mirror of
https://github.com/mihakralj/QuanTAlib.git
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Add TRAMA implementation and comprehensive tests
- 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.
This commit is contained in:
Miha Kralj
@@ -8,6 +8,8 @@ Cycle analysis identifies repeating patterns in price data. John Ehlers pioneere
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| Indicator | Full Name | Description |
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| :--- | :--- | :--- |
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| [CCOR](ccor/Ccor.md) | Ehlers Correlation Cycle | Ehlers. Dual Pearson correlation (cos + -sin). Phasor angle + market state. |
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| [CCYC](ccyc/Ccyc.md) | Ehlers Cyber Cycle | Ehlers. 4-tap FIR + 2-pole high-pass IIR. Isolates dominant cycle component. |
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| [CG](cg/Cg.md) | Ehlers Center of Gravity | Ehlers. Weighted sum position. Minimal lag cycle indicator. |
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| [DSP](dsp/Dsp.md) | Ehlers Detrended Synthetic Price | Removes trend to reveal underlying cycles. |
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| [EACP](eacp/Eacp.md) | Ehlers Autocorrelation Periodogram | Ehlers. Spectral analysis via autocorrelation. Detects dominant period. |
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@@ -0,0 +1,170 @@
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using TradingPlatform.BusinessLayer;
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namespace QuanTAlib.Quantower.Tests;
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public class CcorIndicatorTests
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{
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[Fact]
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public void CcorIndicator_Constructor_SetsDefaults()
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{
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var indicator = new CcorIndicator();
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Assert.Equal(20, indicator.Period);
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Assert.Equal(9.0, indicator.Threshold);
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Assert.Equal(SourceType.Close, indicator.Source);
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Assert.True(indicator.ShowColdValues);
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Assert.Equal("CCOR - Ehlers Correlation Cycle", indicator.Name);
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Assert.True(indicator.SeparateWindow);
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Assert.True(indicator.OnBackGround);
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}
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[Fact]
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public void CcorIndicator_MinHistoryDepths_EqualsZero()
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{
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var indicator = new CcorIndicator();
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Assert.Equal(0, CcorIndicator.MinHistoryDepths);
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Assert.Equal(0, ((IWatchlistIndicator)indicator).MinHistoryDepths);
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}
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[Fact]
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public void CcorIndicator_ShortName_IncludesPeriodAndThreshold()
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{
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var indicator = new CcorIndicator { Period = 20, Threshold = 9.0 };
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Assert.True(indicator.ShortName.Contains("CCOR", StringComparison.Ordinal));
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Assert.True(indicator.ShortName.Contains("20", StringComparison.Ordinal));
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Assert.True(indicator.ShortName.Contains("9.0", StringComparison.Ordinal));
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}
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[Fact]
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public void CcorIndicator_Initialize_CreatesInternalCcor()
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{
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var indicator = new CcorIndicator { Period = 20, Threshold = 9.0 };
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// Initialize should not throw
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indicator.Initialize();
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// After init, line series should exist (Real + Imag + Angle + State)
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Assert.Equal(4, indicator.LinesSeries.Count);
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}
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[Fact]
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public void CcorIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
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{
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var indicator = new CcorIndicator { Period = 20, Threshold = 9.0 };
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indicator.Initialize();
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// Add historical data
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var now = DateTime.UtcNow;
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indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
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// Process update
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var args = new UpdateArgs(UpdateReason.HistoricalBar);
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indicator.ProcessUpdate(args);
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// All 4 line series should have a value
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for (int s = 0; s < 4; s++)
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{
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Assert.Equal(1, indicator.LinesSeries[s].Count);
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Assert.True(double.IsFinite(indicator.LinesSeries[s].GetValue(0)),
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$"Line series {s} should be finite");
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}
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}
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[Fact]
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public void CcorIndicator_ProcessUpdate_NewBar_ComputesValue()
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{
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var indicator = new CcorIndicator { Period = 20, Threshold = 9.0 };
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indicator.Initialize();
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var now = DateTime.UtcNow;
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indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
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indicator.HistoricalData.AddBar(now.AddMinutes(1), 102, 108, 100, 106);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewBar));
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for (int s = 0; s < 4; s++)
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{
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Assert.Equal(2, indicator.LinesSeries[s].Count);
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}
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}
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[Fact]
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public void CcorIndicator_ProcessUpdate_NewTick_ProcessesWithoutError()
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{
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var indicator = new CcorIndicator { Period = 20, Threshold = 9.0 };
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indicator.Initialize();
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// Should not throw an exception
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewTick));
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// Assert that the indicator still exists
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Assert.NotNull(indicator);
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}
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[Fact]
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public void CcorIndicator_SourceCodeLink_IsValid()
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{
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var indicator = new CcorIndicator();
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Assert.False(string.IsNullOrEmpty(indicator.SourceCodeLink));
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Assert.Contains("Ccor.Quantower.cs", indicator.SourceCodeLink, StringComparison.Ordinal);
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}
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[Fact]
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public void CcorIndicator_MultipleHistoricalBars_AllFinite()
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{
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var indicator = new CcorIndicator { Period = 10, Threshold = 9.0 };
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indicator.Initialize();
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var now = DateTime.UtcNow;
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for (int i = 0; i < 30; i++)
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{
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double price = 100 + 5 * Math.Sin(2 * Math.PI * i / 20.0);
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indicator.HistoricalData.AddBar(now.AddMinutes(i), price, price + 2, price - 2, price + 1);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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}
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for (int s = 0; s < 4; s++)
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{
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Assert.Equal(30, indicator.LinesSeries[s].Count);
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for (int i = 0; i < 30; i++)
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{
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Assert.True(double.IsFinite(indicator.LinesSeries[s].GetValue(i)),
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$"Line series {s} at bar {i} should be finite");
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}
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}
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}
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[Fact]
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public void CcorIndicator_CustomPeriod_ReflectedInShortName()
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{
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var indicator = new CcorIndicator { Period = 30, Threshold = 5.0 };
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Assert.Contains("30", indicator.ShortName, StringComparison.Ordinal);
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Assert.Contains("5.0", indicator.ShortName, StringComparison.Ordinal);
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}
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[Theory]
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[InlineData(SourceType.Open)]
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[InlineData(SourceType.High)]
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[InlineData(SourceType.Low)]
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[InlineData(SourceType.Close)]
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public void CcorIndicator_DifferentSources_DoNotThrow(SourceType sourceType)
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{
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var indicator = new CcorIndicator
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{
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Period = 20,
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Threshold = 9.0,
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Source = sourceType
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};
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indicator.Initialize();
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var now = DateTime.UtcNow;
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indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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Assert.Equal(1, indicator.LinesSeries[0].Count);
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}
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}
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@@ -0,0 +1,80 @@
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using System.Drawing;
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using System.Runtime.CompilerServices;
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using TradingPlatform.BusinessLayer;
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namespace QuanTAlib;
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[SkipLocalsInit]
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public sealed class CcorIndicator : Indicator, IWatchlistIndicator
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{
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[InputParameter("Period", sortIndex: 1, 2, 200, 1, 0)]
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public int Period { get; set; } = 20;
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[InputParameter("Threshold", sortIndex: 2, 0.1, 90.0, 0.1, 1)]
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public double Threshold { get; set; } = 9.0;
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[IndicatorExtensions.DataSourceInput]
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public SourceType Source { get; set; } = SourceType.Close;
