| //+------------------------------------------------------------------+
|
| //| Research.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"
|
| //+------------------------------------------------------------------+
|
| //| Includes |
|
| //+------------------------------------------------------------------+
|
| #include "Trajectory.mqh"
|
| #include <Trade\Trade.mqh>
|
| #include <Trade\SymbolInfo.mqh>
|
| #include <Indicators\Oscilators.mqh>
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input ENUM_TIMEFRAMES TimeFrame = PERIOD_H1;
|
| //---
|
| input group "---- RSI ----"
|
| input int RSIPeriod = 14; //Period
|
| input ENUM_APPLIED_PRICE RSIPrice = PRICE_CLOSE; //Applied price
|
| //---
|
| input group "---- CCI ----"
|
| input int CCIPeriod = 14; //Period
|
| input ENUM_APPLIED_PRICE CCIPrice = PRICE_TYPICAL; //Applied price
|
| //---
|
| input group "---- ATR ----"
|
| input int ATRPeriod = 14; //Period
|
| //---
|
| input group "---- MACD ----"
|
| input int FastPeriod = 12; //Fast
|
| input int SlowPeriod = 26; //Slow
|
| input int SignalPeriod = 9; //Signal
|
| input ENUM_APPLIED_PRICE MACDPrice = PRICE_CLOSE; //Applied price
|
| //---
|
| input int StudyIters = 5; //Iterations to Study
|
| input int StudyPeriod = 120; //Bars between Studies
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| SState sState;
|
| STrajectory Base;
|
| STrajectory Buffer[];
|
| CNet Agent;
|
| CFQF RTG;
|
| CNet AgentStudy;
|
| CFQF RTGStudy;
|
| //---
|
| float dError;
|
| datetime dtStudied;
|
| //---
|
| CSymbolInfo Symb;
|
| CTrade Trade;
|
| //---
|
| MqlRates Rates[];
|
| CiRSI RSI;
|
| CiCCI CCI;
|
| CiATR ATR;
|
| CiMACD MACD;
|
| //---
|
| CBufferFloat bState;
|
| CBufferFloat *Result;
|
| vector<float> AgentResult;
|
| double PrevBalance = 0;
|
| double PrevEquity = 0;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| LoadTotalBase();
|
| //---
|
| if(!Symb.Name(_Symbol))
|
| return INIT_FAILED;
|
| Symb.Refresh();
|
| //---
|
| if(!RSI.Create(Symb.Name(), TimeFrame, RSIPeriod, RSIPrice))
|
| return INIT_FAILED;
|
| //---
|
| if(!CCI.Create(Symb.Name(), TimeFrame, CCIPeriod, CCIPrice))
|
| return INIT_FAILED;
|
| //---
|
| if(!ATR.Create(Symb.Name(), TimeFrame, ATRPeriod))
|
| return INIT_FAILED;
|
| //---
|
| if(!MACD.Create(Symb.Name(), TimeFrame, FastPeriod, SlowPeriod, SignalPeriod, MACDPrice))
|
| return INIT_FAILED;
|
| if(!RSI.BufferResize(NBarInPattern) || !CCI.BufferResize(NBarInPattern) ||
|
| !ATR.BufferResize(NBarInPattern) || !MACD.BufferResize(NBarInPattern))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| return INIT_FAILED;
|
| }
|
| //---
|
| if(!Trade.SetTypeFillingBySymbol(Symb.Name()))
|
| return INIT_FAILED;
|
| //--- load models
|
| float temp;
|
| if(!Agent.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
|
| !RTG.Load(FileName + "RTG.nnw", dtStudied, true) ||
|
| !AgentStudy.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
|
| !RTGStudy.Load(FileName + "RTG.nnw", dtStudied, true))
|
| {
|
| PrintFormat("Can't load pretrained models");
|
| CArrayObj *agent = new CArrayObj();
|
| CArrayObj *rtg = new CArrayObj();
|
| if(!CreateDescriptions(agent, rtg))
|
| {
|
| delete agent;
|
| delete rtg;
|
| PrintFormat("Can't create description of models");
|
