| //+------------------------------------------------------------------+
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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 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 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 Account;
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| CBufferFloat Gradient;
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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(!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true))
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| {
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| CArrayObj *actor = new CArrayObj();
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| if(!CreateDescriptions(actor))
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| {
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| delete actor;
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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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| return INIT_FAILED;
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| }
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| delete actor;
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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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| //---
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| Actor.GetLayerOutput(0, Result);
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| if(Result.Total() != (HistoryBars * BarDescr))
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| {
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| PrintFormat("Input size of Actor doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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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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| Gradient.BufferInit(AccountDescr, 0);
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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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| Actor.Save(FileName + "Act.nnw", Actor.getRecentAverageError(), 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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| //| | 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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| int total_steps = 0;
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| for(int tr = 0; tr < total_tr; tr++)
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| {
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| if(Buffer[tr].Total > total_steps)
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| total_steps = Buffer[tr].Total;
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| }
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| uint ticks = GetTickCount();
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| int total_iter=(Iterations+total_tr-1)/total_tr;
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| //---
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| for(int iter = 0; (iter < total_iter && !IsStopped()); iter ++)
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| {
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| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (total_steps - 2));
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| for(int tr = 0; tr < total_tr; tr++)
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| {
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| if(i >= (Buffer[tr].Total - 1))
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| continue;
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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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| Account.Clear();
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| Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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| Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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| Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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| Account.Add(Buffer[tr].States[i].account[2]);
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| Account.Add(Buffer[tr].States[i].account[3]);
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| Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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| Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
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| Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
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| //---
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| if(Account.GetIndex() >= 0)
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| Account.BufferWrite();
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| if(!Actor.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
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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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| ActorResult = vector<float>::Zeros(NActions);
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| ActorResult[Buffer[tr].Actions[i]] = Buffer[tr].Revards[i];
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| Result.AssignArray(ActorResult);
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| if(!Actor.backProp(Result, GetPointer(Account), GetPointer(Gradient)))
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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)(total_iter), 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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| }
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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;
|
| 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;
|
| }
|
| //--- 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;
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| //--- calculate reward
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| vector<float> result = vector<float>::Zeros(NActions);
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| float mean = (forecast / discriminator_matrix.Mean(0)).Mean();
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| result[action] = MathLog(MathAbs(mean));
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| //--- return result
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| return result;
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| }
|
| //+------------------------------------------------------------------+
|
|
|
