forked from mnbvc188199/Warrior_EA
566 lines
47 KiB
MQL5
566 lines
47 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 = 10000;
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input float Tau = 0.01f;
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input float MaxErrorActorStudy = 0.15f;
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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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CNet Critic1;
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CNet Critic2;
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CNet TargetCritic1;
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CNet TargetCritic2;
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CNet Convolution;
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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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vector<float> check;
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int StartTargetIter;
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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(!Actor.Load(FileName + "Act.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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!TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetCritic2.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 *convolution = new CArrayObj();
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if(!CreateDescriptions(actor, critic, convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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if(!Actor.Create(actor) || !Critic1.Create(critic) || !Critic2.Create(critic) ||
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!Convolution.Create(convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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if(!TargetCritic1.Create(critic) || !TargetCritic2.Create(critic))
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{
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delete actor;
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delete critic;
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delete convolution;
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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 convolution;
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//---
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TargetCritic1.WeightsUpdate(GetPointer(Critic1), 1.0f);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), 1.0f);
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StartTargetIter = StartTargetIteration;
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}
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else
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StartTargetIter = 0;
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//---
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if(!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new Encoder model");
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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CArrayObj *convolution = new CArrayObj();
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if(!CreateDescriptions(actor, critic, convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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if(!Convolution.Create(convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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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 convolution;
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}
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//---
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OpenCL = Actor.GetOpenCL();
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Critic1.SetOpenCL(OpenCL);
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Critic2.SetOpenCL(OpenCL);
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TargetCritic1.SetOpenCL(OpenCL);
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TargetCritic2.SetOpenCL(OpenCL);
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Convolution.SetOpenCL(OpenCL);
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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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Actor.GetLayerOutput(LatentLayer, Result);
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int latent_state = Result.Total();
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Critic1.GetLayerOutput(0, Result);
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if(Result.Total() != latent_state)
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{
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PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
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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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TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
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Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
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TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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TargetCritic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Convolution.Save(FileName + "CNN.nnw", 0, 0, 0, TimeCurrent(), true);
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delete Result;
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}
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//+------------------------------------------------------------------+
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//| ChartEvent function |
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//+------------------------------------------------------------------+
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void OnChartEvent(const int id,
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const long &lparam,
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const double &dparam,
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const string &sparam)
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{
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//---
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if(id == 1001)
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Train();
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}
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//+------------------------------------------------------------------+
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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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int total_tr = ArraySize(Buffer);
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uint ticks = GetTickCount();
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//---
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int total_states = Buffer[0].Total;
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for(int i = 1; i < total_tr; i++)
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total_states += Buffer[i].Total;
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vector<float> temp, next;
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Convolution.getResults(temp);
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matrix<float> state_embedding = matrix<float>::Zeros(total_states, temp.Size());
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matrix<float> rewards = matrix<float>::Zeros(total_states, NRewards);
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matrix<float> actions = matrix<float>::Zeros(total_states, NActions);
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int state = 0;
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for(int tr = 0; tr < total_tr; tr++)
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{
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for(int st = 0; st < Buffer[tr].Total; st++)
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{
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State.AssignArray(Buffer[tr].States[st].state);
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float PrevBalance = Buffer[tr].States[MathMax(st - 1, 0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(st - 1, 0)].account[1];
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State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
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State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
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State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
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State.Add(Buffer[tr].States[st].account[2]);