| return INIT_FAILED;
|
| }
|
| if(!Agent.Create(agent) ||
|
| !RTG.Create(rtg) ||
|
| !AgentStudy.Create(agent) ||
|
| !RTGStudy.Create(rtg))
|
| {
|
| delete agent;
|
| delete rtg;
|
| PrintFormat("Can't create models");
|
| return INIT_FAILED;
|
| }
|
| delete agent;
|
| delete rtg;
|
| //---
|
| }
|
| //---
|
| Agent.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;
|
| }
|
| AgentResult = vector<float>::Zeros(NActions);
|
| //---
|
| Agent.GetLayerOutput(0, Result);
|
| if(Result.Total() != (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions))
|
| {
|
| PrintFormat("Input size of Actor doesn't match state description (%d <> %d)", Result.Total(), (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions));
|
| return INIT_FAILED;
|
| }
|
| Agent.Clear();
|
| RTG.Clear();
|
| //---
|
| PrevBalance = AccountInfoDouble(ACCOUNT_BALANCE);
|
| PrevEquity = AccountInfoDouble(ACCOUNT_EQUITY);
|
| //---
|
| return(INIT_SUCCEEDED);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Expert deinitialization function |
|
| //+------------------------------------------------------------------+
|
| void OnDeinit(const int reason)
|
| {
|
| //---
|
| AgentStudy.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
|
| RTGStudy.Save(FileName + "RTG.nnw", TimeCurrent(), true);
|
| delete Result;
|
| int total = ArraySize(Buffer);
|
| printf("Saving %d", MathMin(total + 1, MaxReplayBuffer));
|
| SaveTotalBase();
|
| Print("Saved");
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Expert tick function |
|
| //+------------------------------------------------------------------+
|
| void OnTick()
|
| {
|
| //---
|
| if(!IsNewBar())
|
| return;
|
| //---
|
| int bars = CopyRates(Symb.Name(), TimeFrame, iTime(Symb.Name(), TimeFrame, 1), NBarInPattern, Rates);
|
| if(!ArraySetAsSeries(Rates, true))
|
| return;
|
| //---
|
| RSI.Refresh();
|
| CCI.Refresh();
|
| ATR.Refresh();
|
| MACD.Refresh();
|
| Symb.Refresh();
|
| Symb.RefreshRates();
|
| //--- History data
|
| float atr = 0;
|
| for(int b = 0; b < (int)NBarInPattern; b++)
|
| {
|
| float open = (float)Rates[b].open;
|
| float rsi = (float)RSI.Main(b);
|
| float cci = (float)CCI.Main(b);
|
| atr = (float)ATR.Main(b);
|
| float macd = (float)MACD.Main(b);
|
| float sign = (float)MACD.Signal(b);
|
| if(rsi == EMPTY_VALUE || cci == EMPTY_VALUE || atr == EMPTY_VALUE || macd == EMPTY_VALUE || sign == EMPTY_VALUE)
|
| continue;
|
| //---
|
| int shift = b * BarDescr;
|
| sState.state[shift] = (float)(Rates[b].close - open);
|
| sState.state[shift + 1] = (float)(Rates[b].high - open);
|
| sState.state[shift + 2] = (float)(Rates[b].low - open);
|
| sState.state[shift + 3] = (float)(Rates[b].tick_volume / 1000.0f);
|
| sState.state[shift + 4] = rsi;
|
| sState.state[shift + 5] = cci;
|
| sState.state[shift + 6] = atr;
|
| sState.state[shift + 7] = macd;
|
| sState.state[shift + 8] = sign;
|
| }
|
| bState.AssignArray(sState.state);
|
| //--- Account description
|
| sState.account[0] = (float)AccountInfoDouble(ACCOUNT_BALANCE);
|
| sState.account[1] = (float)AccountInfoDouble(ACCOUNT_EQUITY);
|
| //---
|
| double buy_value = 0, sell_value = 0, buy_profit = 0, sell_profit = 0;
|
| double position_discount = 0;
|
| double multiplyer = 1.0 / (60.0 * 60.0 * 10.0);
|
