| //+------------------------------------------------------------------+
|
| //| 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
|
| #include "Trajectory.mqh"
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input int Iterations = 100000;
|
| input float Tau = 0.001f;
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| STrajectory Buffer[];
|
| CNet Encoder;
|
| CNet TargetEncoder;
|
| CNet Actor;
|
| CNet TargetActor;
|
| CNet Critic1;
|
| CNet Critic2;
|
| CNet TargetCritic1;
|
| CNet TargetCritic2;
|
| CNet Convolution;
|
| CNet Scheduler;
|
| //---
|
| float dError;
|
| datetime dtStudied;
|
| //---
|
| CBufferFloat State;
|
| CBufferFloat Account;
|
| CBufferFloat TargetState;
|
| CBufferFloat TargetAccount;
|
| CBufferFloat Actions;
|
| CBufferFloat Gradient;
|
| CBufferFloat Skills;
|
| CBufferFloat *Result;
|
| vector<float> check;
|
| //---
|
| COpenCLMy *OpenCL;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| //---
|
| ResetLastError();
|
| if(!LoadTotalBase())
|
| {
|
| PrintFormat("Error of load study data: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //--- load models
|
| float temp;
|
| if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
|
| !Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
|
| !Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
|
| !Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true) ||
|
| !Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
|
| !TargetEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
|
| !TargetActor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
|
| !TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
|
| !TargetCritic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| Print("No pretrained models found");
|
| return INIT_FAILED;
|
| }
|
| if(!Scheduler.Load(FileName + "Sch.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| CArrayObj *descr = new CArrayObj();
|
| if(!SchedulerDescriptions(descr) || !Scheduler.Create(descr))
|
| {
|
| delete descr;
|
| return INIT_FAILED;
|
| }
|
| delete descr;
|
| }
|
| //---
|
| OpenCL = Actor.GetOpenCL();
|
| Encoder.SetOpenCL(OpenCL);
|
| Critic1.SetOpenCL(OpenCL);
|
| Critic2.SetOpenCL(OpenCL);
|
| TargetEncoder.SetOpenCL(OpenCL);
|
| TargetActor.SetOpenCL(OpenCL);
|
| TargetCritic1.SetOpenCL(OpenCL);
|
| TargetCritic2.SetOpenCL(OpenCL);
|
| Scheduler.SetOpenCL(OpenCL);
|
| Convolution.SetOpenCL(OpenCL);
|
| //---
|
| Actor.TrainMode(false);
|
| Encoder.TrainMode(false);
|
| //---
|
| vector<float> ActorResult;
|
| Actor.getResults(ActorResult);
|
| if(ActorResult.Size() != 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 State Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
|
| return INIT_FAILED;
|
| }
|
| //---
|
| vector<float> EncoderResults;
|
| Actor.GetLayerOutput(0,Result);
|
| Encoder.getResults(EncoderResults);
|
| if(Result.Total() != int(EncoderResults.Size()))
|
| {
|
| PrintFormat("Input size of Actor doesn't match Encoder outputs (%d <> %d)", Result.Total(), EncoderResults.Size());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| Actor.GetLayerOutput(LatentLayer, Result);
|
| int latent_state = Result.Total();
|
| Critic1.GetLayerOutput(0, Result);
|
| if(Result.Total() != latent_state)
|
| {
|
| PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
|
| return INIT_FAILED;
|
| }
|
| //---
|
| Gradient.BufferInit(AccountDescr, 0);
|
| //---
|
| 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)
|
| {
|
| //---
|
| TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
|
| TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
|
| TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
|
| TargetCritic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
|
| Scheduler.Save(FileName + "Sch.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)
|
| {
|
| int total_tr = ArraySize(Buffer);
|
| uint ticks = GetTickCount();
|
| float loss = 0;
|
| //---
|
| int total_states = Buffer[0].Total - 1;
|
| for(int i = 1; i < total_tr; i++)
|
| total_states += Buffer[i].Total - 1;
|
| vector<float> temp;
|
| Convolution.getResults(temp);
|
| matrix<float> state_embedding = matrix<float>::Zeros(total_states,temp.Size());
|
