309 lines
23 KiB
MQL5
309 lines
23 KiB
MQL5
//+------------------------------------------------------------------+
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//| REINFORCE.mq5 |
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//| Copyright 2022, DNG |
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//| https://www.mql5.com/ru/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2022, DNG"
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#property link "https://www.mql5.com/ru/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 "..\NeuroNet_DNG\NeuroNet.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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//| |
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//+------------------------------------------------------------------+
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#define FileName Symb.Name()+"_"+EnumToString((ENUM_TIMEFRAMES)Period())+"_"+StringSubstr(__FILE__,0,StringFind(__FILE__,".",0))
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input int StudyPeriod = 2; //Study period, years
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uint HistoryBars = 20; //Depth of history
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ENUM_TIMEFRAMES TimeFrame = PERIOD_H1;
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input int SessionSize = 24 * 22;
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input int Iterations = 1000;
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input double DiscountFactor = 0.999;
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int Actions = 3;
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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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//| |
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//+------------------------------------------------------------------+
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CSymbolInfo Symb;
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MqlRates Rates[];
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CNet StudyNet;
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CArrayObj States;
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vectorf vActions;
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vectorf vRewards;
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vectorf vProbs;
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CBufferFloat *TempData;
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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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float dError;
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datetime dtStudied;
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bool bEventStudy;
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MqlDateTime sTime;
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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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//---
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float temp1, temp2;
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if(!StudyNet.Load(FileName + ".nnw", dError, temp1, temp2, dtStudied, false))
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return INIT_FAILED;
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//---
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if(!StudyNet.GetLayerOutput(0, TempData))
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return INIT_FAILED;
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HistoryBars = TempData.Total() / 12;
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StudyNet.getResults(TempData);
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if(TempData.Total() != Actions)
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return INIT_PARAMETERS_INCORRECT;
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if(!vActions.Resize(SessionSize) ||
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!vRewards.Resize(SessionSize) ||
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!vProbs.Resize(SessionSize))
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return INIT_FAILED;
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StudyNet.TrainMode(true);
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//MaxProfit = -1e-37f;
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//---
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bEventStudy = EventChartCustom(ChartID(), 1, 0, 0, "Init");
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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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if(!!TempData)
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delete TempData;
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//---
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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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}
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//+------------------------------------------------------------------+
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//| ChartEvent function |
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//+------------------------------------------------------------------+
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void OnChartEvent(const int id,
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const long &lparam,
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const double &dparam,
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const string &sparam)
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{
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//---
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if(id == 1001)
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Train();
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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//---
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MqlDateTime start_time;
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TimeCurrent(start_time);
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start_time.year -= StudyPeriod;
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if(start_time.year <= 0)
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start_time.year = 1900;
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datetime st_time = StructToTime(start_time);
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//---
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int bars = CopyRates(Symb.Name(), TimeFrame, st_time, TimeCurrent(), Rates);
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if(!RSI.BufferResize(bars) || !CCI.BufferResize(bars) || !ATR.BufferResize(bars) || !MACD.BufferResize(bars))
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{
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ExpertRemove();
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return;
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}
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if(!ArraySetAsSeries(Rates, true))
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{
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ExpertRemove();
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return;
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}
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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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//---
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int total = bars - (int)(HistoryBars + 2 * SessionSize);
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//---
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CBufferFloat* State;
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for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
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{
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int error_code;
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int shift = (int)(fmin(fabs(Math::MathRandomNormal(0, 1, error_code)), 1) * (total) + SessionSize);
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States.Clear();
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for(int batch = 0; batch < SessionSize; batch++)
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{
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int i = shift - batch;
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State = new CBufferFloat();
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if(!State)
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{
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ExpertRemove();
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return;
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}
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int r = i + (int)HistoryBars;
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if(r > bars)
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continue;
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for(int b = 0; b < (int)HistoryBars; b++)
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{
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int bar_t = r - b;
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float open = (float)Rates[bar_t].open;
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TimeToStruct(Rates[bar_t].time, sTime);
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float rsi = (float)RSI.Main(bar_t);
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float cci = (float)CCI.Main(bar_t);
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float atr = (float)ATR.Main(bar_t);
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float macd = (float)MACD.Main(bar_t);
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float sign = (float)MACD.Signal(bar_t);
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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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if(!State.Add((float)Rates[bar_t].close - open) || !State.Add((float)Rates[bar_t].high - open) || !State.Add((float)Rates[bar_t].low - open) || !State.Add((float)Rates[bar_t].tick_volume / 1000.0f) ||
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!State.Add(sTime.hour) || !State.Add(sTime.day_of_week) || !State.Add(sTime.mon) ||
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!State.Add(rsi) || !State.Add(cci) || !State.Add(atr) || !State.Add(macd) || !State.Add(sign))
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break;
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}
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if(IsStopped())
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{
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ExpertRemove();
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return;
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}
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if(State.Total() < (int)HistoryBars * 12)
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continue;
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if(!StudyNet.feedForward(GetPointer(State), 12, true))
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{
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ExpertRemove();
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return;
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}
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StudyNet.getResults(TempData);
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int action = GetAction(TempData);
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if(action < 0)
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{
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ExpertRemove();
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return;
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}
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double reward = Rates[i - 1].close - Rates[i - 1].open;
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switch(action)
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{
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case 0:
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if(reward < 0)
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reward *= 20;
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else
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reward *= 1;
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break;
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case 1:
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if(reward > 0)
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reward *= -20;
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else
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reward *= -1;
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break;
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default:
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reward = -fabs(reward);
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break;
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}
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if(!States.Add(State))
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{
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ExpertRemove();
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return;
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}
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vActions[batch] = (float)action;
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vRewards[SessionSize - batch - 1] = (float)(reward * pow(DiscountFactor, (double)batch));
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vProbs[SessionSize - batch - 1] = TempData.At(action);
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//---
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}
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float cum_reward = vRewards.Sum();
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vRewards = vRewards.CumSum();
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vRewards = vRewards / fmax(vRewards.Max(), fabs(vRewards.Min()));
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float loss = (vRewards * MathLog(vProbs) * (-1)).Sum();
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//if(MaxProfit < cum_reward)
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{
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if(!StudyNet.Save(FileName + ".nnw", loss, 0, 0, Rates[shift - SessionSize].time, false))
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return;
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//MaxProfit = cum_reward;
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}
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//---
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for(int batch = 0; batch < SessionSize; batch++)
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{
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State = States.At(batch);
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if(!StudyNet.feedForward(State))
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{
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ExpertRemove();
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return;
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}
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if(!TempData.BufferInit(Actions, 0) || !TempData.Update((int)vActions[batch], vRewards[SessionSize - batch - 1]))
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{
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ExpertRemove();
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return;
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}
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if(!StudyNet.backProp(TempData))
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{
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ExpertRemove();
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return;
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}
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}
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PrintFormat("Iteration %d, Cummulative reward %.5f, loss %.5f", iter, cum_reward, loss);
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}
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Comment("");
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//---
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ExpertRemove();
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int GetAction(CBufferFloat* probability)
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{
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vectorf prob;
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if(!probability.GetData(prob))
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return -1;
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prob = prob.CumSum();
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prob = prob / prob.Max();
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int err_code;
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float random = (float)Math::MathRandomNormal(0.5, 0.5, err_code);
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if(random >= 1)
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return (int)prob.Size() - 1;
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for(int i = 0; i < (int)prob.Size(); i++)
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if(random <= prob[i])
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return i;
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//---
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return -1;
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}
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//+------------------------------------------------------------------+
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