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[InputParameter("Show cold values", sortIndex: 21)]
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public bool ShowColdValues { get; set; } = true;
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private Ccor _ccor = null!;
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private readonly LineSeries _realSeries;
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private readonly LineSeries _imagSeries;
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private readonly LineSeries _angleSeries;
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private readonly LineSeries _stateSeries;
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private Func<IHistoryItem, double> _priceSelector = null!;
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public static int MinHistoryDepths => 0;
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int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths;
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public override string ShortName => $"CCOR ({Period},{Threshold:F1})";
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public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/cycles/ccor/Ccor.Quantower.cs";
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public CcorIndicator()
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{
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OnBackGround = true;
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SeparateWindow = true;
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Name = "CCOR - Ehlers Correlation Cycle";
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Description = "Ehlers' Correlation Cycle uses dual Pearson correlation (cosine + negative sine) to derive a phasor, monotonic angle, and market state classification";
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_realSeries = new LineSeries(name: "Real", color: IndicatorExtensions.Oscillators, width: 2, style: LineStyle.Solid);
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_imagSeries = new LineSeries(name: "Imag", color: Color.FromArgb(128, 128, 255), width: 1, style: LineStyle.Dash);
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_angleSeries = new LineSeries(name: "Angle", color: Color.FromArgb(200, 200, 100), width: 1, style: LineStyle.Dot);
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_stateSeries = new LineSeries(name: "State", color: Color.FromArgb(255, 165, 0), width: 2, style: LineStyle.Histogramm);
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AddLineSeries(_realSeries);
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AddLineSeries(_imagSeries);
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AddLineSeries(_angleSeries);
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AddLineSeries(_stateSeries);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void OnInit()
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{
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_ccor = new Ccor(Period, Threshold);
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_priceSelector = Source.GetPriceSelector();
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base.OnInit();
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void OnUpdate(UpdateArgs args)
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{
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if (args.Reason != UpdateReason.NewBar && args.Reason != UpdateReason.HistoricalBar)
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{
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return;
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}
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var item = this.HistoricalData[this.Count - 1, SeekOriginHistory.Begin];
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double value = _priceSelector(item);
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var time = this.HistoricalData.Time();
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var input = new TValue(time, value);
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TValue result = _ccor.Update(input, args.IsNewBar());
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_realSeries.SetValue(result.Value, _ccor.IsHot, ShowColdValues);
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_imagSeries.SetValue(_ccor.Imag, _ccor.IsHot, ShowColdValues);
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_angleSeries.SetValue(_ccor.Angle, _ccor.IsHot, ShowColdValues);
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_stateSeries.SetValue(_ccor.MarketState, _ccor.IsHot, ShowColdValues);
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}
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}
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@@ -0,0 +1,592 @@
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using Xunit;
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namespace QuanTAlib.Tests;
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public class CcorTests
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{
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private static readonly GBM TestData = new(startPrice: 100, mu: 0.05, sigma: 0.5, seed: 42);
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private static TSeries GetTestSeries(int count = 500)
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{
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return TestData.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)).Close;
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}
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// ── A) Constructor validation ──
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[Fact]
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public void Ccor_DefaultConstructor_SetsDefaults()
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{
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var ind = new Ccor();
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Assert.Equal("Ccor(20,9.0)", ind.Name);
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Assert.Equal(20, ind.WarmupPeriod);
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}
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[Fact]
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public void Ccor_CustomPeriod_SetsCorrectName()
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{
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var ind = new Ccor(period: 30, threshold: 5.0);
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Assert.Equal("Ccor(30,5.0)", ind.Name);
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Assert.Equal(30, ind.WarmupPeriod);
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}
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[Fact]
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public void Ccor_ZeroPeriod_Throws()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Ccor(period: 0));
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Assert.Equal("period", ex.ParamName);
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}
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[Fact]
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public void Ccor_NegativePeriod_Throws()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Ccor(period: -5));
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Assert.Equal("period", ex.ParamName);
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}
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[Fact]
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public void Ccor_ZeroThreshold_Throws()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Ccor(period: 20, threshold: 0.0));
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Assert.Equal("threshold", ex.ParamName);
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}
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[Fact]
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public void Ccor_NegativeThreshold_Throws()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Ccor(period: 20, threshold: -1.0));
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Assert.Equal("threshold", ex.ParamName);
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}
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[Fact]
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public void Ccor_ChainConstructor_NullSource_Throws()
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{
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Assert.Throws<ArgumentNullException>(() => new Ccor(null!, 20, 9.0));
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}
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// ── B) Basic calculation ──
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[Fact]
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public void Ccor_Update_ReturnsTValue()
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{
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var ind = new Ccor();
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var result = ind.Update(new TValue(DateTime.UtcNow, 100.0));
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Assert.IsType<TValue>(result);
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}
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[Fact]
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public void Ccor_AfterUpdate_LastIsAccessible()
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{
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var ind = new Ccor();
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_ = ind.Update(new TValue(DateTime.UtcNow, 100.0));
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Assert.True(double.IsFinite(ind.Last.Value));
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Assert.Equal("Ccor(20,9.0)", ind.Name);
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}
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[Fact]
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public void Ccor_MultiOutput_AllAccessible()
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{
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var ind = new Ccor();
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var series = GetTestSeries(50);
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foreach (var tv in series)
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{
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_ = ind.Update(tv);
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}
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// All multi-output properties should be accessible and finite
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Assert.True(double.IsFinite(ind.Real));
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Assert.True(double.IsFinite(ind.Imag));
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Assert.True(double.IsFinite(ind.Angle));
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Assert.Contains(ind.MarketState, new[] { -1, 0, 1 });
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}
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[Fact]
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public void Ccor_Real_BoundedMinusOneToOne()
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{
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var ind = new Ccor();
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var series = GetTestSeries(200);
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foreach (var tv in series)
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{
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_ = ind.Update(tv);
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Assert.InRange(ind.Real, -1.0, 1.0);
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}
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}
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[Fact]
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public void Ccor_Imag_BoundedMinusOneToOne()
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{
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var ind = new Ccor();
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var series = GetTestSeries(200);
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foreach (var tv in series)
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{
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_ = ind.Update(tv);
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Assert.InRange(ind.Imag, -1.0, 1.0);
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}
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}
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// ── C) State + bar correction ──
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[Fact]
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public void Ccor_IsNew_True_AdvancesState()
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{
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var ind = new Ccor(period: 10);
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var series = GetTestSeries(20);
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foreach (var tv in series)
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{
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_ = ind.Update(tv, isNew: true);
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}
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Assert.True(ind.IsHot);
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}
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[Fact]
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public void Ccor_IsNew_False_DoesNotAdvance()
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{
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var ind = new Ccor(period: 10);
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var series = GetTestSeries(5);
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// Process 5 bars normally
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foreach (var tv in series)
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{
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_ = ind.Update(tv, isNew: true);
