forked from mnbvc188199/Warrior_EA
530 lines
41 KiB
MQL5
530 lines
41 KiB
MQL5
//+------------------------------------------------------------------+
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//| Research.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/en/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/en/users/dng"
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#property version "1.00"
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//+------------------------------------------------------------------+
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//| Includes |
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//+------------------------------------------------------------------+
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#include "Trajectory.mqh"
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#include <Trade\Trade.mqh>
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#include <Trade\SymbolInfo.mqh>
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#include <Indicators\Oscilators.mqh>
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input ENUM_TIMEFRAMES TimeFrame = PERIOD_H1;
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//---
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input group "---- RSI ----"
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input int RSIPeriod = 14; //Period
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input ENUM_APPLIED_PRICE RSIPrice = PRICE_CLOSE; //Applied price
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//---
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input group "---- CCI ----"
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input int CCIPeriod = 14; //Period
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input ENUM_APPLIED_PRICE CCIPrice = PRICE_TYPICAL; //Applied price
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//---
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input group "---- ATR ----"
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input int ATRPeriod = 14; //Period
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//---
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input group "---- MACD ----"
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input int FastPeriod = 12; //Fast
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input int SlowPeriod = 26; //Slow
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input int SignalPeriod = 9; //Signal
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input ENUM_APPLIED_PRICE MACDPrice = PRICE_CLOSE; //Applied price
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//---
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input int StudyIters = 5; //Iterations to Study
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input int StudyPeriod = 120; //Bars between Studies
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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SState sState;
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STrajectory Base;
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STrajectory Buffer[];
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CNet Agent;
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CFQF RTG;
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CNet AgentStudy;
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CFQF RTGStudy;
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//---
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float dError;
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datetime dtStudied;
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//---
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CSymbolInfo Symb;
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CTrade Trade;
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//---
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MqlRates Rates[];
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CiRSI RSI;
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CiCCI CCI;
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CiATR ATR;
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CiMACD MACD;
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//---
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CBufferFloat bState;
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CBufferFloat *Result;
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vector<float> AgentResult;
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double PrevBalance = 0;
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double PrevEquity = 0;
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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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LoadTotalBase();
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//---
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if(!Symb.Name(_Symbol))
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return INIT_FAILED;
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Symb.Refresh();
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//---
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if(!RSI.Create(Symb.Name(), TimeFrame, RSIPeriod, RSIPrice))
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return INIT_FAILED;
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//---
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if(!CCI.Create(Symb.Name(), TimeFrame, CCIPeriod, CCIPrice))
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return INIT_FAILED;
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//---
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if(!ATR.Create(Symb.Name(), TimeFrame, ATRPeriod))
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return INIT_FAILED;
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//---
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if(!MACD.Create(Symb.Name(), TimeFrame, FastPeriod, SlowPeriod, SignalPeriod, MACDPrice))
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return INIT_FAILED;
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if(!RSI.BufferResize(NBarInPattern) || !CCI.BufferResize(NBarInPattern) ||
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!ATR.BufferResize(NBarInPattern) || !MACD.BufferResize(NBarInPattern))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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return INIT_FAILED;
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}
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//---
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if(!Trade.SetTypeFillingBySymbol(Symb.Name()))
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return INIT_FAILED;
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//--- load models
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float temp;
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if(!Agent.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
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!RTG.Load(FileName + "RTG.nnw", dtStudied, true) ||
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!AgentStudy.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
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!RTGStudy.Load(FileName + "RTG.nnw", dtStudied, true))
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{
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PrintFormat("Can't load pretrained models");
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CArrayObj *agent = new CArrayObj();
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CArrayObj *rtg = new CArrayObj();
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if(!CreateDescriptions(agent, rtg))
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{
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delete agent;
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delete rtg;
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PrintFormat("Can't create description of models");
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return INIT_FAILED;
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}
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if(!Agent.Create(agent) ||
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!RTG.Create(rtg) ||
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!AgentStudy.Create(agent) ||
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!RTGStudy.Create(rtg))
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{
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delete agent;
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delete rtg;
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PrintFormat("Can't create models");
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return INIT_FAILED;
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}
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delete agent;
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delete rtg;
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//---
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}
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//---
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Agent.getResults(Result);
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if(Result.Total() != NActions)
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{
