| //+------------------------------------------------------------------+
|
| //| Study.mq5 |
|
| //| Copyright DNG® |
|
| //| https://www.mql5.com/ru/users/dng |
|
| //+------------------------------------------------------------------+
|
| #property copyright "Copyright DNG®"
|
| #property link "https://www.mql5.com/ru/users/dng"
|
| #property version "1.00"
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| #define Study
|
| #define Critic
|
| #include "Trajectory.mqh"
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input int Iterations = 100000;
|
| input int Batch = 10;
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| STrajectory Buffer[];
|
| CNet Encoder;
|
| CNet Actor;
|
| CNet Probability;
|
| //---
|
| float dError;
|
| datetime dtStudied;
|
| //---
|
| CBufferFloat bState;
|
| CBufferFloat bTime;
|
| CBufferFloat bAccount;
|
| CBufferFloat bGradient;
|
| CBufferFloat bActions;
|
| CBufferFloat *Result;
|
| vector<float> check;
|
| vector<float> STD_Actor;
|
| vector<float> STD_Goal;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| //---
|
| ResetLastError();
|
| if(!LoadTotalBase())
|
| {
|
| PrintFormat("Error of load study data: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //--- load models
|
| float temp;
|
| CArrayObj *encoder = new CArrayObj();
|
| CArrayObj *actor = new CArrayObj();
|
| CArrayObj *probability = new CArrayObj();
|
| if(!CreateDescriptions(encoder, actor, probability))
|
| {
|
| delete encoder;
|
| delete actor;
|
| delete probability;
|
| return INIT_FAILED;
|
| }
|
| if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| Print("Create new State Encoder");
|
| if(!Encoder.Create(encoder))
|
| {
|
| delete encoder;
|
| delete actor;
|
| delete probability;
|
| return INIT_FAILED;
|
| }
|
| }
|
| if(!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| Print("Create new Actor");
|
| if(!Actor.Create(actor))
|
| {
|
| delete encoder;
|
| delete actor;
|
| delete probability;
|
| return INIT_FAILED;
|
| }
|
| }
|
| if(!Probability.Load(FileName + "Prb.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| Print("Create new Probability model");
|
| if(!Probability.Create(probability))
|
| {
|
| delete encoder;
|
| delete actor;
|
| delete probability;
|
| return INIT_FAILED;
|
| }
|
| }
|
| delete encoder;
|
| delete actor;
|
| delete probability;
|
| //---
|
| Encoder.TrainMode(true);
|
| Actor.TrainMode(true);
|
| Probability.TrainMode(true);
|
| Probability.SetOpenCL(Actor.GetOpenCL());
|
| Encoder.SetOpenCL(Actor.GetOpenCL());
|
| //---
|
| Actor.getResults(Result);
|
| if(Result.Total() != NActions)
|
| {
|
| PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| Encoder.GetLayerOutput(0, Result);
|
| if(Result.Total() != (HistoryBars * BarDescr))
|
| {
|
| PrintFormat("Input size of Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
|
| return INIT_FAILED;
|
| }
|
| //---
|
| if(!bGradient.BufferInit(MathMax(AccountDescr, NActions), 0) ||
|
| !bGradient.BufferCreate(Actor.GetOpenCL()))
|
| {
|
| PrintFormat("Error of create buffers: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
|
| {
|
| PrintFormat("Error of create study event: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| return(INIT_SUCCEEDED);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Expert deinitialization function |
|
| //+------------------------------------------------------------------+
|
| void OnDeinit(const int reason)
|
| {
|
| //---
|
| if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
|
| {
|
| Encoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
|
| Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
|
| Probability.Save(FileName + "Prb.nnw", 0, 0, 0, TimeCurrent(), true);
|
| }
|
| delete Result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| ChartEvent function |
|
| //+------------------------------------------------------------------+
|
| void OnChartEvent(const int id,
|
| const long &lparam,
|
| const double &dparam,
|
| const string &sparam)
|
| {
|
| //---
|
| if(id == 1001)
|
| Train();
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Train function |
|
| //+------------------------------------------------------------------+
|
| void Train(void)
|
| {
|
| //---
|
| vector<float> probability = vector<float>::Full(Buffer.Size(), 1.0f / Buffer.Size());
|
| //---
|
| vector<float> result, target, state;
|
| matrix<float> fstate = matrix<float>::Zeros(1, NForecast * BarDescr);
|
| bool Stop = false;
|
| //---
|
| uint ticks = GetTickCount();
|
| //---
|
| for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter += Batch)
|
| {
|
| int tr = SampleTrajectory(probability);
|
| int start = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2 - NForecast - Batch));
|
| if(start <= 0)
|
| {
|
| iter -= Batch;
|
| continue;
|
| }
|
| if(!Encoder.Clear() ||
|
| !Actor.Clear())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| result = vector<float>::Zeros(NActions);
|
| for(int i = start; i < MathMin(Buffer[tr].Total, start + Batch); i++)
|
| {
|
| if(!state.Assign(Buffer[tr].States[i].state) ||
|
| // Implements MathAbs.
