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https://github.com/softwaredevelop/mql5.git
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Toh4iem9
395 lines
14 KiB
Plaintext
395 lines
14 KiB
Plaintext
//+------------------------------------------------------------------+
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//| LinearRegression_Calculator.mqh |
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//| VERSION 4.00: Integrated R-Squared and Slope calculation. |
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//| Copyright 2026, xxxxxxxx |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2026, xxxxxxxx"
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#include <MyIncludes\HeikinAshi_Tools.mqh>
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enum ENUM_CHANNEL_MODE
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{
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DEVIATION_STANDARD,
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DEVIATION_MAXIMUM
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};
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//+==================================================================+
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//| CLASS: CLinearRegressionCalculator |
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//+==================================================================+
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class CLinearRegressionCalculator
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{
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protected:
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int m_period;
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ENUM_CHANNEL_MODE m_channel_mode;
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double m_deviations;
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// Precalc for optimization
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double m_sum_x, m_sum_x2;
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double m_denom_x;
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//--- Persistent Buffer
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double m_price[];
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virtual bool PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type, const double &open[], const double &high[], const double &low[], const double &close[]);
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public:
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CLinearRegressionCalculator(void) {};
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virtual ~CLinearRegressionCalculator(void) {};
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// Init 1: Full (For Channels)
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bool Init(int period, ENUM_CHANNEL_MODE mode, double deviations);
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// Init 2: Simple (For R2/Slope only)
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bool Init(int period);
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//--- Method 1: Moving Regression (The "Wavy" line)
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void CalculateMoving(int rates_total, int prev_calculated, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &middle_buffer[], double &upper_buffer[], double &lower_buffer[]);
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//--- Method 2: Static Channel (The "Straight" segment for current bars)
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void CalculateStaticChannel(int rates_total, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &middle_buffer[], double &upper_buffer[], double &lower_buffer[]);
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//--- Method 3: Rolling Statistics (R-Squared & Slope) - NEW
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void CalculateState(int rates_total, int prev_calculated, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &out_slope[], double &out_r2[], double &out_forecast[]);
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};
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//+------------------------------------------------------------------+
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//| Init (Full) |
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//+------------------------------------------------------------------+
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bool CLinearRegressionCalculator::Init(int period, ENUM_CHANNEL_MODE mode, double deviations)
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{
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m_period = (period < 2) ? 2 : period;
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m_channel_mode = mode;
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m_deviations = (deviations <= 0) ? 2.0 : deviations;
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// Pre-calc X sums (0..N-1)
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m_sum_x = 0;
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m_sum_x2 = 0;
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for(int k = 0; k < m_period; k++)
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{
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m_sum_x += k;
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m_sum_x2 += k * k;
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}
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m_denom_x = m_period * m_sum_x2 - m_sum_x * m_sum_x;
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return true;
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}
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//+------------------------------------------------------------------+
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//| Init (Simple) |
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//+------------------------------------------------------------------+
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bool CLinearRegressionCalculator::Init(int period)
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{
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return Init(period, DEVIATION_STANDARD, 2.0); // Delegate with defaults
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}
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//+------------------------------------------------------------------+
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//| Method 1: Moving Regression (Wavy) |
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//+------------------------------------------------------------------+
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void CLinearRegressionCalculator::CalculateMoving(int rates_total, int prev_calculated, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &middle_buffer[], double &upper_buffer[], double &lower_buffer[])
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{
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if(rates_total < m_period)
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return;
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int start_index = (prev_calculated == 0) ? 0 : prev_calculated - 1;
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if(ArraySize(m_price) != rates_total)
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ArrayResize(m_price, rates_total);
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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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// Pre-calc X sums
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double sum_x = 0, sum_x2 = 0;
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for(int k = 0; k < m_period; k++)
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{
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sum_x += k;
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sum_x2 += k * k;
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}
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double denominator = m_period * sum_x2 - sum_x * sum_x;
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int loop_start = MathMax(m_period - 1, start_index);
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for(int i = loop_start; i < rates_total; i++)
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{
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double sum_y = 0, sum_xy = 0;
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for(int k = 0; k < m_period; k++)
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{
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double price = m_price[i - m_period + 1 + k];
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sum_y += price;
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sum_xy += k * price;
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}
