new indicators

lib/volatility/Sv.cs

@@ -0,0 +1,105 @@
+using System.Runtime.CompilerServices;
+namespace QuanTAlib;
+
+/// <summary>
+/// SV: Stochastic Volatility
+/// A volatility measure that models price volatility as a random process,
+/// capturing both the magnitude and the rate of change in price movements.
+/// </summary>
+/// <remarks>
+/// The SV calculation process:
+/// 1. Calculate log returns
+/// 2. Compute exponentially weighted variance
+/// 3. Apply smoothing to variance estimate
+/// 4. Take square root for volatility
+///
+/// Key characteristics:
+/// - Time-varying volatility
+/// - Mean-reverting process
+/// - Captures volatility clustering
+/// - Handles leverage effects
+/// - Accounts for fat tails
+///
+/// Formula:
+/// Returns = ln(Close/PrevClose)
+/// Variance = λ * PrevVariance + (1-λ) * Returns²
+/// SV = sqrt(Variance)
+/// where λ is the decay factor
+///
+/// Market Applications:
+/// - Option pricing
+/// - Risk management
+/// - Trading strategies
+/// - Portfolio optimization
+/// - Market regime detection
+///
+/// Note: More sophisticated than simple volatility measures, better captures market dynamics
+/// </remarks>
+[SkipLocalsInit]
+public sealed class Sv : AbstractBase
+{
+    private readonly double _lambda;
+    private readonly Sma _ma;
+    private double _prevClose;
+    private double _prevVariance;
+    private double _prevValue;
+    private const int DefaultPeriod = 20;
+    private const double DefaultLambda = 0.94;
+
+    /// <param name="period">The number of periods for smoothing (default 20).</param>
+    /// <param name="lambda">The decay factor for variance calculation (default 0.94).</param>
+    /// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1 or lambda is not between 0 and 1.</exception>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public Sv(int period = DefaultPeriod, double lambda = DefaultLambda)
+    {
+        if (period < 1)
+            throw new ArgumentOutOfRangeException(nameof(period));
+        if (lambda <= 0 || lambda >= 1)
+            throw new ArgumentOutOfRangeException(nameof(lambda));
+
+        _lambda = lambda;
+        _ma = new(period);
+        WarmupPeriod = period;
+        Name = $"SV({period},{lambda:F2})";
+    }
+
+    /// <param name="source">The data source object that publishes updates.</param>
+    /// <param name="period">The number of periods for smoothing.</param>
+    /// <param name="lambda">The decay factor for variance calculation.</param>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public Sv(object source, int period = DefaultPeriod, double lambda = DefaultLambda) : this(period, lambda)
+    {
+        var pubEvent = source.GetType().GetEvent("Pub");
+        pubEvent?.AddEventHandler(source, new BarSignal(Sub));
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    protected override void ManageState(bool isNew)
+    {
+        if (isNew)
+        {
+            _prevClose = BarInput.Close;
+            _index++;
+        }
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
+    protected override double Calculation()
+    {
+        if (!BarInput.IsNew)
+            return _prevValue;
+
+        ManageState(true);
+
+        // Calculate log return
+        double logReturn = Math.Log(BarInput.Close / _prevClose);
+        double squaredReturn = logReturn * logReturn;
+
+        // Update variance estimate
+        _prevVariance = _lambda * _prevVariance + (1.0 - _lambda) * squaredReturn;
+
+        // Apply smoothing and take square root
+        _prevValue = Math.Sqrt(_ma.Calc(_prevVariance, true));
+        return _prevValue;
+    }
+}