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//+------------------------------------------------------------------+
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//| Laguerre_Adaptive_Score_Calculator.mqh |
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//| Copyright 2026, xxxxxxxx|
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2026, xxxxxxxx"
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#property version "1.10" // Refactored class names to eliminate redundant LScore references
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#property description "Stateful calculator implementing Statistical Laguerre Z-Score with adaptive Gamma scaling."
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#ifndef LAGUERRE_ADAPTIVE_SCORE_CALCULATOR_MQH
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#define LAGUERRE_ADAPTIVE_SCORE_CALCULATOR_MQH
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#include <MyIncludes\EfficiencyRatio_Calculator.mqh>
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#include <MyIncludes\ATR_Calculator.mqh>
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#include <MyIncludes\HeikinAshi_Tools.mqh>
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#include <MyIncludes\Laguerre_Adaptive_Filter_Calculator.mqh> // Share adaptive enums
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//+==================================================================+
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//| CLASS: CLaguerreAdaptiveScoreCalculator |
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//+==================================================================+
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class CLaguerreAdaptiveScoreCalculator
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{
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protected:
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ENUM_ADAPTIVE_METHOD m_method;
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int m_adaptive_period;
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double m_gamma_min;
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double m_gamma_max;
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int m_sigma_period; // Volatility lookback period (N)
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bool m_is_ha;
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CEfficiencyRatioCalculator *m_er_calc;
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CATRCalculator *m_atr_calc;
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//--- Persistent State Registers
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double m_price[];
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double m_L0[], m_L1[], m_L2[], m_L3[];
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double m_filter[];
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double m_adaptive_metric[];
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double m_temp_atr[];
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double m_temp_stdev[];
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bool PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type,
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const double &open[], const double &high[], const double &low[], const double &close[]);
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void NormalizeMetric(int rates_total, int prev_calculated, const double &src_array[]);
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public:
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CLaguerreAdaptiveScoreCalculator(void);
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virtual ~CLaguerreAdaptiveScoreCalculator(void);
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bool Init(ENUM_ADAPTIVE_METHOD method, int adaptive_period, double gamma_min, double gamma_max, int sigma_period, bool is_ha);
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void Calculate(int rates_total, int prev_calculated, ENUM_APPLIED_PRICE price_type,
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const double &open[], const double &high[], const double &low[], const double &close[],
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double &score_buffer[]);
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};
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//+------------------------------------------------------------------+
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//| Constructor |
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//+------------------------------------------------------------------+
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CLaguerreAdaptiveScoreCalculator::CLaguerreAdaptiveScoreCalculator(void)
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: m_er_calc(NULL),
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m_atr_calc(NULL),
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m_is_ha(false)
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{
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}
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//+------------------------------------------------------------------+
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//| Destructor |
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//+------------------------------------------------------------------+
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CLaguerreAdaptiveScoreCalculator::~CLaguerreAdaptiveScoreCalculator(void)
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{
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if(CheckPointer(m_er_calc) != POINTER_INVALID)
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delete m_er_calc;
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if(CheckPointer(m_atr_calc) != POINTER_INVALID)
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delete m_atr_calc;
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}
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//+------------------------------------------------------------------+
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//| Init |
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//+------------------------------------------------------------------+
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bool CLaguerreAdaptiveScoreCalculator::Init(ENUM_ADAPTIVE_METHOD method, int adaptive_period, double gamma_min, double gamma_max, int sigma_period, bool is_ha)
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{
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m_method = method;
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m_adaptive_period = (adaptive_period < 2) ? 2 : adaptive_period;
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m_gamma_min = fmax(0.0, fmin(1.0, gamma_min));
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m_gamma_max = fmax(0.0, fmin(1.0, gamma_max));
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m_sigma_period = (sigma_period < 2) ? 2 : sigma_period;
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m_is_ha = is_ha;
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if(CheckPointer(m_er_calc) != POINTER_INVALID)
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{
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delete m_er_calc;
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m_er_calc = NULL;
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}
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if(CheckPointer(m_atr_calc) != POINTER_INVALID)
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{
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delete m_atr_calc;
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m_atr_calc = NULL;
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}
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if(m_method == METHOD_EFFICIENCY_RATIO)
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{
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m_er_calc = new CEfficiencyRatioCalculator();
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if(CheckPointer(m_er_calc) == POINTER_INVALID || !m_er_calc.Init(m_adaptive_period))
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return false;
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}
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else
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if(m_method == METHOD_ATR)
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{
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if(m_is_ha)
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m_atr_calc = new CATRCalculator_HA();
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else
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m_atr_calc = new CATRCalculator();
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if(CheckPointer(m_atr_calc) == POINTER_INVALID || !m_atr_calc.Init(m_adaptive_period, ATR_POINTS))
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return false;
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}
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return true;
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}
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//+------------------------------------------------------------------+
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//| Calculate (Stateful O(1) Z-Score calculation) |
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//+------------------------------------------------------------------+
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void CLaguerreAdaptiveScoreCalculator::Calculate(int rates_total, int prev_calculated, ENUM_APPLIED_PRICE price_type,
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const double &open[], const double &high[], const double &low[], const double &close[],
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double &score_buffer[])