| int total = PositionsTotal();
|
| datetime current = TimeCurrent();
|
| for(int i = 0; i < total; i++)
|
| {
|
| if(PositionGetSymbol(i) != Symb.Name())
|
| continue;
|
| double profit = PositionGetDouble(POSITION_PROFIT);
|
| switch((int)PositionGetInteger(POSITION_TYPE))
|
| {
|
| case POSITION_TYPE_BUY:
|
| buy_value += PositionGetDouble(POSITION_VOLUME);
|
| buy_profit += profit;
|
| break;
|
| case POSITION_TYPE_SELL:
|
| sell_value += PositionGetDouble(POSITION_VOLUME);
|
| sell_profit += profit;
|
| break;
|
| }
|
| position_discount += profit - (current - PositionGetInteger(POSITION_TIME)) * multiplyer * MathAbs(profit);
|
| }
|
| sState.account[2] = (float)buy_value;
|
| sState.account[3] = (float)sell_value;
|
| sState.account[4] = (float)buy_profit;
|
| sState.account[5] = (float)sell_profit;
|
| sState.account[6] = (float)position_discount;
|
| sState.account[7] = (float)Rates[0].time;
|
| //---
|
| bState.Add((float)((sState.account[0] - PrevBalance) / PrevBalance));
|
| bState.Add((float)(sState.account[1] / PrevBalance));
|
| bState.Add((float)((sState.account[1] - PrevEquity) / PrevEquity));
|
| bState.Add(sState.account[2]);
|
| bState.Add(sState.account[3]);
|
| bState.Add((float)(sState.account[4] / PrevBalance));
|
| bState.Add((float)(sState.account[5] / PrevBalance));
|
| bState.Add((float)(sState.account[6] / PrevBalance));
|
| //--- Time label
|
| double x = (double)Rates[0].time / (double)(D'2024.01.01' - D'2023.01.01');
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_MN1);
|
| bState.Add((float)MathCos(2.0 * M_PI * x));
|
| x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_W1);
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_D1);
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| //--- Prev action
|
| bState.AddArray(AgentResult);
|
| //--- Return to go
|
| if(!RTG.feedForward(GetPointer(bState)))
|
| return;
|
| RTG.getResults(Result);
|
| bState.AddArray(Result);
|
| //---
|
| if(!Agent.feedForward(GetPointer(bState), 1, false, (CBufferFloat*)NULL))
|
| return;
|
| //---
|
| PrevBalance = sState.account[0];
|
| PrevEquity = sState.account[1];
|
| //---
|
| vector<float> temp;
|
| Agent.getResults(temp);
|
| //---
|
| double min_lot = Symb.LotsMin();
|
| double step_lot = Symb.LotsStep();
|
| double stops = MathMax(Symb.StopsLevel(), 1) * Symb.Point();
|
| if(temp[0] >= temp[3])
|
| {
|
| temp[0] -= temp[3];
|
| temp[3] = 0;
|
| }
|
| else
|
| {
|
| temp[3] -= temp[0];
|
| temp[0] = 0;
|
| }
|
| float delta = MathAbs(AgentResult - temp).Sum();
|
| AgentResult = temp;
|
| //--- buy control
|
| if(temp[0] < min_lot || (temp[1] * MaxTP * Symb.Point()) <= stops || (temp[2] * MaxSL * Symb.Point()) <= stops)
|
| {
|
| if(buy_value > 0)
|
| CloseByDirection(POSITION_TYPE_BUY);
|
| }
|
| else
|
| {
|
| double buy_lot = min_lot + MathRound((double)(temp[0] - min_lot) / step_lot) * step_lot;
|
| double buy_tp = Symb.NormalizePrice(Symb.Ask() + temp[1] * MaxTP * Symb.Point());
|
| double buy_sl = Symb.NormalizePrice(Symb.Ask() - temp[2] * MaxSL * Symb.Point());
|
| if(buy_value > 0)
|
| TrailPosition(POSITION_TYPE_BUY, buy_sl, buy_tp);
|
| if(buy_value != buy_lot)
|
| {
|
| if(buy_value > buy_lot)
|
| ClosePartial(POSITION_TYPE_BUY, buy_value - buy_lot);
|
| else
|
