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Warrior_EA/references/MQL5/Experts/CIC/Pretrain.mq5

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fix(cl): stabilize network training with tighter limits, weight decay, and sign-agreement gate - Tighten MAX_WEIGHT from 1.0e6 to 100.0 to prevent unbounded weight growth. - Add MIN_ACTIVATION_DERIVATIVE (1e-4) to avoid zero gradients for saturated units. - Introduce WEIGHT_DECAY (0.01) to decouple decay from Adam updates. - Add MAX_WEIGHT_DELTA (0.1) to clamp per-step Adam updates and prevent overshoot. - Apply sign-agreement gate on weight updates in Adam kernel to only apply steps aligned with current gradient direction. - Fix tanh/sigmoid derivative calculations to use the new floor instead of hard-coded edge-case values.
2026-07-15 21:47:37 -04:00
<EFBFBD><EFBFBD>//+------------------------------------------------------------------+
//| Study.mq5 |
//| Copyright DNG<EFBFBD> |
//| https://www.mql5.com/en/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG<00>"
#property link "https://www.mql5.com/en/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
#define Study
#include "Trajectory.mqh"
//+------------------------------------------------------------------+
//| Input parameters |
//+------------------------------------------------------------------+
input int Iterations = 100000;
input float Tau = 0.001f;
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory Buffer[];
CNet Encoder;
CNet TargetEncoder;
CNet Actor;
CNet Critic1;
CNet Critic2;
CNet Convolution;
CNet Descriminator;
CNet SkillProject;
//---
float dError;
datetime dtStudied;
//---
CBufferFloat State;
CBufferFloat Account;
CBufferFloat TargetState;
CBufferFloat TargetAccount;
CBufferFloat Actions;
CBufferFloat Gradient;
CBufferFloat Skills;
CBufferFloat *Result;
vector<float> check;
//---
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) ||
!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
!Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
!Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true) ||
!Descriminator.Load(FileName + "Des.nnw", temp, temp, temp, dtStudied, true) ||
!SkillProject.Load(FileName + "Skp.nnw", temp, temp, temp, dtStudied, true) ||
!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
!TargetEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true))
{
CArrayObj *encoder = new CArrayObj();
CArrayObj *actor = new CArrayObj();
CArrayObj *critic = new CArrayObj();
CArrayObj *descrim = new CArrayObj();
CArrayObj *convolution = new CArrayObj();
CArrayObj *skill_poject = new CArrayObj();
if(!CreateDescriptions(encoder,actor, critic, convolution,descrim,skill_poject))
{
delete encoder;
delete actor;
delete critic;
delete descrim;
delete convolution;
delete skill_poject;
return INIT_FAILED;
}
if(!Encoder.Create(encoder) || !Actor.Create(actor) ||
!Critic1.Create(critic) || !Critic2.Create(critic) ||
!Descriminator.Create(descrim) || !SkillProject.Create(skill_poject) ||
!Convolution.Create(convolution))
{
delete encoder;
delete actor;
delete critic;
delete descrim;
delete convolution;
delete skill_poject;
return INIT_FAILED;
}
if(!TargetEncoder.Create(encoder))
{
delete encoder;
delete actor;
delete critic;
delete descrim;
delete convolution;
delete skill_poject;
return INIT_FAILED;
}
delete encoder;
delete actor;
delete critic;
delete descrim;
delete convolution;
delete skill_poject;
//---
TargetEncoder.WeightsUpdate(GetPointer(Encoder), 1.0f);
}
//---
OpenCL = Actor.GetOpenCL();
Encoder.SetOpenCL(OpenCL);
Critic1.SetOpenCL(OpenCL);
Critic2.SetOpenCL(OpenCL);
TargetEncoder.SetOpenCL(OpenCL);
Descriminator.SetOpenCL(OpenCL);
SkillProject.SetOpenCL(OpenCL);
Convolution.SetOpenCL(OpenCL);
//---
vector<float> ActorResult;
Actor.getResults(ActorResult);
if(ActorResult.Size() != NActions)
{
PrintFormat("The scope of the actor 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 State Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
return INIT_FAILED;
}
//---
vector<float> EncoderResults;
Actor.GetLayerOutput(0,Result);
Encoder.getResults(EncoderResults);
