499 lignes
41 Kio
MQL5
499 lignes
41 Kio
MQL5
//+------------------------------------------------------------------+
|
|
//| Research.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"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "Trajectory.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| Input parameters |
|
|
//+------------------------------------------------------------------+
|
|
input int Iterations = 1000;
|
|
input int DiscriminatorBatch = 500;
|
|
input int AgentBatch = 500;
|
|
input int SchedulerBatch = 500;
|
|
input float DiscountFactor = 0.99f;
|
|
bool TrainMode = true;
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
STrajectory Buffer[];
|
|
CFQF Actor;
|
|
CFQF Scheduler;
|
|
CNet Discriminator;
|
|
int Models = 1;
|
|
//---
|
|
float dError;
|
|
datetime dtStudied;
|
|
//---
|
|
CBufferFloat State;
|
|
CBufferFloat StateSkill;
|
|
CBufferFloat *Result;
|
|
vector<float> ActorResult;
|
|
vector<float> SchedulerResult;
|
|
vector<float> DiscriminatorResult;
|
|
//+------------------------------------------------------------------+
|
|
//| Expert initialization function |
|
|
//+------------------------------------------------------------------+
|
|
int OnInit()
|
|
{
|
|
//---
|
|
ResetLastError();
|
|
if(!LoadTotalBase())
|
|
{
|
|
PrintFormat("Error of load study data: %d", GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
//--- load models
|
|
float temp;
|
|
if(!Actor.Load(FileName + "Act.nnw", dtStudied, true) ||
|
|
!Scheduler.Load(FileName + "Sch.nnw", dtStudied, true) ||
|
|
!Discriminator.Load(FileName + "Dscr.nnw", temp, temp, temp, dtStudied, true))
|
|
{
|
|
CArrayObj *actor = new CArrayObj();
|
|
CArrayObj *scheduler = new CArrayObj();
|
|
CArrayObj *discriminator = new CArrayObj();
|
|
if(!CreateDescriptions(actor, scheduler, discriminator))
|
|
{
|
|
delete actor;
|
|
delete scheduler;
|
|
delete discriminator;
|
|
return INIT_FAILED;
|
|
}
|
|
if(!Actor.Create(actor) ||
|
|
!Scheduler.Create(scheduler) ||
|
|
!Discriminator.Create(discriminator))
|
|
{
|
|
delete actor;
|
|
delete scheduler;
|
|
delete discriminator;
|
|
return INIT_FAILED;
|
|
}
|
|
delete actor;
|
|
delete scheduler;
|
|
delete discriminator;
|
|
//---
|
|
#ifdef FileName
|
|
if(!Actor.UpdateTarget(FileName + ".nnw") ||
|
|
!Scheduler.UpdateTarget(FileName + ".nnw"))
|
|
#else
|
|
if(!Actor.UpdateTarget("FQF.upd") ||
|
|
!Scheduler.UpdateTarget("FQF.nnw"))
|
|
#endif
|
|
return INIT_FAILED;
|
|
}
|
|
//---
|
|
Discriminator.getResults(DiscriminatorResult);
|
|
if(DiscriminatorResult.Size() != NSkills * AccountDescr)
|
|
{
|
|
PrintFormat("The scope of the discriminator does not match the skills count (%d <> %d)", NSkills, Result.Total());
|
|
return INIT_FAILED;
|
|
}
|
|
Scheduler.getResults(SchedulerResult);
|
|
Scheduler.SetUpdateTarget(MathMax(Iterations*SchedulerBatch / 100, 500000 / SchedulerBatch));
|
|
if(SchedulerResult.Size() != NSkills)
|
|
{
|
|
PrintFormat("The scope of the scheduler does not match the skills count (%d <> %d)", NSkills, Result.Total());
|
|
return INIT_FAILED;
|
|
}
|
|
Actor.getResults(ActorResult);
|
|
Actor.SetUpdateTarget(MathMax(Iterations*AgentBatch / 100, 500000 / AgentBatch * NSkills));
|
|
if(ActorResult.Size() != NActions)
|
|
{
|
|
PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
|
|
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)
|
|
{
|
|
//---
|
|
Actor.Save(FileName + "Act.nnw", TimeCurrent(), true);
|
|
Scheduler.Save(FileName + "Sch.nnw", TimeCurrent(), true);
|
|
Discriminator.Save(FileName + "Dscr.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();
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool SaveTotalBase(void)
|
|
{
|
|
int total = ArraySize(Buffer);
|
|
if(total < 0)
|
|
return true;
|
|
int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
|
|
if(handle < 0)
|
|
return false;
|
|
if(FileWriteInteger(handle, total) < INT_VALUE)
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
for(int i = 0; i < total; i++)
|
|
if(!Buffer[i].Save(handle))
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
FileFlush(handle);
|
|
FileClose(handle);
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool LoadTotalBase(void)
|
|
{
|
|
int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
|
|
if(handle < 0)
|
|
return false;
|
|
int total = FileReadInteger(handle);
|
|
if(total <= 0)
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
if(ArrayResize(Buffer, total) < total)
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
