228 lines
9.7 KiB
MQL5
228 lines
9.7 KiB
MQL5
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//+------------------------------------------------------------------+
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//| EGARCH_InnovationZscore.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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// Preprocessor directives and indicator properties
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#define __SLSQP__ // Enable Sequential Least Squares Programming optimization algorithm
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#property indicator_separate_window // Render indicator in an independent subwindow
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#include "Arch\Univariate\mean.mqh" // Include statistical mean model header dependencies
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// Define fixed indicator visual threshold levels
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#property indicator_level1 2.0 // Upper extreme threshold line
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#property indicator_level2 0.0 // Zero baseline
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#property indicator_level3 -2.0 // Lower extreme threshold line
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// Define buffer and plotting counts
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#property indicator_buffers 1
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#property indicator_plots 1
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// --- Plot 1: Standardized Innovation Z-Score
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#property indicator_label1 "Eiz"
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#property indicator_type1 DRAW_LINE
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#property indicator_color1 clrRed
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#property indicator_style1 STYLE_SOLID
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#property indicator_width1 1
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//+------------------------------------------------------------------+
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//| INPUT PARAMETERS |
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//+------------------------------------------------------------------+
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input int BarsToDraw = 500; // Number of historical bars to calculate and render
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input ulong HistoryLen = 200; // Rolling sample size (lookback period) for EGARCH fitting
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input ulong WindowLen = 20; // Rolling window length for Z-score standardizing residuals
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input double ScaleFactor = 100.; // Multiplier applied to log returns (improves numerical optimizer stability)
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input ENUM_MEAN_MODEL MeanModel = MEAN_CONSTANT; // Model type for time-series conditional mean
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input bool MeanConstant = true; // Include intercept constant in mean specification
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input string MeanLags = ""; // Comma-separated list of AR lag terms (e.g. "1,2")
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ENUM_VOLATILITY_MODEL VolatilityModel = VOL_EGARCH; // Volatility process specified as Exponential GARCH
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input ulong _P_ = 1; // GARCH order (lagged conditional log-variances)
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input ulong _O_ = 1; // Asymmetry order (leverage/asymmetric magnitude terms)
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input ulong _Q_ = 1; // ARCH order (lagged innovations)
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input int Volatility_Seed = 0; // Seed for volatility model random generator initialization
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input ENUM_DISTRIBUTION_MODEL ErrorDistribution = DIST_NORMAL; // Innovation error distribution assumption
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input int Distribution_Seed = 0; // Seed for distribution model random generator initialization
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//+------------------------------------------------------------------+
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//| GLOBAL INDICATOR BUFFERS AND STATE VARIABLES |
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//+------------------------------------------------------------------+
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// Dynamic array mapped to indicator plot buffer
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double EizBuffer[];
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// Computational vectors and model pointers
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vector returns = vector::Zeros(HistoryLen); // Rolling logarithmic returns vector
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vector stdresid = returns; // Model-standardized residuals vector
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vector window; // Sliced sub-vector of standardized residuals for local normalization
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ArchParameters model_spec; // Struct holding model specifications and hyperparameters
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HARX* full_model; // Polymorphic pointer to mean model instance
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//+------------------------------------------------------------------+
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//| Custom indicator initialization function |
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//+------------------------------------------------------------------+
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int OnInit()
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{
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// Validate lookback window length
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if(HistoryLen < 30)
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{
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Print("Invalid input value for HistoryLen");
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return INIT_FAILED;
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}
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// --- Map dynamic array to primary indicator buffer
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SetIndexBuffer(0, EizBuffer, INDICATOR_DATA);
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// --- Set plot properties and drawing offset
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PlotIndexSetInteger(0, PLOT_DRAW_BEGIN, BarsToDraw);
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PlotIndexSetDouble(0, PLOT_EMPTY_VALUE, EMPTY_VALUE);
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// --- Configure model specifications
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model_spec.mean_model_type = MeanModel;
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// Parse comma-separated mean lag configuration string if provided
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if(StringLen(MeanLags))
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{
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string lag_info[];
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int nlags = StringSplit(MeanLags, StringGetCharacter(",", 0), lag_info);
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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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if(model_spec.mean_lags.Resize(model_spec.mean_lags.Size() + 1, 3))
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model_spec.mean_lags[model_spec.mean_lags.Size() - 1] = StringToDouble(lag_info[i]);
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else
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{
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Print(" error ", GetLastError());
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return INIT_FAILED;
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}
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}
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}
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}
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}
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// Set EGARCH structural dynamics and seeds
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model_spec.vol_rng_seed = Volatility_Seed;
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model_spec.garch_o = _O_;