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{
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int min_warmup = MathMax(m_adaptive_period * 2, m_sigma_period) + 5;
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if(rates_total < min_warmup)
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return;
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//--- Resize state buffers and enforce chronological safety
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if(ArraySize(m_price) != rates_total)
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{
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ArrayResize(m_price, rates_total);
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ArrayResize(m_L0, rates_total);
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ArrayResize(m_L1, rates_total);
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ArrayResize(m_L2, rates_total);
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ArrayResize(m_L3, rates_total);
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ArrayResize(m_filter, rates_total);
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ArrayResize(m_adaptive_metric, rates_total);
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ArraySetAsSeries(m_price, false);
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ArraySetAsSeries(m_L0, false);
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ArraySetAsSeries(m_L1, false);
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ArraySetAsSeries(m_L2, false);
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ArraySetAsSeries(m_L3, false);
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ArraySetAsSeries(m_filter, false);
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ArraySetAsSeries(m_adaptive_metric, false);
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}
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//--- Prepare prices and calculate metrics
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int start_index = (prev_calculated > 0) ? prev_calculated - 1 : 0;
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if(!PreparePriceSeries(rates_total, start_index, price_type, open, high, low, close))
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return;
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if(m_method == METHOD_EFFICIENCY_RATIO)
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{
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m_er_calc.Calculate(rates_total, prev_calculated, price_type, open, high, low, close, m_adaptive_metric);
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}
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else
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if(m_method == METHOD_ATR)
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{
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if(ArraySize(m_temp_atr) != rates_total)
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{
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ArrayResize(m_temp_atr, rates_total);
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ArraySetAsSeries(m_temp_atr, false);
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}
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m_atr_calc.Calculate(rates_total, prev_calculated, open, high, low, close, m_temp_atr);
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NormalizeMetric(rates_total, prev_calculated, m_temp_atr);
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}
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else // METHOD_STAND_DEV
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{
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if(ArraySize(m_temp_stdev) != rates_total)
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{
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ArrayResize(m_temp_stdev, rates_total);
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ArraySetAsSeries(m_temp_stdev, false);
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}
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int start_sync = (prev_calculated > 0) ? prev_calculated - 1 : 0;
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int loop_start = MathMax(m_adaptive_period - 1, start_sync);
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if(loop_start == m_adaptive_period - 1)
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{
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for(int i = 0; i < loop_start; i++)
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m_temp_stdev[i] = 0.0;
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}
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for(int i = loop_start; i < rates_total; i++)
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{
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double sum = 0.0;
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for(int j = 0; j < m_adaptive_period; j++)
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sum += m_price[i - j];
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double mean = sum / m_adaptive_period;
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double sum_sq = 0.0;
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for(int j = 0; j < m_adaptive_period; j++)
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sum_sq += pow(m_price[i - j] - mean, 2);
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m_temp_stdev[i] = sqrt(sum_sq / m_adaptive_period);
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}
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NormalizeMetric(rates_total, prev_calculated, m_temp_stdev);
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}
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//--- Stateful Adaptive Laguerre Baseline (Mean)
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if(start_index == 0)
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{
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m_L0[0] = m_price[0];
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m_L1[0] = m_price[0];
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m_L2[0] = m_price[0];
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m_L3[0] = m_price[0];
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m_filter[0] = m_price[0];
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score_buffer[0] = 0.0;
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start_index = 1;
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}
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for(int i = start_index; i < rates_total; i++)
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{
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double metric = m_adaptive_metric[i];
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metric = fmax(0.0, fmin(1.0, metric));
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double gamma = m_gamma_max - metric * (m_gamma_max - m_gamma_min);
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gamma = fmax(0.0, fmin(1.0, gamma));
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m_L0[i] = (1.0 - gamma) * m_price[i] + gamma * m_L0[i - 1];
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m_L1[i] = -gamma * m_L0[i] + m_L0[i - 1] + gamma * m_L1[i - 1];
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m_L2[i] = -gamma * m_L1[i] + m_L1[i - 1] + gamma * m_L2[i - 1];
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m_L3[i] = -gamma * m_L2[i] + m_L2[i - 1] + gamma * m_L3[i - 1];
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m_filter[i] = (m_L0[i] + 2.0 * m_L1[i] + 2.0 * m_L2[i] + m_L3[i]) / 6.0;
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}
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//--- Calculate Volatility Distance in Sigma Units (Adaptive Score)
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int sigma_start = (prev_calculated > 0) ? prev_calculated - 1 : m_sigma_period - 1;
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if(sigma_start < m_sigma_period - 1)
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{
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for(int i = 0; i < m_sigma_period - 1; i++)
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score_buffer[i] = 0.0;
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sigma_start = m_sigma_period - 1;
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}
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for(int i = sigma_start; i < rates_total; i++)
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{
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double sum_sq = 0.0;
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double current_mean = m_filter[i];
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// Standard deviation over N period relative to the dynamic adaptive mean
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for(int k = 0; k < m_sigma_period; k++)
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{
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double diff = m_price[i - k] - current_mean;
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sum_sq += diff * diff;
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}
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double std_dev = sqrt(sum_sq / m_sigma_period);
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if(std_dev > 1.0e-9) // Protection against flat-market division-by-zero
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score_buffer[i] = (m_price[i] - current_mean) / std_dev;
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else
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score_buffer[i] = 0.0;
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}
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}
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//+------------------------------------------------------------------+
