| //+------------------------------------------------------------------+
|
| //| Research.mq5 |
|
| //| Copyright DNG® |
|
| //| https://www.mql5.com/ru/users/dng |
|
| //+------------------------------------------------------------------+
|
| #property copyright "Copyright DNG®"
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| #property link "https://www.mql5.com/ru/users/dng"
|
| #property version "1.00"
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| #include "Trajectory.mqh"
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input int Iterations = 100000;
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| input float DiscountFactor = 0.99f;
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| bool TrainMode = true;
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| STrajectory Buffer[];
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| CFQF Actor;
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| CFQF Scheduler;
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| CNet Discriminator;
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| int Models = 1;
|
| //---
|
| float dError;
|
| datetime dtStudied;
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| //---
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| CBufferFloat State1;
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| CBufferFloat *Result;
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| vector<float> ActorResult;
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| vector<float> SchedulerResult;
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| vector<float> DiscriminatorResult;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
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| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| //---
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| ResetLastError();
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| if(!LoadTotalBase())
|
| {
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| PrintFormat("Error of load study data: %d", GetLastError());
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| return INIT_FAILED;
|
| }
|
| //--- load models
|
| float temp;
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| if(!Actor.Load(FileName + "Act.nnw", dtStudied, true) ||
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| !Scheduler.Load(FileName + "Sch.nnw", dtStudied, true) ||
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| !Discriminator.Load(FileName + "Dscr.nnw", temp, temp, temp, dtStudied, true))
|
| {
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| CArrayObj *actor = new CArrayObj();
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| CArrayObj *scheduler = new CArrayObj();
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| CArrayObj *discriminator = new CArrayObj();
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| if(!CreateDescriptions(actor, scheduler, discriminator))
|
| {
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| delete actor;
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| delete scheduler;
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| delete discriminator;
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| return INIT_FAILED;
|
| }
|
| if(!Actor.Create(actor) ||
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| !Scheduler.Create(scheduler) ||
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| !Discriminator.Create(discriminator))
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| {
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| delete actor;
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| delete scheduler;
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| delete discriminator;
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| return INIT_FAILED;
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| }
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| delete actor;
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| delete scheduler;
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| delete discriminator;
|
| //---
|
| #ifdef FileName
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| if(!Actor.UpdateTarget(FileName + ".nnw") ||
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| !Scheduler.UpdateTarget(FileName + ".nnw"))
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| #else
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| if(!Actor.UpdateTarget("FQF.upd") ||
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| !Scheduler.UpdateTarget("FQF.nnw"))
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| #endif
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| return INIT_FAILED;
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| }
|
| //---
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| Discriminator.getResults(Result);
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| if(Result.Total() != NSkills)
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| {
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| PrintFormat("The scope of the discriminator does not match the skills count (%d <> %d)", NSkills, Result.Total());
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| return INIT_FAILED;
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| }
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| Scheduler.getResults(Result);
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| Scheduler.SetUpdateTarget(MathMax(Iterations / 100, 50000));
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| if(Result.Total() != NSkills)
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| {
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| PrintFormat("The scope of the scheduler does not match the skills count (%d <> %d)", NSkills, Result.Total());
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| return INIT_FAILED;
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| }
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| Actor.getResults(Result);
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| Actor.SetUpdateTarget(MathMax(Iterations / 100, 50000));
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| if(Result.Total() != NActions)
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| {
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| PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
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| return INIT_FAILED;
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| }
|
| //---
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| if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
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| {
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| PrintFormat("Error of create study event: %d", GetLastError());
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| return INIT_FAILED;
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| }
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| //---
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| return(INIT_SUCCEEDED);
|
| }
|
| //+------------------------------------------------------------------+
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| //| Expert deinitialization function |
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| //+------------------------------------------------------------------+
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| void OnDeinit(const int reason)
|
| {
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| //---
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| Actor.Save(FileName + "Act.nnw", TimeCurrent(), true);
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| Scheduler.Save(FileName + "Sch.nnw", TimeCurrent(), true);
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| Discriminator.Save(FileName + "Dscr.nnw", 0, 0, 0, TimeCurrent(), true);
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| delete Result;
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| }
|
| //+------------------------------------------------------------------+
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| //| ChartEvent function |
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| //+------------------------------------------------------------------+
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| void OnChartEvent(const int id,
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| const long &lparam,
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| const double &dparam,
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| const string &sparam)
|
| {
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| //---
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| if(id == 1001)
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| Train();
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| }
|
| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| //| | Saves TotalBase. |
|
| //+------------------------------------------------------------------+
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| bool SaveTotalBase(void)
|
| {
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| int total = ArraySize(Buffer);
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| if(total < 0)
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| return true;
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| int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
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| if(handle < 0)
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| return false;
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| if(FileWriteInteger(handle, total) < INT_VALUE)
