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Add Close-to-Close Volatility (CCV) implementation and validation tests
- Implemented CCV class for calculating annualized log return volatility using SMA, EMA, and WMA smoothing methods. - Added comprehensive unit tests for CCV to validate mathematical correctness, consistency across methods, and edge cases. - Created documentation for CCV detailing its mathematical foundation, smoothing methods, and performance metrics.
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Miha Kralj
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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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/// BBW: Bollinger Band Width (Normalized)
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/// </summary>
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/// <remarks>
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/// Measures the normalized width between upper and lower Bollinger Bands as a
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/// fraction of the SMA. BBW quantifies volatility relative to price level and is
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/// useful for identifying "squeeze" conditions (low volatility) that often precede
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/// significant price moves.
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///
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/// Formula:
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/// <c>BBW = (2 × multiplier × StdDev(source, period)) / SMA(source, period)</c>
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///
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/// Since Bollinger Bands are calculated as SMA ± (multiplier × StdDev), the raw width
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/// is 2 × multiplier × StdDev. This implementation normalizes by dividing by the SMA,
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/// expressing the band width as a percentage/fraction of the mean price. This makes
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/// BBW comparable across instruments with different price levels.
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///
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/// This implementation uses O(1) running variance calculation via the sum-of-squares
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/// method, with periodic resynchronization to prevent floating-point drift.
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///
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/// Key properties:
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/// - Always non-negative (output is normalized as fraction of SMA)
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/// - High BBW indicates high relative volatility
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/// - Low BBW indicates low relative volatility ("squeeze")
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/// - Default: 20-period, 2.0 multiplier (same as standard Bollinger Bands)
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Bbw : AbstractBase
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{
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private readonly int _period;
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private readonly double _multiplier;
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private readonly RingBuffer _buffer;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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double Sum,
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double SumSq,
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double LastValid);
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private State _state;
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private State _p_state;
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private const int ResyncInterval = 1000;
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private int _tickCount;
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/// <summary>
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/// Creates BBW with specified period and multiplier.
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/// </summary>
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/// <param name="period">Lookback period (must be > 0)</param>
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/// <param name="multiplier">Standard deviation multiplier (must be > 0)</param>
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public Bbw(int period, double multiplier = 2.0)
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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 (multiplier <= 0)
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{
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throw new ArgumentException("Multiplier must be greater than 0", nameof(multiplier));
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}
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_period = period;
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_multiplier = multiplier;
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_buffer = new RingBuffer(period);
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Name = $"Bbw({period},{multiplier:F1})";
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WarmupPeriod = period;
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}
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/// <summary>
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/// Creates BBW with specified source, period, and multiplier.
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/// </summary>
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public Bbw(ITValuePublisher source, int period, double multiplier = 2.0) : this(period, multiplier)
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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 => _buffer.IsFull;
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/// <summary>
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/// Period of the indicator.
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/// </summary>
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public int Period => _period;
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/// <summary>
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/// Standard deviation multiplier.
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/// </summary>
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public double Multiplier => _multiplier;
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/// <inheritdoc/>
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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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double value = input.Value;
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// Sanitize input
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if (!double.IsFinite(value))
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{
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value = double.IsFinite(_state.LastValid) ? _state.LastValid : 0.0;
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}
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else
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{
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_state.LastValid = value;
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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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// Remove oldest value contribution if buffer full
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if (_buffer.Count == _buffer.Capacity)
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{
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double oldest = _buffer.Oldest;
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_state.Sum -= oldest;
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_state.SumSq -= oldest * oldest;
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}
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// Add new value
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_state.Sum += value;
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_state.SumSq += value * value;
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_buffer.Add(value);
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_tickCount++;
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if (_buffer.IsFull && _tickCount >= ResyncInterval)
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{
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_tickCount = 0;
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RecalculateSums();
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}
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}
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else
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{
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_state = _p_state;
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// Update the newest value in buffer
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_buffer.UpdateNewest(value);
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RecalculateSums();
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}
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// Calculate variance: Var = E[X²] - E[X]²
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int count = _buffer.Count;
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double mean = _state.Sum / count;
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double variance = Math.Max(0.0, (_state.SumSq / count) - (mean * mean));
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double stddev = Math.Sqrt(variance);
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// BBW = 2 × multiplier × StdDev / SMA (normalized band width)
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// Guard against division by zero
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double bbw = mean > 0 ? (2.0 * _multiplier * stddev) / mean : 0.0;
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Last = new TValue(input.Time, bbw);
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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 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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Batch(source.Values, vSpan, _period, _multiplier);
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source.Times.CopyTo(tSpan);
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// Update internal state to match final position
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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 void RecalculateSums()
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{
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_state.Sum = 0.0;
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_state.SumSq = 0.0;
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for (int i = 0; i < _buffer.Count; i++)
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{
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double v = _buffer[i];
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_state.Sum += v;
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_state.SumSq += v * v;
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}
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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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_buffer.Clear();
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_state = default;
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_p_state = default;
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_tickCount = 0;
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Last = default;
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}
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/// <summary>
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/// Calculates BBW for entire series.
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/// </summary>
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public static TSeries Calculate(TSeries source, int period, double multiplier = 2.0)
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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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Batch(source.Values, vSpan, period, multiplier);
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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 BBW calculation with O(1) rolling variance.
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Batch(ReadOnlySpan<double> source, Span<double> output, int period, double multiplier = 2.0)
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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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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 (multiplier <= 0)
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{
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throw new ArgumentException("Multiplier must be greater than 0", nameof(multiplier));
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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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double sum = 0.0;
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double sumSq = 0.0;
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double mult2 = 2.0 * multiplier;
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double lastValid = 0.0;
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// Buffer to track sanitized values for correct window removal
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var valueBuffer = new RingBuffer(period);
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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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// Sanitize input - mirror Update method behavior
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if (!double.IsFinite(val))
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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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lastValid = val;
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}
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// Remove oldest sanitized value if past warmup
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if (i >= period)
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{
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double oldest = valueBuffer.Oldest;
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sum -= oldest;
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sumSq -= oldest * oldest;
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}
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// Add new sanitized value
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sum += val;
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sumSq += val * val;
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valueBuffer.Add(val);
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// Calculate variance and BBW
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int count = Math.Min(i + 1, period);
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double mean = sum / count;
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double variance = Math.Max(0.0, (sumSq / count) - (mean * mean));
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double stddev = Math.Sqrt(variance);
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// BBW = 2 × multiplier × StdDev / SMA (normalized)
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output[i] = mean > 0 ? (mult2 * stddev) / mean : 0.0;
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}
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}
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}
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