for(int i = 0; i < total; i++)
|
|
if(!Buffer[i].Load(handle))
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
FileClose(handle);
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Train function |
|
|
//+------------------------------------------------------------------+
|
|
void Train(void)
|
|
{
|
|
int total_tr = ArraySize(Buffer);
|
|
uint ticks = GetTickCount();
|
|
vector<float> account, reward;
|
|
int bar, action;
|
|
int skill, shift;
|
|
//---
|
|
for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
|
|
{
|
|
for(int phase = 0; phase < 3; phase++)
|
|
{
|
|
int batch = 0;
|
|
switch(phase)
|
|
{
|
|
case 0:
|
|
batch = DiscriminatorBatch;
|
|
break;
|
|
case 1:
|
|
batch = AgentBatch;
|
|
break;
|
|
case 2:
|
|
batch = SchedulerBatch;
|
|
break;
|
|
default:
|
|
PrintFormat("Incorrect phase %d");
|
|
batch = 0;
|
|
break;
|
|
}
|
|
for(int batch_iter = 0; batch_iter < batch; batch_iter++)
|
|
{
|
|
int tr = (int)(((double)MathRand() / 32767.0) * (total_tr - 1));
|
|
int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
|
|
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];
|
|
State.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
|
|
State.Add(Buffer[tr].States[i].account[1] / PrevBalance);
|
|
State.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
|
|
State.Add(Buffer[tr].States[i].account[2] / PrevBalance);
|
|
State.Add(Buffer[tr].States[i].account[4] / PrevBalance);
|
|
State.Add(Buffer[tr].States[i].account[5]);
|
|
State.Add(Buffer[tr].States[i].account[6]);
|
|
State.Add(Buffer[tr].States[i].account[7] / PrevBalance);
|
|
State.Add(Buffer[tr].States[i].account[8] / PrevBalance);
|
|
//---
|
|
bar = (HistoryBars - 1) * BarDescr;
|
|
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);
|
|
PrevBalance = Buffer[tr].States[i].account[0];
|
|
PrevEquity = Buffer[tr].States[i].account[1];
|
|
if(IsStopped())
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
//---
|
|
switch(phase)
|
|
{
|
|
case 0:
|
|
if(!Discriminator.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
for(skill = 0; skill < NSkills; skill++)
|
|
{
|
|
SchedulerResult = vector<float>::Zeros(NSkills);
|
|
SchedulerResult[skill] = 1;
|
|
StateSkill.AssignArray(GetPointer(State));
|
|
StateSkill.AddArray(SchedulerResult);
|
|
if(IsStopped())
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
break;
|
|
}
|
|
if(!Actor.feedForward(GetPointer(State), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
break;
|
|
}
|
|
action = Actor.getSample();
|
|
account = GetNewState(Buffer[tr].States[i].account, action, prof_1l);
|
|
shift = skill * AccountDescr;
|
|
DiscriminatorResult[shift] = (account[0] - PrevBalance) / PrevBalance;
|
|
DiscriminatorResult[shift + 1] = account[1] / PrevBalance;
|
|
DiscriminatorResult[shift + 2] = (account[1] - PrevEquity) / PrevEquity;
|
|
DiscriminatorResult[shift + 3] = account[2] / PrevBalance;
|
|
DiscriminatorResult[shift + 4] = account[4] / PrevBalance;
|
|
DiscriminatorResult[shift + 5] = account[5];
|
|
DiscriminatorResult[shift + 6] = account[6];
|
|
DiscriminatorResult[shift + 7] = account[7] / PrevBalance;
|
|
DiscriminatorResult[shift + 8] = account[8] / PrevBalance;
|
|
}
|
|
if(!Result)
|
|
{
|
|
Result = new CBufferFloat();
|
|
if(!Result)
|
|
{
|
|
PrintFormat("Error of create buffer %d", GetLastError());
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
}
|
|
Result.AssignArray(DiscriminatorResult);
|
|
if(!Discriminator.backProp(Result,(CBufferFloat*)NULL))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
break;
|
|
case 1:
|
|
if(!Discriminator.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
Discriminator.getResults(DiscriminatorResult);
|
|
for(skill = 0; skill < NSkills; skill++)
|
|
{
|
|
SchedulerResult = vector<float>::Zeros(NSkills);
|
|
SchedulerResult[skill] = 1;
|
|
StateSkill.AssignArray(GetPointer(State));
|
|
StateSkill.AddArray(SchedulerResult);
|
|
if(IsStopped())
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
if(!Actor.feedForward(GetPointer(State), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
reward = GetAgentReward(skill, DiscriminatorResult, Buffer[tr].States[i].account);
|
|
Result.AssignArray(reward);
|
|
StateSkill.AssignArray(Buffer[tr].States[i + 1].state);
|
|
account = GetNewState(Buffer[tr].States[i].account, Actor.getAction(), prof_1l);
|
|
shift = skill * AccountDescr;
|
|
StateSkill.Add((account[0] - PrevBalance) / PrevBalance);
|
|
StateSkill.Add(account[1] / PrevBalance);
|
|
