//+------------------------------------------------------------------+
//|                          AsymmetricVolatilityRegimeOscillator.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"

// Preprocessor directives and indicator properties
#define __SLSQP__                                 // Enable Sequential Least Squares Programming optimization algorithm
#property indicator_separate_window               // Render indicator in an independent subwindow

#include "Arch\Univariate\mean.mqh"         // Include statistical mean model header dependencies

// Define buffer and plotting counts
#property indicator_buffers 3
#property indicator_plots   3
#property indicator_level1  0.0                   // Zero crossover baseline

// --- Plot 1: Fast Mean Residual Window
#property indicator_label1  "Fast"
#property indicator_type1   DRAW_LINE
#property indicator_color1  clrRed
#property indicator_style1  STYLE_SOLID
#property indicator_width1  1

// --- Plot 2: Slow Mean Residual Window
#property indicator_label2  "Slow"
#property indicator_type2   DRAW_LINE
#property indicator_color2  clrGreen
#property indicator_style2  STYLE_SOLID
#property indicator_width2  1

// --- Plot 3: Asymmetric Volatility Regime Oscillator (AVRO = Fast - Slow)
#property indicator_label3  "Avro"
#property indicator_type3   DRAW_LINE
#property indicator_color3  clrMediumBlue
#property indicator_style3  STYLE_SOLID
#property indicator_width3  1

//+------------------------------------------------------------------+
//| INPUT PARAMETERS                                                 |
//+------------------------------------------------------------------+
input int                   BarsToDraw        = 500;           // Number of historical bars to calculate and render
input ulong                 FastWindowLen     = 20;            // Fast window length for standardized residual mean
input ulong                 SlowWindowLen     = 40;            // Slow window length for standardized residual mean
input ulong                 HistoryLen        = 200;           // Rolling sample size (lookback period) for EGARCH fitting
input double                ScaleFactor       = 1000.;         // Multiplier applied to log returns (improves numerical optimizer stability)
input ENUM_MEAN_MODEL       MeanModel         = MEAN_CONSTANT; // Model type for time-series conditional mean
input bool                  MeanConstant      = true;          // Include intercept constant in mean specification
input string                MeanLags          = "";            // Comma-separated list of AR lag terms (e.g. "1,2")
ENUM_VOLATILITY_MODEL       VolatilityModel   = VOL_EGARCH;    // Volatility process specified as Exponential GARCH
input ulong                 _P_               = 1;             // GARCH order (lagged conditional log-variances)
input ulong                 _O_               = 1;             // Asymmetry order (leverage/asymmetric magnitude terms)
input ulong                 _Q_               = 1;             // ARCH order (lagged innovations)
input int                   Volatility_Seed   = 0;             // Seed for volatility model random generator initialization
input ENUM_DISTRIBUTION_MODEL ErrorDistribution = DIST_NORMAL; // Innovation error distribution assumption
input int                   Distribution_Seed = 0;             // Seed for distribution model random generator initialization

//+------------------------------------------------------------------+
//| GLOBAL INDICATOR BUFFERS AND STATE VARIABLES                     |
//+------------------------------------------------------------------+
// Dynamic arrays mapped to indicator plot buffers
double         FastBuffer[];
double         SlowBuffer[];
double         AvroBuffer[];

// Computational vectors and model pointers
vector         returns = vector::Zeros(HistoryLen);            // Rolling logarithmic returns vector
vector         stdresid = returns;                             // Model-standardized residuals vector
vector         fastwindow = vector::Zeros(FastWindowLen);       // Sliced fast window vector for local mean calculation
vector         slowwindow = vector::Zeros(SlowWindowLen);       // Sliced slow window vector for local mean calculation
ArchParameters model_spec;                                     // Struct holding model specifications and hyperparameters
HARX*          full_model;                                     // Polymorphic pointer to mean model instance

//+------------------------------------------------------------------+
//| Custom indicator initialization function                         |
//+------------------------------------------------------------------+
int OnInit()
  {
   // Validate lookback window length
   if(HistoryLen < 30)
     {
      Print("Invalid input value for HistoryLen");
      return INIT_FAILED;
     }

   // Validate window hierarchy: Fast window must be strictly smaller than Slow window
   if(FastWindowLen >= SlowWindowLen)
     {
      Print("FastWindowLen should Not be more than or equal to SlowWindowLen");
      return INIT_FAILED;
     }

   // Validate window bounds against total history lookback length
   if(MathMax(FastWindowLen, SlowWindowLen) > HistoryLen)
     {
      Print("Neither FastWindowLen nor SlowWindowLen should be more than HistoryLen");
      return INIT_FAILED;
     }

   // --- Map dynamic dynamic arrays to indicator buffers
   SetIndexBuffer(0, FastBuffer, INDICATOR_DATA);
   SetIndexBuffer(1, SlowBuffer, INDICATOR_DATA);
   SetIndexBuffer(2, AvroBuffer, INDICATOR_DATA);

   // --- Set plot drawing offset to suppress initial uncalculated bars
   PlotIndexSetInteger(0, PLOT_DRAW_BEGIN, BarsToDraw);
   PlotIndexSetInteger(1, PLOT_DRAW_BEGIN, BarsToDraw);
   PlotIndexSetInteger(2, PLOT_DRAW_BEGIN, BarsToDraw);

