| //+------------------------------------------------------------------+
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| //| Study.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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| #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 = 1000;
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| STrajectory Buffer[];
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| CNet Agent;
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| //---
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| float dError;
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| datetime dtStudied;
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| //---
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| CBufferFloat State;
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| CBufferFloat *Result;
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| vector<float> Actions;
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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(!Agent.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true))
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| {
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| Print("Init new models");
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| CArrayObj *agent = new CArrayObj();
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| if(!CreateDescriptions(agent))
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| {
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| delete agent;
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| return INIT_FAILED;
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| }
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| if(!Agent.Create(agent))
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| {
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| delete agent;
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| return INIT_FAILED;
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| }
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| delete agent;
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| }
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| //---
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| Agent.getResults(Result);
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| if(Result.Total() != NActions)
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| {
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| PrintFormat("The scope of the Agent 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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| Agent.GetLayerOutput(0, Result);
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| if(Result.Total() != (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions))
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| {
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| PrintFormat("Input size of Agent doesn't match state description (%d <> %d)", Result.Total(), (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions));
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| return INIT_FAILED;
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| }
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| //---
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| if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
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| {
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| PrintFormat("Error of create study event: %d", GetLastError());
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| return INIT_FAILED;
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| }
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| //---
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| return(INIT_SUCCEEDED);
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| }
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| //+------------------------------------------------------------------+
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| //| Expert deinitialization function |
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| //+------------------------------------------------------------------+
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| void OnDeinit(const int reason)
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| {
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| //---
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| Agent.Save(FileName + "Act.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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| float max_reward = 0, quanitle = 0;
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| vector<float> std;
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| vector<float> probability = GetProbTrajectories(Buffer, max_reward, quanitle, std, 0.95, 0.1f);
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| uint ticks = GetTickCount();
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| //---
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| bool StopFlag = false;
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| for(int iter = 0; (iter < Iterations && !IsStopped() && !StopFlag); 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)) * MathMax(Buffer[tr].Total - 2 * HistoryBars - ValueBars, MathMin(Buffer[tr].Total, 20)));
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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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| Actions = vector<float>::Zeros(NActions);
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| Agent.Clear();
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| for(int state = i; state < MathMin(Buffer[tr].Total - 1 - ValueBars, i + HistoryBars * 3); state++)
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| {
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| //--- History data
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| State.AssignArray(Buffer[tr].States[state].state);
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| //--- Account description
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| float PrevBalance = (state == 0 ? Buffer[tr].States[state].account[0] : Buffer[tr].States[state - 1].account[0]);
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| float PrevEquity = (state == 0 ? Buffer[tr].States[state].account[1] : Buffer[tr].States[state - 1].account[1]);
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| State.Add((Buffer[tr].States[state].account[0] - PrevBalance) / PrevBalance);
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| State.Add(Buffer[tr].States[state].account[1] / PrevBalance);
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| State.Add((Buffer[tr].States[state].account[1] - PrevEquity) / PrevEquity);
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| State.Add(Buffer[tr].States[state].account[2]);
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| State.Add(Buffer[tr].States[state].account[3]);
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| State.Add(Buffer[tr].States[state].account[4] / PrevBalance);
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| State.Add(Buffer[tr].States[state].account[5] / PrevBalance);
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| State.Add(Buffer[tr].States[state].account[6] / PrevBalance);
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| //--- Time label
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| double x = (double)Buffer[tr].States[state].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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| State.Add((float)MathSin(2.0 * M_PI * x));
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| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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| State.Add((float)MathCos(2.0 * M_PI * x));
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| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_W1);
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| State.Add((float)MathSin(2.0 * M_PI * x));
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| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_D1);
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| State.Add((float)MathSin(2.0 * M_PI * x));
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| //--- Prev action
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| if(state > 0)
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| State.AddArray(Buffer[tr].States[state - 1].action);
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| else
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| State.AddArray(vector<float>::Zeros(NActions));
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| //--- Return to go
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| vector<float> target, result;
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| vector<float> noise = vector<float>::Zeros(NRewards);
