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volatility indicators
This commit is contained in:
Miha Kralj
@@ -0,0 +1,551 @@
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// High-Low Volatility (HLV) Indicator
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// A range-based volatility estimator using the Parkinson method with RMA smoothing
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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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/// HLV: High-Low Volatility (Parkinson)
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/// A range-based volatility estimator that uses only High-Low prices,
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/// providing more efficient volatility estimates than close-to-close methods.
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/// </summary>
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/// <remarks>
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/// <b>Calculation steps:</b>
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/// <list type="number">
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/// <item>Calculate log prices: lnH, lnL</item>
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/// <item>parkinsonEstimator = (1/(4×ln(2))) × (lnH - lnL)²</item>
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/// <item>Smooth using bias-corrected RMA</item>
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/// <item>volatility = √(smoothedEstimator)</item>
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/// <item>If annualize: volatility × √(annualPeriods)</item>
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/// </list>
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///
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/// <b>Key characteristics:</b>
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/// <list type="bullet">
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/// <item>Uses only High-Low data (simpler than Garman-Klass)</item>
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/// <item>RMA (Wilder's) smoothing with bias correction</item>
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/// <item>Optional annualization (default 252 trading days)</item>
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/// <item>5× more efficient than close-to-close estimators</item>
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/// </list>
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///
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/// <b>Sources:</b>
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/// Michael Parkinson (1980). "The Extreme Value Method for Estimating the Variance
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/// of the Rate of Return." Journal of Business, 53(1), 61-65.
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Hlv : AbstractBase
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{
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private const double C_4LN2_INV = 0.36067376022224085; // 1 / (4 * ln(2))
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private const double Epsilon = 1e-10;
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private readonly int _period;
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private readonly bool _annualize;
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private readonly int _annualPeriods;
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private readonly double _alpha;
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private readonly double _decay;
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private readonly double _annualFactor;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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double RawRma,
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double E,
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double LastValidPk,
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double LastValue,
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int Count
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);
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private State _s;
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private State _ps;
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/// <summary>
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/// Initializes a new instance of the Hlv class.
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/// </summary>
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/// <param name="period">The smoothing period (default 20).</param>
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/// <param name="annualize">Whether to annualize the volatility (default true).</param>
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/// <param name="annualPeriods">Number of periods per year (default 252).</param>
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/// <exception cref="ArgumentException">
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/// Thrown when period is less than 1, or annualPeriods is less than 1 when annualizing.
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/// </exception>
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public Hlv(int period = 20, bool annualize = true, int annualPeriods = 252)
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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 (annualize && annualPeriods <= 0)
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{
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throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
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}
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_period = period;
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_annualize = annualize;
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_annualPeriods = annualPeriods;
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_alpha = 1.0 / period;
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_decay = 1.0 - _alpha;
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_annualFactor = annualize ? Math.Sqrt(annualPeriods) : 1.0;
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WarmupPeriod = period;
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Name = $"Hlv({period})";
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_s = new State(0, 1.0, 0, 0, 0);
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_ps = _s;
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}
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/// <summary>
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/// Initializes a new instance of the Hlv class with a source.
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/// </summary>
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/// <param name="source">The data source for chaining.</param>
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/// <param name="period">The smoothing period (default 20).</param>
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/// <param name="annualize">Whether to annualize the volatility (default true).</param>
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/// <param name="annualPeriods">Number of periods per year (default 252).</param>
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public Hlv(ITValuePublisher source, int period = 20, bool annualize = true, int annualPeriods = 252)
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: this(period, annualize, annualPeriods)
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{
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source.Pub += Handle;
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}
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private void Handle(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
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/// <summary>
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/// True if the indicator has enough data for valid results.
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/// </summary>
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public override bool IsHot => _s.Count >= WarmupPeriod;
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/// <summary>
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/// The smoothing period.
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/// </summary>
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public int Period => _period;
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/// <summary>
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/// Whether volatility is annualized.
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/// </summary>
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public bool Annualize => _annualize;
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/// <summary>
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/// Number of periods per year for annualization.
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/// </summary>
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public int AnnualPeriods => _annualPeriods;
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/// <summary>
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/// Computes the Parkinson estimator for a single bar.
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private static double ComputeParkinsonEstimator(double high, double low)
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{
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double lnH = Math.Log(high);
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double lnL = Math.Log(low);
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double hlRange = lnH - lnL;
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// parkinsonEstimator = (1/(4*ln(2))) * (lnH - lnL)^2
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return C_4LN2_INV * hlRange * hlRange;
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}
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/// <summary>
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/// Updates the indicator with a TValue input.
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/// For HLV, this treats the value as a pre-computed Parkinson estimator.
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/// Prefer Update(TBar) for standard OHLC data.
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/// </summary>
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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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return UpdateCore(input.Time, input.Value, isNew);
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}
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/// <summary>
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/// Updates the indicator with a new bar (preferred method).