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}
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// Rewrite last bar with same value — should produce same result each time
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_ = ind.Update(series[^1], isNew: false);
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double realAfterFirst = ind.Real;
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_ = ind.Update(series[^1], isNew: false);
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double realAfterSecond = ind.Real;
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Assert.Equal(realAfterFirst, realAfterSecond, 10);
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}
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[Fact]
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public void Ccor_BarCorrection_IterativeUpdatesRestore()
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{
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var ind = new Ccor(period: 10);
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var series = GetTestSeries(30);
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// Process first 25 bars
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for (int i = 0; i < 25; i++)
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{
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_ = ind.Update(series[i]);
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}
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double realSnapshot = ind.Real;
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// Apply 5 corrections (isNew=false)
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for (int i = 0; i < 5; i++)
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{
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_ = ind.Update(new TValue(series[24].Time, 100.0 + i), isNew: false);
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}
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// Reapply original — should restore
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_ = ind.Update(series[24], isNew: false);
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Assert.Equal(realSnapshot, ind.Real, 10);
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}
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[Fact]
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public void Ccor_Reset_ClearsState()
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{
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var ind = new Ccor();
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var series = GetTestSeries(50);
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foreach (var tv in series)
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||||
{
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_ = ind.Update(tv);
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}
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Assert.True(ind.IsHot);
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ind.Reset();
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Assert.False(ind.IsHot);
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Assert.Equal(0.0, ind.Real);
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Assert.Equal(0.0, ind.Imag);
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Assert.Equal(0.0, ind.Angle);
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Assert.Equal(0, ind.MarketState);
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Assert.Equal(default, ind.Last);
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}
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||||
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// ── D) Warmup/convergence ──
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||||
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||||
[Fact]
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||||
public void Ccor_IsHot_FlipsAtWarmupPeriod()
|
||||
{
|
||||
int period = 15;
|
||||
var ind = new Ccor(period: period);
|
||||
var series = GetTestSeries(period + 5);
|
||||
|
||||
for (int i = 0; i < period - 1; i++)
|
||||
{
|
||||
_ = ind.Update(series[i]);
|
||||
Assert.False(ind.IsHot, $"Should not be hot at bar {i + 1}");
|
||||
}
|
||||
|
||||
_ = ind.Update(series[period - 1]);
|
||||
Assert.True(ind.IsHot, $"Should be hot at bar {period}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_WarmupPeriod_EqualsPeriod()
|
||||
{
|
||||
var ind = new Ccor(period: 30);
|
||||
Assert.Equal(30, ind.WarmupPeriod);
|
||||
}
|
||||
|
||||
// ── E) Robustness ──
|
||||
|
||||
[Fact]
|
||||
public void Ccor_NaN_UsesLastValid()
|
||||
{
|
||||
var ind = new Ccor(period: 5);
|
||||
var series = GetTestSeries(10);
|
||||
|
||||
for (int i = 0; i < 8; i++)
|
||||
{
|
||||
_ = ind.Update(series[i]);
|
||||
}
|
||||
|
||||
_ = ind.Update(new TValue(DateTime.UtcNow, double.NaN));
|
||||
Assert.True(double.IsFinite(ind.Real));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Infinity_UsesLastValid()
|
||||
{
|
||||
var ind = new Ccor(period: 5);
|
||||
var series = GetTestSeries(10);
|
||||
|
||||
for (int i = 0; i < 8; i++)
|
||||
{
|
||||
_ = ind.Update(series[i]);
|
||||
}
|
||||
|
||||
_ = ind.Update(new TValue(DateTime.UtcNow, double.PositiveInfinity));
|
||||
Assert.True(double.IsFinite(ind.Real));
|
||||
Assert.True(double.IsFinite(ind.Imag));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_BatchNaN_AllFinite()
|
||||
{
|
||||
var series = GetTestSeries(50);
|
||||
var ind = new Ccor(period: 10);
|
||||
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind.Update(tv);
|
||||
}
|
||||
|
||||
// Inject NaN batch
|
||||
for (int i = 0; i < 5; i++)
|
||||
{
|
||||
_ = ind.Update(new TValue(DateTime.UtcNow, double.NaN));
|
||||
}
|
||||
|
||||
Assert.True(double.IsFinite(ind.Real));
|
||||
Assert.True(double.IsFinite(ind.Imag));
|
||||
Assert.True(double.IsFinite(ind.Angle));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_EmptyTSeries_ReturnsEmpty()
|
||||
{
|
||||
var ind = new Ccor();
|
||||
var result = ind.Update(new TSeries());
|
||||
Assert.Empty(result);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_LargeDataset_NoBlowup()
|
||||
{
|
||||
var largeData = TestData.Fetch(10000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)).Close;
|
||||
var ind = new Ccor();
|
||||
|
||||
for (int i = 0; i < largeData.Count; i++)
|
||||
{
|
||||
_ = ind.Update(largeData[i]);
|
||||
Assert.True(double.IsFinite(ind.Real), $"Non-finite Real at index {i}");
|
||||
Assert.True(double.IsFinite(ind.Imag), $"Non-finite Imag at index {i}");
|
||||
}
|
||||
}
|
||||
|
||||
// ── F) Consistency (4 API modes match) ──
|
||||
|
||||
[Fact]
|
||||
public void Ccor_FourApiModes_Match()
|
||||
{
|
||||
var series = GetTestSeries(100);
|
||||
int period = 20;
|
||||
double threshold = 9.0;
|
||||
|
||||
// Mode 1: Streaming
|
||||
var ind1 = new Ccor(period, threshold);
|
||||
var streaming = new double[series.Count];
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
streaming[i] = ind1.Update(series[i]).Value;
|
||||
}
|
||||
|
||||
// Mode 2: Batch(TSeries)
|
||||
var batchResult = Ccor.Batch(series, period, threshold);
|
||||
|
||||
// Mode 3: Batch(Span)
|
||||
double[] srcVals = new double[series.Count];
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
srcVals[i] = series[i].Value;
|
||||
}
|
||||
double[] spanResult = new double[series.Count];
|
||||
Ccor.Batch(srcVals, spanResult, period, threshold);
|
||||
|
||||
// Mode 4: Eventing
|
||||
var ind4 = new Ccor(period, threshold);
|
||||
var eventResults = new List<double>();
|
||||
ind4.Pub += (object? _, in TValueEventArgs e) => eventResults.Add(e.Value.Value);
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind4.Update(tv);
|
||||
}
|
||||
|
||||
// Compare all modes
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
Assert.Equal(streaming[i], batchResult[i].Value, 10);
|
||||
Assert.Equal(streaming[i], spanResult[i], 10);
|
||||
Assert.Equal(streaming[i], eventResults[i], 10);
|
||||
}
|
||||
}
|
||||
|
||||
// ── G) Span API tests ──
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_MismatchedLengths_Throws()
|
||||
{
|
||||
double[] src = new double[10];
|
||||
double[] output = new double[5];
|
||||
var ex = Assert.Throws<ArgumentException>(() => Ccor.Batch(src, output));
|
||||
Assert.Equal("output", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_ZeroPeriod_Throws()
|
||||
{
|
||||
double[] src = new double[10];
|
||||
double[] output = new double[10];
|
||||
var ex = Assert.Throws<ArgumentException>(() => Ccor.Batch(src, output, period: 0));
|
||||
Assert.Equal("period", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_ZeroThreshold_Throws()
|
||||
{
|
||||
double[] src = new double[10];
|
||||
double[] output = new double[10];
|
||||
var ex = Assert.Throws<ArgumentException>(() => Ccor.Batch(src, output, period: 20, threshold: 0.0));
|
||||
Assert.Equal("threshold", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_Empty_NoException()
|
||||
{
|
||||
double[] src = Array.Empty<double>();
|
||||
double[] output = Array.Empty<double>();
|
||||
Ccor.Batch(src, output); // should not throw
|
||||
Assert.Empty(output);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_MatchesTSeries()
|
||||
{
|
||||
var series = GetTestSeries(100);
|
||||
int period = 15;
|
||||
|
||||
var batchResult = Ccor.Batch(series, period);
|
||||
|
||||
double[] srcVals = new double[series.Count];
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
srcVals[i] = series[i].Value;
|
||||
}
|
||||
double[] spanResult = new double[series.Count];
|
||||
Ccor.Batch(srcVals, spanResult, period);
|
||||
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
Assert.Equal(batchResult[i].Value, spanResult[i], 10);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanBatch_NaN_Handled()
|
||||
{
|
||||
double[] src = { 100, 101, double.NaN, 103, 104, 105, 106, 107, 108, 109 };
|
||||
double[] output = new double[10];
|
||||
Ccor.Batch(src, output, period: 5);
|
||||
|
||||
for (int i = 0; i < output.Length; i++)
|
||||
{
|
||||
Assert.True(double.IsFinite(output[i]), $"Non-finite at index {i}");
|
||||
}
|
||||
}
|
||||
|
||||
// ── H) Chainability ──
|
||||
|
||||
[Fact]
|
||||
public void Ccor_PubEvent_Fires()
|
||||
{
|
||||
var ind = new Ccor();
|
||||
int count = 0;
|
||||
ind.Pub += (object? _, in TValueEventArgs _) => count++;
|
||||
|
||||
var series = GetTestSeries(10);
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind.Update(tv);
|
||||
}
|
||||
|
||||
Assert.Equal(10, count);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_EventChaining_Works()
|
||||
{
|
||||
var source = new Ccor(period: 10);
|
||||
var chained = new Ccor(source, period: 5);
|
||||
|
||||
var series = GetTestSeries(50);
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = source.Update(tv);
|
||||
}
|
||||
|
||||
Assert.True(chained.IsHot);
|
||||
Assert.True(double.IsFinite(chained.Real));
|
||||
}
|
||||
|
||||
// ── CCOR-specific tests ──
|
||||
|
||||
[Fact]
|
||||
public void Ccor_ConstantInput_RealIsZero()
|
||||
{
|
||||
var ind = new Ccor(period: 10);
|
||||
for (int i = 0; i < 30; i++)
|
||||
{
|
||||
_ = ind.Update(new TValue(DateTime.UtcNow.AddMinutes(i), 100.0));
|
||||
}
|
||||
|
||||
// Constant price → zero variance in x → correlation = 0
|
||||
Assert.Equal(0.0, ind.Real, 10);
|
||||
Assert.Equal(0.0, ind.Imag, 10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SineWave_DetectsCorrelation()
|
||||
{
|
||||
int period = 20;
|
||||
var ind = new Ccor(period: period);
|
||||
|
||||
// Feed a perfect sine wave of the same period
|
||||
for (int i = 0; i < 100; i++)
|
||||
{
|
||||
double val = 100.0 + 10.0 * Math.Sin(2.0 * Math.PI * i / period);
|
||||
_ = ind.Update(new TValue(DateTime.UtcNow.AddMinutes(i), val));
|
||||
}
|
||||
|
||||
// After warmup, Real correlation with cosine should be significant (not zero)
|
||||
double absReal = Math.Abs(ind.Real);
|
||||
double absImag = Math.Abs(ind.Imag);
|
||||
Assert.True(absReal > 0.1 || absImag > 0.1,
|
||||
$"Sine wave should produce non-trivial correlation: Real={ind.Real:F4}, Imag={ind.Imag:F4}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_AngleMonotonic_NeverDecreases()
|
||||
{
|
||||
var ind = new Ccor(period: 15);
|
||||
var series = GetTestSeries(200);
|
||||
double prevAngle = double.MinValue;
|
||||
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind.Update(tv);
|
||||
Assert.True(ind.Angle >= prevAngle,
|
||||
$"Angle decreased: {ind.Angle:F4} < prev {prevAngle:F4}");
|
||||
prevAngle = ind.Angle;
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_MarketState_OnlyValidValues()
|
||||
{
|
||||
var ind = new Ccor();
|
||||
var series = GetTestSeries(200);
|
||||
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind.Update(tv);
|
||||
Assert.Contains(ind.MarketState, new[] { -1, 0, 1 });
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_DifferentPeriods_ProduceDifferentResults()
|
||||
{
|
||||
var series = GetTestSeries(100);
|
||||
var ind10 = new Ccor(period: 10);
|
||||
var ind30 = new Ccor(period: 30);
|
||||
|
||||
foreach (var tv in series)
|
||||
{
|
||||
_ = ind10.Update(tv);
|
||||
_ = ind30.Update(tv);
|
||||
}
|
||||
|
||||
// Different periods should produce different Real values
|
||||
Assert.NotEqual(ind10.Real, ind30.Real, 5);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Prime_SetsState()
|
||||
{
|
||||
var ind = new Ccor(period: 10);
|
||||
var series = GetTestSeries(20);
|
||||
double[] vals = new double[series.Count];
|
||||
for (int i = 0; i < series.Count; i++)
|
||||
{
|
||||
vals[i] = series[i].Value;
|
||||
}
|
||||
|
||||
ind.Prime(vals);
|
||||
Assert.True(ind.IsHot);
|
||||
Assert.True(double.IsFinite(ind.Real));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Calculate_ReturnsBothResultsAndIndicator()
|
||||
{
|
||||
var series = GetTestSeries(50);
|
||||
var (results, indicator) = Ccor.Calculate(series);
|
||||
|
||||
Assert.Equal(50, results.Count);
|
||||
Assert.True(indicator.IsHot);
|
||||
Assert.True(double.IsFinite(indicator.Real));
|
||||
Assert.True(double.IsFinite(indicator.Imag));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Batch_TSeries_CorrectLength()
|
||||
{
|
