| //+------------------------------------------------------------------+
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| //| StudyActor.mq5 |
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| //| Copyright DNG® |
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| //| https://www.mql5.com/ru/users/dng |
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| //+------------------------------------------------------------------+
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| #property copyright "Copyright DNG®"
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| #property link "https://www.mql5.com/ru/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 int LatentLayer = 9;
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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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| CNet Scheduler;
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| CFQF Actor;
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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 State;
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| CBufferFloat *Result;
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| vector<float> SchedulerResult;
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| vector<float> check;
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| vector<float> ActorResult;
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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(!Scheduler.Load(FileName + "Sch.nnw", temp, temp, temp, dtStudied, true))
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| {
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| PrintFormat("Error of load scheduler model: %d", GetLastError());
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| return INIT_FAILED;
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| }
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| if(!Actor.Load(FileName + "Act.nnw", 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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| if(!CreateDescriptions(actor, scheduler))
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| {
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| delete actor;
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| delete scheduler;
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| return INIT_FAILED;
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| }
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| if(!Actor.Create(actor))
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| {
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| delete actor;
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| delete scheduler;
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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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| //---
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| }
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| //---
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| Actor.getResults(Result);
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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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| Actor.SetOpenCL(Scheduler.GetOpenCL());
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| Actor.SetUpdateTarget(MathMax(Iterations / 100, 10000));
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| //---
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| Scheduler.getResults(Result);
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| if(Result.Total() != AccountDescr)
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| {
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| PrintFormat("The scope of the scheduler does not match the account description (%d <> %d)", AccountDescr, Result.Total());
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| return INIT_FAILED;
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| }
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| //---
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| Actor.GetLayerOutput(0, Result);
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| int inputs = Result.Total();
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| if(!Scheduler.GetLayerOutput(LatentLayer, Result))
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| {
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| PrintFormat("Error of load latent layer %d", LatentLayer);
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| return INIT_FAILED;
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| }
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| if(inputs != Result.Total())
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| {
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| PrintFormat("Size of latent layer does not match input size of Actor (%d <> %d)", Result.Total(), inputs);
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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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| 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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| //| | 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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| {
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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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| int total_tr = ArraySize(Buffer);
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| uint ticks = GetTickCount();
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| vector<float> account, reward;
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| int bar, action;
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| //---
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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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| State.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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| State.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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| State.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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| State.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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| State.Add(Buffer[tr].States[i].account[2] / PrevBalance);
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| State.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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| State.Add(Buffer[tr].States[i].account[5]);
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| State.Add(Buffer[tr].States[i].account[6]);
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| State.Add(Buffer[tr].States[i].account[7] / PrevBalance);
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| State.Add(Buffer[tr].States[i].account[8] / PrevBalance);
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| //---
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| bar = (HistoryBars - 1) * BarDescr;
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| double cl_op = Buffer[tr].States[i + 1].state[bar];
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| // Implements SymbolInfoDouble.
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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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| PrevBalance = Buffer[tr].States[i].account[0];
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| PrevEquity = Buffer[tr].States[i].account[1];
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| if(IsStopped())
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| //---
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| if(!Scheduler.feedForward(GetPointer(State), 1, false,(CBufferFloat*)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| //---
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| if(!Scheduler.GetLayerOutput(LatentLayer, Result))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| //---
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| if(!Actor.feedForward(Result, 1, false))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| //---
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| Scheduler.getResults(SchedulerResult);
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| ActorResult = vector<float>::Zeros(NActions);
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| for(action = 0; action < NActions; action++)
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| {
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| reward = GetNewState(Buffer[tr].States[i].account, action, prof_1l);
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| reward[0] = reward[0] / PrevBalance - 1.0f;
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| reward[3] = reward[2] / PrevBalance;
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| reward[2] = reward[1] / PrevEquity - 1.0f;
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| reward[1] /= PrevBalance;
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| reward[4] /= PrevBalance;
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| reward[7] /= PrevBalance;
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| reward[8] /= PrevBalance;
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| reward=MathPow(SchedulerResult - reward, 2.0);
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| ActorResult[action] = -reward.Sum();
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| }
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| ActorResult = ActorResult - ActorResult.Min();
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| State.AssignArray(Buffer[tr].States[i+1].state);
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| State.Add((Buffer[tr].States[i+1].account[0] - PrevBalance) / PrevBalance);
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| State.Add(Buffer[tr].States[i+1].account[1] / PrevBalance);
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| State.Add((Buffer[tr].States[i+1].account[1] - PrevEquity) / PrevEquity);
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| State.Add(Buffer[tr].States[i+1].account[2] / PrevBalance);
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| State.Add(Buffer[tr].States[i+1].account[4] / PrevBalance);
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| State.Add(Buffer[tr].States[i+1].account[5]);
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| State.Add(Buffer[tr].States[i+1].account[6]);
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| State.Add(Buffer[tr].States[i+1].account[7] / PrevBalance);
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| State.Add(Buffer[tr].States[i+1].account[8] / PrevBalance);
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| //---
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| if(!Scheduler.feedForward(GetPointer(State), 1, false,(CBufferFloat*)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| //---
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| if(!Scheduler.GetLayerOutput(LatentLayer, Result))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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| break;
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| }
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| State.AssignArray(Result);
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| Result.AssignArray(ActorResult);
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| if(!Actor.backProp(Result,DiscountFactor,GetPointer(State),1,false))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| ExpertRemove();
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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", "Actor", iter * 100.0 / (double)(Iterations), Actor.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__, "Actor", Actor.getRecentAverageError());
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| ExpertRemove();
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| //---
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| }
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| //| | Gets NewState. |
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| //+------------------------------------------------------------------+
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| vector<float> GetNewState(float &prev_account[], int action, double prof_1l)
|
| {
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| vector<float> result;
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| //---
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| result.Assign(prev_account);
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| switch(action)
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| {
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| case 0:
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| result[5] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
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| result[7] += result[5] * (float)prof_1l;
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| result[8] -= result[6] * (float)prof_1l;
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| result[4] = result[7] + result[8];
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| result[1] = result[0] + result[4];
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| break;
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| case 1:
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| result[6] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
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| result[7] += result[5] * (float)prof_1l;
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| result[8] -= result[6] * (float)prof_1l;
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| result[4] = result[7] + result[8];
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| result[1] = result[0] + result[4];
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| break;
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| case 2:
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| result[0] += result[4];
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| result[1] = result[0];
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| result[2] = result[0];
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| for(int i = 3; i < AccountDescr; i++)
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| result[i] = 0;
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| break;
|
| case 3:
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| result[7] += result[5] * (float)prof_1l;
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| result[8] -= result[6] * (float)prof_1l;
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| result[4] = result[7] + result[8];
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| result[1] = result[0] + result[4];
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| break;
|
| }
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| //--- return result
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| return result;
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| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Gets AgentReward. |
|
| //+------------------------------------------------------------------+
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| vector<float> GetAgentReward(int skill, vector<float> &discriminator, float &prev_account[])
|
| {
|
| //--- prepare
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| matrix<float> discriminator_matrix;
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| discriminator_matrix.Init(1, discriminator.Size());
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| discriminator_matrix.Row(discriminator, 0);
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| discriminator_matrix.Reshape(NSkills, AccountDescr);
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| vector<float> forecast = discriminator_matrix.Row(skill);
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| //--- check action
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| int action = 3;
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| float buy = forecast[5] - prev_account[5];
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| float sell = forecast[6] - prev_account[6];
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| if(buy < 0 && sell < 0)
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| action = 2;
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| else
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| if(buy > sell)
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| action = 0;
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| else
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| if(buy < sell)
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| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