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| target.Assign(Buffer[tr].States[0].rewards);
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| if(target.Sum() >= quanitle)
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| noise = Noise(std, 100);
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| target.Assign(Buffer[tr].States[state + 1].rewards);
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| result.Assign(Buffer[tr].States[state + ValueBars].rewards);
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| target = target - result * MathPow(DiscFactor, ValueBars) + noise;
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| State.AddArray(target);
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| //--- Feed Forward
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| if(!Agent.feedForward(GetPointer(State), 1, false, (CBufferFloat*)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| StopFlag = true;
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| break;
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| }
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| //--- Policy study
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| Result.AssignArray(Buffer[tr].States[state].action);
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| if(!Agent.backProp(Result, (CBufferFloat*)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| StopFlag = true;
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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", "Agent", iter * 100.0 / (double)(Iterations), Agent.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__, "Agent", Agent.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 ProbTrajectories. |
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| //+------------------------------------------------------------------+
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| vector<float> GetProbTrajectories(STrajectory &buffer[], float &max_reward, float &quanitle, vector<float> &std, double quant, float lanbda)
|
| {
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| ulong total = buffer.Size();
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| matrix<float> rewards = matrix<float>::Zeros(total, NRewards);
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| vector<float> result;
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| for(ulong i = 0; i < total; i++)
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| {
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| result.Assign(buffer[i].States[0].rewards);
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| rewards.Row(result, i);
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| }
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| std = rewards.Std(0);
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| result = rewards.Sum(1);
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| max_reward = result.Max();
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| //---
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| vector<float> sorted = result;
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| bool sort = true;
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| int iter = 0;
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| while(sort)
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| {
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| sort = false;
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| for(ulong i = 0; i < sorted.Size() - 1; i++)
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| if(sorted[i] > sorted[i + 1])
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| {
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| float temp = sorted[i];
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| sorted[i] = sorted[i + 1];
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| sorted[i + 1] = temp;
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| sort = true;
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| }
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| iter++;
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| }
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| quanitle = sorted.Quantile(quant);
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| //---
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| float min = result.Min() - 0.1f * std.Sum();
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| if(max_reward > min)
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| {
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| float k=result.Percentile(90) - max_reward;
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| vector<float> multipl = MathAbs(result - max_reward)/ (k==0 ? -std.Sum() : k);
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| multipl=exp(multipl);
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| result = (result - min) / (max_reward - min);
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| result = result / (result + lanbda) * multipl;
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| result.ReplaceNan(0);
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| }
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| else
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| result.Fill(1);
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| result = result / result.Sum();
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| result = result.CumSum();
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| //---
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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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| //| | Implements SampleTrajectory. |
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| //+------------------------------------------------------------------+
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| int SampleTrajectory(vector<float> &probability)
|
| {
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| //--- check
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| ulong total = probability.Size();
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| if(total <= 0)
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| return -1;
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| //--- randomize
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| float rnd = float(MathRand() / 32767.0);
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| //--- search
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| if(rnd <= probability[0] || total == 1)
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| return 0;
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| if(rnd > probability[total - 2])
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| return int(total - 1);
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| int result = int(rnd * total);
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| if(probability[result] < rnd)
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| while(probability[result] < rnd)
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| result++;
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| else
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| while(probability[result - 1] >= rnd)
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| result--;
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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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| //| | Implements Noise. |
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| //+------------------------------------------------------------------+
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| vector<float> Noise(vector<float> &std, float multiplyer)
|
| {
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| //--- check
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| ulong total = std.Size();
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| if(total <= 0)
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| return vector<float>::Zeros(0);
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| //---
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| vector<float> result = vector<float>::Zeros(total);
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| for(ulong i = 0; i < total; i++)
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| {
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| float rnd = float(MathRand() / 32767.0);
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| result[i] = std[i] * rnd * multiplyer;
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| }
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| //--- return result
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| return result;
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| }
|
| //+------------------------------------------------------------------+
|
|
|
