mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-22 12:38:06 +00:00
volatility indicators
This commit is contained in:
Miha Kralj
@@ -0,0 +1,353 @@
|
||||
using System.Runtime.CompilerServices;
|
||||
using System.Runtime.InteropServices;
|
||||
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// EWMA: Exponentially Weighted Moving Average Volatility
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// EWMA Volatility calculates volatility using an exponentially weighted moving average
|
||||
/// of squared log returns. This approach gives more weight to recent observations while
|
||||
/// still considering historical data, making it responsive to market changes.
|
||||
///
|
||||
/// Formula:
|
||||
/// <c>r_t = ln(Close_t / Close_{t-1})</c>
|
||||
/// <c>RMA_t = (RMA_{t-1} × (period - 1) + r²_t) / period</c>
|
||||
/// <c>BiasCorrection = 1 - (1 - 1/period)^n</c>
|
||||
/// <c>CorrectedVariance = RMA_t / BiasCorrection</c>
|
||||
/// <c>EWMA = √(CorrectedVariance × AnnualPeriods)</c>
|
||||
///
|
||||
/// Key properties:
|
||||
/// - Uses RMA (Running Moving Average) for exponential smoothing
|
||||
/// - Includes bias correction for accurate early estimates
|
||||
/// - Can be annualized or returned as periodic volatility
|
||||
/// - More responsive than simple moving average approaches
|
||||
/// </remarks>
|
||||
[SkipLocalsInit]
|
||||
public sealed class Ewma : AbstractBase
|
||||
{
|
||||
private readonly int _period;
|
||||
private readonly bool _annualize;
|
||||
private readonly int _annualPeriods;
|
||||
private readonly double _decay;
|
||||
|
||||
private const double MinPrice = 1e-10;
|
||||
private const double Epsilon = 1e-10;
|
||||
|
||||
[StructLayout(LayoutKind.Auto)]
|
||||
private record struct State(
|
||||
double RawRmaSqRet,
|
||||
double BiasE,
|
||||
double PrevClose,
|
||||
double LastValid,
|
||||
int Count);
|
||||
private State _s;
|
||||
private State _ps;
|
||||
|
||||
/// <summary>
|
||||
/// Creates EWMA Volatility indicator with specified parameters.
|
||||
/// </summary>
|
||||
/// <param name="period">The period for EWMA calculation (must be > 0)</param>
|
||||
/// <param name="annualize">Whether to annualize the volatility output (default: true)</param>
|
||||
/// <param name="annualPeriods">Number of periods in a year for annualization (default: 252 for daily data)</param>
|
||||
/// <exception cref="ArgumentException">Thrown when parameters are invalid</exception>
|
||||
public Ewma(int period = 20, bool annualize = true, int annualPeriods = 252)
|
||||
{
|
||||
if (period <= 0)
|
||||
{
|
||||
throw new ArgumentException("Period must be greater than 0", nameof(period));
|
||||
}
|
||||
if (annualize && annualPeriods <= 0)
|
||||
{
|
||||
throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
|
||||
}
|
||||
|
||||
_period = period;
|
||||
_annualize = annualize;
|
||||
_annualPeriods = annualPeriods;
|
||||
_decay = 1.0 - (1.0 / period);
|
||||
Name = annualize ? $"Ewma({period},{annualPeriods})" : $"Ewma({period})";
|
||||
WarmupPeriod = period;
|
||||
_s = new State(0.0, 1.0, double.NaN, 0.0, 0);
|
||||
_ps = _s;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Creates EWMA Volatility indicator with specified source and parameters.
|
||||
/// </summary>
|
||||
public Ewma(ITValuePublisher source, int period = 20, bool annualize = true, int annualPeriods = 252)
|
||||
: this(period, annualize, annualPeriods)
|
||||
{
|
||||
source.Pub += Handle;
|
||||
}
|
||||
|
||||
private void Handle(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
|
||||
|
||||
/// <summary>
|
||||
/// True if the indicator has completed the warmup period.
|
||||
/// </summary>
|
||||
public override bool IsHot => _s.Count >= _period;
|
||||
|
||||
/// <summary>
|
||||
/// Period for EWMA calculation.
|
||||
/// </summary>
|
||||
public int Period => _period;
|
||||
|
||||
/// <summary>
|
||||
/// Whether volatility is annualized.
|
||||
/// </summary>
|
||||
public bool Annualize => _annualize;
|
||||
|
||||
/// <summary>
|
||||
/// Number of periods per year for annualization.
