forked from mnbvc188199/Warrior_EA
250 lines
19 KiB
MQL5
250 lines
19 KiB
MQL5
//+------------------------------------------------------------------+
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//| Genetic.mq5 |
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//| Copyright 2022, DNG |
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//| https://www.mql5.com/en/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/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 "NetEvolution.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 MODEL Symb.Name()+"_"+EnumToString((ENUM_TIMEFRAMES)Period())+"_Evolution"
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#define NeuronsToBar 12
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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 PopulationSize = 50;
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input int Generations = 1000;
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input float Mutation = 0.01f;
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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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CNetEvolution Models;
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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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float BestLoss;
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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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if(!Models.Load(MODEL + ".nnw", PopulationSize, false))
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return INIT_FAILED;
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//---
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if(!Models.GetLayerOutput(0, TempData))
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return INIT_FAILED;
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HistoryBars = TempData.Total() / NeuronsToBar;
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Models.getResults(TempData);
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if(TempData.Total() != Actions)
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return INIT_PARAMETERS_INCORRECT;
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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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CBufferFloat* State = new CBufferFloat();
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float loss = 0;
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uint count = 0;
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uint total = bars - HistoryBars - 2;
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ulong ticks = GetTickCount64();
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uint test_size = 22 * 24;
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for(int gen = 0; (gen < Generations && !IsStopped()); gen ++)
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{
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for(uint i = total; i > test_size; i--)
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{
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uint r = i + HistoryBars;
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if(r > (uint)bars)
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{
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continue;
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}
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State.Clear();
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for(uint b = 0; b < HistoryBars; b++)
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{
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uint 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) ||
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!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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{
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break;
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}
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}
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if(IsStopped())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(State.Total() < (int)HistoryBars * NeuronsToBar)
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{
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continue;
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}
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if(!Models.feedForward(State, NeuronsToBar, true))
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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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double reward = Rates[i - 1].close - Rates[i - 1].open;
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TempData.Clear();
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if(!TempData.Add((float)(reward < 0 ? 20 * reward : reward)) ||
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!TempData.Add((float)(reward > 0 ? -reward * 20 : -reward)) ||
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!TempData.Add((float) - fabs(reward * 10)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(!Models.Rewards(TempData))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(GetTickCount64() - ticks > 250)
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{
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uint x = total - i;
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double perc = x * 100.0 / (total - test_size);
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Comment(StringFormat("%d from %d -> %.2f%% from %.2f%%", x, total - test_size, perc, 100));
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ticks = GetTickCount64();
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}
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}
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//---
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if(!IsStopped())
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Models.Save(MODEL + ".nnw", false);
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//---
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float average, maximum;
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if(!Models.NextGeneration(Mutation, average, maximum))
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{
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PrintFormat("Error of create next generation: %d", GetLastError());
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//---
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PrintFormat("Generation %d, Average Cummulative reward %.5f, Max Reward %.5f", gen, average, maximum);
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}
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delete State;
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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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