if(Result.Total() != int(EncoderResults.Size()))
{
PrintFormat("Input size of Actor doesn't match Encoder outputs (%d <> %d)", Result.Total(), EncoderResults.Size());
return INIT_FAILED;
}
//---
Actor.GetLayerOutput(LatentLayer, Result);
int latent_state = Result.Total();
Critic1.GetLayerOutput(0, Result);
if(Result.Total() != latent_state)
{
PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
return INIT_FAILED;
}
//---
Gradient.BufferInit(MathMax(AccountDescr,NSkills), 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)
{
//---
TargetEncoder.WeightsUpdate(GetPointer(Encoder), Tau);
Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
TargetEncoder.Save(FileName + "Enc.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
Critic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
Critic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
Convolution.Save(FileName + "CNN.nnw", 0, 0, 0, TimeCurrent(), true);
Descriminator.Save(FileName + "Des.nnw", 0, 0, 0, TimeCurrent(), true);
SkillProject.Save(FileName + "Skp.nnw", 0, 0, 0, TimeCurrent(), true);
delete Result;
}
//+------------------------------------------------------------------+
//| 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 total_states = Buffer[0].Total - 1;
for(int i = 1; i < total_tr; i++)
total_states += Buffer[i].Total - 1;
vector<float> temp;
Convolution.getResults(temp);
matrix<float> state_embedding = matrix<float>::Zeros(total_states,temp.Size());
int state = 0;
for(int tr = 0; tr < total_tr; tr++)
{
for(int st = 0; st < Buffer[tr].Total - 1; st++)
{
State.AssignArray(Buffer[tr].States[st].state);
float PrevBalance = Buffer[tr].States[MathMax(st,0)].account[0];
float PrevEquity = Buffer[tr].States[MathMax(st,0)].account[1];
State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
State.Add(Buffer[tr].States[st].account[2]);
State.Add(Buffer[tr].States[st].account[3]);
State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
//---
State.AddArray(Buffer[tr].States[st + 1].state);
State.Add((Buffer[tr].States[st + 1].account[0] - PrevBalance) / PrevBalance);
State.Add(Buffer[tr].States[st + 1].account[1] / PrevBalance);
State.Add((Buffer[tr].States[st + 1].account[1] - PrevEquity) / PrevEquity);
State.Add(Buffer[tr].States[st + 1].account[2]);
State.Add(Buffer[tr].States[st + 1].account[3]);
State.Add(Buffer[tr].States[st + 1].account[4] / PrevBalance);
State.Add(Buffer[tr].States[st + 1].account[5] / PrevBalance);
State.Add(Buffer[tr].States[st + 1].account[6] / PrevBalance);
x = (double)Buffer[tr].States[st + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
if(!Convolution.feedForward(GetPointer(State),1,false,NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
ExpertRemove();
return;
}
Convolution.getResults(temp);
state_embedding.Row(temp,state);
state++;
if(GetTickCount() - ticks > 500)
{
string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
Comment(str);
ticks = GetTickCount();
}
}
}
if(state != total_states)
{
state_embedding.Reshape(state,state_embedding.Cols());
total_states = state;
}
//---
vector<float> reward = vector<float>::Zeros(NRewards);
vector<float> rewards1 = reward, rewards2 = reward;
int bar = (HistoryBars-1) * BarDescr;
for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
{
int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
if(i < 0)
{
iter--;
continue;
}
//--- State
State.AssignArray(Buffer[tr].States[i].state);
float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
Account.Clear();
Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
Account.Add(Buffer[tr].States[i].account[2]);
Account.Add(Buffer[tr].States[i].account[3]);
Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
if(Account.GetIndex() >= 0)
Account.BufferWrite();
//--- Skills
vector<float> skills = vector<float>::Zeros(NSkills);
//for(int sk = 0; sk < NSkills; sk++)
// skills[sk] = (float)((double)MathRand() / 32767.0);
//skills.Activation(skills,AF_SOFTMAX);
skills[int((double)MathRand() / 32768.0 * NSkills)] = 1;
Skills.AssignArray(skills);
if(Skills.GetIndex() >= 0 && !Skills.BufferWrite())