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/// </summary>
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/// <param name="bar">The input bar.</param>
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/// <param name="isNew">Whether this is a new bar or an update.</param>
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/// <returns>The calculated volatility value.</returns>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(TBar bar, bool isNew = true)
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{
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// Handle invalid High-Low data
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if (!double.IsFinite(bar.High) || !double.IsFinite(bar.Low) ||
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bar.High <= 0 || bar.Low <= 0)
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{
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// Pass NaN to trigger last-valid-value substitution
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return UpdateCore(bar.Time, double.NaN, isNew);
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}
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double pkEstimator = ComputeParkinsonEstimator(bar.High, bar.Low);
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return UpdateCore(bar.Time, pkEstimator, isNew);
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}
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/// <summary>
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/// Updates the indicator with a bar series.
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/// </summary>
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/// <param name="source">The source bar series.</param>
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/// <returns>A TSeries containing the volatility values.</returns>
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public TSeries Update(TBarSeries source)
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{
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if (source.Count == 0)
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{
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return [];
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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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// Extract High-Low data
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Span<double> highs = len <= 128 ? stackalloc double[len] : new double[len];
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Span<double> lows = len <= 128 ? stackalloc double[len] : new double[len];
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for (int i = 0; i < len; i++)
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{
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highs[i] = source[i].High;
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lows[i] = source[i].Low;
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tSpan[i] = source[i].Time;
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}
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Batch(highs, lows, vSpan, _period, _annualize, _annualPeriods);
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// Update internal state
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for (int i = 0; i < len; i++)
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{
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Update(source[i], isNew: true);
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}
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return new TSeries(t, v);
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}
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/// <inheritdoc/>
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public override TSeries Update(TSeries source)
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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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// Treat source values as pre-computed Parkinson estimators
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BatchFromEstimators(source.Values, vSpan, _period, _annualize, _annualPeriods);
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source.Times.CopyTo(tSpan);
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// Update internal state
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for (int i = 0; i < len; i++)
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{
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Update(new TValue(source.Times[i], source.Values[i]), isNew: true);
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}
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return new TSeries(t, v);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private TValue UpdateCore(long timeTicks, double pkEstimator, bool isNew)
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{
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if (isNew)
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{
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_ps = _s;
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}
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else
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{
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_s = _ps;
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}
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var s = _s;
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// Handle non-finite estimator - use last valid value
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if (!double.IsFinite(pkEstimator))
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{
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pkEstimator = s.LastValidPk;
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}
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else
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{
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s.LastValidPk = pkEstimator;
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}
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// RMA smoothing with bias correction
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double rawRma, e;
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if (s.Count == 0)
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{
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rawRma = pkEstimator;
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e = _decay;
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}
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else
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{
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// RMA: raw_rma = prev_rma * decay + alpha * value
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rawRma = Math.FusedMultiplyAdd(s.RawRma, _decay, _alpha * pkEstimator);
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e = _decay * s.E;
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}
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// Bias correction
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double correctedRma = e > Epsilon ? rawRma / (1.0 - e) : rawRma;
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// Calculate volatility
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double volatility;
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if (correctedRma < 0)
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{
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volatility = 0; // Can't take sqrt of negative
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}
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else
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{
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volatility = Math.Sqrt(correctedRma) * _annualFactor;
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}
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if (!double.IsFinite(volatility))
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{
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volatility = s.LastValue;
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}
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// Update state using direct field assignment (like Cvi pattern)
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s.RawRma = rawRma;
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s.E = e;
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s.LastValue = volatility;
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if (isNew)
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{
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s.Count++;
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}
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_s = s;
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Last = new TValue(timeTicks, volatility);
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PubEvent(Last, isNew);
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return Last;
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}
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/// <inheritdoc/>
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public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
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{
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for (int i = 0; i < source.Length; i++)
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{
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Update(new TValue(DateTime.UtcNow, source[i]), isNew: true);
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}
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}
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/// <inheritdoc/>
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public override void Reset()
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{
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_s = new State(0, 1.0, 0, 0, 0);
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_ps = _s;
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Last = default;
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}
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/// <summary>
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/// Calculates High-Low Volatility for a bar series (static).
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/// </summary>
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/// <param name="source">The source bar series.</param>
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/// <param name="period">The smoothing period.</param>
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/// <param name="annualize">Whether to annualize.</param>
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/// <param name="annualPeriods">Periods per year.</param>
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/// <returns>A TSeries containing the volatility values.</returns>
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public static TSeries Calculate(TBarSeries source, int period = 20, bool annualize = true, int annualPeriods = 252)
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{
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var hlv = new Hlv(period, annualize, annualPeriods);
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return hlv.Update(source);
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}
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/// <summary>
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/// Calculates HLV for a TSeries (treats values as pre-computed Parkinson estimators).