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double b = (m_period * sum_xy - sum_x * sum_y) / denominator;
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double a = (sum_y - b * sum_x) / m_period;
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double regression_value = a + b * (m_period - 1); // End point
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middle_buffer[i] = regression_value;
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double deviation_offset = 0;
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if(m_channel_mode == DEVIATION_STANDARD)
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{
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double dev_sum_sq = 0;
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for(int k = 0; k < m_period; k++)
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{
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double price = m_price[i - m_period + 1 + k];
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double reg_val_at_k = a + b * k;
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dev_sum_sq += MathPow(price - reg_val_at_k, 2);
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}
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deviation_offset = m_deviations * MathSqrt(dev_sum_sq / m_period);
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}
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else
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{
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double max_dev = 0;
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for(int k = 0; k < m_period; k++)
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{
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double price = m_price[i - m_period + 1 + k];
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double reg_val_at_k = a + b * k;
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max_dev = MathMax(max_dev, MathAbs(price - reg_val_at_k));
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}
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deviation_offset = max_dev;
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}
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upper_buffer[i] = regression_value + deviation_offset;
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lower_buffer[i] = regression_value - deviation_offset;
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}
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}
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//+------------------------------------------------------------------+
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//| Method 2: Static Channel (Straight) |
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//+------------------------------------------------------------------+
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void CLinearRegressionCalculator::CalculateStaticChannel(int rates_total, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &middle_buffer[], double &upper_buffer[], double &lower_buffer[])
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{
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if(rates_total < m_period)
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return;
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// Always update price buffer for the last segment
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if(ArraySize(m_price) != rates_total)
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ArrayResize(m_price, rates_total);
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// We only need to prepare the last 'm_period' prices
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int start_prep = rates_total - m_period;
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if(!PreparePriceSeries(rates_total, start_prep, price_type, open, high, low, close))
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return;
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// 1. Clear old history (Optimization: only clear if necessary, but for visual clarity we clear all before start)
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if(start_prep > 0)
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{
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middle_buffer[start_prep - 1] = EMPTY_VALUE;
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upper_buffer[start_prep - 1] = EMPTY_VALUE;
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lower_buffer[start_prep - 1] = EMPTY_VALUE;
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}
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// 2. Calculate Regression for the SINGLE window [rates_total-period ... rates_total-1]
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double sum_x = 0, sum_y = 0, sum_xy = 0, sum_x2 = 0;
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for(int k = 0; k < m_period; k++)
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{
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double x = k;
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double y = m_price[start_prep + k];
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sum_x += x;
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sum_x2 += x * x;
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sum_y += y;
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sum_xy += x * y;
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}
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double denominator = m_period * sum_x2 - sum_x * sum_x;
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double b = (m_period * sum_xy - sum_x * sum_y) / denominator;
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double a = (sum_y - b * sum_x) / m_period;
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// 3. Calculate Deviation
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double deviation_offset = 0;
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if(m_channel_mode == DEVIATION_STANDARD)
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{
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double dev_sum_sq = 0;
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for(int k = 0; k < m_period; k++)
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{
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double y = m_price[start_prep + k];
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double reg_val = a + b * k;
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dev_sum_sq += MathPow(y - reg_val, 2);
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}
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deviation_offset = m_deviations * MathSqrt(dev_sum_sq / m_period);
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}
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else
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{
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double max_dev = 0;
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for(int k = 0; k < m_period; k++)
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{
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double y = m_price[start_prep + k];
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double reg_val = a + b * k;
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max_dev = MathMax(max_dev, MathAbs(y - reg_val));
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}
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deviation_offset = max_dev;
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}
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// 4. Draw the Straight Line Segment
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for(int k = 0; k < m_period; k++)
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{
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int bar_index = start_prep + k;
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double reg_val = a + b * k;
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middle_buffer[bar_index] = reg_val;
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upper_buffer[bar_index] = reg_val + deviation_offset;
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lower_buffer[bar_index] = reg_val - deviation_offset;
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}
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}
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//+------------------------------------------------------------------+
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//| Method 3: Rolling Statistics (NEW) |
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//+------------------------------------------------------------------+
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void CLinearRegressionCalculator::CalculateState(int rates_total, int prev_calculated, const double &open[], const double &high[], const double &low[], const double &close[], ENUM_APPLIED_PRICE price_type,
