117 Zeilen
3,5 KiB
MQL5
117 Zeilen
3,5 KiB
MQL5
//+------------------------------------------------------------------+
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//| HAR_as_ARX_Demo.mq5 |
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//| Copyright 2025, MetaQuotes Ltd. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2025, MetaQuotes Ltd."
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#property link "https://www.mql5.com"
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#property version "1.00"
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#property script_show_inputs
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#include<Arch\Univariate\mean.mqh>
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//--- input parameters
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input string Symbol_="AUDUSD";
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input ENUM_TIMEFRAMES TimeFrame=PERIOD_D1;
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input datetime StartDate=D'2025.01.01';
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input ulong HistoryLen = 504;
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input double ScaleFactor=100.;
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input bool MeanConstant = true;
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input string MeanLags ="1,5,22";
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//+------------------------------------------------------------------+
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//| Script program start function |
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//+------------------------------------------------------------------+
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void OnStart()
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{
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//---download data
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vector prices;
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if(!prices.CopyRates(Symbol_,TimeFrame,COPY_RATES_CLOSE,StartDate,HistoryLen))
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{
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Print(" failed to get close prices for ", Symbol_,". Error ", GetLastError());
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return;
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}
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//---
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prices = log(prices);
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//---
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vector returns = np::diff(prices);
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//---
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string lag_info[];
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//---
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vector lags=vector::Zeros(0);
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//---
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int nlags = StringSplit(MeanLags,StringGetCharacter(",",0),lag_info);
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//---
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double atod;
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if(nlags>0)
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{
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for(uint i = 0; i<uint(nlags); ++i)
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{
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if(StringLen(lag_info[i])>0)
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{
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atod = StringToDouble(lag_info[i]);
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if(atod>0 && ulong(atod)<returns.Size()-1)
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if(lags.Resize(lags.Size()+1,3))
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lags[lags.Size()-1] = atod;
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}
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}
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}
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//---
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if(lags.Size())
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np::sort(lags);
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//---build the HAR model
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ArchParameters har_spec;
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har_spec.observations=returns*ScaleFactor;
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har_spec.include_constant = MeanConstant;
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har_spec.mean_lags = lags;
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har_spec.vol_model_type = VOL_CONST;
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//---
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HAR harmodel;
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//---
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if(!harmodel.initialize(har_spec))
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return;
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//---
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ArchModelResult har = harmodel.fit(ScaleFactor);
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//---
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if(!har.params.Size())
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{
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Print("Convergence failed ", GetLastError());
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return;
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}
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//---
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Print(" Har model parameters\n", har.params);
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//---Now we build an equivalent ARX model
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matrix exogvars = matrix::Zeros(returns.Size(),lags.Size());
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//---calculate averages
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double sum;
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ulong lag,count;
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for(ulong i = 0; i<exogvars.Cols(); ++i)
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{
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lag = (ulong)lags[i];
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count = lag;
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for(ulong k = lag; k<exogvars.Rows(); ++k)
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{
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sum = 0.0;
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for(ulong j = 0; j<count; ++j)
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sum+=returns[k-j-1];
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exogvars[k,i] = sum/double(count);
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}
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}
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//---
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ArchParameters arx_spec;
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arx_spec.observations = ScaleFactor*np::sliceVector(returns,long(lags[lags.Size()-1]));
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arx_spec.exog_data = ScaleFactor*np::sliceMatrixRows(exogvars,long(lags[lags.Size()-1]));
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arx_spec.include_constant = MeanConstant;
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arx_spec.vol_model_type=VOL_CONST;
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//---
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ARX arxmodel;
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if(!arxmodel.initialize(arx_spec))
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return;
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//---
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ArchModelResult arx = arxmodel.fit(ScaleFactor);
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if(!arx.params.Size())
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{
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Print(" convergence failed ", GetLastError());
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return;
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}
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//---
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Print("ARX model parameters\n", arx.params);
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}
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//+------------------------------------------------------------------+
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