forked from mnbvc188199/Warrior_EA
630 lines
46 KiB
MQL5
630 lines
46 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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input double MinProfit = 10;
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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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input int Agent = 1;
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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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STrajectory Frame[1];
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CNet Actor;
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CNet Critic;
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CNet Convolution;
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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 bAccount;
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CBufferFloat bActions;
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CBufferFloat bGradient;
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CBufferFloat *Result;
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vector<float> check;
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double PrevBalance = 0;
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double PrevEquity = 0;
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bool BaseLoaded;
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matrix<float> state_embeddings;
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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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//---
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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(HistoryBars) || !CCI.BufferResize(HistoryBars) ||
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!ATR.BufferResize(HistoryBars) || !MACD.BufferResize(HistoryBars))
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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(!Actor.Load(StringFormat("%sAct%d.nnw", FileName, Agent), temp, temp, temp, dtStudied, true))
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{
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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if(!CreateDescriptions(actor, critic, critic))
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{
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delete actor;
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delete critic;
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return INIT_FAILED;
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}
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if(!Actor.Create(actor))
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{
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delete actor;
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delete critic;
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return INIT_FAILED;
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}
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delete actor;
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delete critic;
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//---
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}
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//---
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if(!Critic.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new Critic and Encoder models");
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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CArrayObj *convolution = new CArrayObj();
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if(!CreateDescriptions(actor, critic, convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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if(!Critic.Create(critic))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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delete actor;
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delete critic;
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delete convolution;
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//---
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}
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//---
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if(!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new Critic and Encoder models");
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CArrayObj *actor = new CArrayObj();
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CArrayObj *critic = new CArrayObj();
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CArrayObj *convolution = new CArrayObj();
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if(!CreateDescriptions(actor, critic, convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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if(!Convolution.Create(convolution))
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{
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delete actor;
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delete critic;
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delete convolution;
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return INIT_FAILED;
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}
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delete actor;
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delete critic;
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delete convolution;
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//---
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}
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//---
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Critic.SetOpenCL(Actor.GetOpenCL());
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Convolution.SetOpenCL(Actor.GetOpenCL());
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Critic.TrainMode(false);
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//---
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Actor.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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//---
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Actor.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Input size of Actor doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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return INIT_FAILED;
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}
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//---
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PrevBalance = AccountInfoDouble(ACCOUNT_BALANCE);
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PrevEquity = AccountInfoDouble(ACCOUNT_EQUITY);
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BaseLoaded = false;
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bGradient.BufferInit(MathMax(AccountDescr, NActions), 0);
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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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ResetLastError();
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if(!Actor.Save(StringFormat("%sActEx%d.nnw", FileName, Agent), 0, 0, 0, TimeCurrent(), true))
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PrintFormat("Error of saving Agent %d: %d", Agent, GetLastError());
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delete Result;
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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), HistoryBars, 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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//---
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float atr = 0;
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for(int b = 0; b < (int)HistoryBars; 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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//---
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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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bAccount.Clear();
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bAccount.Add((float)((sState.account[0] - PrevBalance) / PrevBalance));
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bAccount.Add((float)(sState.account[1] / PrevBalance));
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bAccount.Add((float)((sState.account[1] - PrevEquity) / PrevEquity));
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bAccount.Add(sState.account[2]);