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State.Add(Buffer[tr].States[st].account[3]);
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State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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State.AddArray(vector<float>::Zeros(NActions));
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if(!Convolution.feedForward(GetPointer(State), 1, false, NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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ExpertRemove();
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return;
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}
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Convolution.getResults(temp);
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if(!state_embedding.Row(temp, state))
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continue;
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if(!temp.Assign(Buffer[tr].States[st].rewards) ||
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!next.Assign(Buffer[tr].States[st + 1].rewards) ||
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!rewards.Row(temp - next * DiscFactor, state))
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continue;
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if(!temp.Assign(Buffer[tr].States[st].action) ||
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!actions.Row(temp, state))
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continue;
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state++;
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
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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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if(state != total_states)
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{
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rewards.Resize(state, NRewards);
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actions.Resize(state, NActions);
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state_embedding.Reshape(state, state_embedding.Cols());
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total_states = state;
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}
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//---
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vector<float> rewards1, rewards2, target_reward;
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STarget target;
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//---
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vector<float> probability = GetProbTrajectories(Buffer, 0.9);
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int bar = (HistoryBars - 1) * BarDescr;
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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 - 2));
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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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target_reward = vector<float>::Zeros(NRewards);
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//--- Target
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if(iter >= StartTargetIter)
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{
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State.AssignArray(Buffer[tr].States[i + 1].state);
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float PrevBalance = Buffer[tr].States[i].account[0];
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float PrevEquity = Buffer[tr].States[i].account[1];
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Account.Clear();
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Account.Add((Buffer[tr].States[i + 1].account[0] - PrevBalance) / PrevBalance);
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Account.Add(Buffer[tr].States[i + 1].account[1] / PrevBalance);
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Account.Add((Buffer[tr].States[i + 1].account[1] - PrevEquity) / PrevEquity);
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Account.Add(Buffer[tr].States[i + 1].account[2]);
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Account.Add(Buffer[tr].States[i + 1].account[3]);
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Account.Add(Buffer[tr].States[i + 1].account[4] / PrevBalance);
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Account.Add(Buffer[tr].States[i + 1].account[5] / PrevBalance);
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Account.Add(Buffer[tr].States[i + 1].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[i + 1].account[7] / (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 = (double)Buffer[tr].States[i + 1].account[7] / (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 = (double)Buffer[tr].States[i + 1].account[7] / (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 = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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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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break;
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}
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//---
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if(!TargetCritic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)) ||
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!TargetCritic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)))
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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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TargetCritic1.getResults(rewards1);
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TargetCritic2.getResults(rewards2);
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target_reward.Assign(Buffer[tr].States[i + 1].rewards);
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if(rewards1.Sum() <= rewards2.Sum())
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target_reward = rewards1 - target_reward;
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else
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target_reward = rewards2 - target_reward;
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target_reward *= DiscFactor;
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target_reward[NRewards - 1] = EntropyLatentState(Actor);
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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 x = (double)Buffer[tr].States[i].account[7] / (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 = (double)Buffer[tr].States[i].account[7] / (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 = (double)Buffer[tr].States[i].account[7] / (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 = (double)Buffer[tr].States[i].account[7] / (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(!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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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(Actor), LatentLayer, GetPointer(Actions)) ||
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!Critic2.feedForward(GetPointer(Actor), LatentLayer, 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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if(!State.AddArray(GetPointer(Account)) || !State.AddArray(vector<float>::Zeros(NActions)) ||
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!Convolution.feedForward(GetPointer(State), 1, false, NULL))
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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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Convolution.getResults(temp);
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target = GetTargets(Quant, temp, state_embedding, rewards, actions);
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//---
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Critic1.getResults(rewards1);
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Result.AssignArray(CAGrad(target.rewards + target_reward - rewards1) + rewards1);
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if(!Critic1.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
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!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
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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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Critic2.getResults(rewards2);
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Result.AssignArray(CAGrad(target.rewards + target_reward - rewards2) + rewards2);
