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NN_in_Trading/Experts/ADAPT/Study.mq5

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//+------------------------------------------------------------------+
//| 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 = 10000;
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory Buffer[];
CNet ADAPTEncoder;
CNet ADAPTEndpoints;
CNet ADAPTProbability;
CNet StateEncoder;
CNet EndpointEncoder;
CNet Actor;
//---
float dError;
datetime dtStudied;
//---
CBufferFloat bState;
CBufferFloat bAccount;
CBufferFloat bGradient;
CBufferFloat bProbs;
CBufferFloat *Result;
vector<float> check;
vector<float> STD_Actor;
vector<float> STD_Goal;
//---
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(!ADAPTEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
!ADAPTEndpoints.Load(FileName + "Endp.nnw", temp, temp, temp, dtStudied, true) ||
!ADAPTProbability.Load(FileName + "Prob.nnw", temp, temp, temp, dtStudied, true)
)
{
CArrayObj *encoder = new CArrayObj();
CArrayObj *endpoint = new CArrayObj();
CArrayObj *prob = new CArrayObj();
if(!CreateTrajNetDescriptions(encoder, endpoint, prob))
{
delete endpoint;
delete prob;
delete encoder;
return INIT_FAILED;
}
if(!ADAPTEncoder.Create(encoder) ||
!ADAPTEndpoints.Create(endpoint) ||
!ADAPTProbability.Create(prob))
{
delete endpoint;
delete prob;
delete encoder;
return INIT_FAILED;
}
delete endpoint;
delete prob;
delete encoder;
}
//---
if(!StateEncoder.Load(FileName + "StEnc.nnw", temp, temp, temp, dtStudied, true) ||
!EndpointEncoder.Load(FileName + "EndEnc.nnw", temp, temp, temp, dtStudied, true) ||
!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true))
{
CArrayObj *actor = new CArrayObj();
CArrayObj *endpoint = new CArrayObj();
CArrayObj *encoder = new CArrayObj();
if(!CreateDescriptions(actor, endpoint, encoder))
{
delete actor;
delete endpoint;
delete encoder;
return INIT_FAILED;
}
if(!Actor.Create(actor) || !StateEncoder.Create(encoder) || !EndpointEncoder.Create(endpoint))
{
delete actor;
delete endpoint;
delete encoder;
return INIT_FAILED;
}
delete actor;
delete endpoint;
delete encoder;
//---
}
//---
OpenCL = Actor.GetOpenCL();
StateEncoder.SetOpenCL(OpenCL);
EndpointEncoder.SetOpenCL(OpenCL);
ADAPTEncoder.SetOpenCL(OpenCL);
ADAPTEndpoints.SetOpenCL(OpenCL);
ADAPTProbability.SetOpenCL(OpenCL);
//---
Actor.getResults(Result);
if(Result.Total() != NActions)
{
PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
return INIT_FAILED;
}
//---
ADAPTEndpoints.getResults(Result);
if(Result.Total() != 3 * NForecast)
{
PrintFormat("The scope of the Endpoints does not match forecat endpoints (%d <> %d)", 3 * NForecast, Result.Total());
return INIT_FAILED;
}
//---
ADAPTEncoder.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;
}
//---
if(!bGradient.BufferInit(MathMax(AccountDescr, NForecast), 0) ||
!bGradient.BufferCreate(OpenCL))
{
PrintFormat("Error of create buffers: %d", GetLastError());
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)
{
//---
if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
{
Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
StateEncoder.Save(FileName + "StEnc.nnw", 0, 0, 0, TimeCurrent(), true);
EndpointEncoder.Save(FileName + "EndEnc.nnw", 0, 0, 0, TimeCurrent(), true);
ADAPTEncoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
ADAPTEndpoints.Save(FileName + "Endp.nnw", 0, 0, 0, TimeCurrent(), true);
ADAPTProbability.Save(FileName + "Prob.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;
matrix<float> targets, temp_m;
bool Stop = false;
//---
uint ticks = GetTickCount();
//---
for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter ++)
{
int tr = SampleTrajectory(probability);
int batch = GPTBars + 48;
int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2 - PrecoderBars - batch));
if(state <= 0)
{
iter--;
continue;
}
ADAPTEncoder.Clear();
int end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
for(int i = state; i < end; i++)
{
bState.AssignArray(Buffer[tr].States[i].state);
//--- Trajectory
if(!ADAPTEncoder.feedForward((CBufferFloat*)GetPointer(bState), 1, false, (CBufferFloat*)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
if(!ADAPTEndpoints.feedForward((CNet*)GetPointer(ADAPTEncoder), -1, (CBufferFloat*)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
if(!ADAPTProbability.feedForward((CNet*)GetPointer(ADAPTEncoder), -1, (CNet*)GetPointer(ADAPTEndpoints)))
{
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(target.Size() > BarDescr)
{
matrix<float> temp(1, target.Size());
temp.Row(target, 0);
temp.Reshape(target.Size() / BarDescr, BarDescr);
temp.Resize(temp.Rows(), 3);
target = temp.Row(temp.Rows() - 1);
}
targets.Row(target, t);
}
target = targets.Col(0).CumSum();
targets.Col(target, 0);
targets.Col(target + targets.Col(1), 1);
targets.Col(target + targets.Col(2), 2);
int extr = 1;
if(target[0] == 0)
target[0] = target[1];
int direct = (target[0] > 0 ? 1 : -1);
for(int i = 1; i < PrecoderBars; i++)
{
if((target[i]*direct) < 0)
break;
extr++;
}
