forked from mnbvc188199/Warrior_EA
343 lines
26 KiB
MQL5
343 lines
26 KiB
MQL5
//+------------------------------------------------------------------+
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//| Research.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/en/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/en/users/dng"
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#property version "1.00"
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#include "Trajectory.mqh"
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input int Iterations = 100000;
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input float DiscountFactor = 0.99f;
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bool TrainMode = true;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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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;
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//---
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float dError;
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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;
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//+------------------------------------------------------------------+
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//| Expert initialization function |
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//+------------------------------------------------------------------+
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int OnInit()
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{
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//---
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ResetLastError();
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if(!LoadTotalBase())
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{
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PrintFormat("Error of load study data: %d", GetLastError());
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return INIT_FAILED;
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}
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//--- load models
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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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{
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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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{
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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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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;
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//---
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#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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//---
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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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//---
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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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}
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//+------------------------------------------------------------------+
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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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//---
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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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//+------------------------------------------------------------------+
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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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//---
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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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//+------------------------------------------------------------------+
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bool SaveTotalBase(void)
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{
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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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{
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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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bool LoadTotalBase(void)
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{
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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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{
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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].Load(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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FileClose(handle);
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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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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{
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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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//---
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if(IsStopped())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(!Scheduler.feedForward(GetPointer(State1), 1, false))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//---
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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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//---
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if(IsStopped())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(!Actor.feedForward(GetPointer(State1), 1, false))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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int action = Actor.getAction();
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if(action < 0 || action >= NActions)
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{
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iter--;
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continue;
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}
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Actor.getResults(ActorResult);;
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State1.AssignArray(Buffer[tr].States[i + 1].state);
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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];
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double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
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SymbolInfoDouble(_Symbol, SYMBOL_POINT);
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switch(action)
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{
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case 0:
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account[5] += (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;
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case 1:
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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;
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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;
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break;
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case 3:
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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;
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}
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PrevBalance = Buffer[tr].States[i].account[0];
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PrevEquity = Buffer[tr].States[i].account[1];
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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);
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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);
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//---
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if(!Discriminator.feedForward(GetPointer(State1), 1, false))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Discriminator.getResults(DiscriminatorResult);
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ActorResult[action] = DiscriminatorResult.Loss(SchedulerResult, LOSS_CCE);
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Result.AssignArray(SchedulerResult);
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if(!Discriminator.backProp(Result))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//---
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Result.AssignArray(SchedulerResult * ((account[0] - PrevBalance) / PrevBalance));
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if(!Scheduler.backProp(Result, DiscountFactor, GetPointer(State1), 1, false))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Result.AssignArray(ActorResult);
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State1.AddArray(SchedulerResult);
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if(!Actor.backProp(Result, DiscountFactor, GetPointer(State1), 1, false))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Scheduler", iter * 100.0 / (double)(Iterations), Scheduler.getRecentAverageError());
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str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Discriminator", iter * 100.0 / (double)(Iterations), Discriminator.getRecentAverageError());
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Comment(str);
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ticks = GetTickCount();
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}
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}
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Comment("");
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//---
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", Scheduler.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Discriminator", Discriminator.getRecentAverageError());
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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