| matrix<float> rewards = matrix<float>::Zeros(total_states,NRewards);
|
| int state = 0;
|
| for(int tr = 0; tr < total_tr; tr++)
|
| {
|
| for(int st = 0; st < Buffer[tr].Total - 1; st++)
|
| {
|
| State.AssignArray(Buffer[tr].States[st].state);
|
| float PrevBalance = Buffer[tr].States[MathMax(st,0)].account[0];
|
| float PrevEquity = Buffer[tr].States[MathMax(st,0)].account[1];
|
| State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
|
| State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
|
| State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
|
| State.Add(Buffer[tr].States[st].account[2]);
|
| State.Add(Buffer[tr].States[st].account[3]);
|
| State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
|
| State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
|
| State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
|
| double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
|
| State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| //---
|
| State.AddArray(Buffer[tr].States[st + 1].state);
|
| State.Add((Buffer[tr].States[st + 1].account[0] - PrevBalance) / PrevBalance);
|
| State.Add(Buffer[tr].States[st + 1].account[1] / PrevBalance);
|
| State.Add((Buffer[tr].States[st + 1].account[1] - PrevEquity) / PrevEquity);
|
| State.Add(Buffer[tr].States[st + 1].account[2]);
|
| State.Add(Buffer[tr].States[st + 1].account[3]);
|
| State.Add(Buffer[tr].States[st + 1].account[4] / PrevBalance);
|
| State.Add(Buffer[tr].States[st + 1].account[5] / PrevBalance);
|
| State.Add(Buffer[tr].States[st + 1].account[6] / PrevBalance);
|
| x = (double)Buffer[tr].States[st + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
|
| State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| return;
|
| }
|
| Convolution.getResults(temp);
|
| state_embedding.Row(temp,state);
|
| temp.Assign(Buffer[tr].States[st].rewards);
|
| for(ulong r = 0; r < temp.Size(); r++)
|
| temp[r] -= Buffer[tr].States[st + 1].rewards[r] * DiscFactor;
|
| rewards.Row(temp,state);
|
| state++;
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
|
| Comment(str);
|
| ticks = GetTickCount();
|
| }
|
| }
|
| }
|
| if(state != total_states)
|
| {
|
| state_embedding.Reshape(state,state_embedding.Cols());
|
| rewards.Reshape(state,NRewards);
|
| total_states = state;
|
| }
|
| //---
|
| vector<float> reward, rewards1, rewards2, target_reward;
|
| int bar = (HistoryBars - 1) * BarDescr;
|
| for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
|
| {
|
| int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
|
| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
|
| if(i < 0)
|
| {
|
| iter--;
|
| continue;
|
| }
|
| reward = vector<float>::Zeros(NRewards);
|
| rewards1 = reward;
|
| rewards2 = reward;
|
| target_reward = reward;
|
| //--- State
|
| State.AssignArray(Buffer[tr].States[i].state);
|
| float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
|
| float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
|
| if(PrevBalance == 0.0f || PrevEquity == 0.0f)
|
| continue;
|
| Account.Clear();
|
| Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
|
| Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
|
| Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
|
| Account.Add(Buffer[tr].States[i].account[2]);
|
| Account.Add(Buffer[tr].States[i].account[3]);
|
| Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
|
| Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
|
| Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
|
| double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
|
| Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
|
| if(Account.GetIndex() >= 0)
|
| Account.BufferWrite();
|
| //--- Encoder State
|
| if(!Encoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //--- Skills
|
| if(!Scheduler.feedForward(GetPointer(Encoder), -1, NULL,-1))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //--- Actor
|
| if(!Actor.feedForward(GetPointer(Encoder), -1, GetPointer(Scheduler),-1))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //--- Next State
|
| TargetState.AssignArray(Buffer[tr].States[i + 1].state);
|
| double cl_op = Buffer[tr].States[i + 1].state[bar];
|
| // Implements SymbolInfoDouble.