targets.Resize(extr, 3);
if(direct >= 0)
{
target = targets.Max(AXIS_HORZ);
target[2] = targets.Col(2).Min();
}
else
{
target = targets.Min(AXIS_HORZ);
target[1] = targets.Col(1).Max();
}
//---
ADAPTEndpoints.getResults(result);
targets.Reshape(1, result.Size());
targets.Row(result, 0);
targets.Reshape(NForecast, 3);
temp_m = targets;
for(int i = 0; i < 3; i++)
temp_m.Col(temp_m.Col(i) - target[i], i);
temp_m = MathPow(temp_m, 2.0f);
ulong pos = temp_m.Sum(AXIS_VERT).ArgMin();
targets.Row(target, pos);
Result.AssignArray(targets);
//---
if(!ADAPTEndpoints.backProp(Result, (CBufferFloat*)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
if(!ADAPTEncoder.backPropGradient((CBufferFloat*)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
bProbs.AssignArray(vector<float>::Zeros(NForecast));
bProbs.Update((int)pos, 1);
bProbs.BufferWrite();
if(!ADAPTProbability.backProp(GetPointer(bProbs), GetPointer(ADAPTEndpoints)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
//--- Policy
float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
bAccount.Clear();
bAccount.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
bAccount.Add(Buffer[tr].States[i].account[1] / PrevBalance);
bAccount.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
bAccount.Add(Buffer[tr].States[i].account[2]);
bAccount.Add(Buffer[tr].States[i].account[3]);
bAccount.Add(Buffer[tr].States[i].account[4] / PrevBalance);
bAccount.Add(Buffer[tr].States[i].account[5] / PrevBalance);
bAccount.Add(Buffer[tr].States[i].account[6] / PrevBalance);
double time = (double)Buffer[tr].States[i].account[7];
double x = time / (double)(D'2024.01.01' - D'2023.01.01');
bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = time / (double)PeriodSeconds(PERIOD_MN1);
bAccount.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = time / (double)PeriodSeconds(PERIOD_W1);
bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = time / (double)PeriodSeconds(PERIOD_D1);
bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
if(bAccount.GetIndex() >= 0)
bAccount.BufferWrite();
//--- State embedding
if(!StateEncoder.feedForward((CNet *)GetPointer(ADAPTEncoder), -1, (CBufferFloat*)GetPointer(bAccount)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
//--- Endpoint embedding
if(!EndpointEncoder.feedForward(Result, -1, false, (CBufferFloat*)GetPointer(bProbs)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
//--- Actor
if(!Actor.feedForward((CNet *)GetPointer(StateEncoder), -1, (CNet*)GetPointer(EndpointEncoder)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
result = vector<float>::Zeros(NActions);
double value = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_LOSS);
double risk = AccountInfoDouble(ACCOUNT_EQUITY) * 0.01;
if(direct > 0)
{
if(Buffer[tr].States[i].state[4] > 50 &&
Buffer[tr].States[i].state[5] > 0 &&
Buffer[tr].States[i].state[8] > Buffer[tr].States[i].state[7]
)
{
float tp = float(target[1] / _Point / MaxTP);
result[1] = tp;
int sl = int(MathMax(MathMax(target[1] / 3, -target[2]) / _Point, MaxSL / 10));
result[2] = float(sl) / MaxSL;
result[0] = float(MathMax(risk / (value * sl), 0.01)) + FLT_EPSILON;
}
}
else
{
if(Buffer[tr].States[i].state[4] < 50 &&
Buffer[tr].States[i].state[5] < 0 &&
Buffer[tr].States[i].state[8] < Buffer[tr].States[i].state[7]
)
{
float tp = float((-target[2]) / _Point / MaxTP);
result[4] = tp;
int sl = int(MathMax(MathMax((-target[2]) / 3, target[1]) / _Point, MaxSL / 10));
result[5] = float(sl) / MaxSL;
result[3] = float(MathMax(risk / (value * sl), 0.01)) + FLT_EPSILON;
}
}
Result.AssignArray(result);
if(!Actor.backProp(Result, (CNet *)GetPointer(EndpointEncoder)) ||
!StateEncoder.backPropGradient(GetPointer(bAccount), (CBufferFloat *)GetPointer(bGradient)) ||
!EndpointEncoder.backPropGradient(GetPointer(bProbs), (CBufferFloat *)GetPointer(bGradient))
)
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
if(!ADAPTEncoder.backPropGradient((CBufferFloat*)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
Stop = true;
break;
}
//---
if(GetTickCount() - ticks > 500)
{
double percent = (double(i - state) / ((end - state)) + iter) * 100.0 / (Iterations);
string str = StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Actor", percent, Actor.getRecentAverageError());
str += StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Endpoints", percent, ADAPTEndpoints.getRecentAverageError());
str += StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Probability", percent, ADAPTProbability.getRecentAverageError());
Comment(str);
ticks = GetTickCount();
}
}
}
Comment("");
//---
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Actor", Actor.getRecentAverageError());
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Endpoints", ADAPTEndpoints.getRecentAverageError());
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Probability", ADAPTProbability.getRecentAverageError());
ExpertRemove();
//---
}
//+------------------------------------------------------------------+
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