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/// </summary>
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public static TSeries Calculate(TSeries source, int period = 20, bool annualize = true, int annualPeriods = 252)
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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 (annualize && annualPeriods <= 0)
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{
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throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
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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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BatchFromEstimators(source.Values, vSpan, period, annualize, annualPeriods);
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source.Times.CopyTo(tSpan);
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return new TSeries(t, v);
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}
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/// <summary>
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/// Batch calculation using spans for High-Low data.
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/// </summary>
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/// <param name="high">High prices.</param>
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/// <param name="low">Low prices.</param>
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/// <param name="output">Output volatility values.</param>
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/// <param name="period">The smoothing period.</param>
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/// <param name="annualize">Whether to annualize.</param>
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/// <param name="annualPeriods">Periods per year.</param>
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public static void Batch(
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ReadOnlySpan<double> high,
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ReadOnlySpan<double> low,
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Span<double> output,
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int period = 20,
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bool annualize = true,
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int annualPeriods = 252)
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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 (annualize && annualPeriods <= 0)
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{
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throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
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}
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int len = high.Length;
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if (low.Length != len)
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{
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throw new ArgumentException("High and low spans must have the same length", nameof(low));
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}
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if (output.Length < len)
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{
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throw new ArgumentException("Output span must be at least as long as input spans", nameof(output));
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}
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if (len == 0)
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{
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return;
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}
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double alpha = 1.0 / period;
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double decay = 1.0 - alpha;
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double annualFactor = annualize ? Math.Sqrt(annualPeriods) : 1.0;
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double rawRma = 0;
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double e = 1.0;
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double lastValidPk = 0;
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double lastValue = 0;
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for (int i = 0; i < len; i++)
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{
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double h = high[i];
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double l = low[i];
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double pkEstimator;
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// Handle invalid data
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if (!double.IsFinite(h) || !double.IsFinite(l) ||
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h <= 0 || l <= 0)
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{
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pkEstimator = lastValidPk;
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}
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else
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{
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pkEstimator = ComputeParkinsonEstimator(h, l);
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if (!double.IsFinite(pkEstimator))
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{
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pkEstimator = lastValidPk;
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}
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else
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{
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lastValidPk = pkEstimator;
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}
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}
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if (i == 0)
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{
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rawRma = pkEstimator;
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e = decay;
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}
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else
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{
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rawRma = Math.FusedMultiplyAdd(rawRma, decay, alpha * pkEstimator);
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e *= decay;
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}
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double correctedRma = e > Epsilon ? rawRma / (1.0 - e) : rawRma;
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double volatility = correctedRma < 0 ? 0 : Math.Sqrt(correctedRma) * annualFactor;
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if (!double.IsFinite(volatility))
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{
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volatility = lastValue;
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}
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else
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{
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lastValue = volatility;
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}
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output[i] = volatility;
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}
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}
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/// <summary>
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/// Batch calculation from pre-computed Parkinson estimators.
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/// </summary>
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private static void BatchFromEstimators(
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ReadOnlySpan<double> estimators,
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Span<double> output,
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int period,
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||||
bool annualize,
|
||||
int annualPeriods)
|
||||
{
|
||||
if (period <= 0)
|
||||
{
|
||||
throw new ArgumentException("Period must be greater than 0", nameof(period));
|
||||
}
|
||||
if (estimators.Length != output.Length)
|
||||
{
|
||||
throw new ArgumentException("Source and output must have the same length", nameof(output));
|
||||
}
|
||||
|
||||
int len = estimators.Length;
|
||||
if (len == 0)
|
||||
{
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||||
return;
|
||||
}
|
||||
|
||||
double alpha = 1.0 / period;
|
||||
double decay = 1.0 - alpha;
|
||||
double annualFactor = annualize ? Math.Sqrt(annualPeriods) : 1.0;
|
||||
|
||||
double rawRma = 0;
|
||||
double e = 1.0;
|
||||
double lastValidPk = 0;
|
||||
double lastValue = 0;
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
double pkEstimator = estimators[i];
|
||||
|
||||
if (!double.IsFinite(pkEstimator))
|
||||
{
|
||||
pkEstimator = lastValidPk;
|
||||
}
|
||||
else
|
||||
{
|
||||
lastValidPk = pkEstimator;
|
||||
}
|
||||
|
||||
if (i == 0)
|
||||
{
|
||||
rawRma = pkEstimator;
|
||||
e = decay;
|
||||
}
|
||||
else
|
||||
{
|
||||
rawRma = Math.FusedMultiplyAdd(rawRma, decay, alpha * pkEstimator);
|
||||
e *= decay;
|
||||
}
|
||||
|
||||
double correctedRma = e > Epsilon ? rawRma / (1.0 - e) : rawRma;
|
||||
|
||||
double volatility = correctedRma < 0 ? 0 : Math.Sqrt(correctedRma) * annualFactor;
|
||||
|
||||
if (!double.IsFinite(volatility))
|
||||
{
|
||||
volatility = lastValue;
|
||||
}
|
||||
else
|
||||
{
|
||||
lastValue = volatility;
|
||||
}
|
||||
|
||||
output[i] = volatility;
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user