   // --- Initialize buffers with empty markers
   ArrayInitialize(FastBuffer, EMPTY_VALUE);
   ArrayInitialize(SlowBuffer, EMPTY_VALUE);

   // --- Configure model specifications
   model_spec.mean_model_type = MeanModel;

   // Parse comma-separated mean lag configuration string if provided
   if(StringLen(MeanLags))
     {
      string lag_info[];
      int nlags = StringSplit(MeanLags, StringGetCharacter(",", 0), lag_info);
      if(nlags > 0)
        {
         for(uint i = 0; i < uint(nlags); ++i)
           {
            if(StringLen(lag_info[i]) > 0)
              {
               if(model_spec.mean_lags.Resize(model_spec.mean_lags.Size() + 1, 3))
                  model_spec.mean_lags[model_spec.mean_lags.Size() - 1] = StringToDouble(lag_info[i]);
               else
                 {
                  Print(" error ", GetLastError());
                  return INIT_FAILED;
                 }
              }
           }
        }
     }

   // Set EGARCH structural dynamics and seeds
   model_spec.vol_rng_seed  = Volatility_Seed;
   model_spec.garch_o       = _O_;
   model_spec.garch_p       = _P_;
   model_spec.garch_q       = _Q_;
   model_spec.dist_type     = ErrorDistribution;
   model_spec.dist_rng_seed = Distribution_Seed;

   // Factory instantiation of the requested mean model class
   switch(MeanModel)
     {
      case MEAN_CONSTANT:
         full_model = new ConstantMean();
         break;
      case MEAN_ZERO:
         full_model = new ZeroMean();
         break;
      case MEAN_AR:
         full_model = new AR();
         break;
      default:
         full_model = new ConstantMean();
         break;
     }

   // Ensure memory allocation for mean model succeeded
   if(CheckPointer(full_model) == POINTER_INVALID)
      return INIT_FAILED;

   model_spec.vol_model_type = VolatilityModel;

   return(INIT_SUCCEEDED);
  }

//+------------------------------------------------------------------+
//| Custom indicator deinitialization function                       |
//+------------------------------------------------------------------+
void OnDeinit(const int reason)
  {
   // Free dynamic memory allocated for the mean model instance
   if(CheckPointer(full_model) == POINTER_DYNAMIC)
      delete full_model;
  }

//+------------------------------------------------------------------+
//| Custom indicator iteration function                              |
//+------------------------------------------------------------------+
int OnCalculate(const int32_t rates_total,
                const int32_t prev_calculated,
                const datetime &time[],
                const double &open[],
                const double &high[],
                const double &low[],
                const double &close[],
                const long &tick_volume[],
                const long &volume[],
                const int32_t &spread[])
  {
   // Verify total bars available meet required history length
   if(rates_total < int32_t(HistoryLen))
     {
      Print("Not enough bars for indicator calculation");
      return -1;
     }

   // Determine starting index for incremental bar calculation
   int32_t limit = 0;
   if(prev_calculated <= 0)
      limit = rates_total - int32_t(fabs(BarsToDraw)); // First run: calculate specified historical depth
   else
      limit = prev_calculated - 1;                     // Subsequent runs: update only latest bar(s)

   // Main calculation loop iterating through historical price bars
   for(int32_t shift = limit; shift < rates_total; ++shift)
     {
      int32_t from = (shift - int32_t(HistoryLen)) + 1;
      
      // Calculate logarithmic returns over the rolling lookback window
      for(int32_t i = from, k = 0; k < int32_t(HistoryLen); ++i, ++k)
         returns[k] = log(close[i] / close[i - 1]);
      
      // Scale returns to assist optimizer convergence
      returns *= fabs(ScaleFactor);
      
      // Load current sample window into specification structure
      model_spec.observations = returns;
      
      // Re-initialize model state with new sample window
      if(!full_model.initialize(model_spec))
        {
         Print(" initialization error ");
         return 0;
        }
      
      // Fit EGARCH model via maximum likelihood optimization
      ArchModelResult result = full_model.fit();
      if(!result.conditional_volatility.Size())
        {
         Print(" model fit error ");
         return 0;
        }

      // Extract full vector of standardized residuals
      stdresid = result.std_resid();

      // Populate fast and slow evaluation windows from the tail of standardized residuals
      for(ulong i = 0; i < FastWindowLen; ++i)
         fastwindow[i] = stdresid[HistoryLen - FastWindowLen + i];
      for(ulong i = 0; i < SlowWindowLen; ++i)
         slowwindow[i] = stdresid[HistoryLen - SlowWindowLen + i];

      // Compute local mean values and derive the oscillator buffer (AVRO)
      FastBuffer[shift] = fastwindow.Mean();
      SlowBuffer[shift] = slowwindow.Mean();
      AvroBuffer[shift] = FastBuffer[shift] - SlowBuffer[shift];
     }

   // Return calculated count to optimize subsequent iteration calls
   return(rates_total);
  }
//+------------------------------------------------------------------+