|
| MathAbs(state).Sum() == 0 ||
|
| !bState.AssignArray(state))
|
| {
|
| iter -= Batch + start - i;
|
| break;
|
| }
|
| //---
|
| bTime.Clear();
|
| double time = (double)Buffer[tr].States[i].account[7];
|
| double x = time / (double)(D'2024.01.01' - D'2023.01.01');
|
| bTime.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = time / (double)PeriodSeconds(PERIOD_MN1);
|
| bTime.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = time / (double)PeriodSeconds(PERIOD_W1);
|
| bTime.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = time / (double)PeriodSeconds(PERIOD_D1);
|
| bTime.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| if(bTime.GetIndex() >= 0)
|
| bTime.BufferWrite();
|
| //--- Account
|
| float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
|
| float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
|
| float profit = float(bState[0] / _Point * (result[0] - result[3]));
|
| bAccount.Clear();
|
| bAccount.Add(1);
|
| bAccount.Add((PrevEquity + profit) / PrevEquity);
|
| bAccount.Add(profit / PrevEquity);
|
| bAccount.Add(MathMax(result[0] - result[3], 0));
|
| bAccount.Add(MathMax(result[3] - result[0], 0));
|
| bAccount.Add((bAccount[3] > 0 ? profit / PrevEquity : 0));
|
| bAccount.Add((bAccount[4] > 0 ? profit / PrevEquity : 0));
|
| bAccount.Add(0);
|
| bAccount.AddArray(GetPointer(bTime));
|
| if(bAccount.GetIndex() >= 0)
|
| bAccount.BufferWrite();
|
| //--- Feed Forward
|
| if(!Encoder.feedForward((CBufferFloat*)GetPointer(bState), 1, false, (CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| if(!Actor.feedForward((CBufferFloat*)GetPointer(bAccount), 1, false, GetPointer(Encoder), LatentLayer))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| if(!Probability.feedForward(GetPointer(Encoder), LatentLayer, (CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| //--- Look for target
|
| target = vector<float>::Zeros(NActions);
|
| bActions.AssignArray(target);
|
| if(!state.Assign(Buffer[tr].States[i + NForecast].state) ||
|
| !state.Resize(NForecast * BarDescr) ||
|
| // Implements MathAbs.
|
| MathAbs(state).Sum() == 0)
|
| {
|
| iter -= Batch + start - i;
|
| break;
|
| }
|
| if(!fstate.Resize(1, NForecast * BarDescr) ||
|
| !fstate.Row(state, 0) ||
|
| !fstate.Reshape(NForecast, BarDescr))
|
| {
|
| iter -= Batch + start - i;
|
| break;
|
| }
|
| for(int j = 0; j < NForecast / 2; j++)
|
| {
|
| if(!fstate.SwapRows(j, NForecast - j - 1))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| }
|
| target = fstate.Col(0).CumSum();
|
| if(result[0] > result[3])
|
| {
|
| float tp = 0;
|
| float sl = 0;
|
| float cur_sl = float(-(result[2] > 0 ? result[2] : 1) * MaxSL * Point());
|
| int pos = 0;
|
| for(int j = 0; j < NForecast; j++)
|
| {
|
| tp = MathMax(tp, target[j] + fstate[j, 1] - fstate[j, 0]);
|
| pos = j;
|
| if(cur_sl >= target[j] + fstate[j, 2] - fstate[j, 0])
|
| break;
|
| sl = MathMin(sl, target[j] + fstate[j, 2] - fstate[j, 0]);
|
| }
|
| if(tp > 0)
|
| {
|
| sl = (float)MathMax(MathMin(MathAbs(sl) / (MaxSL * Point()), 1), 0.01);
|
| tp = float(MathMin(tp / (MaxTP * Point()), 1));
|
| result[0] = MathMax(result[0] - result[3], 0.011f);
|
| result[5] = result[1] = tp;
|
| result[4] = result[2] = sl;
|
| result[3] = 0;
|
| bActions.AssignArray(result);
|
| }
|
| }
|
| else
|
| {
|
| if(result[0] < result[3])
|
| {
|
| float tp = 0;
|
| float sl = 0;
|
| float cur_sl = float((result[5] > 0 ? result[5] : 1) * MaxSL * Point());
|
| int pos = 0;
|
| for(int j = 0; j < NForecast; j++)
|
| {
|
| tp = MathMin(tp, target[j] + fstate[j, 2] - fstate[j, 0]);
|
| pos = j;
|
| if(cur_sl <= target[j] + fstate[j, 1] - fstate[j, 0])
|
| break;
|
| sl = MathMax(sl, target[j] + fstate[j, 1] - fstate[j, 0]);
|
| }
|
| if(tp < 0)
|
| {
|
| sl = (float)MathMax(MathMin(MathAbs(sl) / (MaxSL * Point()), 1), 0.01);
|
| tp = float(MathMin(-tp / (MaxTP * Point()), 1));
|
| result[3] = MathMax(result[3] - result[0], 0.011f);