|
| {
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| FileClose(handle);
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| return false;
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| }
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| for(int i = 0; i < total; i++)
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| if(!Buffer[i].Save(handle))
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| {
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| FileClose(handle);
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| return false;
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| }
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| FileFlush(handle);
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| FileClose(handle);
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| //---
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| return true;
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| }
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| //| | Loads TotalBase. |
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| //+------------------------------------------------------------------+
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| bool LoadTotalBase(void)
|
| {
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| int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
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| if(handle < 0)
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| return false;
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| int total = FileReadInteger(handle);
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| if(total <= 0)
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| {
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| FileClose(handle);
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| return false;
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| }
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| if(ArrayResize(Buffer, total) < total)
|
| {
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| FileClose(handle);
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| return false;
|
| }
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| for(int i = 0; i < total; i++)
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| if(!Buffer[i].Load(handle))
|
| {
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| FileClose(handle);
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| return false;
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| }
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| FileClose(handle);
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| //---
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Train function |
|
| //+------------------------------------------------------------------+
|
| void Train(void)
|
| {
|
| int total_tr = ArraySize(Buffer);
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| uint ticks = GetTickCount();
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| for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
|
| {
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| int tr = (int)(((double)MathRand() / 32767.0) * (total_tr - 1));
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| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
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| State1.AssignArray(Buffer[tr].States[i].state);
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| float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
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| float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
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| State1.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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| State1.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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| State1.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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| State1.Add(Buffer[tr].States[i].account[3] / 100.0f);
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| State1.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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| State1.Add(Buffer[tr].States[i].account[5]);
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| State1.Add(Buffer[tr].States[i].account[6]);
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| State1.Add(Buffer[tr].States[i].account[7] / PrevBalance);
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| State1.Add(Buffer[tr].States[i].account[8] / PrevBalance);
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| //---
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| break;
|
| }
|
| if(!Scheduler.feedForward(GetPointer(State1), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| break;
|
| }
|
| //---
|
| int skill = Scheduler.getAction();
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| SchedulerResult = vector<float>::Zeros(NSkills);
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| SchedulerResult[skill] = 1;
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| State1.AddArray(SchedulerResult);
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| //---
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| if(!Actor.feedForward(GetPointer(State1), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| int action = Actor.getAction();
|
| if(action < 0 || action >= NActions)
|
| {
|
| iter--;
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| continue;
|
| }
|
| Actor.getResults(ActorResult);;
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| State1.AssignArray(Buffer[tr].States[i + 1].state);
|
| vector<float> account;
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| account.Assign(Buffer[tr].States[i].account);
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| int bar = (HistoryBars - 1) * BarDescr;
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| double cl_op = Buffer[tr].States[i + 1].state[bar];
|
| // Implements SymbolInfoDouble.
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| double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
|
| SymbolInfoDouble(_Symbol, SYMBOL_POINT);
|
| switch(action)
|
| {
|
| case 0:
|
| account[5] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
|
| account[7] += account[5] * (float)prof_1l;
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| account[8] -= account[6] * (float)prof_1l;
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| account[4] = account[7] + account[8];
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| account[1] = account[0] + account[4];
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| break;
|
| case 1:
|
| account[6] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
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| account[7] += account[5] * (float)prof_1l;
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| account[8] -= account[6] * (float)prof_1l;
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| account[4] = account[7] + account[8];
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| account[1] = account[0] + account[4];
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| break;
|
| case 2:
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| account[0] += account[4];
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| account[1] = account[0];
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| account[2] = account[0];
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| for(bar = 3; bar < AccountDescr; bar++)
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| account[bar] = 0;
|
| break;
|
| case 3:
|
| account[7] += account[5] * (float)prof_1l;
|
| account[8] -= account[6] * (float)prof_1l;
|
| account[4] = account[7] + account[8];
|
| account[1] = account[0] + account[4];
|
| break;
|
| }
|
| PrevBalance = Buffer[tr].States[i].account[0];
|
| PrevEquity = Buffer[tr].States[i].account[1];
|
| State1.Add((account[0] - PrevBalance) / PrevBalance);
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| State1.Add(account[1] / PrevBalance);
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| State1.Add((account[1] - PrevEquity) / PrevEquity);
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| State1.Add(account[3] / 100.0f);
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| State1.Add(account[4] / PrevBalance);
|
| State1.Add(account[5]);
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| State1.Add(account[6]);
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| State1.Add(account[7] / PrevBalance);
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| State1.Add(account[8] / PrevBalance);
|
| //---
|
| if(!Discriminator.feedForward(GetPointer(State1), 1, false,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| Discriminator.getResults(DiscriminatorResult);
|
| ActorResult[action] = DiscriminatorResult.Loss(SchedulerResult, LOSS_CCE);
|
| Result.AssignArray(SchedulerResult);
|
| if(!Discriminator.backProp(Result,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //---
|
| Result.AssignArray(SchedulerResult * ((account[0] - PrevBalance) / PrevBalance));
|
| if(!Scheduler.backProp(Result, DiscountFactor, GetPointer(State1), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| Result.AssignArray(ActorResult);
|
| State1.AddArray(SchedulerResult);
|
| if(!Actor.backProp(Result, DiscountFactor, GetPointer(State1), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| 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();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
