| //+------------------------------------------------------------------+
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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 = 1e3;
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| STrajectory Buffer[];
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| CNet Scheduler;
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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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| //+------------------------------------------------------------------+
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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(!Scheduler.Load(FileName + "Sch.nnw", temp, temp, temp, dtStudied, true))
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| {
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| CArrayObj *scheduler = new CArrayObj();
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| if(!CreateSchedulerDescriptions(scheduler))
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| {
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| delete scheduler;
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| return INIT_FAILED;
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| }
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| if(!Scheduler.Create(scheduler))
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| {
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| delete scheduler;
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| return INIT_FAILED;
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| }
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| delete scheduler;
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| //---
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| }
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| //---
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| Scheduler.getResults(Result);
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| if(Result.Total() != EmbeddingSize)
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| {
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| PrintFormat("The scope of the Scheduler-model does not match the Embedding count (%d <> %d)", EmbeddingSize, Result.Total());
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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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| Scheduler.Save(FileName + "Sch.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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| //| | T ra in implementation. |
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| //+------------------------------------------------------------------+
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| void Train(void)
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| {
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| vector<double> probability = GetProbTrajectories(Buffer, 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_p = SampleTrajectory(probability);
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| int tr_m = SampleTrajectory(probability);
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| while(tr_p == tr_m)
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| tr_m = SampleTrajectory(probability);
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| if(Buffer[tr_p].Profit < Buffer[tr_m].Profit)
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| {
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| int t = tr_p;
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| tr_p = tr_m;
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| tr_m = t;
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| }
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| //--- Positive
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| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * MathMax(Buffer[tr_p].Total - 2 * HistoryBars - NBarInPattern, MathMin(Buffer[tr_p].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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| Scheduler.Clear();
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| for(int state = i; state < MathMin(Buffer[tr_p].Total - 1 - NBarInPattern, i + HistoryBars * 2); state++)
|
| {
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| //--- History data
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| State.AssignArray(Buffer[tr_p].States[state].state);
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| //--- Account description
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| float PrevBalance = (state == 0 ? Buffer[tr_p].States[state].account[0] : Buffer[tr_p].States[state - 1].account[0]);
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| float PrevEquity = (state == 0 ? Buffer[tr_p].States[state].account[1] : Buffer[tr_p].States[state - 1].account[1]);
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| State.Add((Buffer[tr_p].States[state].account[0] - PrevBalance) / PrevBalance);
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| State.Add(Buffer[tr_p].States[state].account[1] / PrevBalance);
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| State.Add((Buffer[tr_p].States[state].account[1] - PrevEquity) / PrevEquity);
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| State.Add(Buffer[tr_p].States[state].account[2]);
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| State.Add(Buffer[tr_p].States[state].account[3]);
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| State.Add(Buffer[tr_p].States[state].account[4] / PrevBalance);
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| State.Add(Buffer[tr_p].States[state].account[5] / PrevBalance);
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| State.Add(Buffer[tr_p].States[state].account[6] / PrevBalance);
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| //--- Time label
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| double x = (double)Buffer[tr_p].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_p].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_p].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_p].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_p].States[state - 1].action);
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| else
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| State.AddArray(vector<float>::Zeros(NActions));
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| //--- Feed Forward
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| if(!Scheduler.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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| //--- Study
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| Result.AssignArray(Buffer[tr_p].States[state].scheduler);
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| if(!Scheduler.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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| string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Scheeduler", iter * 100.0 / (double)(Iterations), Scheduler.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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| //--- Negotive
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| i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * MathMax(Buffer[tr_m].Total - 2 * HistoryBars - NBarInPattern, MathMin(Buffer[tr_m].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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| Scheduler.Clear();
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| for(int state = i; state < MathMin(Buffer[tr_m].Total - 1 - NBarInPattern, i + HistoryBars * 2); state++)
|
| {
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| //--- History data
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| State.AssignArray(Buffer[tr_m].States[state].state);
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| //--- Account description
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| float PrevBalance = (state == 0 ? Buffer[tr_m].States[state].account[0] : Buffer[tr_m].States[state - 1].account[0]);
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| float PrevEquity = (state == 0 ? Buffer[tr_m].States[state].account[1] : Buffer[tr_m].States[state - 1].account[1]);
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| State.Add((Buffer[tr_m].States[state].account[0] - PrevBalance) / PrevBalance);
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| State.Add(Buffer[tr_m].States[state].account[1] / PrevBalance);
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| State.Add((Buffer[tr_m].States[state].account[1] - PrevEquity) / PrevEquity);
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| State.Add(Buffer[tr_m].States[state].account[2]);
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| State.Add(Buffer[tr_m].States[state].account[3]);
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| State.Add(Buffer[tr_m].States[state].account[4] / PrevBalance);
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| State.Add(Buffer[tr_m].States[state].account[5] / PrevBalance);
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| State.Add(Buffer[tr_m].States[state].account[6] / PrevBalance);
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| //--- Time label
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| double x = (double)Buffer[tr_m].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_m].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_m].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_m].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_m].States[state - 1].action);
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| else
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| State.AddArray(vector<float>::Zeros(NActions));
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| //--- Feed Forward
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| if(!Scheduler.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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| //--- Study
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| if(Buffer[tr_p].Profit == Buffer[tr_m].Profit)
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| Result.AssignArray(Buffer[tr_m].States[state].scheduler);
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| else
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| {
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| vector<float> target, forecast;
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| target.Assign(Buffer[tr_m].States[state].scheduler);
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| Scheduler.getResults(forecast);
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| target = forecast - (target - forecast) / 2;
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| Result.AssignArray(target);
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| }
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| if(!Scheduler.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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| if(GetTickCount() - ticks > 500)
|
| {
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| string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Scheeduler", (iter + 0.5) * 100.0 / (double)(Iterations), Scheduler.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__, "Scheduler", Scheduler.getRecentAverageError());
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| ExpertRemove();
|
| //---
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| }
|
| //+------------------------------------------------------------------+
|
|
|