StateSkill.Add((account[1] - PrevEquity) / PrevEquity);
|
|
StateSkill.Add(account[2] / PrevBalance);
|
|
StateSkill.Add(account[4] / PrevBalance);
|
|
StateSkill.Add(account[5]);
|
|
StateSkill.Add(account[6]);
|
|
StateSkill.Add(account[7] / PrevBalance);
|
|
StateSkill.Add(account[8] / PrevBalance);
|
|
if(!Actor.backProp(Result, DiscountFactor, GetPointer(StateSkill), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
}
|
|
break;
|
|
case 2:
|
|
if(!Scheduler.feedForward(GetPointer(State), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
Scheduler.getResults(SchedulerResult);
|
|
State.AddArray(SchedulerResult);
|
|
if(IsStopped())
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
if(!Actor.feedForward(GetPointer(State), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
action = Actor.getAction();
|
|
account = GetNewState(Buffer[tr].States[i].account, action, prof_1l);
|
|
SchedulerResult = SchedulerResult * (account[0] / PrevBalance - 1.0);
|
|
Result.AssignArray(SchedulerResult);
|
|
State.AssignArray(Buffer[tr].States[i + 1].state);
|
|
State.Add((account[0] - PrevBalance) / PrevBalance);
|
|
State.Add(account[1] / PrevBalance);
|
|
State.Add((account[1] - PrevEquity) / PrevEquity);
|
|
State.Add(account[2] / PrevBalance);
|
|
State.Add(account[4] / PrevBalance);
|
|
State.Add(account[5]);
|
|
State.Add(account[6]);
|
|
State.Add(account[7] / PrevBalance);
|
|
State.Add(account[8] / PrevBalance);
|
|
if(!Scheduler.backProp(Result, DiscountFactor, GetPointer(State), 1, false))
|
|
{
|
|
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
|
ExpertRemove();
|
|
break;
|
|
}
|
|
break;
|
|
default:
|
|
PrintFormat("Wrong phase %d", phase);
|
|
break;
|
|
}
|
|
if(GetTickCount() - ticks > 500)
|
|
{
|
|
string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Scheduler", iter * 100.0 / (double)(Iterations), Scheduler.getRecentAverageError());
|
|
str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Discriminator", iter * 100.0 / (double)(Iterations), Discriminator.getRecentAverageError());
|
|
Comment(str);
|
|
ticks = GetTickCount();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
Comment("");
|
|
//---
|
|
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", Scheduler.getRecentAverageError());
|
|
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Discriminator", Discriminator.getRecentAverageError());
|
|
ExpertRemove();
|
|
//---
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
vector<float> GetNewState(float &prev_account[], int action, double prof_1l)
|
|
{
|
|
vector<float> result;
|
|
//---
|
|
result.Assign(prev_account);
|
|
switch(action)
|
|
{
|
|
case 0:
|
|
result[5] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
|
|
result[7] += result[5] * (float)prof_1l;
|
|
result[8] -= result[6] * (float)prof_1l;
|
|
result[4] = result[7] + result[8];
|
|
result[1] = result[0] + result[4];
|
|
break;
|
|
case 1:
|
|
result[6] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
|
|
result[7] += result[5] * (float)prof_1l;
|
|
result[8] -= result[6] * (float)prof_1l;
|
|
result[4] = result[7] + result[8];
|
|
result[1] = result[0] + result[4];
|
|
break;
|
|
case 2:
|
|
result[0] += result[4];
|
|
result[1] = result[0];
|
|
result[2] = result[0];
|
|
for(int i = 3; i < AccountDescr; i++)
|
|
result[i] = 0;
|
|
break;
|
|
case 3:
|
|
result[7] += result[5] * (float)prof_1l;
|
|
result[8] -= result[6] * (float)prof_1l;
|
|
result[4] = result[7] + result[8];
|
|
result[1] = result[0] + result[4];
|
|
break;
|
|
}
|
|
//--- return result
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
vector<float> GetAgentReward(int skill, vector<float> &discriminator, float &prev_account[])
|
|
{
|
|
//--- prepare
|
|
matrix<float> discriminator_matrix;
|
|
discriminator_matrix.Init(1, discriminator.Size());
|
|
discriminator_matrix.Row(discriminator, 0);
|
|
discriminator_matrix.Reshape(NSkills, AccountDescr);
|
|
vector<float> forecast = discriminator_matrix.Row(skill);
|
|
//--- check action
|
|
int action = 3;
|
|
float buy = forecast[5] - prev_account[5];
|
|
float sell = forecast[6] - prev_account[6];
|
|
if(buy < 0 && sell < 0)
|
|
action = 2;
|
|
else
|
|
if(buy > sell)
|
|
action = 0;
|
|
else
|
|
if(buy < sell)
|
|
action = 1;
|
|
//--- calculate reward
|
|
vector<float> result = vector<float>::Zeros(NActions);
|
|
float mean = (forecast / discriminator_matrix.Mean(0)).Mean();
|
|
result[action] = MathLog(MathAbs(mean));
|
|
//--- return result
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|