forked from mnbvc188199/Warrior_EA
246 lines
19 KiB
MQL5
246 lines
19 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.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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#define Study
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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 = 1e6;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet StateEncoder;
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CNet Critic1;
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CNet Critic2;
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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 Actions;
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CBufferFloat Gradient;
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CBufferFloat *Result;
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//---
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COpenCLMy *OpenCL;
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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(!StateEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new models");
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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CArrayObj *encoder = new CArrayObj();
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if(!CreateDescriptions(actor, critic, encoder))
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{
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delete actor;
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delete critic;
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delete encoder;
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return INIT_FAILED;
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}
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if(!Critic1.Create(critic) || !Critic2.Create(critic) ||
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!StateEncoder.Create(encoder))
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{
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delete actor;
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delete critic;
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delete encoder;
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return INIT_FAILED;
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}
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delete actor;
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delete critic;
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delete encoder;
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//---
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}
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//---
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OpenCL = Critic1.GetOpenCL();
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Critic2.SetOpenCL(OpenCL);
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StateEncoder.SetOpenCL(OpenCL);
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//---
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StateEncoder.getResults(Result);
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if(Result.Total() != LatentCount)
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{
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PrintFormat("The scope of the State Encoder does not match the latent size count (%d <> %d)", LatentCount, Result.Total());
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return INIT_FAILED;
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}
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//---
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StateEncoder.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Input size of State Encoder 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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Critic1.GetLayerOutput(0, Result);
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if(Result.Total() != LatentCount)
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{
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PrintFormat("Input size of Critic1 doesn't match State Encoder output (%d <> %d)", Result.Total(), LatentCount);
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return INIT_FAILED;
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}
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//---
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Critic2.GetLayerOutput(0, Result);
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if(Result.Total() != LatentCount)
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{
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PrintFormat("Input size of Critic2 doesn't match State Encoder output (%d <> %d)", Result.Total(), LatentCount);
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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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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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if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
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{
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StateEncoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
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Critic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Critic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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}
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delete Result;
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delete OpenCL;
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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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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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//---
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vector<float> probability = GetProbTrajectories(Buffer, 0.9);
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//---
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vector<float> rewards, rewards1, rewards2, target_reward;
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uint ticks = GetTickCount();
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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 = SampleTrajectory(probability);
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int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3));
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if(i < 0)
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{
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iter--;
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continue;
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}
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//--- Q-function study
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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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double time = (double)Buffer[tr].States[i].account[7];
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double x = time / (double)(D'2024.01.01' - D'2023.01.01');
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_MN1);
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Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_W1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_D1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(Account.GetIndex() >= 0)
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Account.BufferWrite();
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//---
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if(!StateEncoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
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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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Actions.AssignArray(Buffer[tr].States[i].action);
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if(Actions.GetIndex() >= 0)
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Actions.BufferWrite();
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//---
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if(!Critic1.feedForward(GetPointer(StateEncoder), -1, GetPointer(Actions)) ||
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!Critic2.feedForward(GetPointer(StateEncoder), -1, GetPointer(Actions)))
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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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Critic1.getResults(rewards1);
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Critic2.getResults(rewards2);
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//---
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rewards.Assign(Buffer[tr].States[i + 1].rewards);
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target_reward.Assign(Buffer[tr].States[i + 2].rewards);
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rewards = rewards - target_reward * DiscFactor;
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Result.AssignArray(CAGrad(rewards - rewards1) + rewards1);
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if(!Critic1.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
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!StateEncoder.backPropGradient(GetPointer(Account), GetPointer(Gradient)))
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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(CAGrad(rewards - rewards2) + rewards2);
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if(!Critic2.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
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!StateEncoder.backPropGradient(GetPointer(Account), GetPointer(Gradient)))
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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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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
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str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.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__, "Critic1", Critic1.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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