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Encoder State
if(!Encoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Actor
if(!Actor.feedForward(GetPointer(Encoder), -1, GetPointer(Skills)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Next State
TargetState.AssignArray(Buffer[tr].States[i + 1].state);
double cl_op = Buffer[tr].States[i + 1].state[bar];
double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
SymbolInfoDouble(_Symbol, SYMBOL_POINT);
Actor.getResults(Result);
vector<float> forecast = ForecastAccount(Buffer[tr].States[i].account,Result,prof_1l,Buffer[tr].States[i + 1].account[7]);
TargetAccount.AssignArray(forecast);
if(TargetAccount.GetIndex() >= 0 && !TargetAccount.BufferWrite())
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
if(!TargetEncoder.feedForward(GetPointer(TargetState), 1, false, GetPointer(TargetAccount)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Descriminator
if(!Descriminator.feedForward(GetPointer(Encoder),-1,GetPointer(TargetEncoder),-1) ||
!SkillProject.feedForward(GetPointer(Skills),1,false,NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
Descriminator.getResults(rewards1);
SkillProject.getResults(rewards2);
float norm1 = rewards1.Norm(VECTOR_NORM_P,2);
float norm2 = rewards2.Norm(VECTOR_NORM_P,2);
reward[0] = 0;//(rewards1 / norm1).Dot(rewards2 / norm2);
Result.AssignArray(rewards2);
if(!Descriminator.backProp(Result,GetPointer(TargetEncoder)) ||
!Encoder.backPropGradient(GetPointer(Account),GetPointer(Gradient)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
Result.AssignArray(rewards1);
if(!SkillProject.backProp(Result,(CNet *)NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(forecast[3] == 0.0f && forecast[4] == 0.f)
reward[0] -= Buffer[tr].States[i + 1].state[bar + 6] / PrevBalance;
//---
State.AddArray(GetPointer(Account));
State.AddArray(GetPointer(TargetState));
State.AddArray(GetPointer(TargetAccount));
if(!Convolution.feedForward(GetPointer(State),1,false,NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
Convolution.getResults(rewards1);
reward[0] += KNNReward(7,rewards1,state_embedding);
Result.AssignArray(reward);
//---
if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1) ||
!Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
if(Critic1.getRecentAverageError() <= Critic2.getRecentAverageError())
{
if(!Critic1.backProp(Result, GetPointer(Actor)) ||
!Actor.backPropGradient(GetPointer(Skills), GetPointer(Gradient), -1) ||
!Critic2.backProp(Result, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
}
else
{
if(!Critic2.backProp(Result, GetPointer(Actor)) ||
!Actor.backPropGradient(GetPointer(Skills), GetPointer(Gradient), -1) ||
!Critic1.backProp(Result, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
}
//--- Update Target Nets
TargetEncoder.WeightsUpdate(GetPointer(Encoder), Tau);
//---
if(GetTickCount() - ticks > 500)
{
string str = StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Descriminator", iter * 100.0 / (double)(Iterations), Descriminator.getRecentAverageError());
Comment(str);
ticks = GetTickCount();
}
}
Comment("");
//---
PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
ExpertRemove();
//---
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
float KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding)
{
if(embedding.Size() != state_embedding.Cols())
{
PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
return (0);
}
//---
ulong size = embedding.Size();
ulong states = state_embedding.Rows();
matrix<float> temp = matrix<float>::Zeros(states,size);
//---
for(ulong i = 0; i < size; i++)
temp.Col(MathPow(state_embedding.Col(i) - embedding[i],2.0f),i);
vector<float> dist = MathSqrt(temp.Sum(1));
vector<float> min_dist = vector<float>::Zeros(k);
for(ulong i = 0; i < k; i++)
{
ulong pos = dist.ArgMin();
min_dist[i] = dist[pos];
dist[pos] = FLT_MAX;
}
//---
vector<float> ri = MathLog(min_dist + 1.0f);
//---
float result = ri.Mean();
//---
return (result);
}
//+------------------------------------------------------------------+
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