|
||||
/// </summary>
|
||||
public int AnnualPeriods => _annualPeriods;
|
||||
|
||||
/// <inheritdoc/>
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public override TValue Update(TValue input, bool isNew = true)
|
||||
{
|
||||
double close = input.Value;
|
||||
|
||||
if (isNew)
|
||||
{
|
||||
_ps = _s;
|
||||
}
|
||||
else
|
||||
{
|
||||
_s = _ps;
|
||||
}
|
||||
|
||||
var s = _s;
|
||||
|
||||
// Sanitize input - use state's LastValid for consistency
|
||||
double lastValid = double.IsFinite(s.LastValid) && s.LastValid > 0 ? s.LastValid : 1.0;
|
||||
if (!double.IsFinite(close) || close <= 0)
|
||||
{
|
||||
close = lastValid;
|
||||
}
|
||||
else if (isNew)
|
||||
{
|
||||
s.LastValid = close;
|
||||
}
|
||||
|
||||
double safeClose = Math.Max(close, MinPrice);
|
||||
double safePrevClose = double.IsFinite(s.PrevClose) && s.PrevClose > 0 ? s.PrevClose : safeClose;
|
||||
|
||||
// Calculate log return
|
||||
double logReturn = 0.0;
|
||||
if (safeClose > 0.0 && safePrevClose > 0.0)
|
||||
{
|
||||
logReturn = Math.Log(safeClose / safePrevClose);
|
||||
}
|
||||
|
||||
double squaredReturn = logReturn * logReturn;
|
||||
|
||||
// RMA calculation: raw_rma_sq_ret = (raw_rma_sq_ret * (period - 1) + squaredReturn) / period
|
||||
double rawRmaSqRet;
|
||||
double biasE;
|
||||
|
||||
if (s.Count == 0)
|
||||
{
|
||||
// First value: initialize with squared return
|
||||
rawRmaSqRet = squaredReturn;
|
||||
biasE = _decay;
|
||||
}
|
||||
else
|
||||
{
|
||||
// RMA update: (prev * (period - 1) + current) / period
|
||||
rawRmaSqRet = Math.FusedMultiplyAdd(s.RawRmaSqRet, _period - 1, squaredReturn) / _period;
|
||||
// Update bias correction factor: e = (1 - alpha) * e_prev
|
||||
biasE = _decay * s.BiasE;
|
||||
}
|
||||
|
||||
// Bias correction: corrected = raw / (1 - e)
|
||||
double biasCorrection = 1.0 - biasE;
|
||||
double correctedRmaSqRet = biasCorrection > Epsilon ? rawRmaSqRet / biasCorrection : rawRmaSqRet;
|
||||
|
||||
// Ensure non-negative variance
|
||||
double currentEwmaSqReturns = Math.Max(correctedRmaSqRet, 0.0);
|
||||
|
||||
// Calculate volatility
|
||||
double volatility = Math.Sqrt(currentEwmaSqReturns);
|
||||
|
||||
// Annualize if requested
|
||||
double result = _annualize ? volatility * Math.Sqrt(_annualPeriods) : volatility;
|
||||
|
||||
if (isNew)
|
||||
{
|
||||
s.RawRmaSqRet = rawRmaSqRet;
|
||||
s.BiasE = biasE;
|
||||
s.PrevClose = safeClose;
|
||||
s.Count++;
|
||||
_s = s;
|
||||
}
|
||||
|
||||
if (!double.IsFinite(result))
|
||||
{
|
||||
result = 0.0;
|
||||
}
|
||||
|
||||
Last = new TValue(input.Time, result);
|
||||
PubEvent(Last, isNew);
|
||||
return Last;
|
||||
}
|
||||
|
||||
/// <inheritdoc/>
|
||||
public override TSeries Update(TSeries source)
|
||||
{
|
||||
int len = source.Count;
|
||||
var t = new List<long>(len);
|
||||
var v = new List<double>(len);
|
||||
CollectionsMarshal.SetCount(t, len);
|
||||
CollectionsMarshal.SetCount(v, len);
|
||||
|
||||
var tSpan = CollectionsMarshal.AsSpan(t);
|
||||
var vSpan = CollectionsMarshal.AsSpan(v);
|
||||
|
||||
Batch(source.Values, vSpan, _period, _annualize, _annualPeriods);
|
||||
source.Times.CopyTo(tSpan);
|
||||
|
||||
// Update internal state to match final position
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
Update(new TValue(source.Times[i], source.Values[i]), isNew: true);
|
||||
}
|
||||
|
||||
return new TSeries(t, v);
|
||||
}
|
||||
|
||||
/// <inheritdoc/>
|
||||
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
|
||||
{
|
||||
for (int i = 0; i < source.Length; i++)
|
||||
{
|
||||
Update(new TValue(DateTime.UtcNow, source[i]), isNew: true);
|
||||
}
|
||||
}
|
||||
|
||||
/// <inheritdoc/>
|
||||
public override void Reset()
|
||||
{
|
||||
_s = new State(0.0, 1.0, double.NaN, 0.0, 0);
|
||||
_ps = _s;
|
||||
Last = default;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates EWMA Volatility for entire series.
