349 lines
12 KiB
MQL5
349 lines
12 KiB
MQL5
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//+------------------------------------------------------------------+
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//| base.mqh |
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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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#include"..\..\np.mqh"
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#include"..\Utility\wald.mqh"
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#include"..\..\Regression\OLS.mqh"
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#include"..\..\Regression\utils.mqh"
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//---
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#define EMPTY_VECTOR vector::Zeros(0)
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#define EMPTY_MATRIX matrix::Zeros(0,0)
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//---
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vector EMPTY_VECTOR_ARRAY[];
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matrix EMPTY_MATRIX_ARRAY[];
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//+------------------------------------------------------------------+
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//| covariance type |
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//+------------------------------------------------------------------+
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enum ENUM_COVAR_TYPE
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{
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COVAR_CLASSIC = 0,//classic
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COVAR_ROBUST//robust
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};
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//+------------------------------------------------------------------+
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//| forecasting method |
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//+------------------------------------------------------------------+
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enum ENUM_FORECAST_METHOD
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{
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FORECAST_ANALYTIC=0,//analytic
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FORECAST_SIMULATION,//simulation
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FORECAST_BOOTSTRAP//bootstrap
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};
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//+------------------------------------------------------------------+
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//| forecast alignment |
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//+------------------------------------------------------------------+
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enum ENUM_FORECAST_ALIGNMENT
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{
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ALIGN_ORIGIN = 0,//origin
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ALIGN_TARGET//target
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};
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//+------------------------------------------------------------------+
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//| mean models |
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//+------------------------------------------------------------------+
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enum ENUM_MEAN_MODEL
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{
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MEAN_CONSTANT=0,//Constant Mean
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MEAN_ZERO,//Zero Mean
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MEAN_AR,//AR
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MEAN_ARX,//AR-X
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MEAN_HAR,//HAR
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MEAN_HARX//HAR-X
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};
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//---
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const string MEAN_MODELS = "Constant Mean,Zero Mean,AR,AR-X,HAR,HAR-X";
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//+------------------------------------------------------------------+
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//| volatility models |
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//+------------------------------------------------------------------+
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enum ENUM_VOLATILITY_MODEL
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{
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VOL_CONST=0,//Constant Variance
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VOL_ARCH,//ARCH
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VOL_AVARCH,//AVARCH
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VOL_AVGARCH,//AVGARCH
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VOL_TARCH,//TARCH ZARCH
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VOL_GARCH,//GARCH
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VOL_GJR_GARCH//GJR_GARCH
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};
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//---
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const string VOLATILITY_MODELS = "Constant Variance,ARCH,AVARCH,AVGARCH,TARCH ZARCH,GARCH,GJR GARCH";
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//+------------------------------------------------------------------+
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//| distributions |
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//+------------------------------------------------------------------+
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enum ENUM_DISTRIBUTION_MODEL
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{
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DIST_NORMAL=0,//Normal
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DIST_STUDENT,//Standardized Student's T
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DIST_SKEW_STUDENT,//Standardized Skew Student's T
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DIST_GEN_ERROR,//Generalized Error Distribution
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};
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//---
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const string DISTRIBUTIONS = "Normal,Standardized Student's t,Standardized Skew Student's t,Generalized Error Distribution";
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//+------------------------------------------------------------------+
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//|Test a matrix for an implicit constant |
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//+------------------------------------------------------------------+
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bool implicit_constant(matrix &exog_data)
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{
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ulong nobs = exog_data.Rows();
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matrix temp(nobs,exog_data.Cols()+1);
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vector ones = vector::Ones(nobs);
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temp.Col(ones,0);
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for(ulong i = 1; i<temp.Cols(); ++i)
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temp.Col(exog_data.Col(i-1),i);
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ulong rank = temp.Rank();
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return (rank == exog_data.Cols());
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}
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//+------------------------------------------------------------------+
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//|reindex |
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//+------------------------------------------------------------------+
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bool _reindex(ulong actual, matrix& in[], matrix& out[])
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{
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ulong obs = ulong(in.Size());
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if(actual>obs)
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{
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ArrayResize(out,int(actual));
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matrix temp = matrix::Zeros(in[0].Rows(),in[0].Cols());
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temp.Fill(EMPTY_VALUE);
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for(uint i = 0; i<uint(actual - obs); ++i)
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out[i] = (i<uint(actual - obs))?temp:in[i-uint(actual - obs)];