||||
var series = GetTestSeries(100);
|
||||
var result = Ccor.Batch(series);
|
||||
Assert.Equal(100, result.Count);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Update_TSeries_CorrectLength()
|
||||
{
|
||||
var ind = new Ccor();
|
||||
var series = GetTestSeries(100);
|
||||
var result = ind.Update(series);
|
||||
Assert.Equal(100, result.Count);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,368 @@
|
||||
using Xunit;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// Validation tests for CCOR - Ehlers Correlation Cycle.
|
||||
/// Since CCOR is a proprietary Ehlers algorithm with no standard library implementations
|
||||
/// (not in TA-Lib, Skender, Tulip, or Ooples), these tests validate mathematical
|
||||
/// properties of Pearson correlation and internal consistency across API modes.
|
||||
/// </summary>
|
||||
public class CcorValidationTests
|
||||
{
|
||||
private const double Tolerance = 1e-9;
|
||||
private const long StartTime = 946_684_800_000_000_0L; // 2000-01-01 UTC in ticks
|
||||
private static readonly TimeSpan Step = TimeSpan.FromMinutes(1);
|
||||
|
||||
#region Pearson Correlation Mathematical Properties
|
||||
|
||||
[Fact]
|
||||
public void Ccor_ConstantInput_RealAndImagAreZero()
|
||||
{
|
||||
// Constant price → zero variance in x → correlation undefined → returns 0
|
||||
var ccor = new Ccor(period: 10);
|
||||
|
||||
for (int i = 0; i < 100; i++)
|
||||
{
|
||||
ccor.Update(new TValue(DateTime.UtcNow.AddMinutes(i), 100.0), true);
|
||||
}
|
||||
|
||||
Assert.Equal(0.0, ccor.Real, Tolerance);
|
||||
Assert.Equal(0.0, ccor.Imag, Tolerance);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_PerfectCosineInput_RealNearOne()
|
||||
{
|
||||
// If price exactly matches the cosine reference, Real correlation → +1
|
||||
int period = 20;
|
||||
var ccor = new Ccor(period: period);
|
||||
|
||||
double twoPiOverN = 2.0 * Math.PI / period;
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double val = Math.Cos(twoPiOverN * (i % period));
|
||||
ccor.Update(new TValue(DateTime.UtcNow.AddMinutes(i), val), true);
|
||||
}
|
||||
|
||||
// After many full cycles, Real should be very close to +1
|
||||
Assert.True(ccor.Real > 0.95,
|
||||
$"Perfect cosine input should give Real ≈ 1.0, got {ccor.Real:F6}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_PerfectNegSineInput_ImagHighMagnitude()
|
||||
{
|
||||
// If price has -sin periodicity, Imag correlation magnitude should be near 1.0
|
||||
int period = 20;
|
||||
var ccor = new Ccor(period: period);
|
||||
|
||||
double twoPiOverN = 2.0 * Math.PI / period;
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double val = -Math.Sin(twoPiOverN * (i % period));
|
||||
ccor.Update(new TValue(DateTime.UtcNow.AddMinutes(i), val), true);
|
||||
}
|
||||
|
||||
Assert.True(Math.Abs(ccor.Imag) > 0.90,
|
||||
$"Perfect -sin input should give |Imag| ≈ 1.0, got {ccor.Imag:F6}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_RealAndImag_BoundedMinusOneToOne()
|
||||
{
|
||||
// Pearson correlation coefficient is always in [-1, +1]
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(1000, StartTime, Step);
|
||||
var ccor = new Ccor(period: 20);
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
ccor.Update(new TValue(bars[i].Time, bars[i].Close), true);
|
||||
Assert.InRange(ccor.Real, -1.0, 1.0);
|
||||
Assert.InRange(ccor.Imag, -1.0, 1.0);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SineWave_RealAndImagAreOrthogonal()
|
||||
{
|
||||
// For a pure sine wave at the indicator's period, the Real (cosine) and Imag (-sine)
|
||||
// correlations should be approximately orthogonal components of a phasor
|
||||
int period = 20;
|
||||
var ccor = new Ccor(period: period);
|
||||
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double val = 100.0 + 10.0 * Math.Sin(2.0 * Math.PI * i / period);
|
||||
ccor.Update(new TValue(DateTime.UtcNow.AddMinutes(i), val), true);
|
||||
}
|
||||
|
||||
// Both should be non-trivial
|
||||
Assert.True(Math.Abs(ccor.Real) > 0.01 || Math.Abs(ccor.Imag) > 0.01,
|
||||
$"Sine wave should produce non-trivial phasor: Real={ccor.Real:F4}, Imag={ccor.Imag:F4}");
|
||||
|
||||
// R² + I² should be near 1 for a pure tone at the matched frequency
|
||||
double magnitude = Math.Sqrt(ccor.Real * ccor.Real + ccor.Imag * ccor.Imag);
|
||||
Assert.True(magnitude > 0.5,
|
||||
$"Phasor magnitude should be significant for matched sine: {magnitude:F4}");
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Angle Properties
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Angle_MonotonicallyNonDecreasing()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var ccor = new Ccor(period: 20);
|
||||
double prevAngle = double.MinValue;
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
ccor.Update(new TValue(bars[i].Time, bars[i].Close), true);
|
||||
Assert.True(ccor.Angle >= prevAngle,
|
||||
$"Angle decreased at bar {i}: {ccor.Angle:F4} < prev {prevAngle:F4}");
|
||||
prevAngle = ccor.Angle;
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_Angle_AdvancesOnCyclicInput()
|
||||
{
|
||||
// For cyclic input, the angle should advance significantly
|
||||
int period = 20;
|
||||
var ccor = new Ccor(period: period);
|
||||
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double val = 100.0 + 10.0 * Math.Sin(2.0 * Math.PI * i / period);
|
||||
ccor.Update(new TValue(DateTime.UtcNow.AddMinutes(i), val), true);
|
||||
}
|
||||
|
||||
Assert.True(ccor.Angle > 0.0,
|
||||
$"Angle should advance on cyclic input, got {ccor.Angle:F4}");
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Market State Properties
|
||||
|
||||
[Fact]
|
||||
public void Ccor_MarketState_OnlyValidValues()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var ccor = new Ccor(period: 20, threshold: 9.0);
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
ccor.Update(new TValue(bars[i].Time, bars[i].Close), true);
|
||||
Assert.Contains(ccor.MarketState, new[] { -1, 0, 1 });
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_MarketState_HasVariation()
|
||||
{
|
||||
// Over enough data, all three states should appear at least once
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(2000, StartTime, Step);
|
||||
var ccor = new Ccor(period: 20, threshold: 9.0);
|
||||
var states = new HashSet<int>();
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
ccor.Update(new TValue(bars[i].Time, bars[i].Close), true);
|
||||
states.Add(ccor.MarketState);
|
||||
}
|
||||
|
||||
Assert.True(states.Count >= 2,
|
||||
$"Expected at least 2 distinct market states, got {states.Count}: {string.Join(",", states)}");
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Deterministic Reproducibility
|
||||
|
||||
[Theory]
|
||||
[InlineData(42)]
|
||||
[InlineData(123)]
|
||||
[InlineData(456)]
|
||||
public void Ccor_DeterministicOutput(int seed)
|
||||
{
|
||||
var gbm1 = new GBM(seed: seed);
|
||||
var bars1 = gbm1.Fetch(200, StartTime, Step);
|
||||
|
||||
var gbm2 = new GBM(seed: seed);
|
||||
var bars2 = gbm2.Fetch(200, StartTime, Step);
|
||||
|
||||
var ccor1 = new Ccor(period: 20, threshold: 9.0);
|
||||
var ccor2 = new Ccor(period: 20, threshold: 9.0);
|
||||
|
||||
for (int i = 0; i < bars1.Count; i++)
|
||||
{
|
||||
var r1 = ccor1.Update(new TValue(bars1[i].Time, bars1[i].Close));
|
||||
var r2 = ccor2.Update(new TValue(bars2[i].Time, bars2[i].Close));
|
||||
|
||||
Assert.Equal(r1.Value, r2.Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(10)]
|
||||
[InlineData(20)]
|
||||
[InlineData(50)]
|
||||
public void Ccor_AllPeriods_ProduceFiniteOutput(int period)
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var ccor = new Ccor(period: period);
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
var r = ccor.Update(new TValue(bars[i].Time, bars[i].Close));
|
||||
Assert.True(double.IsFinite(r.Value), $"Non-finite at bar {i} with period={period}");
|
||||
Assert.True(double.IsFinite(ccor.Real), $"Non-finite Real at bar {i}");
|
||||
Assert.True(double.IsFinite(ccor.Imag), $"Non-finite Imag at bar {i}");
|
||||
Assert.True(double.IsFinite(ccor.Angle), $"Non-finite Angle at bar {i}");
|
||||
}
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Consistency Validation
|
||||
|
||||
[Fact]
|
||||
public void Ccor_BatchMatchesStreaming_OnGBM()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
// Streaming
|
||||
var ccorStream = new Ccor(period: 20, threshold: 9.0);
|
||||
var streamResults = new double[source.Count];
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
var r = ccorStream.Update(source[i], true);
|
||||
streamResults[i] = r.Value;
|
||||
}
|
||||
|
||||
// Batch
|
||||
var batchResults = Ccor.Batch(source, 20, 9.0);
|
||||
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
Assert.Equal(streamResults[i], batchResults[i].Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_SpanMatchesBatch_OnGBM()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
// TSeries batch
|
||||
var batchResults = Ccor.Batch(source, 20, 9.0);
|
||||
|
||||
// Span batch
|
||||
double[] values = new double[source.Count];
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
values[i] = source[i].Value;
|
||||
}
|
||||
|
||||
double[] output = new double[values.Length];
|
||||
Ccor.Batch(values.AsSpan(), output.AsSpan(), 20, 9.0);
|
||||
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
Assert.Equal(batchResults[i].Value, output[i], Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_ResetAndReprocess_Matches()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(200, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
var ccor = new Ccor(period: 20, threshold: 9.0);
|
||||
var results1 = ccor.Update(source);
|
||||
|
||||
ccor.Reset();
|
||||
var results2 = ccor.Update(source);
|
||||
|
||||
Assert.Equal(results1.Count, results2.Count);
|
||||
for (int i = 0; i < results1.Count; i++)
|
||||
{
|
||||
Assert.Equal(results1[i].Value, results2[i].Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Period Sensitivity
|
||||
|
||||
[Fact]
|
||||
public void Ccor_DifferentPeriods_ProduceDifferentResults()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(200, StartTime, Step);
|
||||
|
||||
var ccor10 = new Ccor(period: 10);
|
||||
var ccor30 = new Ccor(period: 30);
|
||||
double diffEnergy = 0;
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
var tv = new TValue(bars[i].Time, bars[i].Close);
|
||||
var r10 = ccor10.Update(tv);
|
||||
var r30 = ccor30.Update(tv);
|
||||
|
||||
if (i > 30)
|
||||
{
|
||||
double d = r10.Value - r30.Value;
|
||||
diffEnergy += d * d;
|
||||
}
|
||||
}
|
||||
|
||||
Assert.True(diffEnergy > 1e-6,
|
||||
$"Different periods should produce different outputs, diffEnergy={diffEnergy}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccor_DifferentThresholds_ProduceDifferentMarketStates()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
|
||||
var ccorTight = new Ccor(period: 20, threshold: 1.0);
|
||||
var ccorLoose = new Ccor(period: 20, threshold: 50.0);
|
||||
|
||||
int statesDiffer = 0;
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
var tv = new TValue(bars[i].Time, bars[i].Close);
|
||||
ccorTight.Update(tv);
|
||||
ccorLoose.Update(tv);
|
||||
|
||||
if (ccorTight.MarketState != ccorLoose.MarketState)
|
||||
{
|
||||
statesDiffer++;
|
||||
}
|
||||
}
|
||||
|
||||
// Real/Imag/Angle are independent of threshold — only MarketState differs
|
||||
Assert.True(statesDiffer > 0,
|
||||
"Different thresholds should produce different market state classifications");
|
||||
}
|
||||
|
||||
#endregion
|
||||
}
|
||||
@@ -0,0 +1,435 @@
|
||||
using System.Buffers;
|
||||
using System.Runtime.CompilerServices;
|
||||
using System.Runtime.InteropServices;
|
||||
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// CCOR: Ehlers Correlation Cycle — extracts cycle phase by computing Pearson correlation
|
||||
/// of a price window against cosine (Real) and negative-sine (Imaginary) reference waves,
|
||||
/// converting the resulting phasor to an angle with monotonic constraint and classifying
|
||||
/// the market state as trending or cycling.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// From John F. Ehlers, "Correlation As A Cycle Indicator" (Stocks & Commodities, June 2020).
|
||||
///
|
||||
/// Algorithm:
|
||||
/// 1. Dual Pearson correlation over sliding window of N bars:
|
||||
/// Real = corr(price, cos(2πk/N)), Imag = corr(price, -sin(2πk/N))
|
||||
/// 2. Phasor angle = 90° + atan(Real/Imag) with quadrant fix (if Imag > 0: angle -= 180°)
|
||||
/// 3. Monotonic constraint: angle = max(angle, prev_angle) — prevents backward spin
|
||||
/// 4. State detection: |Δangle| < threshold → trending (+1 uptrend / -1 downtrend), else cycling (0)
|
||||
///
|
||||
/// Properties:
|
||||
/// - O(period) per bar for dual correlation loops
|
||||
/// - Precomputed cos/sin tables eliminate per-bar trig calls
|
||||
/// - Real, Imag bounded [-1, +1] by Pearson construction
|
||||
/// - Zero allocation in hot path (RingBuffer is pre-allocated)
|
||||
/// </remarks>
|
||||
[SkipLocalsInit]
|
||||
public sealed class Ccor : AbstractBase
|
||||
{
|
||||
private readonly int _period;
|
||||
private readonly double _threshold;
|
||||
private readonly double[] _cosTable;
|
||||
private readonly double[] _negSinTable;
|
||||
private readonly RingBuffer _buf;
|
||||
|
||||
[StructLayout(LayoutKind.Auto)]
|
||||
private record struct State(double PrevAngle, int Count, double LastValid);
|
||||
|
||||
private State _s;
|
||||
private State _ps;
|
||||
|
||||
/// <summary>Pearson correlation of price with cosine reference wave. Range [-1, +1].</summary>
|
||||
public double Real { get; private set; }
|
||||
|
||||
/// <summary>Pearson correlation of price with negative-sine reference wave. Range [-1, +1].</summary>
|
||||
public double Imag { get; private set; }
|
||||
|
||||
/// <summary>Phasor angle (degrees), monotonically increasing.</summary>
|
||||
public double Angle { get; private set; }
|
||||
|
||||
/// <summary>Market state: +1 = uptrend, -1 = downtrend, 0 = cycling.</summary>
|
||||
public int MarketState { get; private set; }
|
||||
|
||||
/// <inheritdoc />
|
||||
public override bool IsHot => _s.Count >= WarmupPeriod;
|
||||
|
||||
/// <summary>
|
||||
/// Creates a new Ccor indicator.
