//+------------------------------------------------------------------+
//|                  Equity_vs_Forex_Asymmetry_Significance_Test.mq5 |
//|                                  Copyright 2025, MetaQuotes Ltd. |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2025, MetaQuotes Ltd."
#property link      "https://www.mql5.com"
#property version   "1.00"
#resource "\\Files\\VolatilityModels\\sp500.csv" as string equity_data
#property script_show_inputs
#include<VolatilityModels\Arch\Univariate\mean.mqh>
//--- input parameters
input string   Symbol_="AUDUSD";
input ENUM_TIMEFRAMES   TimeFrame=PERIOD_D1;
input datetime StartDate=D'2025.01.01';
input ENUM_VOLATILITY_MODEL VolatilityModel = VOL_GJR_GARCH;
input double ScaleFactor=100.;
input ENUM_MEAN_MODEL MeanModel = MEAN_CONSTANT;
input bool MeanConstant = true;
input ulong _P_ = 1;
input ulong _O_ = 1;
input ulong _Q_ = 1;
//+------------------------------------------------------------------+
//| Script program start function                                    |
//+------------------------------------------------------------------+
void OnStart()
  {
   if(VolatilityModel != VOL_GJR_GARCH && VolatilityModel!=VOL_TARCH)
    {
     Print(" INVALID INPUT: choose either Tarch or Gjr-Garch");
     return;
    }
//---
   matrix data = np::readcsv_from_string(equity_data,false,",",true,0);
   vector equity = data.Col(1);
   vector forex;
   if(!forex.CopyRates(Symbol_,TimeFrame,COPY_RATES_CLOSE,datetime(data[0,0]),datetime(data[data.Rows()-1,0])))
     {
      Print(" failed to get close prices for ", Symbol_,". Error ", GetLastError());
      return;
     }

   forex = log(forex);
   equity = log(equity);

   forex = np::diff(forex);
   equity = np::diff(equity);

   forex*=ScaleFactor;
   equity*=ScaleFactor;

   ArchParameters asym_vol_spec;
   asym_vol_spec.include_constant = MeanConstant;
   asym_vol_spec.mean_model_type = MeanModel;
   asym_vol_spec.vol_model_type = VolatilityModel;
   asym_vol_spec.garch_p = _P_;
   asym_vol_spec.garch_q = _Q_;
   asym_vol_spec.garch_o = _O_;
   
   asym_vol_spec.observations=equity;
   
   
   HARX*  equity_model = NULL;
   HARX*  forex_model = NULL;
   
   
   switch(MeanModel)
     {
      case MEAN_CONSTANT:
         equity_model = new ConstantMean();
         forex_model = new ConstantMean();
         break;
      case MEAN_ZERO:
         equity_model = new ZeroMean();
         forex_model = new ZeroMean();
         break;
      default:
         equity_model = new ConstantMean();
         forex_model = new ConstantMean();
         break;
     }
   
   if(!equity_model.initialize(asym_vol_spec))
    {
      delete equity_model;
      return;
    }
      
   ArchModelResult equity_result = equity_model.fit(ScaleFactor);
   Print("Results for ", EnumToString(VolatilityModel));
   if(!equity_result.params.Size())
    {
     Print(" Equity model fit failed ");
     delete equity_model;
    }
   else
    {
     Print(" Equity: Pvalue of gamma parameter is ", equity_result.pvalues()[3]);
     delete equity_model;
    }
  
  asym_vol_spec.observations = forex;  
  if(!forex_model.initialize(asym_vol_spec))
   {
    delete forex_model;
    return;
   }
  
  ArchModelResult forex_result = forex_model.fit(ScaleFactor);
  
  if(!forex_result.params.Size())
   {
    Print(" Forex model fit failed ");
    delete forex_model;
   } 
  else
   {
    Print(" Forex: Pvalue of gamma parameter is " , forex_result.pvalues()[3]);
    delete forex_model;
   } 
  }
//+------------------------------------------------------------------+