forked from mnbvc188199/Warrior_EA
278 lines
20 KiB
MQL5
278 lines
20 KiB
MQL5
//+------------------------------------------------------------------+
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//| StudyActor.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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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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//---
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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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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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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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vector<float> GetNewState(float &prev_account[], int action, double prof_1l)
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{
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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;
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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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}
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//--- return result
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return result;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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vector<float> GetAgentReward(int skill, vector<float> &discriminator, float &prev_account[])
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{
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//--- 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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}
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//+------------------------------------------------------------------+
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