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PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
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return INIT_FAILED;
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}
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AgentResult = vector<float>::Zeros(NActions);
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//---
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Agent.GetLayerOutput(0, Result);
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if(Result.Total() != (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions))
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{
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PrintFormat("Input size of Actor doesn't match state description (%d <> %d)", Result.Total(), (NRewards + BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions));
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return INIT_FAILED;
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}
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Agent.Clear();
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RTG.Clear();
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//---
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PrevBalance = AccountInfoDouble(ACCOUNT_BALANCE);
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PrevEquity = AccountInfoDouble(ACCOUNT_EQUITY);
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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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AgentStudy.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
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RTGStudy.Save(FileName + "RTG.nnw", TimeCurrent(), true);
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delete Result;
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int total = ArraySize(Buffer);
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printf("Saving %d", MathMin(total + 1, MaxReplayBuffer));
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SaveTotalBase();
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Print("Saved");
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}
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//+------------------------------------------------------------------+
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//| Expert tick function |
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//+------------------------------------------------------------------+
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void OnTick()
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{
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//---
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if(!IsNewBar())
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return;
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//---
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int bars = CopyRates(Symb.Name(), TimeFrame, iTime(Symb.Name(), TimeFrame, 1), NBarInPattern, Rates);
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if(!ArraySetAsSeries(Rates, true))
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return;
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//---
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RSI.Refresh();
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CCI.Refresh();
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ATR.Refresh();
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MACD.Refresh();
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Symb.Refresh();
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Symb.RefreshRates();
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//--- History data
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float atr = 0;
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for(int b = 0; b < (int)NBarInPattern; b++)
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{
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float open = (float)Rates[b].open;
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float rsi = (float)RSI.Main(b);
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float cci = (float)CCI.Main(b);
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atr = (float)ATR.Main(b);
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float macd = (float)MACD.Main(b);
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float sign = (float)MACD.Signal(b);
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if(rsi == EMPTY_VALUE || cci == EMPTY_VALUE || atr == EMPTY_VALUE || macd == EMPTY_VALUE || sign == EMPTY_VALUE)
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continue;
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//---
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int shift = b * BarDescr;
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sState.state[shift] = (float)(Rates[b].close - open);
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sState.state[shift + 1] = (float)(Rates[b].high - open);
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sState.state[shift + 2] = (float)(Rates[b].low - open);
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sState.state[shift + 3] = (float)(Rates[b].tick_volume / 1000.0f);
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sState.state[shift + 4] = rsi;
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sState.state[shift + 5] = cci;
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sState.state[shift + 6] = atr;
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sState.state[shift + 7] = macd;
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sState.state[shift + 8] = sign;
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}
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bState.AssignArray(sState.state);
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//--- Account description
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sState.account[0] = (float)AccountInfoDouble(ACCOUNT_BALANCE);
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sState.account[1] = (float)AccountInfoDouble(ACCOUNT_EQUITY);
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//---
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double buy_value = 0, sell_value = 0, buy_profit = 0, sell_profit = 0;
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double position_discount = 0;
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double multiplyer = 1.0 / (60.0 * 60.0 * 10.0);
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int total = PositionsTotal();
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datetime current = TimeCurrent();
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for(int i = 0; i < total; i++)
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{
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if(PositionGetSymbol(i) != Symb.Name())
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continue;
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double profit = PositionGetDouble(POSITION_PROFIT);
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switch((int)PositionGetInteger(POSITION_TYPE))
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{
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case POSITION_TYPE_BUY:
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buy_value += PositionGetDouble(POSITION_VOLUME);
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buy_profit += profit;
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break;
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case POSITION_TYPE_SELL:
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sell_value += PositionGetDouble(POSITION_VOLUME);
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sell_profit += profit;
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break;
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}
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position_discount += profit - (current - PositionGetInteger(POSITION_TIME)) * multiplyer * MathAbs(profit);
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}
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sState.account[2] = (float)buy_value;
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sState.account[3] = (float)sell_value;
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sState.account[4] = (float)buy_profit;
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sState.account[5] = (float)sell_profit;
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sState.account[6] = (float)position_discount;
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sState.account[7] = (float)Rates[0].time;
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//---
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bState.Add((float)((sState.account[0] - PrevBalance) / PrevBalance));
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bState.Add((float)(sState.account[1] / PrevBalance));
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bState.Add((float)((sState.account[1] - PrevEquity) / PrevEquity));
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bState.Add(sState.account[2]);
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bState.Add(sState.account[3]);
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bState.Add((float)(sState.account[4] / PrevBalance));