|
||||
/// </summary>
|
||||
/// <param name="period">Presumed dominant cycle wavelength. Must be > 0. Default 20.</param>
|
||||
/// <param name="threshold">Angle rate threshold (degrees) for state detection. Must be > 0. Default 9.0.</param>
|
||||
public Ccor(int period = 20, double threshold = 9.0)
|
||||
{
|
||||
if (period <= 0)
|
||||
{
|
||||
throw new ArgumentException("Period must be greater than 0.", nameof(period));
|
||||
}
|
||||
if (threshold <= 0.0)
|
||||
{
|
||||
throw new ArgumentException("Threshold must be greater than 0.", nameof(threshold));
|
||||
}
|
||||
|
||||
_period = period;
|
||||
_threshold = threshold;
|
||||
|
||||
// Precompute cos/sin lookup tables
|
||||
_cosTable = new double[period];
|
||||
_negSinTable = new double[period];
|
||||
double twoPiOverN = 2.0 * Math.PI / period;
|
||||
|
||||
for (int k = 0; k < period; k++)
|
||||
{
|
||||
double angle = twoPiOverN * k;
|
||||
_cosTable[k] = Math.Cos(angle);
|
||||
_negSinTable[k] = -Math.Sin(angle);
|
||||
}
|
||||
|
||||
_buf = new(period);
|
||||
Name = $"Ccor({period},{threshold:F1})";
|
||||
WarmupPeriod = period;
|
||||
_s = default;
|
||||
_ps = default;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Creates a new Ccor indicator chained to a publisher source.
|
||||
/// </summary>
|
||||
public Ccor(ITValuePublisher source, int period = 20, double threshold = 9.0) : this(period, threshold)
|
||||
{
|
||||
ArgumentNullException.ThrowIfNull(source);
|
||||
source.Pub += HandleInput;
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
private void HandleInput(object? sender, in TValueEventArgs e)
|
||||
{
|
||||
Update(e.Value, e.IsNew);
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public override TValue Update(TValue input, bool isNew = true)
|
||||
{
|
||||
// State management: save/restore for bar correction
|
||||
if (isNew)
|
||||
{
|
||||
_ps = _s;
|
||||
_buf.Snapshot();
|
||||
}
|
||||
else
|
||||
{
|
||||
_s = _ps;
|
||||
_buf.Restore();
|
||||
}
|
||||
|
||||
var s = _s;
|
||||
|
||||
double price = input.Value;
|
||||
|
||||
// NaN/Infinity guard: substitute last valid value
|
||||
if (!double.IsFinite(price))
|
||||
{
|
||||
price = s.LastValid;
|
||||
}
|
||||
else
|
||||
{
|
||||
s = s with { LastValid = price };
|
||||
}
|
||||
|
||||
// Increment bar count
|
||||
int count = isNew ? s.Count + 1 : s.Count;
|
||||
|
||||
// Add price to ring buffer
|
||||
_buf.Add(price);
|
||||
|
||||
// Compute dual Pearson correlations
|
||||
int n = Math.Min(count, _period);
|
||||
double realVal = 0, imagVal = 0;
|
||||
double angleVal = 0;
|
||||
int stateVal = 0;
|
||||
|
||||
if (n >= 2)
|
||||
{
|
||||
realVal = ComputeCorrelation(_buf, _cosTable, n);
|
||||
imagVal = ComputeCorrelation(_buf, _negSinTable, n);
|
||||
|
||||
// Phasor angle (degrees) with quadrant resolution
|
||||
if (imagVal != 0.0)
|
||||
{
|
||||
angleVal = 90.0 + Math.Atan(realVal / imagVal) * (180.0 / Math.PI);
|
||||
}
|
||||
if (imagVal > 0.0)
|
||||
{
|
||||
angleVal -= 180.0;
|
||||
}
|
||||
|
||||
// Monotonic constraint: angle cannot decrease
|
||||
double savedPrev = s.PrevAngle;
|
||||
if (angleVal < savedPrev)
|
||||
{
|
||||
angleVal = savedPrev;
|
||||
}
|
||||
|
||||
// Market state detection
|
||||
double angleChange = Math.Abs(angleVal - savedPrev);
|
||||
if (angleChange < _threshold && angleVal >= 0.0)
|
||||
{
|
||||
stateVal = 1; // uptrend
|
||||
}
|
||||
else if (angleChange < _threshold && angleVal <= 0.0)
|
||||
{
|
||||
stateVal = -1; // downtrend
|
||||
}
|
||||
// else stateVal = 0 (cycling)
|
||||
}
|
||||
|
||||
Real = realVal;
|
||||
Imag = imagVal;
|
||||
Angle = angleVal;
|
||||
MarketState = stateVal;
|
||||
|
||||
_s = new State(angleVal, count, s.LastValid);
|
||||
|
||||
Last = new TValue(input.Time, realVal);
|
||||
PubEvent(Last, isNew);
|
||||
return Last;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Processes a full TSeries, returning the Real correlation for each bar.
|
||||
/// </summary>
|
||||
public override TSeries Update(TSeries source)
|
||||
{
|
||||
if (source.Count == 0)
|
||||
{
|
||||
return [];
|
||||
}
|
||||
|
||||
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);
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
var result = Update(source[i]);
|
||||
vSpan[i] = result.Value;
|
||||
}
|
||||
source.Times.CopyTo(tSpan);
|
||||
|
||||
return new TSeries(t, v);
|
||||
}
|
||||
|
||||
/// <inheritdoc />
|
||||
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
|
||||
{
|
||||
foreach (double value in source)
|
||||
{
|
||||
Update(new TValue(DateTime.UtcNow, value));
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static batch: creates a Ccor, processes source, returns output TSeries.
|
||||
/// </summary>
|
||||
public static TSeries Batch(TSeries source, int period = 20, double threshold = 9.0)
|
||||
{
|
||||
var ind = new Ccor(period, threshold);
|
||||
return ind.Update(source);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static span-based batch: computes correlation cycle Real component into output span.
|
||||
/// </summary>
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public static void Batch(ReadOnlySpan<double> source, Span<double> output, int period = 20, double threshold = 9.0)
|
||||
{
|
||||
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 (threshold <= 0.0)
|
||||
{
|
||||
throw new ArgumentException("Threshold must be greater than 0.", nameof(threshold));
|
||||
}
|
||||
|
||||
int len = source.Length;
|
||||
if (len == 0)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Precompute trig tables
|
||||
const int StackallocThreshold = 256;
|
||||
double[]? rentedCos = null;
|
||||
scoped Span<double> cosTab;
|
||||
|
||||
if (period <= StackallocThreshold)
|
||||
{
|
||||
cosTab = stackalloc double[period];
|
||||
}
|
||||
else
|
||||
{
|
||||
rentedCos = ArrayPool<double>.Shared.Rent(period);
|
||||
cosTab = rentedCos.AsSpan(0, period);
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
double twoPiOverN = 2.0 * Math.PI / period;
|
||||
for (int k = 0; k < period; k++)
|
||||
{
|
||||
cosTab[k] = Math.Cos(twoPiOverN * k);
|
||||
}
|
||||
|
||||
// Price ring buffer (manual circular)
|
||||
double[]? rentedBuf = null;
|
||||
scoped Span<double> priceBuf;
|
||||
if (period <= StackallocThreshold)
|
||||
{
|
||||
priceBuf = stackalloc double[period];
|
||||
}
|
||||
else
|
||||
{
|
||||
rentedBuf = ArrayPool<double>.Shared.Rent(period);
|
||||
priceBuf = rentedBuf.AsSpan(0, period);
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
priceBuf.Clear();
|
||||
int bufIdx = 0;
|
||||
int filled = 0;
|
||||
double lastValid = 0;
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
double val = source[i];
|
||||
if (!double.IsFinite(val))
|
||||
{
|
||||
val = lastValid;
|
||||
}
|
||||
else
|
||||
{
|
||||
lastValid = val;
|
||||
}
|
||||
|
||||
priceBuf[bufIdx] = val;
|
||||
bufIdx = (bufIdx + 1) % period;
|
||||
if (filled < period)
|
||||
{
|
||||
filled++;
|
||||
}
|
||||
|
||||
int n = filled;
|
||||
double realVal = 0;
|
||||
|
||||
if (n >= 2)
|
||||
{
|
||||
// Compute Real correlation (cosine)
|
||||
double sx = 0, sxx = 0, sxy = 0;
|
||||
double sy = 0, syy = 0;
|
||||
|
||||
for (int k = 0; k < n; k++)
|
||||
{
|
||||
int idx = ((bufIdx - 1 - k) % period + period) % period;
|
||||
double x = priceBuf[idx];
|
||||
double y = cosTab[k];
|
||||
sx += x;
|
||||
sxx += x * x;
|
||||
sxy += x * y;
|
||||
sy += y;
|
||||
syy += y * y;
|
||||
}
|
||||
|
||||
double nd = n;
|
||||
double dp = (nd * sxx - sx * sx) * (nd * syy - sy * sy);
|
||||
realVal = dp > 0.0 ? Math.Clamp((nd * sxy - sx * sy) / Math.Sqrt(dp), -1.0, 1.0) : 0.0;
|
||||
}
|
||||
|
||||
output[i] = realVal;
|
||||
}
|
||||
}
|
||||
finally
|
||||
{
|
||||
if (rentedBuf != null)
|
||||
{
|
||||
ArrayPool<double>.Shared.Return(rentedBuf);
|
||||
}
|
||||
}
|
||||
}
|
||||
finally
|
||||
{
|
||||
if (rentedCos != null)
|
||||
{
|
||||
ArrayPool<double>.Shared.Return(rentedCos);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static convenience method: returns (TSeries results, Ccor indicator) for inspection.
|
||||
/// </summary>
|
||||
public static (TSeries Results, Ccor Indicator) Calculate(TSeries source, int period = 20, double threshold = 9.0)
|
||||
{
|
||||
var ind = new Ccor(period, threshold);
|
||||
var results = ind.Update(source);
|
||||
return (results, ind);
|
||||
}
|
||||
|
||||
/// <inheritdoc />
|
||||
public override void Reset()
|
||||
{
|
||||
_s = default;
|
||||
_ps = default;
|
||||
_buf.Clear();
|
||||
Last = default;
|
||||
Real = 0;
|
||||
Imag = 0;
|
||||
Angle = 0;
|
||||
MarketState = 0;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Computes Pearson correlation between the most recent n values in RingBuffer
|
||||
/// and the first n entries of a reference wave table.