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if(!Critic2.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
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!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
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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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//--- Policy study
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Actor.getResults(rewards1);
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Result.AssignArray(CAGrad(target.actions - rewards1) + rewards1);
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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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break;
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}
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//---
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CNet *critic = NULL;
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if(Critic1.getRecentAverageError() <= Critic2.getRecentAverageError())
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critic = GetPointer(Critic1);
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else
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critic = GetPointer(Critic2);
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if(MathAbs(critic.getRecentAverageError()) <= MaxErrorActorStudy)
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{
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if(!critic.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)))
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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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critic.getResults(rewards1);
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Result.AssignArray(CAGrad(target.rewards + target_reward - rewards1) + rewards1);
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critic.TrainMode(false);
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if(!critic.backProp(Result, GetPointer(Actor)) ||
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!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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critic.TrainMode(true);
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break;
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}
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critic.TrainMode(true);
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}
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//--- Update Target Nets
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if(iter >= StartTargetIter)
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{
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TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
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}
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else
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{
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TargetCritic1.WeightsUpdate(GetPointer(Critic1), 1);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), 1);
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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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str += StringFormat("%-14s %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__, "Critic1", Critic1.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
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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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//+------------------------------------------------------------------+
| |
vector<float> CAGrad(vector<float> &grad)
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{
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matrix<float> GG = grad.Outer(grad);
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GG.ReplaceNan(0);
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if(MathAbs(GG).Sum() == 0)
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return grad;
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float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
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GG = GG / MathPow(scale, 2);
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vector<float> Gg = GG.Mean(1);
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float gg = Gg.Mean();
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vector<float> w = vector<float>::Zeros(grad.Size());
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float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
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vector<float> w_best = w;
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float obj_best = FLT_MAX;
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vector<float> moment = vector<float>::Zeros(w.Size());
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for(int i = 0; i < iCAGrad_Iters; i++)
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{
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vector<float> ww;
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w.Activation(ww, AF_SOFTMAX);
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float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
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if(MathAbs(obj) < obj_best)
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{
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obj_best = MathAbs(obj);
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w_best = w;
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}
| |
if(i < (iCAGrad_Iters - 1))
| |
{
| |
float loss = -obj;
| |
vector<float> derev = Gg + GG.MatMul(ww) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2) + ww.MatMul(GG) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2);
| |
vector<float> delta = derev * loss;
| |
ulong size = delta.Size();
| |
matrix<float> ident = matrix<float>::Identity(size, size);
| |
vector<float> ones = vector<float>::Ones(size);
| |
matrix<float> sm_der = ones.Outer(ww);
| |
sm_der = sm_der.Transpose() * (ident - sm_der);
| |
delta = sm_der.MatMul(delta);
| |
if(delta.Ptp() != 0)
| |
delta = delta / delta.Ptp();
| |
moment = delta * 0.8f + moment * 0.5f;
| |
w += moment;
| |
if(w.Ptp() != 0)
| |
w = w / w.Ptp();
| |
}
| |
}
| |
w_best.Activation(w, AF_SOFTMAX);
| |
float gw_norm = MathSqrt(w.MatMul(GG).Dot(w) + 1.0e-4f);
| |
float lmbda = c / (gw_norm + 1.0e-4f);
| |
vector<float> result = ((w * lmbda + 1.0f / (float)grad.Size()) * grad) / (1 + MathPow(fCAGrad_C, 2));
| |
//---
| |
return result;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
STarget GetTargets(double quant, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards, matrix<float> &actions)
| |
{
| |
STarget result;
| |
if(embedding.Size() != state_embedding.Cols())
| |
{
| |
PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
| |
return result;
| |
}
| |
//---
| |
ulong size = embedding.Size();
| |
ulong states = state_embedding.Rows();
| |
ulong k = ulong(states * quant);
| |
matrix<float> temp = matrix<float>::Zeros(states, size);
| |
for(ulong i = 0; i < size; i++)
| |
temp.Col(MathAbs(state_embedding.Col(i) - embedding[i]), i);
| |
float alpha = temp.Max();
| |
vector<float> dist = MathLog(MathExp(temp / (-alpha)).Sum(1)) * (-alpha);
| |
vector<float> min_dist = vector<float>::Zeros(k);
| |
matrix<float> k_rewards = matrix<float>::Zeros(k, NRewards);
| |
matrix<float> k_actions = matrix<float>::Zeros(k, NActions);
| |
matrix<float> k_embedding = matrix<float>::Zeros(k + 1, size);
| |
matrix<float> U, V;
| |
vector<float> S;
| |
float max = dist.Quantile(quant);
| |
float min = dist.Min();
| |
for(ulong i = 0, cur = 0; (i < states && cur < k); i++)
| |
{
| |
if(max < dist[i])
| |
continue;
| |
min_dist[cur] = dist[i];
| |
k_rewards.Row(rewards.Row(i), cur);
| |
k_actions.Row(actions.Row(i), cur);
| |
k_embedding.Row(state_embedding.Row(i), cur);
| |
cur++;
| |
}
| |
k_embedding.Row(embedding, k);
| |
//---
| |
vector<float> sf;
| |
(min_dist * (-1)).Activation(sf, AF_SOFTMAX);
| |
result.rewards = sf.MatMul(k_rewards);
| |
//---
| |
k_embedding.SVD(U, V, S);
| |
result.rewards[NRewards - 2] = S.Sum() / (MathSqrt(MathPow(k_embedding, 2.0f).Sum() * MathMax(k + 1, size)));
| |
result.rewards[NRewards - 1] = EntropyLatentState(Actor);
| |
//---
| |
vector<float> act_sf;
| |
alpha = MathAbs(k_rewards).Max();
| |
dist = MathLog(MathExp(k_rewards / (-alpha)).Sum(1)) * (-alpha);
| |
dist.Activation(act_sf, AF_SOFTMAX);
| |
result.actions = act_sf.MatMul(k_actions);
| |
//---
| |
return result;
| |
}
| |
//+------------------------------------------------------------------+
|