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model_spec.garch_p = _P_;
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model_spec.garch_q = _Q_;
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model_spec.dist_type = ErrorDistribution;
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model_spec.dist_rng_seed = Distribution_Seed;
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// Factory instantiation of the requested mean model class
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switch(MeanModel)
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{
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case MEAN_CONSTANT:
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full_model = new ConstantMean();
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break;
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case MEAN_ZERO:
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full_model = new ZeroMean();
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break;
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case MEAN_AR:
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full_model = new AR();
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break;
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default:
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full_model = new ConstantMean();
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break;
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}
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// Ensure memory allocation for mean model succeeded
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if(CheckPointer(full_model) == POINTER_INVALID)
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return INIT_FAILED;
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model_spec.vol_model_type = VolatilityModel;
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return(INIT_SUCCEEDED);
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}
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//+------------------------------------------------------------------+
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//| Custom indicator deinitialization function |
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//+------------------------------------------------------------------+
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void OnDeinit(const int reason)
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{
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// Free dynamic memory allocated for the mean model instance
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if(CheckPointer(full_model) == POINTER_DYNAMIC)
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delete full_model;
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}
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//+------------------------------------------------------------------+
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//| Custom indicator iteration function |
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//+------------------------------------------------------------------+
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int OnCalculate(const int32_t rates_total,
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const int32_t prev_calculated,
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const datetime &time[],
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const double &open[],
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const double &high[],
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const double &low[],
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const double &close[],
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const long &tick_volume[],
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const long &volume[],
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const int32_t &spread[])
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{
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int32_t limit = 0;
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// Verify total bars available meet the required lookback + rendering window
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if(rates_total < int32_t(HistoryLen + BarsToDraw))
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{
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Print("Not enough bars for indicator calculation");
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return -1;
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}
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// Determine starting index for incremental bar calculation
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if(prev_calculated <= 0)
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{
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limit = rates_total - int32_t(fabs(BarsToDraw)); // First run: calculate specified historical depth
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ArrayInitialize(EizBuffer, EMPTY_VALUE);
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}
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else
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limit = prev_calculated - 1; // Subsequent runs: update only latest bar(s)
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// Main calculation loop iterating through historical price bars
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for(int32_t shift = limit; shift < rates_total; ++shift)
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{
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int32_t from = (shift - int32_t(HistoryLen)) + 1;
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// Calculate logarithmic returns over the rolling lookback window
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for(int32_t i = from, k = 0; k < int32_t(HistoryLen); ++i, ++k)
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returns[k] = log(close[i] / close[i - 1]);
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// Scale returns to assist optimizer convergence
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returns *= fabs(ScaleFactor);
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// Load current sample window into specification structure
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model_spec.observations = returns;
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// Re-initialize model state with new sample window
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if(!full_model.initialize(model_spec))
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{
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Print(" initialization error ");
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return 0;
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}
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// Fit EGARCH model via maximum likelihood optimization
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ArchModelResult result = full_model.fit();
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if(!result.conditional_volatility.Size())
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{
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Print(" model fit error ");
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return 0;
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}
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// Extract full vector of standardized residuals (innovations divided by fitted volatility)
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stdresid = result.std_resid();
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// Slice out the dynamic evaluation window (last WindowLen entries) from standardized residuals
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window = np::sliceVector(stdresid, long(stdresid.Size() - WindowLen));
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// Calculate local Z-score of the most recent innovation over the sliced window (with epsilon to prevent division by zero)
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EizBuffer[shift] = (window[window.Size() - 1] - window.Mean()) / (window.Std() + 1.e-8);
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}
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// Return calculated count to optimize subsequent iteration calls
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return(rates_total);
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}
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//+------------------------------------------------------------------+
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//+------------------------------------------------------------------+
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