|
||||
/// </summary>
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
private static double ComputeCorrelation(RingBuffer buf, double[] refTable, int n)
|
||||
{
|
||||
double sx = 0, sxx = 0, sxy = 0;
|
||||
double sy = 0, syy = 0;
|
||||
int newest = buf.Count - 1;
|
||||
|
||||
for (int k = 0; k < n; k++)
|
||||
{
|
||||
double x = buf[newest - k];
|
||||
double y = refTable[k];
|
||||
sx += x;
|
||||
sxx += x * x;
|
||||
sxy += x * y;
|
||||
sy += y;
|
||||
syy += y * y;
|
||||
}
|
||||
|
||||
double nd = n;
|
||||
double denomProd = (nd * sxx - sx * sx) * (nd * syy - sy * sy);
|
||||
if (denomProd <= 0.0)
|
||||
{
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
double r = (nd * sxy - sx * sy) / Math.Sqrt(denomProd);
|
||||
return Math.Clamp(r, -1.0, 1.0);
|
||||
}
|
||||
}
|
||||
@@ -129,6 +129,32 @@ function CCOR(source, period, threshold):
|
||||
| `angle` | monotonically increasing degrees | Phasor angle of detected cycle |
|
||||
| `state` | $\{-1, 0, +1\}$ | $-1$ = downtrend, $0$ = cycling, $+1$ = uptrend |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count | Cost (cycles) | Subtotal |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| ADD/SUB | 5×N | 1 | 5N |
|
||||
| MUL | 5×N | 3 | 15N |
|
||||
| DIV | 2 | 15 | 30 |
|
||||
| SQRT | 1 | 15 | 15 |
|
||||
| ATAN | 1 | 20 | 20 |
|
||||
| CMP | 3 | 1 | 3 |
|
||||
| CLAMP | 1 | 1 | 1 |
|
||||
| **Total** | **~10N+8** | — | **~20N+69** |
|
||||
|
||||
For default period $N = 20$: ~269 cycles per bar. The O(N) cost comes from dual Pearson correlation loops over the sliding window. Precomputed cos/sin tables eliminate per-bar trig calls.
|
||||
|
||||
### Quality Metrics
|
||||
|
||||
| Metric | Score | Notes |
|
||||
| :--- | :---: | :--- |
|
||||
| **Accuracy** | 9/10 | Pearson correlation bounded [-1, +1] by construction |
|
||||
| **Timeliness** | 8/10 | Full-window correlation; no recursive lag |
|
||||
| **Smoothness** | 7/10 | Monotonic angle constraint prevents backward jumps |
|
||||
| **Memory** | 8/10 | O(N) ring buffer + precomputed trig tables |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** "Correlation As A Cycle Indicator." *Technical Analysis of Stocks & Commodities*, June 2020.
|
||||
|
||||
@@ -0,0 +1,161 @@
|
||||
using TradingPlatform.BusinessLayer;
|
||||
|
||||
namespace QuanTAlib.Quantower.Tests;
|
||||
|
||||
public class CcycIndicatorTests
|
||||
{
|
||||
[Fact]
|
||||
public void CcycIndicator_Constructor_SetsDefaults()
|
||||
{
|
||||
var indicator = new CcycIndicator();
|
||||
|
||||
Assert.Equal(0.07, indicator.Alpha);
|
||||
Assert.Equal(SourceType.Close, indicator.Source);
|
||||
Assert.True(indicator.ShowColdValues);
|
||||
Assert.Equal("CCYC - Ehlers Cyber Cycle", indicator.Name);
|
||||
Assert.True(indicator.SeparateWindow);
|
||||
Assert.True(indicator.OnBackGround);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_MinHistoryDepths_EqualsZero()
|
||||
{
|
||||
var indicator = new CcycIndicator();
|
||||
|
||||
Assert.Equal(0, CcycIndicator.MinHistoryDepths);
|
||||
Assert.Equal(0, ((IWatchlistIndicator)indicator).MinHistoryDepths);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_ShortName_IncludesAlpha()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
|
||||
Assert.True(indicator.ShortName.Contains("CCYC", StringComparison.Ordinal));
|
||||
Assert.True(indicator.ShortName.Contains("0.07", StringComparison.Ordinal));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_Initialize_CreatesInternalCcyc()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
|
||||
// Initialize should not throw
|
||||
indicator.Initialize();
|
||||
|
||||
// After init, line series should exist (Cycle + Trigger)
|
||||
Assert.Equal(2, indicator.LinesSeries.Count);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
indicator.Initialize();
|
||||
|
||||
// Add historical data
|
||||
var now = DateTime.UtcNow;
|
||||
indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
|
||||
|
||||
// Process update
|
||||
var args = new UpdateArgs(UpdateReason.HistoricalBar);
|
||||
indicator.ProcessUpdate(args);
|
||||
|
||||
// Line series should have a value
|
||||
Assert.Equal(1, indicator.LinesSeries[0].Count);
|
||||
Assert.True(double.IsFinite(indicator.LinesSeries[0].GetValue(0)));
|
||||
Assert.Equal(1, indicator.LinesSeries[1].Count);
|
||||
Assert.True(double.IsFinite(indicator.LinesSeries[1].GetValue(0)));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_ProcessUpdate_NewBar_ComputesValue()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
indicator.Initialize();
|
||||
|
||||
var now = DateTime.UtcNow;
|
||||
indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
|
||||
indicator.HistoricalData.AddBar(now.AddMinutes(1), 102, 108, 100, 106);
|
||||
|
||||
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
|
||||
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewBar));
|
||||
|
||||
Assert.Equal(2, indicator.LinesSeries[0].Count);
|
||||
Assert.Equal(2, indicator.LinesSeries[1].Count);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_ProcessUpdate_NewTick_ProcessesWithoutError()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
indicator.Initialize();
|
||||
|
||||
// Should not throw an exception
|
||||
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewTick));
|
||||
|
||||
// Assert that the indicator still exists
|
||||
Assert.NotNull(indicator);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_SourceCodeLink_IsValid()
|
||||
{
|
||||
var indicator = new CcycIndicator();
|
||||
|
||||
Assert.False(string.IsNullOrEmpty(indicator.SourceCodeLink));
|
||||
Assert.Contains("Ccyc.Quantower.cs", indicator.SourceCodeLink, StringComparison.Ordinal);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_MultipleHistoricalBars_AllFinite()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.07 };
|
||||
indicator.Initialize();
|
||||
|
||||
var now = DateTime.UtcNow;
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
double price = 100 + 5 * Math.Sin(2 * Math.PI * i / 20.0);
|
||||
indicator.HistoricalData.AddBar(now.AddMinutes(i), price, price + 2, price - 2, price + 1);
|
||||
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
|
||||
}
|
||||
|
||||
Assert.Equal(20, indicator.LinesSeries[0].Count);
|
||||
Assert.Equal(20, indicator.LinesSeries[1].Count);
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
Assert.True(double.IsFinite(indicator.LinesSeries[0].GetValue(i)));
|
||||
Assert.True(double.IsFinite(indicator.LinesSeries[1].GetValue(i)));
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CcycIndicator_CustomAlpha_ReflectedInShortName()
|
||||
{
|
||||
var indicator = new CcycIndicator { Alpha = 0.15 };
|
||||
Assert.Contains("0.15", indicator.ShortName, StringComparison.Ordinal);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(SourceType.Open)]
|
||||
[InlineData(SourceType.High)]
|
||||
[InlineData(SourceType.Low)]
|
||||
[InlineData(SourceType.Close)]
|
||||
public void CcycIndicator_DifferentSources_DoNotThrow(SourceType sourceType)
|
||||
{
|
||||
var indicator = new CcycIndicator
|
||||
{
|
||||
Alpha = 0.07,
|
||||
Source = sourceType
|
||||
};
|
||||
indicator.Initialize();
|
||||
|
||||
var now = DateTime.UtcNow;
|
||||
indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
|
||||
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
|
||||
|
||||
Assert.Equal(1, indicator.LinesSeries[0].Count);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,69 @@
|
||||
using System.Drawing;
|
||||
using System.Runtime.CompilerServices;
|
||||
using TradingPlatform.BusinessLayer;
|
||||
|
||||
namespace QuanTAlib;
|
||||
|
||||
[SkipLocalsInit]
|
||||
public sealed class CcycIndicator : Indicator, IWatchlistIndicator
|
||||
{
|
||||
[InputParameter("Alpha", sortIndex: 1, 0.01, 0.99, 0.01, 2)]
|
||||
public double Alpha { get; set; } = 0.07;
|
||||
|
||||
[IndicatorExtensions.DataSourceInput]
|
||||
public SourceType Source { get; set; } = SourceType.Close;
|
||||
|
||||
[InputParameter("Show cold values", sortIndex: 21)]
|
||||
public bool ShowColdValues { get; set; } = true;
|
||||
|
||||
private Ccyc _ccyc = null!;
|
||||
private readonly LineSeries _cycleSeries;
|
||||
private readonly LineSeries _triggerSeries;
|
||||
private Func<IHistoryItem, double> _priceSelector = null!;
|
||||
|
||||
public static int MinHistoryDepths => 0;
|
||||
int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths;
|
||||
|
||||
public override string ShortName => $"CCYC ({Alpha:F2})";
|
||||
public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/cycles/ccyc/Ccyc.Quantower.cs";
|
||||
|
||||
public CcycIndicator()
|
||||
{
|
||||
OnBackGround = true;
|
||||
SeparateWindow = true;
|
||||
Name = "CCYC - Ehlers Cyber Cycle";
|
||||
Description = "Ehlers' Cyber Cycle isolates the dominant cycle component using a 4-tap FIR pre-smoother and a 2-pole high-pass IIR filter";
|
||||
|
||||
_cycleSeries = new LineSeries(name: "Cycle", color: IndicatorExtensions.Oscillators, width: 2, style: LineStyle.Solid);
|
||||
_triggerSeries = new LineSeries(name: "Trigger", color: Color.FromArgb(128, 128, 255), width: 1, style: LineStyle.Dash);
|
||||
AddLineSeries(_cycleSeries);
|
||||
AddLineSeries(_triggerSeries);
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
protected override void OnInit()
|
||||
{
|
||||
_ccyc = new Ccyc(Alpha);
|
||||
_priceSelector = Source.GetPriceSelector();
|
||||
base.OnInit();
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
protected override void OnUpdate(UpdateArgs args)
|
||||
{
|
||||
if (args.Reason != UpdateReason.NewBar && args.Reason != UpdateReason.HistoricalBar)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
var item = this.HistoricalData[this.Count - 1, SeekOriginHistory.Begin];
|
||||
double value = _priceSelector(item);
|
||||
var time = this.HistoricalData.Time();
|
||||
|
||||
var input = new TValue(time, value);
|
||||
TValue result = _ccyc.Update(input, args.IsNewBar());
|
||||
|
||||
_cycleSeries.SetValue(result.Value, _ccyc.IsHot, ShowColdValues);
|
||||
_triggerSeries.SetValue(_ccyc.Trigger, _ccyc.IsHot, ShowColdValues);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,517 @@
|
||||
using Xunit;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
public class CcycTests
|
||||
{
|
||||
private const long StartTime = 946_684_800_000_000_0L; // 2000-01-01 UTC in ticks
|
||||
private static readonly TimeSpan Step = TimeSpan.FromMinutes(1);
|
||||
private static readonly GBM TestData = new(startPrice: 100, mu: 0.05, sigma: 0.5, seed: 42);
|
||||
|
||||
private static TSeries GetTestSeries(int count = 500)
|
||||
{
|
||||
return TestData.Fetch(count, StartTime, Step).Close;
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// A) Constructor Defaults
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_DefaultAlpha_NoThrow()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
Assert.NotNull(ccyc);
|
||||
Assert.Equal(7, ccyc.WarmupPeriod);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_CustomAlpha_NoThrow()
|
||||
{
|
||||
var ccyc = new Ccyc(alpha: 0.15);
|
||||
Assert.NotNull(ccyc);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_AlphaZero_Throws()
|
||||
{
|
||||
Assert.Throws<ArgumentException>(() => new Ccyc(alpha: 0.0));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_AlphaOne_Throws()
|
||||
{
|
||||
Assert.Throws<ArgumentException>(() => new Ccyc(alpha: 1.0));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_AlphaNegative_Throws()
|
||||
{
|
||||
Assert.Throws<ArgumentException>(() => new Ccyc(alpha: -0.1));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_AlphaAboveOne_Throws()
|
||||
{
|
||||
Assert.Throws<ArgumentException>(() => new Ccyc(alpha: 1.5));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Name_ContainsAlpha()
|
||||
{
|
||||
var ccyc = new Ccyc(0.07);
|
||||
Assert.Contains("0.07", ccyc.Name, StringComparison.Ordinal);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_WarmupPeriod_IsSeven()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
Assert.Equal(7, ccyc.WarmupPeriod);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// B) Basic Calculation
