88 lines
4.2 KiB
MQL5
88 lines
4.2 KiB
MQL5
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//+------------------------------------------------------------------+
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//| Aparch_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 for script customization
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input datetime StartDate = D'2025.01.01'; //--- Historical capture anchor start date
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input ulong HistoryLen = 1000; //--- Total historical data bars to request
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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_ = 1; //--- Asymmetric term
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input ulong _Q_ = 1; //--- Long-term variance persistence GARCH lag order
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input double Delta = EMPTY_VALUE; //--- Delta value
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input bool CommonAsymmetry=false; //--- Enable common asymmetric coefficents
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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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//--- Pull close prices directly into an array using native vector operations
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if(!prices.CopyRates(NULL,PERIOD_CURRENT, 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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//--- --- 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 aparch container
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ArchParameters aparch_spec;
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//--- Apply the scaling factor (multiplying by 100 scales returns to percentage form)
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aparch_spec.observations = ScaleFactor * returns;
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aparch_spec.vol_model_type = VOL_APARCH;
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aparch_spec.garch_p = _P_;
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aparch_spec.garch_o = _O_;
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aparch_spec.garch_q = _Q_;
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aparch_spec.aparch_common_asym = CommonAsymmetry;
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aparch_spec.aparch_delta = Delta;
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//--- --- Step 5: Model Initialization ---
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//--- Instantiate the continuous tracking zero mean container wrapper
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ZeroMean aparch_model;
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//--- Pass structural parameters down into the optimization initialization routine
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if(!aparch_model.initialize(aparch_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 aparch_params = aparch_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(aparch_params.solver_return_code)
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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 = aparch_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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//---
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vector pv = aparch_params.pvalues();
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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("Aparch 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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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], aparch_params.params[i],pv[i]);
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}
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}
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//+------------------------------------------------------------------+
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