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}
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return true;
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}
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//+------------------------------------------------------------------+
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//|reindex |
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//+------------------------------------------------------------------+
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matrix _reindex(ulong actual, matrix& in)
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{
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ulong obs = ulong(in.Rows());
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if(actual>obs)
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{
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matrix out(actual);
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vector temp = vector::Zeros(in.Cols());
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temp.Fill(EMPTY_VALUE);
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for(ulong i = 0; i<(actual - obs); ++i)
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out.Row((i<(actual - obs))?temp:in.Row(i-(actual - obs)),i);
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return out;
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}
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return in;
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}
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//+------------------------------------------------------------------+
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//|reindex |
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//+------------------------------------------------------------------+
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vector _reindex(ulong actual, vector& in)
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{
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ulong obs = ulong(in.Size());
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if(actual>obs)
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{
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vector out(actual);
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double temp = EMPTY_VALUE;
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for(ulong i = 0; i<(actual - obs); ++i)
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out[i] = (i<(actual - obs))?temp:in[i-(actual - obs)];
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return out;
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}
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return in;
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}
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//+------------------------------------------------------------------+
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//| generate parameter names |
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//+------------------------------------------------------------------+
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string common_names(ulong p, ulong o, ulong q)
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{
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string names = "omega";
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for(ulong i = 0; i<p; ++i)
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names += ",alpha["+string(i+1)+"]";
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for(ulong i = 0; i<o; ++i)
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names += ",gamma["+string(i+1)+"]";
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for(ulong i = 0; i<q; ++i)
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names += ",beta["+string(i+1)+"]";
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return names;
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}
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//+------------------------------------------------------------------+
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//| Arch parameters |
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//+------------------------------------------------------------------+
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struct ArchParameters
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{
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// --- Data & Core Configuration
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vector observations;
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matrix exog_data;
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vector mean_lags;
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ENUM_MEAN_MODEL mean_model_type;
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ENUM_VOLATILITY_MODEL vol_model_type;
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ENUM_DISTRIBUTION_MODEL dist_type;
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ulong holdout_size;
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bool is_rescale_enabled;
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double scaling_factor;
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// --- Mean Model Parameters
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bool include_constant;
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bool use_har_rotation;
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// --- Volatility Process Parameters (GARCH/ARCH)
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int vol_rng_seed;
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ulong garch_p;
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ulong garch_o;
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ulong garch_q;
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double vol_power;
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long sample_start_idx;
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long sample_end_idx;
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ulong min_bootstrap_sims;
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// --- Distribution Parameters
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vector dist_init_params;
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int dist_rng_seed;
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ArchParameters(void)
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{
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observations = vector::Zeros(0);
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exog_data = matrix::Zeros(0,0);
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mean_lags = vector::Zeros(0);
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vol_model_type=WRONG_VALUE;
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dist_type=WRONG_VALUE;
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holdout_size=0;
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scaling_factor = 1.0;
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is_rescale_enabled=false;
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mean_model_type = WRONG_VALUE;
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include_constant = true;
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use_har_rotation =false;
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vol_rng_seed = 0;
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garch_p = 1;
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garch_o = 0;
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garch_q = 1;
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vol_power=2.;
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sample_start_idx=1;
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sample_end_idx=-1;
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min_bootstrap_sims=100;
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dist_init_params = vector::Zeros(0);
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dist_rng_seed = 0;
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}
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ArchParameters(ArchParameters &other)
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{
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observations = other.observations;
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exog_data = other.exog_data;
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scaling_factor = other.scaling_factor;
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mean_lags = other.mean_lags;
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vol_model_type=other.vol_model_type;
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dist_type=other.dist_type;
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holdout_size=other.holdout_size;
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is_rescale_enabled=other.is_rescale_enabled;
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include_constant = other.include_constant;
