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https://github.com/softwaredevelop/mql5.git
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Toh4iem9
242 lines
8.8 KiB
Plaintext
242 lines
8.8 KiB
Plaintext
//+------------------------------------------------------------------+
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//| LinearRegression_Calculator.mqh |
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//| VERSION 2.00: Optimized for incremental calculation. |
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//| Copyright 2025, xxxxxxxx |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2025, xxxxxxxx"
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#include <MyIncludes\HeikinAshi_Tools.mqh>
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//--- Enum for Channel Calculation Mode ---
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enum ENUM_CHANNEL_MODE
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{
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DEVIATION_STANDARD, // Channel width based on Standard Deviation
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DEVIATION_MAXIMUM // Channel width based on Maximum Deviation
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};
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//+==================================================================+
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//| CLASS 1: CLinearRegressionCalculator (Base Class) |
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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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//--- Persistent Buffer for Incremental Calculation
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double m_price[];
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//--- Updated: Accepts start_index
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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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bool Init(int period, ENUM_CHANNEL_MODE mode, double deviations);
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//--- Updated: Accepts prev_calculated
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void Calculate(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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//+------------------------------------------------------------------+
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//| Init |
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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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return true;
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}
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//+------------------------------------------------------------------+
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//| Main Calculation (Optimized) |
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//+------------------------------------------------------------------+
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void CLinearRegressionCalculator::Calculate(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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//--- 1. Determine Start Index
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int start_index;
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if(prev_calculated == 0)
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start_index = 0;
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else
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start_index = prev_calculated - 1;
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//--- 2. Resize Buffer
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if(ArraySize(m_price) != rates_total)
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ArrayResize(m_price, rates_total);
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//--- 3. Prepare Price (Optimized)
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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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//--- 4. Calculate Linear Regression (Always recalculate for the window)
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int regression_start_index = rates_total - m_period;
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// Calculate Sums
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double sum_x = 0, sum_y = 0, sum_xy = 0, sum_x2 = 0;
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for(int i = 0; i < m_period; i++)
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{
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double y = m_price[regression_start_index + i];
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double x = i;
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sum_x += x;
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sum_y += y;
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sum_xy += x * y;
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sum_x2 += x * x;
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}
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double b = (m_period * sum_xy - sum_x * sum_y) / (m_period * sum_x2 - sum_x * sum_x);
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double a = (sum_y - b * sum_x) / m_period;
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double deviation_offset = 0;
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double regression_values[];
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ArrayResize(regression_values, m_period);
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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 i = 0; i < m_period; i++)
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{
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regression_values[i] = a + b * i;
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dev_sum_sq += MathPow(m_price[regression_start_index + i] - regression_values[i], 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 // DEVIATION_MAXIMUM
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{
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double max_dev = 0;
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for(int i = 0; i < m_period; i++)
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{
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regression_values[i] = a + b * i;
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max_dev = MathMax(max_dev, MathAbs(m_price[regression_start_index + i] - regression_values[i]));
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}
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deviation_offset = max_dev;
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}
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// Fill Buffers
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for(int i = 0; i < m_period; i++)
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{
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int buffer_index = regression_start_index + i;
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middle_buffer[buffer_index] = regression_values[i];
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upper_buffer[buffer_index] = regression_values[i] + deviation_offset;
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lower_buffer[buffer_index] = regression_values[i] - deviation_offset;
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}
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if(regression_start_index > 0)
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{
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middle_buffer[regression_start_index-1] = EMPTY_VALUE;
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upper_buffer[regression_start_index-1] = EMPTY_VALUE;
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lower_buffer[regression_start_index-1] = EMPTY_VALUE;
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}
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}
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//+------------------------------------------------------------------+
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//| Prepare Price (Standard - Optimized) |
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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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// Optimized copy loop
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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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// Internal HA buffers
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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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//| Prepare Price (Heikin Ashi - Optimized) |
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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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// Resize internal HA buffers
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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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//--- STRICT CALL: Use the optimized 10-param HA calculation
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m_ha_calculator.Calculate(rates_total, start_index, open, high, low, close,
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m_ha_open, m_ha_high, m_ha_low, m_ha_close);
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//--- Copy to m_price (Optimized loop)
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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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