| Trade.Buy(buy_lot - buy_value, Symb.Name(), Symb.Ask(), buy_sl, buy_tp);
|
| }
|
| }
|
| //--- sell control
|
| if(temp[3] < min_lot || (temp[4] * MaxTP * Symb.Point()) <= stops || (temp[5] * MaxSL * Symb.Point()) <= stops)
|
| {
|
| if(sell_value > 0)
|
| CloseByDirection(POSITION_TYPE_SELL);
|
| }
|
| else
|
| {
|
| double sell_lot = min_lot + MathRound((double)(temp[3] - min_lot) / step_lot) * step_lot;;
|
| double sell_tp = Symb.NormalizePrice(Symb.Bid() - temp[4] * MaxTP * Symb.Point());
|
| double sell_sl = Symb.NormalizePrice(Symb.Bid() + temp[5] * MaxSL * Symb.Point());
|
| if(sell_value > 0)
|
| TrailPosition(POSITION_TYPE_SELL, sell_sl, sell_tp);
|
| if(sell_value != sell_lot)
|
| {
|
| if(sell_value > sell_lot)
|
| ClosePartial(POSITION_TYPE_SELL, sell_value - sell_lot);
|
| else
|
| Trade.Sell(sell_lot - sell_value, Symb.Name(), Symb.Bid(), sell_sl, sell_tp);
|
| }
|
| }
|
| //---
|
| int shift = BarDescr * (NBarInPattern - 1);
|
| sState.rewards[0] = bState[shift];
|
| sState.rewards[1] = bState[shift + 1] - 1.0f;
|
| if((buy_value + sell_value) == 0)
|
| sState.rewards[2] -= (float)(atr / PrevBalance);
|
| else
|
| sState.rewards[2] = 0;
|
| for(ulong i = 0; i < NActions; i++)
|
| sState.action[i] = AgentResult[i];
|
| if(!Base.Add(sState))
|
| ExpertRemove();
|
| //---
|
| if((Bars(_Symbol, TimeFrame) % StudyPeriod) == 0)
|
| Train();
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Train function |
|
| //+------------------------------------------------------------------+
|
| void Train(void)
|
| {
|
| int total_tr = ArraySize(Buffer);
|
| if(Base.Total >= StudyPeriod)
|
| if(ArrayResize(Buffer, total_tr + 1) == (total_tr + 1))
|
| {
|
| Buffer[total_tr] = Base;
|
| Buffer[total_tr].CumRevards();
|
| total_tr++;
|
| }
|
| int clear = Base.Total + StudyPeriod - Buffer_Size;
|
| if(clear > 0)
|
| Base.ClearFirstN(clear);
|
| //---
|
| int count = 0;
|
| for(int i = 0; i < (total_tr + count); i++)
|
| {
|
| if(Buffer[i + count].Total < StudyPeriod)
|
| {
|
| count++;
|
| i--;
|
| continue;
|
| }
|
| if(count > 0)
|
| Buffer[i] = Buffer[i + count];
|
| }
|
| if(count > 0)
|
| {
|
| ArrayResize(Buffer, total_tr - count);
|
| total_tr = ArraySize(Buffer);
|
| }
|
| uint ticks = GetTickCount();
|
| //---
|
| bool StopFlag = false;
|
| for(int iter = 0; (iter < StudyIters && !IsStopped() && !StopFlag); iter ++)
|
| {
|
| int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
|
| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * MathMax(Buffer[tr].Total - 2 * HistoryBars, MathMin(Buffer[tr].Total, 20)));
|
| if(i < 0)
|
| {
|
| iter--;
|
| continue;
|
| }
|
| vector<float> Actions = vector<float>::Zeros(NActions);
|
| AgentStudy.Clear();
|
| RTGStudy.Clear();
|
| for(int state = i; state < MathMin(Buffer[tr].Total - 2, int(i + HistoryBars * 1.5)); state++)
|
| {
|
| //--- History data
|
| bState.AssignArray(Buffer[tr].States[state].state);
|
| //--- Account description
|
| float prevBalance = (state == 0 ? Buffer[tr].States[state].account[0] : Buffer[tr].States[state - 1].account[0]);
|
| float prevEquity = (state == 0 ? Buffer[tr].States[state].account[1] : Buffer[tr].States[state - 1].account[1]);
|
| bState.Add((Buffer[tr].States[state].account[0] - prevBalance) / prevBalance);
|