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bAccount.Add(sState.account[3]);
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bAccount.Add((float)(sState.account[4] / PrevBalance));
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bAccount.Add((float)(sState.account[5] / PrevBalance));
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bAccount.Add((float)(sState.account[6] / PrevBalance));
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double x = (double)Rates[0].time / (double)(D'2024.01.01' - D'2023.01.01');
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bAccount.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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bAccount.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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bAccount.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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bAccount.Add((float)MathSin(2.0 * M_PI * x));
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//---
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if(bAccount.GetIndex() >= 0)
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if(!bAccount.BufferWrite())
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return;
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//---
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if(!Actor.feedForward(GetPointer(bState), 1, false, GetPointer(bAccount)))
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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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Actor.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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//--- 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 = NormalizeDouble(Symb.Ask() + temp[1] * MaxTP * Symb.Point(), Symb.Digits());
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double buy_sl = NormalizeDouble(Symb.Ask() - temp[2] * MaxSL * Symb.Point(), Symb.Digits());
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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 = NormalizeDouble(Symb.Bid() - temp[4] * MaxTP * Symb.Point(), Symb.Digits());
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double sell_sl = NormalizeDouble(Symb.Bid() + temp[5] * MaxSL * Symb.Point(), Symb.Digits());
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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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sState.rewards[0] = bAccount[0];
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sState.rewards[1] = 1.0f - bAccount[1];
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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] = temp[i];
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sState.rewards[3] = 0;
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sState.rewards[4] = 0;
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if(!Base.Add(sState))
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ExpertRemove();
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//---
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bState.AddArray(GetPointer(bAccount));
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bState.AddArray(temp);
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bActions.AssignArray(temp);
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if(!Convolution.feedForward(GetPointer(bState), 1, false, NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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return;
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}
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Convolution.getResults(temp);
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//---
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if(!BaseLoaded)
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state_embeddings = CreateEmbeddings();
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BaseLoaded = true;
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//---
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ulong total_states = state_embeddings.Rows();
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if(total_states <= 0)
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{
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ResetLastError();
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if(!state_embeddings.Resize(total_states + 1, state_embeddings.Cols()) ||
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!state_embeddings.Row(temp, total_states))
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PrintFormat("%s -> %d: Error of adding new embedding %", __FUNCTION__, __LINE__, GetLastError());
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return;
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}
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//---
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vector<float> rewards = ResearchReward(Quant, temp, state_embeddings);
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ResetLastError();
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if(!state_embeddings.Resize(total_states + 1, state_embeddings.Cols()) ||
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!state_embeddings.Row(temp, total_states))
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PrintFormat("%s -> %d: Error of adding new embedding %", __FUNCTION__, __LINE__, GetLastError());
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//---
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Result.AssignArray(rewards);
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if(!Critic.feedForward(GetPointer(Actor), LatentLayer, GetPointer(bActions)) ||
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!Critic.backProp(Result, GetPointer(bActions), GetPointer(bGradient)) ||
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!Actor.backPropGradient(GetPointer(bAccount), GetPointer(bGradient), LatentLayer))
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PrintFormat("%s -> %d: Error of backpropagation %", __FUNCTION__, __LINE__, GetLastError());
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}
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//+------------------------------------------------------------------+
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//| Tester function |
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//+------------------------------------------------------------------+
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double OnTester()
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{
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//---
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double ret = 0.0;
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//---
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double profit = TesterStatistics(STAT_PROFIT);
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Frame[0] = Base;
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if(profit >= MinProfit && profit != 0)
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FrameAdd(MQLInfoString(MQL_PROGRAM_NAME), 1, profit, Frame);
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//---
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return(ret);
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}
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//+------------------------------------------------------------------+
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//| TesterInit function |
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//+------------------------------------------------------------------+
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void OnTesterInit()
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{
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//---
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BaseLoaded = LoadTotalBase();
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}
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//+------------------------------------------------------------------+
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//| TesterPass function |
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//+------------------------------------------------------------------+
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void OnTesterPass()
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{
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//---
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ulong pass;
