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Warrior_EA/references/MQL5/Experts/CIC/Finetune.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 TargetActor;
CNet Critic1;
CNet Critic2;
CNet TargetCritic1;
CNet TargetCritic2;
CNet Convolution;
CNet Scheduler;
//---
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) ||
!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
!TargetEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
!TargetActor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
!TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
!TargetCritic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
{
Print("No pretrained models found");
return INIT_FAILED;
}
if(!Scheduler.Load(FileName + "Sch.nnw", temp, temp, temp, dtStudied, true))
{
CArrayObj *descr = new CArrayObj();
if(!SchedulerDescriptions(descr) || !Scheduler.Create(descr))
{
delete descr;
return INIT_FAILED;
}
delete descr;
}
//---
OpenCL = Actor.GetOpenCL();
Encoder.SetOpenCL(OpenCL);
Critic1.SetOpenCL(OpenCL);
Critic2.SetOpenCL(OpenCL);
TargetEncoder.SetOpenCL(OpenCL);
TargetActor.SetOpenCL(OpenCL);
TargetCritic1.SetOpenCL(OpenCL);
TargetCritic2.SetOpenCL(OpenCL);
Scheduler.SetOpenCL(OpenCL);
Convolution.SetOpenCL(OpenCL);
//---
Actor.TrainMode(false);
Encoder.TrainMode(false);
//---
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(AccountDescr, 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)
{
//---
TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
TargetCritic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
Scheduler.Save(FileName + "Sch.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();
float loss = 0;
//---
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());
matrix<float> rewards = matrix<float>::Zeros(total_states,NRewards);
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);
temp.Assign(Buffer[tr].States[st].rewards);
for(ulong r = 0; r < temp.Size(); r++)
temp[r] -= Buffer[tr].States[st + 1].rewards[r] * DiscFactor;
rewards.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());
rewards.Reshape(state,NRewards);
total_states = state;
}
//---
vector<float> reward, rewards1, rewards2, target_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;
}
reward = vector<float>::Zeros(NRewards);
rewards1 = reward;
rewards2 = reward;
target_reward = reward;
//--- 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];
if(PrevBalance == 0.0f || PrevEquity == 0.0f)
continue;
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();
//--- Encoder State
if(!Encoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Skills
if(!Scheduler.feedForward(GetPointer(Encoder), -1, NULL,-1))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Actor
if(!Actor.feedForward(GetPointer(Encoder), -1, GetPointer(Scheduler),-1))
{
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;
}
//--- Target
if(!TargetActor.feedForward(GetPointer(TargetEncoder), -1, GetPointer(Scheduler),-1))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(!TargetCritic1.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)) ||
!TargetCritic2.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
TargetCritic1.getResults(rewards1);
TargetCritic2.getResults(rewards2);
if(rewards1.Sum() <= rewards2.Sum())
target_reward = rewards1;
else
target_reward = rewards2;
target_reward *= DiscFactor;
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,rewards);
reward += target_reward;
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;
}
Critic1.getResults(rewards1);
Critic2.getResults(rewards2);
if(rewards1.Sum() <= rewards2.Sum())
{
loss = (loss * MathMin(iter,999) + (reward - rewards1).Sum()) / MathMin(iter + 1,1000);
if(!Critic1.backProp(Result, GetPointer(Actor)) ||
!Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
!Scheduler.backPropGradient() ||
!Critic2.backProp(Result, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
}
else
{
loss = (loss * MathMin(iter,999) + (reward - rewards2).Sum()) / MathMin(iter + 1,1000);
if(!Critic2.backProp(Result, GetPointer(Actor)) ||
!Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
!Scheduler.backPropGradient() ||
!Critic1.backProp(Result, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
}
//--- Update Target Nets
TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), 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", "Scheduler", iter * 100.0 / (double)(Iterations), loss);
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());
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", loss);
ExpertRemove();
//---
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
float KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards)
{
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));
matrix<float> min_dist = matrix<float>::Zeros(k,NRewards + 1);
for(ulong i = 0; i < k; i++)
{
ulong pos = dist.ArgMin();
min_dist[i,0] = dist[pos];
dist[pos] = FLT_MAX;
for(int c = 0; c < NRewards; c++)
min_dist[i,c + 1] = rewards[pos,c];
}
//---
min_dist.Col(vector<float>::Zeros(k),0);
//---
float result = min_dist.Sum() / (k * NRewards);
//---
return (result);
}
//+------------------------------------------------------------------+
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