|
||||
/// </summary>
|
||||
public static TSeries Calculate(TSeries source, int period = 20, bool annualize = true, int annualPeriods = 252)
|
||||
{
|
||||
if (period <= 0)
|
||||
{
|
||||
throw new ArgumentException("Period must be greater than 0", nameof(period));
|
||||
}
|
||||
if (annualize && annualPeriods <= 0)
|
||||
{
|
||||
throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
|
||||
}
|
||||
|
||||
int len = source.Count;
|
||||
var t = new List<long>(len);
|
||||
var v = new List<double>(len);
|
||||
CollectionsMarshal.SetCount(t, len);
|
||||
CollectionsMarshal.SetCount(v, len);
|
||||
|
||||
var tSpan = CollectionsMarshal.AsSpan(t);
|
||||
var vSpan = CollectionsMarshal.AsSpan(v);
|
||||
|
||||
Batch(source.Values, vSpan, period, annualize, annualPeriods);
|
||||
source.Times.CopyTo(tSpan);
|
||||
|
||||
return new TSeries(t, v);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Batch EWMA Volatility calculation.
|
||||
/// </summary>
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public static void Batch(ReadOnlySpan<double> source, Span<double> output, int period = 20, bool annualize = true, int annualPeriods = 252)
|
||||
{
|
||||
if (source.Length != output.Length)
|
||||
{
|
||||
throw new ArgumentException("Source and output must have the same length", nameof(output));
|
||||
}
|
||||
if (period <= 0)
|
||||
{
|
||||
throw new ArgumentException("Period must be greater than 0", nameof(period));
|
||||
}
|
||||
if (annualize && annualPeriods <= 0)
|
||||
{
|
||||
throw new ArgumentException("Annual periods must be greater than 0 when annualizing", nameof(annualPeriods));
|
||||
}
|
||||
|
||||
int len = source.Length;
|
||||
if (len == 0)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
double alpha = 1.0 / period;
|
||||
double decay = 1.0 - alpha;
|
||||
double annualFactor = annualize ? Math.Sqrt(annualPeriods) : 1.0;
|
||||
|
||||
double rawRmaSqRet = 0.0;
|
||||
double biasE = 1.0;
|
||||
double prevClose = double.NaN;
|
||||
double lastValidClose = 1.0;
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
double close = source[i];
|
||||
|
||||
// Sanitize input
|
||||
if (!double.IsFinite(close) || close <= 0)
|
||||
{
|
||||
close = lastValidClose;
|
||||
}
|
||||
else
|
||||
{
|
||||
lastValidClose = close;
|
||||
}
|
||||
|
||||
double safeClose = Math.Max(close, MinPrice);
|
||||
double safePrevClose = double.IsFinite(prevClose) && prevClose > 0 ? prevClose : safeClose;
|
||||
|
||||
// Calculate log return
|
||||
double logReturn = 0.0;
|
||||
if (safeClose > 0.0 && safePrevClose > 0.0)
|
||||
{
|
||||
logReturn = Math.Log(safeClose / safePrevClose);
|
||||
}
|
||||
|
||||
double squaredReturn = logReturn * logReturn;
|
||||
|
||||
// RMA calculation
|
||||
if (i == 0)
|
||||
{
|
||||
rawRmaSqRet = squaredReturn;
|
||||
biasE = decay;
|
||||
}
|
||||
else
|
||||
{
|
||||
rawRmaSqRet = Math.FusedMultiplyAdd(rawRmaSqRet, period - 1, squaredReturn) / period;
|
||||
biasE = decay * biasE;
|
||||
}
|
||||
|
||||
// Bias correction
|
||||
double biasCorrection = 1.0 - biasE;
|
||||
double correctedRmaSqRet = biasCorrection > Epsilon ? rawRmaSqRet / biasCorrection : rawRmaSqRet;
|
||||
|
||||
// Calculate volatility
|
||||
double currentEwmaSqReturns = Math.Max(correctedRmaSqRet, 0.0);
|
||||
double volatility = Math.Sqrt(currentEwmaSqReturns);
|
||||
double result = volatility * annualFactor;
|
||||
|
||||
prevClose = safeClose;
|
||||
|
||||
output[i] = double.IsFinite(result) ? result : 0.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user