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string name;
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long id;
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double value;
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STrajectory array[];
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while(FrameNext(pass, name, id, value, array))
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{
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int total = ArraySize(Buffer);
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if(name != MQLInfoString(MQL_PROGRAM_NAME))
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continue;
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if(id <= 0)
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continue;
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if(total >= MaxReplayBuffer)
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{
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for(int a = 0; a < id; a++)
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{
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float min = FLT_MAX;
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int min_tr = 0;
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for(int i = 0; i < total; i++)
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{
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float prof = Buffer[i].States[Buffer[i].Total - 1].account[1];
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if(prof < min)
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{
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min = MathMin(prof, min);
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min_tr = i;
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}
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}
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float prof = array[a].States[array[a].Total - 1].account[1];
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if(min <= prof)
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{
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Buffer[min_tr] = array[a];
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PrintFormat("Replace %.2f to %.2f -> bars %d", min, prof, array[a].Total);
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}
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}
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}
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else
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{
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if(ArrayResize(Buffer, total + (int)id, 10) < 0)
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return;
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ArrayCopy(Buffer, array, total, 0, (int)id);
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}
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}
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}
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//+------------------------------------------------------------------+
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//| TesterDeinit function |
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//+------------------------------------------------------------------+
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void OnTesterDeinit()
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{
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//---
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int total = ArraySize(Buffer);
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printf("total %d", MathMin(total, MaxReplayBuffer));
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Print("Saving...");
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SaveTotalBase();
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Print("Saved");
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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vector<float> ResearchReward(double quant, vector<float> &embedding, matrix<float> &state_embedding)
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{
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vector<float> result = vector<float>::Zeros(NRewards);
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if(embedding.Size() != state_embedding.Cols())
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{
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PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
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return result;
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}
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//---
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ulong size = embedding.Size();
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ulong states = state_embedding.Rows();
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ulong k = ulong(states * quant);
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matrix<float> temp = matrix<float>::Zeros(states, size);
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vector<float> min_dist = vector<float>::Zeros(k);
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matrix<float> k_embedding = matrix<float>::Zeros(k + 1, size);
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matrix<float> U, V;
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vector<float> S;
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//---
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for(ulong i = 0; i < size; i++)
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temp.Col(MathAbs(state_embedding.Col(i) - embedding[i]), i);
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float alpha = temp.Max();
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if(alpha == 0)
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alpha = 1;
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vector<float> dist = MathLog(MathExp(temp / (-alpha)).Sum(1)) * (-alpha);
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//---
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float max = dist.Quantile(quant);
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for(ulong i = 0, cur = 0; (i < states && cur < k); i++)
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{
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if(max < dist[i])
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continue;
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min_dist[cur] = dist[i];
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k_embedding.Row(state_embedding.Row(i), cur);
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cur++;
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}
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k_embedding.Row(embedding, k);
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//---
| |
k_embedding.SVD(U, V, S);
| |
result[NRewards - 2] = S.Sum() / (MathSqrt(MathPow(k_embedding, 2.0f).Sum() * MathMax(k + 1, size)));
| |
result[NRewards - 1] = EntropyLatentState(Actor);
| |
//---
| |
return result;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
matrix<float> CreateEmbeddings(void)
| |
{
| |
vector<float> temp;
| |
CBufferFloat State;
| |
Convolution.getResults(temp);
| |
matrix<float> result = matrix<float>::Zeros(0, temp.Size());
| |
//---
| |
BaseLoaded = LoadTotalBase();
| |
if(!BaseLoaded)
| |
{
| |
PrintFormat("%s - %d => Error of load base", __FUNCTION__, __LINE__);
| |
return result;
| |
}
| |
//---
| |
int total_tr = ArraySize(Buffer);
| |
uint ticks = GetTickCount();
| |
//---
| |
int total_states = Buffer[0].Total;
| |
for(int i = 1; i < total_tr; i++)
| |
total_states += Buffer[i].Total;
| |
result.Resize(total_states, temp.Size());
| |
//---
| |
int state = 0;
| |
for(int tr = 0; tr < total_tr; tr++)
| |
{
| |
for(int st = 0; st < Buffer[tr].Total; st++)
| |
{
| |
State.AssignArray(Buffer[tr].States[st].state);
| |
float prevBalance = Buffer[tr].States[MathMax(st - 1, 0)].account[0];
| |
float prevEquity = Buffer[tr].States[MathMax(st - 1, 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].action);
| |
if(!Convolution.feedForward(GetPointer(State), 1, false, NULL))
| |
{
| |
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
| |
break;
| |
}
| |
Convolution.getResults(temp);
| |
if(!result.Row(temp, state))
| |
continue;
| |
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)
| |
result.Reshape(state, result.Cols());
| |
ArrayFree(Buffer);
| |
//---
| |
return result;
| |
}
| |
//+------------------------------------------------------------------+
|