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bState.Add((float)(sState.account[5] / PrevBalance));
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bState.Add((float)(sState.account[6] / PrevBalance));
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//--- Time label
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double x = (double)Rates[0].time / (double)(D'2024.01.01' - D'2023.01.01');
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bState.Add((float)MathSin(2.0 * M_PI * x));
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x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_MN1);
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bState.Add((float)MathCos(2.0 * M_PI * x));
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x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_W1);
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bState.Add((float)MathSin(2.0 * M_PI * x));
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x = (double)Rates[0].time / (double)PeriodSeconds(PERIOD_D1);
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bState.Add((float)MathSin(2.0 * M_PI * x));
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//--- Prev action
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bState.AddArray(AgentResult);
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//--- Return to go
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if(!RTG.feedForward(GetPointer(bState)))
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return;
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RTG.getResults(Result);
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bState.AddArray(Result);
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//---
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if(!Agent.feedForward(GetPointer(bState), 1, false, (CBufferFloat*)NULL))
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return;
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//---
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PrevBalance = sState.account[0];
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PrevEquity = sState.account[1];
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//---
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vector<float> temp;
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Agent.getResults(temp);
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//---
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double min_lot = Symb.LotsMin();
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double step_lot = Symb.LotsStep();
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double stops = MathMax(Symb.StopsLevel(), 1) * Symb.Point();
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if(temp[0] >= temp[3])
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{
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temp[0] -= temp[3];
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temp[3] = 0;
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}
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else
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{
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temp[3] -= temp[0];
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temp[0] = 0;
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}
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float delta = MathAbs(AgentResult - temp).Sum();
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AgentResult = temp;
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//--- buy control
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if(temp[0] < min_lot || (temp[1] * MaxTP * Symb.Point()) <= stops || (temp[2] * MaxSL * Symb.Point()) <= stops)
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{
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if(buy_value > 0)
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CloseByDirection(POSITION_TYPE_BUY);
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}
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else
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{
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double buy_lot = min_lot + MathRound((double)(temp[0] - min_lot) / step_lot) * step_lot;
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double buy_tp = Symb.NormalizePrice(Symb.Ask() + temp[1] * MaxTP * Symb.Point());
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double buy_sl = Symb.NormalizePrice(Symb.Ask() - temp[2] * MaxSL * Symb.Point());
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if(buy_value > 0)
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TrailPosition(POSITION_TYPE_BUY, buy_sl, buy_tp);
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if(buy_value != buy_lot)
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{
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if(buy_value > buy_lot)
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ClosePartial(POSITION_TYPE_BUY, buy_value - buy_lot);
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else
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Trade.Buy(buy_lot - buy_value, Symb.Name(), Symb.Ask(), buy_sl, buy_tp);
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}
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}
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//--- sell control
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if(temp[3] < min_lot || (temp[4] * MaxTP * Symb.Point()) <= stops || (temp[5] * MaxSL * Symb.Point()) <= stops)
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{
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if(sell_value > 0)
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CloseByDirection(POSITION_TYPE_SELL);
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}
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else
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{
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double sell_lot = min_lot + MathRound((double)(temp[3] - min_lot) / step_lot) * step_lot;;
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double sell_tp = Symb.NormalizePrice(Symb.Bid() - temp[4] * MaxTP * Symb.Point());
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double sell_sl = Symb.NormalizePrice(Symb.Bid() + temp[5] * MaxSL * Symb.Point());
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if(sell_value > 0)
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TrailPosition(POSITION_TYPE_SELL, sell_sl, sell_tp);
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if(sell_value != sell_lot)
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{
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if(sell_value > sell_lot)
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ClosePartial(POSITION_TYPE_SELL, sell_value - sell_lot);
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else
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Trade.Sell(sell_lot - sell_value, Symb.Name(), Symb.Bid(), sell_sl, sell_tp);
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}
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}
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//---
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int shift = BarDescr * (NBarInPattern - 1);
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sState.rewards[0] = bState[shift];
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sState.rewards[1] = bState[shift + 1] - 1.0f;
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if((buy_value + sell_value) == 0)
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sState.rewards[2] -= (float)(atr / PrevBalance);
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else
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sState.rewards[2] = 0;
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for(ulong i = 0; i < NActions; i++)
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sState.action[i] = AgentResult[i];
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if(!Base.Add(sState))
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ExpertRemove();
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//---
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if((Bars(_Symbol, TimeFrame) % StudyPeriod) == 0)
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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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void Train(void)
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{
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int total_tr = ArraySize(Buffer);
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if(Base.Total >= StudyPeriod)
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if(ArrayResize(Buffer, total_tr + 1) == (total_tr + 1))
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{
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Buffer[total_tr] = Base;
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Buffer[total_tr].CumRevards();
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total_tr++;
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}
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int clear = Base.Total + StudyPeriod - Buffer_Size;
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if(clear > 0)
