet Decoder;
//---
float dError;
datetime dtStudied;
//---
CBufferFloat State;
CBufferFloat Actions;
CBufferFloat *Result;
//---
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(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
!Decoder.Load(FileName + "Dec.nnw", temp, temp, temp, dtStudied, true))
{
Print("Init new CVAE");
CArrayObj *encoder = new CArrayObj();
CArrayObj *decoder = new CArrayObj();
if(!CreateCVAEDescriptions(encoder,decoder))
{
delete encoder;
delete decoder;
return INIT_FAILED;
}
if(!Encoder.Create(encoder) || !Decoder.Create(decoder))
{
delete encoder;
delete decoder;
return INIT_FAILED;
}
delete encoder;
delete decoder;
}
//---
OpenCL = Encoder.GetOpenCL();
Decoder.SetOpenCL(OpenCL);
//---
Decoder.getResults(Result);
if(Result.Total() != NActions)
{
PrintFormat("The scope of the Decoder does not match the actions count (%d <> %d)", NActions, Result.Total());
return INIT_FAILED;
}
//---
Encoder.GetLayerOutput(0, Result);
if(Result.Total() != (HistoryBars * BarDescr))
{
PrintFormat("Input size of Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
return INIT_FAILED;
}
//---
Encoder.getResults(Result);
int latent_state = Result.Total();
Decoder.GetLayerOutput(0, Result);
if(Result.Total() != latent_state)
{
PrintFormat("Input size of Decoder doesn't match result of Encoder (%d <> %d)", Result.Total(), latent_state);
return INIT_FAILED;
}
//---
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)
{
//---
Encoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
Decoder.Save(FileName + "Dec.nnw", Decoder.getRecentAverageError(), 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)
{
int total_tr = ArraySize(Buffer);
uint ticks = GetTickCount();
int bar = (HistoryBars - 1) * BarDescr;
//---
for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
{
int tr = int((MathRand() * MathRand() / MathPow(32767, 2)) * (total_tr));
int i = int((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
if(i < 0)
continue;
State.AssignArray(Buffer[tr].States[i].state);
Actions.AssignArray(Buffer[tr].States[i].action);
if(Actions.GetIndex() >= 0)
Actions.BufferWrite();
//---
if(!Encoder.feedForward(GetPointer(State), 1,false, GetPointer(Actions)) ||
!Decoder.feedForward(GetPointer(Encoder), -1, GetPointer(Encoder),1))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(!Decoder.backProp(GetPointer(Actions), GetPointer(Encoder), 1) ||
!Encoder.backPropGradient(GetPointer(Actions), GetPointer(Actions)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(GetTickCount() - ticks > 500)
{
string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Decoder", iter * 100.0 / (double)(Iterations), Decoder.getRecentAverageError());
Comment(str);
ticks = GetTickCount();
}
}
Comment("");
//---
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Decoder", Decoder.getRecentAverageError());
ExpertRemove();
//---
}
//+------------------------------------------------------------------+