mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-12 15:48:05 +00:00
- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic. - Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks. - Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations). - Enhanced documentation for TRAMA, including performance profiles and quality metrics. - Updated workspace configuration by removing unnecessary folder references.
343 lines
9.4 KiB
C#
343 lines
9.4 KiB
C#
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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/// GDEMA: Generalized Double Exponential Moving Average
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/// </summary>
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/// <remarks>
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/// Extends standard DEMA with a tunable volume factor v that controls
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/// the aggressiveness of lag compensation. Two cascaded EMAs with shared
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/// warmup compensator combined via parameterized linear combination.
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///
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/// Calculation: <c>GDEMA = (1+v)×EMA₁ - v×EMA₂</c> where EMA₂ = EMA(EMA₁).
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/// When v=0 → EMA, v=1 → standard DEMA, v>1 → more aggressive lag removal.
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Gdema : AbstractBase
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{
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[StructLayout(LayoutKind.Auto)]
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private record struct EmaState(double Ema, double E, bool IsHot, bool IsCompensated)
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{
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public static EmaState New() => new() { Ema = 0, E = 1.0, IsHot = false, IsCompensated = false };
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}
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private readonly double _alpha;
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private readonly double _decay;
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private readonly double _vfactor;
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private readonly double _onePlusV; // precomputed (1 + v)
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private EmaState _state1 = EmaState.New();
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private EmaState _state2 = EmaState.New();
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private EmaState _p_state1 = EmaState.New();
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private EmaState _p_state2 = EmaState.New();
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private double _lastValidValue = double.NaN;
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private double _p_lastValidValue = double.NaN;
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private readonly ITValuePublisher? _publisher;
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private readonly TValuePublishedHandler? _listener;
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public override bool IsHot => _state2.IsHot;
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public Gdema(int period = 10, double vfactor = 1.0)
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{
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ArgumentOutOfRangeException.ThrowIfLessThan(period, 1);
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_alpha = 2.0 / (period + 1);
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_decay = 1.0 - _alpha;
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_vfactor = vfactor;
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_onePlusV = 1.0 + vfactor;
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Name = $"Gdema({period},{vfactor:F1})";
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WarmupPeriod = period;
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}
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public Gdema(ITValuePublisher source, int period = 10, double vfactor = 1.0) : this(period, vfactor)
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{
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_publisher = source;
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_listener = Handle;
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source.Pub += _listener;
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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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if (isNew)
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{
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_p_state1 = _state1;
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_p_state2 = _state2;
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_p_lastValidValue = _lastValidValue;
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}
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else
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{
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_state1 = _p_state1;
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_state2 = _p_state2;
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_lastValidValue = _p_lastValidValue;
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}
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double val = input.Value;
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if (double.IsFinite(val))
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{
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_lastValidValue = val;
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}
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else
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{
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val = _lastValidValue;
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}
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if (double.IsNaN(val))
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{
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Last = new TValue(input.Time, double.NaN);
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PubEvent(Last, isNew);
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return Last;
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}
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double e1 = ComputeEma(val, _alpha, _decay, ref _state1);
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double e2 = ComputeEma(e1, _alpha, _decay, ref _state2);
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// GDEMA = (1+v)*EMA1 - v*EMA2
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double result = Math.FusedMultiplyAdd(_onePlusV, e1, -_vfactor * e2);
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Last = new TValue(input.Time, result);
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PubEvent(Last, isNew);
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return Last;
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}
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[MethodImpl(MethodImplOptions.AggressiveOptimization)]
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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 [];
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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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source.Times.CopyTo(tSpan);
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EmaState preBatch_s1 = _state1;
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EmaState preBatch_s2 = _state2;
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double preBatch_lastValid = _lastValidValue;
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EmaState s1 = _state1;
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EmaState s2 = _state2;
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double lastValid = _lastValidValue;
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double alpha = _alpha;
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double decay = _decay;
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double onePlusV = _onePlusV;
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double vf = _vfactor;
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for (int i = 0; i < len; i++)
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{
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double val = source.Values[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
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{
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val = lastValid;
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}
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if (double.IsNaN(val))
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{
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vSpan[i] = double.NaN;
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continue;
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}
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double e1 = ComputeEma(val, alpha, decay, ref s1);
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double e2 = ComputeEma(e1, alpha, decay, ref s2);
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vSpan[i] = Math.FusedMultiplyAdd(onePlusV, e1, -vf * e2);
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}
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_state1 = s1;
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_state2 = s2;
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_lastValidValue = lastValid;
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_p_state1 = preBatch_s1;