| bState.Add(Buffer[tr].States[state].account[1] / prevBalance);
|
| bState.Add((Buffer[tr].States[state].account[1] - prevEquity) / prevEquity);
|
| bState.Add(Buffer[tr].States[state].account[2]);
|
| bState.Add(Buffer[tr].States[state].account[3]);
|
| bState.Add(Buffer[tr].States[state].account[4] / prevBalance);
|
| bState.Add(Buffer[tr].States[state].account[5] / prevBalance);
|
| bState.Add(Buffer[tr].States[state].account[6] / prevBalance);
|
| //--- Time label
|
| double x = (double)Buffer[tr].States[state].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_MN1);
|
| bState.Add((float)MathCos(2.0 * M_PI * x));
|
| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_W1);
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_D1);
|
| bState.Add((float)MathSin(2.0 * M_PI * x));
|
| //--- Prev action
|
| bState.AddArray(Actions);
|
| //--- Return to go
|
| if(!RTGStudy.feedForward(GetPointer(bState)))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| StopFlag = true;
|
| break;
|
| }
|
| Result.AssignArray(Buffer[tr].States[state + 1].rewards);
|
| if(!RTGStudy.backProp(Result))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| StopFlag = true;
|
| break;
|
| }
|
| //--- Policy Feed Forward
|
| bState.AddArray(Buffer[tr].States[state + 1].rewards);
|
| if(!AgentStudy.feedForward(GetPointer(bState), 1, false, (CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| StopFlag = true;
|
| break;
|
| }
|
| //--- Policy study
|
| Actions.Assign(Buffer[tr].States[state].action);
|
| vector<float> result;
|
| AgentStudy.getResults(result);
|
| Result.AssignArray(CAGrad(Actions - result) + result);
|
| if(!AgentStudy.backProp(Result, (CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| StopFlag = true;
|
| break;
|
| }
|
| //---
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Agent", iter * 100.0 / (double)(StudyIters), AgentStudy.getRecentAverageError());
|
| str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "RTG", iter * 100.0 / (double)(StudyIters), RTGStudy.getRecentAverageError());
|
| Comment(str);
|
| ticks = GetTickCount();
|
| }
|
| }
|
| }
|
| Comment("");
|
| //---
|
| Agent.WeightsUpdate(GetPointer(AgentStudy), 1.0f);
|
| RTG.WeightsUpdate(GetPointer(RTGStudy), 1.0f);
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates and manages object lifecycle for CAGrad. |
|
| //+------------------------------------------------------------------+
|
| vector<float> CAGrad(vector<float> &grad)
|
| {
|
| matrix<float> GG = grad.Outer(grad);
|
| GG.ReplaceNan(0);
|
| if(MathAbs(GG).Sum() == 0)
|
| return grad;
|
| float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
|
| GG = GG / MathPow(scale, 2);
|
| vector<float> Gg = GG.Mean(1);
|
| float gg = Gg.Mean();
|
| vector<float> w = vector<float>::Zeros(grad.Size());
|
| float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
|
| vector<float> w_best = w;
|
| float obj_best = FLT_MAX;
|
| vector<float> moment = vector<float>::Zeros(w.Size());
|
| for(int i = 0; i < iCAGrad_Iters; i++)
|
| {
|
| vector<float> ww;
|
| w.Activation(ww, AF_SOFTMAX);
|
| float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
|
| if(MathAbs(obj) < obj_best)
|
| {
|
| obj_best = MathAbs(obj);
|
| w_best = w;
|
| }
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