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use_har_rotation = other.use_har_rotation;
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vol_rng_seed = other.vol_rng_seed;
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garch_p = other.garch_p;
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garch_o = other.garch_o;
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garch_q = other.garch_q;
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vol_power=other.vol_power;
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sample_start_idx=other.sample_start_idx;
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sample_end_idx=other.sample_end_idx;
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min_bootstrap_sims=other.min_bootstrap_sims;
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dist_init_params = other.dist_init_params;
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dist_rng_seed = other.dist_rng_seed;
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}
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void operator=(ArchParameters &other)
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{
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observations = other.observations;
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exog_data = other.exog_data;
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scaling_factor = other.scaling_factor;
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mean_lags = other.mean_lags;
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vol_model_type=other.vol_model_type;
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dist_type=other.dist_type;
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holdout_size=other.holdout_size;
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is_rescale_enabled=other.is_rescale_enabled;
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include_constant = other.include_constant;
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use_har_rotation = other.use_har_rotation;
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vol_rng_seed = other.vol_rng_seed;
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garch_p = other.garch_p;
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garch_o = other.garch_o;
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garch_q = other.garch_q;
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vol_power=other.vol_power;
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sample_start_idx=other.sample_start_idx;
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sample_end_idx=other.sample_end_idx;
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min_bootstrap_sims=other.min_bootstrap_sims;
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dist_init_params = other.dist_init_params;
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dist_rng_seed = other.dist_rng_seed;
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}
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};
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//+------------------------------------------------------------------+
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//| arch simulation |
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//+------------------------------------------------------------------+
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struct ArchSimulation
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{
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matrix values[];
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matrix residuals[];
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matrix variances[];
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matrix residual_variances[];
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ArchSimulation(void)
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{
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}
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ArchSimulation(matrix& _values[],matrix& _residuals[], matrix& _variances[], matrix& _residual_variances[])
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{
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np::copy3D(_values,values);
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np::copy3D(_residuals,residuals);
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np::copy3D(_variances,variances);
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np::copy3D(_residual_variances,residual_variances);
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}
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ArchSimulation(ArchSimulation &other)
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{
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np::copy3D(other.values,values);
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np::copy3D(other.residuals,residuals);
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np::copy3D(other.variances,variances);
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np::copy3D(other.residual_variances,residual_variances);
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}
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void operator=(ArchSimulation &other)
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{
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np::copy3D(other.values,values);
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np::copy3D(other.residuals,residuals);
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np::copy3D(other.variances,variances);
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np::copy3D(other.residual_variances,residual_variances);
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}
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};
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//+------------------------------------------------------------------+
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//| arch forecast result |
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//+------------------------------------------------------------------+
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struct ArchForecast
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{
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matrix mean;
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matrix variance;
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matrix residual_variance;
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ArchSimulation simulation;
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ArchForecast(void)
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{
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mean = variance = residual_variance = matrix::Zeros(0,0);
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}
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ArchForecast(ulong startindex, matrix& _mean, matrix& _variance, matrix& _res_var, matrix& _values[], matrix& _residuals[], matrix& _variances[], matrix& _residual_variances[])
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{
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mean = _mean;
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variance = _variance;
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residual_variance = _res_var;
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np::copy3D(_values,simulation.values);
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np::copy3D(_residuals,simulation.residuals);
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np::copy3D(_variances,simulation.variances);
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np::copy3D(_residual_variances,simulation.residual_variances);
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}
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ArchForecast(ArchForecast &other)
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{
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mean = other.mean;
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variance = other.variance;
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residual_variance = other.residual_variance;
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simulation = other.simulation;
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}
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void operator=(ArchForecast &other)
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{
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mean = other.mean;
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variance = other.variance;
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residual_variance = other.residual_variance;
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simulation = other.simulation;
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}
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};
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//+------------------------------------------------------------------+
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