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https://github.com/mihakralj/QuanTAlib.git
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137 lines
4.5 KiB
C#
137 lines
4.5 KiB
C#
using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// REMA: Regularized Exponential Moving Average
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/// A modified exponential moving average that includes a regularization term to reduce
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/// noise and improve trend following. The regularization helps to smooth the output
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/// while maintaining responsiveness to significant price movements.
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/// </summary>
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/// <remarks>
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/// The REMA calculation process:
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/// 1. Uses standard EMA smoothing with adaptive alpha
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/// 2. Adds regularization term based on previous values
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/// 3. Balances new and regularized terms using lambda parameter
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/// 4. Provides smoother output than standard EMA
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///
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/// Key characteristics:
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/// - Improved noise reduction through regularization
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/// - Better trend following than standard EMA
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/// - Adjustable regularization via lambda parameter
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/// - Adaptive alpha based on period
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/// - Reduced whipsaws in choppy markets
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///
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/// Sources:
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/// https://user42.tuxfamily.org/chart/manual/Regularized-Exponential-Moving-Average.html
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/// </remarks>
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public class Rema : AbstractBase
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{
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private readonly int _period;
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private readonly double _lambda;
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private readonly double _lambdaPlus1Recip; // 1/(1 + lambda)
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private double _lastRema, _prevRema;
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private double _savedLastRema, _savedPrevRema;
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/// <summary>
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/// Gets the period used in the REMA calculation.
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/// </summary>
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public int Period => _period;
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/// <summary>
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/// Gets the lambda (regularization) parameter value.
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/// </summary>
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public double Lambda => _lambda;
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/// <param name="period">The number of periods used in the REMA calculation.</param>
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/// <param name="lambda">The regularization parameter (default 0.5). Higher values increase smoothing.</param>
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/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1 or lambda is negative.</exception>
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public Rema(int period, double lambda = 0.5)
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{
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if (period < 1)
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throw new System.ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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if (lambda < 0)
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throw new System.ArgumentOutOfRangeException(nameof(lambda), "Lambda must be non-negative.");
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_period = period;
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_lambda = lambda;
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_lambdaPlus1Recip = 1.0 / (1.0 + lambda);
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Name = $"REMA({period},{lambda:F2})";
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WarmupPeriod = period;
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Init();
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}
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/// <param name="source">The data source object that publishes updates.</param>
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/// <param name="period">The number of periods used in the REMA calculation.</param>
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/// <param name="lambda">The regularization parameter (default 0.5).</param>
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public Rema(object source, int period, double lambda = 0.5) : this(period, lambda)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public override void Init()
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{
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base.Init();
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_lastRema = 0;
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_prevRema = 0;
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_savedLastRema = 0;
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_savedPrevRema = 0;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_savedLastRema = _lastRema;
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_savedPrevRema = _prevRema;
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_index++;
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}
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else
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{
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_lastRema = _savedLastRema;
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_prevRema = _savedPrevRema;
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}
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private double CalculateAlpha()
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{
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return 2.0 / (System.Math.Min(_period, _index) + 1);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private double CalculateRema(double alpha, double input)
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{
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double standardTerm = _lastRema + (alpha * (input - _lastRema));
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double regularizationTerm = _lastRema + (_lastRema - _prevRema);
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return (standardTerm + (_lambda * regularizationTerm)) * _lambdaPlus1Recip;
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}
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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if (_index > 2)
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{
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double alpha = CalculateAlpha();
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double rema = CalculateRema(alpha, Input.Value);
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_prevRema = _lastRema;
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_lastRema = rema;
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}
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else if (_index == 2)
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{
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_prevRema = _lastRema;
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_lastRema = Input.Value;
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}
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else
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{
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_lastRema = Input.Value;
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}
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IsHot = _index >= WarmupPeriod;
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return _lastRema;
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}
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}
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