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double &out_slope[], double &out_r2[], double &out_forecast[])
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{
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if(rates_total < m_period)
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return;
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int start_index = (prev_calculated == 0) ? 0 : prev_calculated - 1;
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if(ArraySize(m_price) != rates_total)
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ArrayResize(m_price, rates_total);
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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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int loop_start = MathMax(m_period - 1, start_index);
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for(int i = loop_start; i < rates_total; i++)
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{
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double sum_y = 0, sum_xy = 0, sum_y2 = 0;
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for(int k = 0; k < m_period; k++)
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{
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double price = m_price[i - m_period + 1 + k];
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double x = k; // Map x to 0..N-1
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sum_y += price;
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sum_xy += x * price;
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sum_y2 += price * price;
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}
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double b = 0;
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if(m_denom_x != 0)
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b = (m_period * sum_xy - m_sum_x * sum_y) / m_denom_x; // Slope
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double a = (sum_y - b * m_sum_x) / m_period; // Intercept
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double forecast = a + b * (m_period - 1); // Current Value
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// R-Squared Calc
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// SST = Total Sum of Squares = Sum(y^2) - (Sum(y)^2)/N
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// SSR = Regression Sum of Squares = b * (Sum(xy) - Sum(x)Sum(y)/N)
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// R2 = SSR / SST
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// Alternative standard formula: R2 = (N*SumXY - SumX*SumY)^2 / (DenomX * DenomY)
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double denom_y = (m_period * sum_y2) - (sum_y * sum_y);
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double r2 = 0;
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if(m_denom_x > 0 && denom_y > 0)
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{
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double num = (m_period * sum_xy - m_sum_x * sum_y);
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r2 = (num * num) / (m_denom_x * denom_y);
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}
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out_slope[i] = b;
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out_r2[i] = r2;
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out_forecast[i] = forecast; // Same as 'middle_buffer' in Moving mode
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}
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}
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//+------------------------------------------------------------------+
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//| Prepare Price (Standard) |
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//+------------------------------------------------------------------+
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bool CLinearRegressionCalculator::PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type, const double &open[], const double &high[], const double &low[], const double &close[])
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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_CLOSE:
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m_price[i] = close[i];
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break;
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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])/2.0;
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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]+2*close[i])/4.0;
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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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return true;
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}
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//+==================================================================+
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//| CLASS 2: CLinearRegressionCalculator_HA |
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//+==================================================================+
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class CLinearRegressionCalculator_HA : public CLinearRegressionCalculator
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{
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private:
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CHeikinAshi_Calculator m_ha_calculator;
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double m_ha_open[], m_ha_high[], m_ha_low[], m_ha_close[];
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protected:
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virtual bool PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type, const double &open[], const double &high[], const double &low[], const double &close[]) override;
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CLinearRegressionCalculator_HA::PreparePriceSeries(int rates_total, int start_index, ENUM_APPLIED_PRICE price_type, const double &open[], const double &high[], const double &low[], const double &close[])
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{
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if(ArraySize(m_ha_open) != rates_total)
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{
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ArrayResize(m_ha_open, rates_total);
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ArrayResize(m_ha_high, rates_total);
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ArrayResize(m_ha_low, rates_total);
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ArrayResize(m_ha_close, rates_total);
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}
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m_ha_calculator.Calculate(rates_total, start_index, open, high, low, close, m_ha_open, m_ha_high, m_ha_low, m_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_CLOSE:
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m_price[i] = m_ha_close[i];
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break;
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case PRICE_OPEN:
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m_price[i] = m_ha_open[i];
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break;
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case PRICE_HIGH:
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m_price[i] = m_ha_high[i];
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break;
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case PRICE_LOW:
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m_price[i] = m_ha_low[i];
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break;
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case PRICE_MEDIAN:
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m_price[i] = (m_ha_high[i]+m_ha_low[i])/2.0;
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break;
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case PRICE_TYPICAL:
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m_price[i] = (m_ha_high[i]+m_ha_low[i]+m_ha_close[i])/3.0;
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break;
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case PRICE_WEIGHTED:
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m_price[i] = (m_ha_high[i]+m_ha_low[i]+2*m_ha_close[i])/4.0;
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break;
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default:
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m_price[i] = m_ha_close[i];
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break;
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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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