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SingleValue_ReturnsFinite()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var result = ccyc.Update(new TValue(DateTime.UtcNow, 100));
|
||||
Assert.True(double.IsFinite(result.Value));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_MultipleValues_AllFinite()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries();
|
||||
var results = ccyc.Update(source);
|
||||
for (int i = 0; i < results.Count; i++)
|
||||
{
|
||||
Assert.True(double.IsFinite(results[i].Value), $"Non-finite at index {i}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_OutputNotZeroWhenHot()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries(200);
|
||||
var results = ccyc.Update(source);
|
||||
|
||||
// After warmup, at least some values should be non-zero
|
||||
bool anyNonZero = false;
|
||||
for (int i = ccyc.WarmupPeriod; i < results.Count; i++)
|
||||
{
|
||||
if (Math.Abs(results[i].Value) > 1e-10)
|
||||
{
|
||||
anyNonZero = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
Assert.True(anyNonZero, "All post-warmup values are zero");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_IsOscillator_ChangesSigns()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries(200);
|
||||
var results = ccyc.Update(source);
|
||||
|
||||
bool hasPositive = false;
|
||||
bool hasNegative = false;
|
||||
for (int i = ccyc.WarmupPeriod; i < results.Count; i++)
|
||||
{
|
||||
if (results[i].Value > 0)
|
||||
{
|
||||
hasPositive = true;
|
||||
}
|
||||
|
||||
if (results[i].Value < 0)
|
||||
{
|
||||
hasNegative = true;
|
||||
}
|
||||
|
||||
if (hasPositive && hasNegative)
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
Assert.True(hasPositive && hasNegative, "Cycle should oscillate around zero");
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// C) State Management / Bar Correction
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_BarCorrection_RestoresState()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries(50);
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
ccyc.Update(source[i], true);
|
||||
}
|
||||
|
||||
// Get state after all bars
|
||||
double lastVal = ccyc.Last.Value;
|
||||
|
||||
// Simulate bar correction: update with isNew=false
|
||||
var correctedTv = new TValue(DateTime.UtcNow, 999.0);
|
||||
ccyc.Update(correctedTv, false);
|
||||
_ = ccyc.Last.Value;
|
||||
|
||||
// Now redo with original last value using isNew=false
|
||||
ccyc.Update(source[^1], false);
|
||||
double restoredVal = ccyc.Last.Value;
|
||||
|
||||
Assert.Equal(lastVal, restoredVal, 10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Reset_ClearsState()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries(100);
|
||||
ccyc.Update(source);
|
||||
|
||||
// Verify hot
|
||||
Assert.True(ccyc.IsHot);
|
||||
|
||||
ccyc.Reset();
|
||||
|
||||
// After reset, should not be hot
|
||||
Assert.False(ccyc.IsHot);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// D) Warmup
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_NotHot_BeforeWarmup()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
for (int i = 0; i < 6; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), 100 + i), true);
|
||||
Assert.False(ccyc.IsHot, $"Should not be hot at bar {i + 1}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_IsHot_AtWarmup()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
for (int i = 0; i < 7; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), 100 + i), true);
|
||||
}
|
||||
Assert.True(ccyc.IsHot);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// E) Robustness
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_NaN_HandledGracefully()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), 100 + i), true);
|
||||
}
|
||||
_ = ccyc.Last.Value;
|
||||
|
||||
// Feed NaN
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(10), double.NaN), true);
|
||||
Assert.True(double.IsFinite(ccyc.Last.Value));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Infinity_HandledGracefully()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), 100 + i), true);
|
||||
}
|
||||
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(10), double.PositiveInfinity), true);
|
||||
Assert.True(double.IsFinite(ccyc.Last.Value));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_EmptyTSeries_ReturnsEmpty()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
_ = ccyc.Update(new TSeries());
|
||||
Assert.True(true); // No throw
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_LargeDataset_NoBlowup()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = TestData.Fetch(10000, StartTime, Step).Close;
|
||||
var results = ccyc.Update(source);
|
||||
for (int i = 0; i < results.Count; i++)
|
||||
{
|
||||
Assert.True(double.IsFinite(results[i].Value), $"Non-finite at {i}");
|
||||
}
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// F) Consistency (4-API-mode)
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_StreamingMatchesBatch()
|
||||
{
|
||||
var source = GetTestSeries(200);
|
||||
|
||||
// Streaming
|
||||
var ccycStreaming = new Ccyc();
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
ccycStreaming.Update(source[i], true);
|
||||
}
|
||||
|
||||
// Batch
|
||||
var batchResults = Ccyc.Batch(source);
|
||||
|
||||
Assert.Equal(source.Count, batchResults.Count);
|
||||
|
||||
// Compare last 50 values
|
||||
for (int i = source.Count - 50; i < source.Count; i++)
|
||||
{
|
||||
// Streaming processes all bars and streaming result is the last one
|
||||
// But for exact comparison, batch results should match streaming approach
|
||||
}
|
||||
|
||||
// The batch method creates a fresh indicator and calls Update(TSeries),
|
||||
// which processes sequentially — should match streaming exactly
|
||||
var ccyc2 = new Ccyc();
|
||||
var results2 = ccyc2.Update(source);
|
||||
Assert.Equal(batchResults.Count, results2.Count);
|
||||
for (int i = 0; i < batchResults.Count; i++)
|
||||
{
|
||||
Assert.Equal(batchResults[i].Value, results2[i].Value, 10);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SpanBatchMatchesTSeriesBatch()
|
||||
{
|
||||
var source = GetTestSeries(200);
|
||||
var batchResults = Ccyc.Batch(source);
|
||||
|
||||
// Span batch
|
||||
double[] values = new double[source.Count];
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
values[i] = source[i].Value;
|
||||
}
|
||||
|
||||
double[] output = new double[values.Length];
|
||||
Ccyc.Batch(values.AsSpan(), output.AsSpan());
|
||||
|
||||
// Compare
|
||||
for (int i = 0; i < batchResults.Count; i++)
|
||||
{
|
||||
Assert.Equal(batchResults[i].Value, output[i], 6);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_CalculateReturnsIndicator()
|
||||
{
|
||||
var source = GetTestSeries(100);
|
||||
var (results, indicator) = Ccyc.Calculate(source);
|
||||
|
||||
Assert.NotNull(indicator);
|
||||
Assert.Equal(source.Count, results.Count);
|
||||
Assert.True(indicator.IsHot);
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// G) Span API
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SpanBatch_LengthMismatch_Throws()
|
||||
{
|
||||
double[] src = [1, 2, 3];
|
||||
double[] outShort = new double[2];
|
||||
Assert.Throws<ArgumentException>(() => Ccyc.Batch(src.AsSpan(), outShort.AsSpan()));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SpanBatch_InvalidAlpha_Throws()
|
||||
{
|
||||
double[] src = [1, 2, 3];
|
||||
double[] output = new double[3];
|
||||
Assert.Throws<ArgumentException>(() => Ccyc.Batch(src.AsSpan(), output.AsSpan(), alpha: 0.0));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SpanBatch_EmptyInput_NoThrow()
|
||||
{
|
||||
double[] src = [];
|
||||
double[] output = [];
|
||||
Ccyc.Batch(src.AsSpan(), output.AsSpan());
|
||||
Assert.True(true); // No throw
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// H) Chainability
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Chainable_ReceivesValues()
|
||||
{
|
||||
var source = GetTestSeries(100);
|
||||
var ema = new Ema(10);
|
||||
var ccyc = new Ccyc(ema, alpha: 0.07);
|
||||
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
ema.Update(source[i], true);
|
||||
}
|
||||
|
||||
Assert.True(ccyc.IsHot, "Chained CCYC should become hot");
|
||||
Assert.True(double.IsFinite(ccyc.Last.Value));
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// I) CCYC-Specific
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Trigger_IsDelayedCycle()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
var source = GetTestSeries(50);
|
||||
|
||||
double prevCycle = 0;
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
ccyc.Update(source[i], true);
|
||||
if (i > 0)
|
||||
{
|
||||
// Trigger should equal previous cycle value
|
||||
Assert.Equal(prevCycle, ccyc.Trigger, 10);
|
||||
}
|
||||
prevCycle = ccyc.Last.Value;
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_ConstantInput_ConvergesToZero()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), 100.0), true);
|
||||
}
|
||||
|
||||
// High-pass filter on constant → 0
|
||||
Assert.True(Math.Abs(ccyc.Last.Value) < 1e-6, $"Expected near-zero, got {ccyc.Last.Value}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SineWave_DetectsCycle()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
int period = 20;
|
||||
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double value = 100 + 10 * Math.Sin(2 * Math.PI * i / period);
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), value), true);
|
||||
}
|
||||
|
||||
// On a sine wave, the cycle output should have significant amplitude
|
||||
Assert.True(Math.Abs(ccyc.Last.Value) > 0.01, "Cycle should detect sine wave");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_DifferentAlphas_ProduceDifferentOutputs()
|
||||
{
|
||||
var source = GetTestSeries(200);
|
||||
var resultsFast = Ccyc.Batch(source, alpha: 0.15);
|
||||
var resultsSlow = Ccyc.Batch(source, alpha: 0.03);
|
||||
|
||||
bool anyDiff = false;
|
||||
for (int i = 20; i < source.Count; i++)
|
||||
{
|
||||
if (Math.Abs(resultsFast[i].Value - resultsSlow[i].Value) > 1e-10)
|
||||
{
|
||||
anyDiff = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
Assert.True(anyDiff, "Different alphas should produce different outputs");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Prime_SetsState()
|
||||
{
|
||||
var ccyc = new Ccyc();
|
||||
double[] primeData = new double[50];
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
primeData[i] = 100 + 5 * Math.Sin(2 * Math.PI * i / 20.0);
|
||||
}
|
||||
|
||||
ccyc.Prime(primeData.AsSpan());
|
||||
Assert.True(ccyc.IsHot);
|
||||
Assert.True(double.IsFinite(ccyc.Last.Value));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Bootstrap_DiffersFromSteadyState()
|
||||
{
|
||||
// First 6 bars use bootstrap; bar 7+ use IIR
|
||||
var ccyc = new Ccyc();
|
||||
var values = new double[] { 100, 102, 99, 101, 103, 98, 100, 104, 97 };
|
||||
var results = new List<double>();
|
||||
|
||||
for (int i = 0; i < values.Length; i++)
|
||||
{
|
||||
var r = ccyc.Update(new TValue(DateTime.UtcNow.AddDays(i), values[i]), true);
|
||||
results.Add(r.Value);
|
||||
}
|
||||
|
||||
// All values should be finite
|
||||
foreach (var v in results)
|
||||
{
|
||||
Assert.True(double.IsFinite(v));
|
||||
}
|
||||
|
||||
// At bar 7 (index 6), we enter steady state — should still be finite
|
||||
Assert.True(double.IsFinite(results[6]));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_ResetAndReprocess_MatchesOriginal()
|
||||
{
|
||||
var source = GetTestSeries(100);
|
||||
var ccyc = new Ccyc();
|
||||
|
||||
var results1 = ccyc.Update(source);
|
||||
ccyc.Reset();
|
||||
var results2 = ccyc.Update(source);
|
||||
|
||||
Assert.Equal(results1.Count, results2.Count);
|
||||
for (int i = 0; i < results1.Count; i++)
|
||||
{
|
||||
Assert.Equal(results1[i].Value, results2[i].Value, 10);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,363 @@
|
||||
using Xunit;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// Validation tests for CCYC - Ehlers Cyber Cycle.
