mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-20 03:28:05 +00:00
452 lines
13 KiB
C#
452 lines
13 KiB
C#
using System.Buffers;
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// GWMA: Gaussian-Weighted Moving Average
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/// </summary>
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/// <remarks>
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/// GWMA uses a centered Gaussian window to weight price data.
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/// Definition:
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/// center = (period - 1) / 2
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/// W_i = exp(-0.5 * ((i - center) / (sigma * period))^2)
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///
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/// The final GWMA is the weighted sum of the price window divided by the sum of weights.
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/// Unlike ALMA (which has an offset parameter), GWMA centers the Gaussian peak at the
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/// middle of the window and uses sigma to control the bell curve width.
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Gwma : AbstractBase
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{
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private readonly int _period;
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private readonly double _sigma;
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private readonly double[] _weights;
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private readonly double _invWeightSum;
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private readonly RingBuffer _buffer;
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private readonly ITValuePublisher? _source;
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private readonly TValuePublishedHandler? _pubHandler;
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private bool _isNew = true;
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[StructLayout(LayoutKind.Auto)]
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private record struct State
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{
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public double LastValidValue;
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public bool IsInitialized;
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}
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private State _state;
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private State _p_state;
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public bool IsNew => _isNew;
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public override bool IsHot => _buffer.IsFull;
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/// <summary>
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/// Creates GWMA with specified parameters.
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/// </summary>
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/// <param name="period">Window size (must be > 0)</param>
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/// <param name="sigma">Controls the width of the Gaussian bell curve (default 0.4). Lower values make the curve narrower.</param>
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public Gwma(int period, double sigma = 0.4)
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{
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if (period <= 0)
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{
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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}
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if (sigma <= 0)
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{
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throw new ArgumentException("Sigma must be greater than 0", nameof(sigma));
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}
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if (sigma > 1)
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{
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throw new ArgumentOutOfRangeException(nameof(sigma), "Sigma must be between 0 and 1");
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}
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_period = period;
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_sigma = sigma;
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_buffer = new RingBuffer(period);
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_weights = new double[period];
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Name = $"Gwma({period}, {sigma:F2})";
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WarmupPeriod = period;
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ComputeWeights(_weights, period, sigma, out _invWeightSum);
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_state = new State { LastValidValue = double.NaN, IsInitialized = false };
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}
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public Gwma(ITValuePublisher source, int period, double sigma = 0.4)
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: this(period, sigma)
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{
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_source = source;
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_pubHandler = Handle;
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_source.Pub += _pubHandler;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private void Handle(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
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protected override void Dispose(bool disposing)
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{
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if (disposing && _source != null && _pubHandler != null)
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{
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_source.Pub -= _pubHandler;
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}
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base.Dispose(disposing);
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}
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/// <summary>
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/// Computes Gaussian weights for GWMA.
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private static void ComputeWeights(Span<double> weights, int period, double sigma, out double invWeightSum)
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{
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double center = (period - 1) / 2.0;
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double invSigmaP = 1.0 / (sigma * period);
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double sum = 0;
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for (int i = 0; i < period; i++)
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{
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double x = (i - center) * invSigmaP;
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double w = Math.Exp(-0.5 * x * x);
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weights[i] = w;
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sum += w;
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}
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invWeightSum = 1.0 / sum;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private double GetValidValue(double input)
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{
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if (double.IsFinite(input))
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{
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return input;
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}
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return _state.IsInitialized ? _state.LastValidValue : double.NaN;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public override TValue Update(TValue input, bool isNew = true)
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{
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_isNew = isNew;
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return Update(input, isNew, publish: true);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private TValue Update(TValue input, bool isNew, bool publish)
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{
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if (isNew)
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{
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_p_state = _state;
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}
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else
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{
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_state = _p_state;
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}
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if (double.IsFinite(input.Value))
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{
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_state.LastValidValue = input.Value;
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_state.IsInitialized = true;
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}
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// Retrieve valid value (handles NaN propagation prevention)
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double val = GetValidValue(input.Value);
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_buffer.Add(val, isNew);
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double result = _buffer.Count > 0 ? CalculateWeightedSum(fallbackValue: val) : 0.0;
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Last = new TValue(input.Time, result);
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if (publish)
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{
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PubEvent(Last, isNew);
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}
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return Last;
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}
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public override TSeries Update(TSeries source)
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{
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if (source.Count == 0)
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{
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return new TSeries([], []);
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}
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int len = source.Count;
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var t = new List<long>(len);
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var v = new List<double>(len);
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CollectionsMarshal.SetCount(t, len);
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CollectionsMarshal.SetCount(v, len);
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var tSpan = CollectionsMarshal.AsSpan(t);
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var vSpan = CollectionsMarshal.AsSpan(v);
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Calculate(source.Values, vSpan, _period, _sigma);
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source.Times.CopyTo(tSpan);
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// Restore internal state to match the streaming path:
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// - seed the last valid value from the history before the replay window (critical for NaN handling)
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// - replay the last window to rebuild buffer + correction state
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_buffer.Clear();
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int windowSize = Math.Min(len, _period);
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int startIndex = len - windowSize;
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_state = default;
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_state.LastValidValue = double.NaN;
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_state.IsInitialized = false;
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if (startIndex > 0)
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{
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for (int i = startIndex - 1; i >= 0; i--)
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{
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double v0 = source.Values[i];
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if (double.IsFinite(v0))
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{
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_state.LastValidValue = v0;
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_state.IsInitialized = true;
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break;
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}
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}
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}
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for (int i = startIndex; i < len; i++)
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{
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Update(source[i], isNew: true, publish: false);
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}
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return new TSeries(t, v);
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}
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public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
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{