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_p_state2 = preBatch_s2;
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_p_lastValidValue = preBatch_lastValid;
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Last = new TValue(tSpan[len - 1], vSpan[len - 1]);
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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 (double 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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public static TSeries Batch(TSeries source, int period = 10, double vfactor = 1.0)
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{
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var gdema = new Gdema(period, vfactor);
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return gdema.Update(source);
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}
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public static void Batch(ReadOnlySpan<double> source, Span<double> output, int period = 10, double vfactor = 1.0)
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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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ArgumentOutOfRangeException.ThrowIfLessThan(period, 1);
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if (source.Length == 0)
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{
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return;
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}
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double alpha = 2.0 / (period + 1);
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double decay = 1.0 - alpha;
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double onePlusV = 1.0 + vfactor;
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double lastValid = double.NaN;
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double ema1_val = 0;
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double ema1_e = 1.0;
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bool ema1_isCompensated = false;
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double ema2_val = 0;
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double ema2_e = 1.0;
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bool ema2_isCompensated = false;
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for (int i = 0; i < source.Length; 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
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{
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val = lastValid;
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}
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if (double.IsNaN(val))
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{
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output[i] = double.NaN;
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continue;
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}
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// EMA1
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ema1_val = Math.FusedMultiplyAdd(ema1_val, decay, alpha * val);
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double e1;
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if (!ema1_isCompensated)
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{
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ema1_e *= decay;
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if (ema1_e <= 1e-10)
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{
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ema1_isCompensated = true;
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e1 = ema1_val;
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}
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else
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{
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e1 = ema1_val / (1.0 - ema1_e);
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}
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}
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else
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{
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e1 = ema1_val;
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}
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// EMA2
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ema2_val = Math.FusedMultiplyAdd(ema2_val, decay, alpha * e1);
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double e2;
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if (!ema2_isCompensated)
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{
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ema2_e *= decay;
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if (ema2_e <= 1e-10)
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{
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ema2_isCompensated = true;
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e2 = ema2_val;
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}
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else
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{
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e2 = ema2_val / (1.0 - ema2_e);
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}
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}
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else
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{
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e2 = ema2_val;
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}
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// GDEMA = (1+v)*EMA1 - v*EMA2
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output[i] = Math.FusedMultiplyAdd(onePlusV, e1, -vfactor * e2);
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}
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}
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public static (TSeries Results, Gdema Indicator) Calculate(TSeries source, int period = 10, double vfactor = 1.0)
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{
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var indicator = new Gdema(period, vfactor);
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TSeries results = indicator.Update(source);
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return (results, indicator);
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}
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public override void Reset()
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{
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_state1 = EmaState.New();
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_state2 = EmaState.New();
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_p_state1 = EmaState.New();
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_p_state2 = EmaState.New();
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_lastValidValue = double.NaN;
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_p_lastValidValue = double.NaN;
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Last = default;
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}
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protected override void Dispose(bool disposing)
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{
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if (disposing && _publisher != null && _listener != null)
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{
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_publisher.Pub -= _listener;
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}
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base.Dispose(disposing);
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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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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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private static double ComputeEma(double input, double alpha, double decay, ref EmaState state)
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{
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state.Ema = Math.FusedMultiplyAdd(state.Ema, decay, alpha * input);
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double result;
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if (!state.IsCompensated)
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{
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state.E *= decay;
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if (!state.IsHot && state.E <= 0.05)
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{
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state.IsHot = true;
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}
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if (state.E <= 1e-10)
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{
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state.IsCompensated = true;
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result = state.Ema;
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}
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else
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{
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result = state.Ema / (1.0 - state.E);
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}
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}
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else
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{
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result = state.Ema;
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}
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return result;
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}
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}
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