|
| result[2] = result[4] = tp;
|
| result[1] = result[5] = sl;
|
| result[0] = 0;
|
| bActions.AssignArray(result);
|
| }
|
| }
|
| else
|
| {
|
| ulong argmin = target.ArgMin();
|
| ulong argmax = target.ArgMax();
|
| float max_sl = float(MaxSL * Point());
|
| while(argmax > 0 && argmin > 0)
|
| {
|
| if(argmax < argmin && target[argmax] / 2 > MathAbs(target[argmin]) && MathAbs(target[argmin]) < max_sl)
|
| break;
|
| if(argmax > argmin && target[argmax] < MathAbs(target[argmin] / 2) && target[argmax] < max_sl)
|
| break;
|
| target.Resize(MathMin(argmax, argmin));
|
| argmin = target.ArgMin();
|
| argmax = target.ArgMax();
|
| }
|
| if(argmin == 0 || (argmax < argmin && argmax > 0))
|
| {
|
| float tp = 0;
|
| float sl = 0;
|
| float cur_sl = - float(MaxSL * Point());
|
| ulong pos = 0;
|
| for(ulong j = 0; j < argmax; j++)
|
| {
|
| tp = MathMax(tp, target[j] + fstate[j, 1] - fstate[j, 0]);
|
| pos = j;
|
| if(cur_sl >= target[j] + fstate[j, 2] - fstate[j, 0])
|
| break;
|
| sl = MathMin(sl, target[j] + fstate[j, 2] - fstate[j, 0]);
|
| }
|
| if(tp > 0)
|
| {
|
| sl = (float)MathMax(MathMin(MathAbs(sl) / (MaxSL * Point()), 1), 0.01);
|
| tp = (float)MathMin(tp / (MaxTP * Point()), 1);
|
| result[0] = float(MathMax(Buffer[tr].States[i].account[0] / 100 * 0.01, 0.011));
|
| result[5] = result[1] = tp;
|
| result[4] = result[2] = sl;
|
| result[3] = 0;
|
| bActions.AssignArray(result);
|
| }
|
| }
|
| else
|
| {
|
| if(argmax == 0 || argmax > argmin)
|
| {
|
| float tp = 0;
|
| float sl = 0;
|
| float cur_sl = float(MaxSL * Point());
|
| ulong pos = 0;
|
| for(ulong j = 0; j < argmin; j++)
|
| {
|
| tp = MathMin(tp, target[j] + fstate[j, 2] - fstate[j, 0]);
|
| pos = j;
|
| if(cur_sl <= target[j] + fstate[j, 1] - fstate[j, 0])
|
| break;
|
| sl = MathMax(sl, target[j] + fstate[j, 1] - fstate[j, 0]);
|
| }
|
| if(tp < 0)
|
| {
|
| sl = (float)MathMax(MathMin(MathAbs(sl) / (MaxSL * Point()), 1), 0.01);
|
| tp = (float)MathMin(-tp / (MaxTP * Point()), 1);
|
| result[3] = float(MathMax(Buffer[tr].States[i].account[0] / 100 * 0.01, 0.011));
|
| result[2] = result[4] = tp;
|
| result[1] = result[5] = sl;
|
| result[0] = 0;
|
| bActions.AssignArray(result);
|
| }
|
| }
|
| }
|
| }
|
| }
|
| //--- State Encoder
|
| if(!Encoder.backProp(GetPointer(bState), (CBufferFloat*)NULL, NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| //--- Actor Policy
|
| if(!Actor.backProp(GetPointer(bActions), (CNet*)GetPointer(Encoder), LatentLayer)
|
| || !Encoder.backPropGradient((CBufferFloat*)NULL, (CBufferFloat*)NULL, LatentLayer, true)
|
| )
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| target = vector<float>::Zeros(NActions / 3);
|
| if(fstate[0, 0] > 0)
|
| target[0] = 1;
|
| else
|
| if(fstate[0, 0] < 0)
|
| target[1] = 1;
|
| if(!Result.AssignArray(target) ||
|
| !Probability.backProp(Result, (CBufferFloat*)NULL)
|
| || !Encoder.backPropGradient((CBufferFloat*)NULL, (CBufferFloat*)NULL, LatentLayer)
|
| )
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| Stop = true;
|
| break;
|
| }
|
| //---
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| double percent = double(iter + i - start) * 100.0 / (Iterations);
|
| string str = StringFormat("%-13s %6.2f%% -> Error %15.8f\n", "Encoder", percent, Encoder.getRecentAverageError());
|
| str += StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Actor", percent, Actor.getRecentAverageError());
|
| str += StringFormat("%-13s %6.2f%% -> Error %15.8f\n", "Probability", percent, Probability.getRecentAverageError());
|
| Comment(str);
|
| ticks = GetTickCount();
|
| }
|
| }
|
| }
|
| Comment("");
|
| //---
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Actor", Actor.getRecentAverageError());
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Probability", Probability.getRecentAverageError());
|
| ExpertRemove();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