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//| Sliding Min-Max Normalization (DRY Helper) |
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//+------------------------------------------------------------------+
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void CLaguerreAdaptiveScoreCalculator::NormalizeMetric(int rates_total, int prev_calculated, const double &src_array[])
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{
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int start_sync = (prev_calculated > 0) ? prev_calculated - 1 : 0;
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int min_lookback = m_adaptive_period;
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int loop_start = MathMax(min_lookback * 2, start_sync);
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if(loop_start == min_lookback * 2)
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{
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for(int i = 0; i < loop_start; i++)
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m_adaptive_metric[i] = 0.0;
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}
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for(int i = loop_start; i < rates_total; i++)
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{
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double min_val = src_array[i];
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double max_val = src_array[i];
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for(int j = 1; j < m_adaptive_period; j++)
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{
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double val = src_array[i - j];
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if(val < min_val)
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min_val = val;
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if(val > max_val)
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max_val = val;
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}
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double diff = max_val - min_val;
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if(diff > 1.0e-9)
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m_adaptive_metric[i] = (src_array[i] - min_val) / diff;
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else
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m_adaptive_metric[i] = 0.0;
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}
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}
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//+------------------------------------------------------------------+
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//| Prepare Price Series |
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//+------------------------------------------------------------------+
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bool CLaguerreAdaptiveScoreCalculator::PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type,
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const double &open[], const double &high[], const double &low[], const double &close[])
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{
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if(m_is_ha)
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{
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static CHeikinAshi_Calculator ha_calc;
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static double ha_open[], ha_high[], ha_low[], ha_close[];
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if(ArraySize(ha_open) != rates_total)
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{
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ArrayResize(ha_open, rates_total);
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ArrayResize(ha_high, rates_total);
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ArrayResize(ha_low, rates_total);
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ArrayResize(ha_close, rates_total);
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ArraySetAsSeries(ha_open, false);
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ArraySetAsSeries(ha_high, false);
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ArraySetAsSeries(ha_low, false);
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ArraySetAsSeries(ha_close, false);
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}
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ha_calc.Calculate(rates_total, start_index, open, high, low, close, ha_open, ha_high, ha_low, ha_close);
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for(int i = start_index; i < rates_total; i++)
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{
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switch(price_type)
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{
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case PRICE_OPEN:
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m_price[i] = ha_open[i];
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break;
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case PRICE_HIGH:
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m_price[i] = ha_high[i];
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break;
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case PRICE_LOW:
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m_price[i] = ha_low[i];
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break;
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case PRICE_MEDIAN:
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m_price[i] = (ha_high[i] + ha_low[i]) * 0.5;
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break;
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case PRICE_TYPICAL:
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m_price[i] = (ha_high[i] + ha_low[i] + ha_close[i]) / 3.0;
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break;
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case PRICE_WEIGHTED:
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m_price[i] = (ha_high[i] + ha_low[i] + ha_close[i] * 2.0) * 0.25;
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break;
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default:
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m_price[i] = ha_close[i];
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break;
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}
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}
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}
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else
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{
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for(int i = start_index; i < rates_total; i++)
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{
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switch(price_type)
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{
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case PRICE_OPEN:
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m_price[i] = open[i];
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break;
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case PRICE_HIGH:
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m_price[i] = high[i];
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break;
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case PRICE_LOW:
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m_price[i] = low[i];
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break;
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case PRICE_MEDIAN:
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m_price[i] = (high[i] + low[i]) * 0.5;
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break;
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case PRICE_TYPICAL:
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m_price[i] = (high[i] + low[i] + close[i]) / 3.0;
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break;
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case PRICE_WEIGHTED:
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m_price[i] = (high[i] + low[i] + close[i] * 2.0) * 0.25;
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break;
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default:
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m_price[i] = close[i];
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break;
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}
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}
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}
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return true;
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}
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//+==================================================================+
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//| CLASS 2: CLaguerreAdaptiveScoreCalculator_HA |
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//+==================================================================+
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class CLaguerreAdaptiveScoreCalculator_HA : public CLaguerreAdaptiveScoreCalculator
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{
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public:
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CLaguerreAdaptiveScoreCalculator_HA(void)
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{
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m_is_ha = true;
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};
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};
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#endif // LAGUERRE_ADAPTIVE_SCORE_CALCULATOR_MQH
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//+------------------------------------------------------------------+
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