|
| double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
|
| SymbolInfoDouble(_Symbol, SYMBOL_POINT);
|
| Actor.getResults(Result);
|
| vector<float> forecast = ForecastAccount(Buffer[tr].States[i].account,Result,prof_1l,Buffer[tr].States[i + 1].account[7]);
|
| TargetAccount.AssignArray(forecast);
|
| if(TargetAccount.GetIndex() >= 0 && !TargetAccount.BufferWrite())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| if(!TargetEncoder.feedForward(GetPointer(TargetState), 1, false, GetPointer(TargetAccount)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //--- Target
|
| if(!TargetActor.feedForward(GetPointer(TargetEncoder), -1, GetPointer(Scheduler),-1))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| //---
|
| if(!TargetCritic1.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)) ||
|
| !TargetCritic2.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| TargetCritic1.getResults(rewards1);
|
| TargetCritic2.getResults(rewards2);
|
| if(rewards1.Sum() <= rewards2.Sum())
|
| target_reward = rewards1;
|
| else
|
| target_reward = rewards2;
|
| target_reward *= DiscFactor;
|
| State.AddArray(GetPointer(TargetState));
|
| State.AddArray(GetPointer(TargetAccount));
|
| if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| Convolution.getResults(rewards1);
|
| reward[0] += KNNReward(7,rewards1,state_embedding,rewards);
|
| reward += target_reward;
|
| Result.AssignArray(reward);
|
| //---
|
| if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1) ||
|
| !Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| Critic1.getResults(rewards1);
|
| Critic2.getResults(rewards2);
|
| if(rewards1.Sum() <= rewards2.Sum())
|
| {
|
| loss = (loss * MathMin(iter,999) + (reward - rewards1).Sum()) / MathMin(iter + 1,1000);
|
| if(!Critic1.backProp(Result, GetPointer(Actor)) ||
|
| !Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
|
| !Scheduler.backPropGradient() ||
|
| !Critic2.backProp(Result, GetPointer(Actor)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| }
|
| else
|
| {
|
| loss = (loss * MathMin(iter,999) + (reward - rewards2).Sum()) / MathMin(iter + 1,1000);
|
| if(!Critic2.backProp(Result, GetPointer(Actor)) ||
|
| !Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
|
| !Scheduler.backPropGradient() ||
|
| !Critic1.backProp(Result, GetPointer(Actor)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| }
|
| //--- Update Target Nets
|
| TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
|
| TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
|
| //---
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| string str = StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
|
| str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
|
| str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Scheduler", iter * 100.0 / (double)(Iterations), loss);
|
| Comment(str);
|
| ticks = GetTickCount();
|
| }
|
| }
|
| Comment("");
|
| //---
|
| PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
|
| PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", loss);
|
| ExpertRemove();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements KNNReward. |
|
| //+------------------------------------------------------------------+
|
| float KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards)
|
| {
|
| if(embedding.Size() != state_embedding.Cols())
|
| {
|
| PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
|
| return (0);
|
| }
|
| //---
|
| ulong size = embedding.Size();
|
| ulong states = state_embedding.Rows();
|
| matrix<float> temp = matrix<float>::Zeros(states,size);
|
| //---
|
| for(ulong i = 0; i < size; i++)
|
| temp.Col(MathPow(state_embedding.Col(i) - embedding[i],2.0f),i);
|
| vector<float> dist = MathSqrt(temp.Sum(1));
|
| matrix<float> min_dist = matrix<float>::Zeros(k,NRewards + 1);
|
| for(ulong i = 0; i < k; i++)
|
| {
|
| ulong pos = dist.ArgMin();
|
| min_dist[i,0] = dist[pos];
|
| dist[pos] = FLT_MAX;
|
| for(int c = 0; c < NRewards; c++)
|
| min_dist[i,c + 1] = rewards[pos,c];
|
| }
|
| //---
|
| min_dist.Col(vector<float>::Zeros(k),0);
|
| //---
|
| float result = min_dist.Sum() / (k * NRewards);
|
| //---
|
| return (result);
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