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foreach (var value in source)
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{
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Update(new TValue(DateTime.MinValue, value));
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}
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private double CalculateWeightedSum(double fallbackValue)
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{
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int count = _buffer.Count;
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if (count == 0)
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{
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return 0;
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}
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if (count < _period)
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{
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return CalculateWeightedSumWarmup(_buffer.GetSpan(), count, _sigma, fallbackValue);
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}
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if (_invWeightSum == 0.0)
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{
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return fallbackValue;
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}
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ReadOnlySpan<double> internalBuf = _buffer.InternalBuffer;
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int head = _buffer.StartIndex;
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int part1Len = _period - head;
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double sum1 = internalBuf.Slice(head, part1Len).DotProduct(_weights.AsSpan(0, part1Len));
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double sum2 = internalBuf[..head].DotProduct(_weights.AsSpan(part1Len));
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return (sum1 + sum2) * _invWeightSum;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private static double CalculateWeightedSumWarmup(ReadOnlySpan<double> window, int p, double sigma, double fallbackValue)
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{
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if (p <= 0)
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{
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return 0.0;
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}
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if (p == 1)
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{
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return fallbackValue;
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}
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double center = (p - 1) * 0.5;
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double invSigmaP = 1.0 / (sigma * p);
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double sum = 0.0;
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double wSum = 0.0;
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for (int i = 0; i < p; i++)
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{
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double x = (i - center) * invSigmaP;
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double w = Math.Exp(-0.5 * x * x);
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sum = Math.FusedMultiplyAdd(window[i], w, sum);
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wSum += w;
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}
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return wSum > 0.0 ? sum / wSum : fallbackValue;
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}
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public static TSeries Batch(TSeries source, int period, double sigma = 0.4)
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{
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var gwma = new Gwma(period, sigma);
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return gwma.Update(source);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Calculate(ReadOnlySpan<double> source, Span<double> output, int period, double sigma = 0.4)
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{
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if (period <= 0)
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{
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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}
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if (sigma <= 0)
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{
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throw new ArgumentException("Sigma must be greater than 0", nameof(sigma));
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}
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if (sigma > 1)
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{
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throw new ArgumentOutOfRangeException(nameof(sigma), "Sigma must be between 0 and 1");
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}
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if (source.Length != output.Length)
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{
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throw new ArgumentException("Source and output must have the same length", nameof(output));
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}
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int len = source.Length;
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if (len == 0)
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{
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return;
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}
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if (period > len)
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{
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double[]? bufferArray = len > 256 ? ArrayPool<double>.Shared.Rent(len) : null;
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Span<double> buffer = len <= 256
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? stackalloc double[len]
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: bufferArray!.AsSpan(0, len);
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double lastValid = double.NaN;
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try
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{
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for (int i = 0; i < len; i++)
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{
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double val = source[i];
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if (double.IsFinite(val))
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{
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lastValid = val;
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}
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else if (double.IsFinite(lastValid))
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{
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val = lastValid;
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}
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else
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{
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val = 0.0;
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}
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buffer[i] = val;
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int p = i + 1;
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output[i] = CalculateWeightedSumWarmup(buffer, p, sigma, fallbackValue: val);
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}
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}
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finally
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{
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if (bufferArray != null)
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{
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ArrayPool<double>.Shared.Return(bufferArray);
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}
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}
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return;
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}
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double[]? weightsArray = period > 256 ? ArrayPool<double>.Shared.Rent(period) : null;
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Span<double> weights = period <= 256
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? stackalloc double[period]
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: weightsArray!.AsSpan(0, period);
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double[]? ringArray = period > 256 ? ArrayPool<double>.Shared.Rent(period) : null;
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Span<double> ring = period <= 256
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? stackalloc double[period]
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: ringArray!.AsSpan(0, period);
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ComputeWeights(weights, period, sigma, out double invWeightSum);
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int ringIdx = 0;
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int count = 0;
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double lastValid2 = double.NaN;
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try
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{
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for (int i = 0; i < len; i++)
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{
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double val = source[i];
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if (double.IsFinite(val))
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{
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lastValid2 = val;
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}
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else if (double.IsFinite(lastValid2))
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{
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val = lastValid2;
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}
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else
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{
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val = 0.0;
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}
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ring[ringIdx] = val;
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ringIdx++;
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if (ringIdx >= period)
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{
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ringIdx = 0;
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}
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if (count < period)
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{
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count++;
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}
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if (count < period)
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{
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output[i] = CalculateWeightedSumWarmup(ring, count, sigma, fallbackValue: val);
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continue;
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}
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if (invWeightSum == 0.0)
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{
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output[i] = val;
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continue;
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}
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int part1Len = period - ringIdx;
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double sum = ring.Slice(ringIdx, part1Len).DotProduct(weights.Slice(0, part1Len))
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+ ring.Slice(0, ringIdx).DotProduct(weights.Slice(part1Len));
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output[i] = sum * invWeightSum;
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}
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}
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finally
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{
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if (weightsArray != null)
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{
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ArrayPool<double>.Shared.Return(weightsArray);
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}
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if (ringArray != null)
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{
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ArrayPool<double>.Shared.Return(ringArray);
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}
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}
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}
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public override void Reset()
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{
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_buffer.Clear();
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_state = new State { LastValidValue = double.NaN, IsInitialized = false };
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_p_state = _state;
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Last = default;
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}
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}
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