81 lines
3.8 KiB
MQL5
81 lines
3.8 KiB
MQL5
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//+------------------------------------------------------------------+
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//| EGARCH_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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#define __SLSQP__
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#include "Arch\univariate\mean.mqh"
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#resource "SPY2017.csv" as string equity_data
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//--- Input parameters for script customization
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input double ScaleFactor = 100.; //--- Rescaling multiplier to prevent optimizer underflow
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input ulong _P_ = 1; //--- Short-term ARCH lag order
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input ulong _O_ = 0; //--- Asymmetric term
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input ulong _Q_ = 1; //--- Long-term variance persistence GARCH lag order
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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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//---
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vector prices;
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//--- --- Step 1: Historical Data Fetch ---
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matrix data = np::readcsv_from_string(equity_data,false,",",true,0);
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prices = data.Col(1);
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//--- --- Step 2: Transform Prices to Returns ---
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//--- Map closing prices to logarithmic space
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prices = log(prices);
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//--- Compute log returns: r_t = ln(P_t) - ln(P_{t-1})
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vector returns = np::diff(prices);
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//--- --- Step 3: Base Model Configuration Setup ---
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//--- Initialize core specification fields mapping onto the EGARCH container
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ArchParameters egarch_spec;
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//--- Apply the scaling factor (multiplying by 100 scales returns to percentage form)
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egarch_spec.observations = ScaleFactor * returns;
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egarch_spec.vol_model_type = VOL_EGARCH;
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egarch_spec.garch_p = _P_;
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egarch_spec.garch_o = _O_;
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egarch_spec.garch_q = _Q_;
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//--- --- Step 5: Model Initialization ---
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//--- Instantiate the continuous tracking constant mean container wrapper
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ZeroMean egarch_model;
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//--- Pass structural parameters down into the optimization initialization routine
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if(!egarch_model.initialize(egarch_spec))
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return;
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//--- --- Step 6: Parameter Optimization (Fitting) ---
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//--- Trigger the non-linear execution optimizer loop (SLSQP engine solver)
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ArchModelResult egarch_params = egarch_model.fit();
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//--- --- Step 7: Optimization Convergence Guard ---
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//--- Verify that the resulting parameters array size matches the model criteria configurations
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if(!egarch_params.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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//--- Prepare output of model parameters
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string pnames = egarch_model.volatility().parameterNames();
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string vol_parameter_labels[];
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//--- Organize parameter names into array for display
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int labels = StringSplit(pnames,StringGetCharacter(",",0),vol_parameter_labels);
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//--- Check number of labels is equal to number of model parameters
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if(labels != int(egarch_params.params.Size()))
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return;
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//--- --- Step 8: Results Output Extraction ---
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//--- Print optimal target parameter solutions to the MT5 journal
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Print("EGARCH model parameters");
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PrintFormat("%10s %10s %10s","Name","Value","Pvalue");
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//--- Extract statistical asymptotic standard deviation errors mapped out to individual p-values
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vector pv = egarch_params.pvalues();
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for(ulong i = 0; i < pv.Size(); ++i)
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{
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//--- Log individual calculated p-values step-by-step to evaluate structural significance
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PrintFormat("%10s %10.4f %10.4f",vol_parameter_labels[i], egarch_params.params[i],pv[i]);
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}
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}
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//+------------------------------------------------------------------+
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