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Base.ClearFirstN(clear);
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//---
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int count = 0;
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for(int i = 0; i < (total_tr + count); i++)
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{
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if(Buffer[i + count].Total < StudyPeriod)
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{
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count++;
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i--;
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continue;
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}
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if(count > 0)
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Buffer[i] = Buffer[i + count];
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}
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if(count > 0)
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{
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ArrayResize(Buffer, total_tr - count);
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total_tr = ArraySize(Buffer);
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}
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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 < StudyIters && !IsStopped() && !StopFlag); iter ++)
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{
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int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
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int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * MathMax(Buffer[tr].Total - 2 * HistoryBars, MathMin(Buffer[tr].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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vector<float> Actions = vector<float>::Zeros(NActions);
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AgentStudy.Clear();
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RTGStudy.Clear();
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for(int state = i; state < MathMin(Buffer[tr].Total - 2, int(i + HistoryBars * 1.5)); state++)
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{
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//--- History data
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bState.AssignArray(Buffer[tr].States[state].state);
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//--- Account description
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float prevBalance = (state == 0 ? Buffer[tr].States[state].account[0] : Buffer[tr].States[state - 1].account[0]);
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float prevEquity = (state == 0 ? Buffer[tr].States[state].account[1] : Buffer[tr].States[state - 1].account[1]);
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bState.Add((Buffer[tr].States[state].account[0] - prevBalance) / prevBalance);
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bState.Add(Buffer[tr].States[state].account[1] / prevBalance);
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bState.Add((Buffer[tr].States[state].account[1] - prevEquity) / prevEquity);
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bState.Add(Buffer[tr].States[state].account[2]);
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bState.Add(Buffer[tr].States[state].account[3]);
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bState.Add(Buffer[tr].States[state].account[4] / prevBalance);
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bState.Add(Buffer[tr].States[state].account[5] / prevBalance);
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bState.Add(Buffer[tr].States[state].account[6] / prevBalance);
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//--- Time label
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double x = (double)Buffer[tr].States[state].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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bState.Add((float)MathSin(2.0 * M_PI * x));
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x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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bState.Add((float)MathCos(2.0 * M_PI * x));
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x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_W1);
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bState.Add((float)MathSin(2.0 * M_PI * x));
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x = (double)Buffer[tr].States[state].account[7] / (double)PeriodSeconds(PERIOD_D1);
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bState.Add((float)MathSin(2.0 * M_PI * x));
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//--- Prev action
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bState.AddArray(Actions);
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//--- Return to go
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if(!RTGStudy.feedForward(GetPointer(bState)))
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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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Result.AssignArray(Buffer[tr].States[state + 1].rewards);
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if(!RTGStudy.backProp(Result))
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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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//--- Policy Feed Forward
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bState.AddArray(Buffer[tr].States[state + 1].rewards);
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if(!AgentStudy.feedForward(GetPointer(bState), 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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//--- Policy study
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Actions.Assign(Buffer[tr].States[state].action);
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vector<float> result;
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AgentStudy.getResults(result);
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Result.AssignArray(CAGrad(Actions - result) + result);
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if(!AgentStudy.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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{
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string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Agent", iter * 100.0 / (double)(StudyIters), AgentStudy.getRecentAverageError());
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str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "RTG", iter * 100.0 / (double)(StudyIters), RTGStudy.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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Agent.WeightsUpdate(GetPointer(AgentStudy), 1.0f);
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RTG.WeightsUpdate(GetPointer(RTGStudy), 1.0f);
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//---
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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vector<float> CAGrad(vector<float> &grad)
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{
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matrix<float> GG = grad.Outer(grad);
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GG.ReplaceNan(0);
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if(MathAbs(GG).Sum() == 0)
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return grad;
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float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
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GG = GG / MathPow(scale, 2);
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vector<float> Gg = GG.Mean(1);
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float gg = Gg.Mean();
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vector<float> w = vector<float>::Zeros(grad.Size());
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float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
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vector<float> w_best = w;
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float obj_best = FLT_MAX;
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vector<float> moment = vector<float>::Zeros(w.Size());
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for(int i = 0; i < iCAGrad_Iters; i++)
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{
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vector<float> ww;
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w.Activation(ww, AF_SOFTMAX);
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float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
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if(MathAbs(obj) < obj_best)
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{
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obj_best = MathAbs(obj);
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w_best = w;
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}
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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;
| |
}
| |
//+------------------------------------------------------------------+
|