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NN_in_Trading/Experts/DoC/OnlineStudy.mq5

530 行
20 KiB
MQL5
原始文件 永久链接 普通视图 历史

Split NeuroNet.mqh
2026-06-05 22:00:59 +03:00
//+------------------------------------------------------------------+
//| 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);
//---
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
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;
}
//+------------------------------------------------------------------+
复制固定链接