|
||||
/// Since CCYC is a proprietary Ehlers algorithm with no standard library implementations,
|
||||
/// these tests validate mathematical properties and internal consistency.
|
||||
/// </summary>
|
||||
public class CcycValidationTests
|
||||
{
|
||||
private const double Tolerance = 1e-9;
|
||||
private const long StartTime = 946_684_800_000_000_0L; // 2000-01-01 UTC in ticks
|
||||
private static readonly TimeSpan Step = TimeSpan.FromMinutes(1);
|
||||
|
||||
#region Mathematical Property Validation
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_ConstantInput_ConvergesToZero()
|
||||
{
|
||||
// High-pass filter on constant input must converge to zero
|
||||
var ccyc = new Ccyc(0.07);
|
||||
|
||||
for (int i = 0; i < 500; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), 100.0), true);
|
||||
}
|
||||
|
||||
Assert.True(Math.Abs(ccyc.Last.Value) < 1e-10,
|
||||
$"Constant input should produce zero output, got {ccyc.Last.Value}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_LinearTrend_ConvergesToZero()
|
||||
{
|
||||
// High-pass filter on linear trend should converge to zero (no oscillation)
|
||||
var ccyc = new Ccyc(0.07);
|
||||
|
||||
for (int i = 0; i < 500; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), 100.0 + 0.5 * i), true);
|
||||
}
|
||||
|
||||
// After warmup, should be near zero since linear trend has no cycle component
|
||||
Assert.True(Math.Abs(ccyc.Last.Value) < 1.0,
|
||||
$"Linear trend should produce near-zero output, got {ccyc.Last.Value}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SineWave_ProducesNonZeroOutput()
|
||||
{
|
||||
// A sine wave should produce non-zero cycle output
|
||||
var ccyc = new Ccyc(0.07);
|
||||
int period = 20;
|
||||
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
double value = 100 + 10 * Math.Sin(2 * Math.PI * i / period);
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), value), true);
|
||||
}
|
||||
|
||||
// Cycle output should be non-trivial
|
||||
Assert.True(Math.Abs(ccyc.Last.Value) > 0.01,
|
||||
$"Sine wave should produce non-zero cycle, got {ccyc.Last.Value}");
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(10)]
|
||||
[InlineData(20)]
|
||||
[InlineData(40)]
|
||||
public void Ccyc_SineWave_OutputOscillates(int period)
|
||||
{
|
||||
// Output should oscillate (have zero crossings) for sinusoidal input
|
||||
var ccyc = new Ccyc(0.07);
|
||||
int zeroCrossings = 0;
|
||||
double prev = 0;
|
||||
|
||||
for (int i = 0; i < 300; i++)
|
||||
{
|
||||
double value = 100 + 10 * Math.Sin(2 * Math.PI * i / period);
|
||||
var r = ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), value), true);
|
||||
|
||||
if (i > 20 && prev * r.Value < 0 && prev != 0)
|
||||
{
|
||||
zeroCrossings++;
|
||||
}
|
||||
prev = r.Value;
|
||||
}
|
||||
|
||||
Assert.True(zeroCrossings > 3,
|
||||
$"Output should oscillate with period={period}, got {zeroCrossings} zero crossings");
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(42)]
|
||||
[InlineData(123)]
|
||||
[InlineData(456)]
|
||||
public void Ccyc_DeterministicOutput(int seed)
|
||||
{
|
||||
// Same input should always produce same output
|
||||
var gbm = new GBM(seed: seed);
|
||||
var bars1 = gbm.Fetch(200, StartTime, Step);
|
||||
|
||||
gbm = new GBM(seed: seed);
|
||||
var bars2 = gbm.Fetch(200, StartTime, Step);
|
||||
|
||||
var ccyc1 = new Ccyc(0.07);
|
||||
var ccyc2 = new Ccyc(0.07);
|
||||
|
||||
for (int i = 0; i < bars1.Count; i++)
|
||||
{
|
||||
var result1 = ccyc1.Update(new TValue(bars1[i].Time, bars1[i].Close));
|
||||
var result2 = ccyc2.Update(new TValue(bars2[i].Time, bars2[i].Close));
|
||||
|
||||
Assert.Equal(result1.Value, result2.Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region High-Pass Filter Property Validation
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_HigherAlpha_ProducesDifferentOutput()
|
||||
{
|
||||
// Different alpha values should produce measurably different cycle outputs
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(200, StartTime, Step);
|
||||
|
||||
var ccycFast = new Ccyc(0.15);
|
||||
var ccycSlow = new Ccyc(0.03);
|
||||
|
||||
double diffEnergy = 0;
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
var tv = new TValue(bars[i].Time, bars[i].Close);
|
||||
var rFast = ccycFast.Update(tv);
|
||||
var rSlow = ccycSlow.Update(tv);
|
||||
|
||||
if (i > 20)
|
||||
{
|
||||
double d = rFast.Value - rSlow.Value;
|
||||
diffEnergy += d * d;
|
||||
}
|
||||
}
|
||||
|
||||
// Different alphas must produce different outputs
|
||||
Assert.True(diffEnergy > 1e-6,
|
||||
$"Different alphas should produce different outputs, diffEnergy={diffEnergy}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_FIR_SmoothsNoise()
|
||||
{
|
||||
// The 4-tap FIR smoother should reduce high-frequency noise
|
||||
// Test: random noise should produce smaller cycle than sine wave
|
||||
var ccycNoise = new Ccyc(0.07);
|
||||
var ccycSine = new Ccyc(0.07);
|
||||
|
||||
var rng = new Random(42);
|
||||
double sineEnergy = 0;
|
||||
|
||||
for (int i = 0; i < 300; i++)
|
||||
{
|
||||
double noiseVal = 100 + rng.NextDouble() * 10;
|
||||
ccycNoise.Update(new TValue(DateTime.UtcNow.AddMinutes(i), noiseVal), true);
|
||||
|
||||
double sineVal = 100 + 10 * Math.Sin(2 * Math.PI * i / 20.0);
|
||||
var sineResult = ccycSine.Update(new TValue(DateTime.UtcNow.AddMinutes(i), sineVal), true);
|
||||
|
||||
if (i > 30)
|
||||
{
|
||||
sineEnergy += sineResult.Value * sineResult.Value;
|
||||
}
|
||||
}
|
||||
|
||||
// Sine wave produces coherent cycle output
|
||||
Assert.True(sineEnergy > 0, "Sine wave should produce energy");
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Trigger Line Validation
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Trigger_IsOnePeriodDelayed()
|
||||
{
|
||||
var ccyc = new Ccyc(0.07);
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(100, StartTime, Step);
|
||||
|
||||
double prevCycle = 0;
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
ccyc.Update(new TValue(bars[i].Time, bars[i].Close));
|
||||
|
||||
if (i > 0)
|
||||
{
|
||||
Assert.Equal(prevCycle, ccyc.Trigger, Tolerance);
|
||||
}
|
||||
prevCycle = ccyc.Last.Value;
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_Trigger_CrossoverDetectable()
|
||||
{
|
||||
// On a sine wave, cycle and trigger should cross each other (sign change in diff)
|
||||
var ccyc = new Ccyc(0.07);
|
||||
int crossoverCount = 0;
|
||||
double prevDiff = 0;
|
||||
|
||||
for (int i = 0; i < 300; i++)
|
||||
{
|
||||
double value = 100 + 10 * Math.Sin(2 * Math.PI * i / 20.0);
|
||||
ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), value), true);
|
||||
|
||||
if (i > 20)
|
||||
{
|
||||
double diff = ccyc.Last.Value - ccyc.Trigger;
|
||||
if (prevDiff != 0 && diff * prevDiff < 0)
|
||||
{
|
||||
crossoverCount++;
|
||||
}
|
||||
prevDiff = diff;
|
||||
}
|
||||
}
|
||||
|
||||
Assert.True(crossoverCount > 0,
|
||||
"Cycle and trigger should cross on sine input");
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Consistency Validation
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_BatchMatchesStreaming_OnGBM()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
// Streaming
|
||||
var ccycStream = new Ccyc(0.07);
|
||||
var streamResults = new double[source.Count];
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
var r = ccycStream.Update(source[i], true);
|
||||
streamResults[i] = r.Value;
|
||||
}
|
||||
|
||||
// Batch
|
||||
var batchResults = Ccyc.Batch(source, 0.07);
|
||||
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
Assert.Equal(streamResults[i], batchResults[i].Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_SpanMatchesBatch_OnGBM()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
// TSeries batch
|
||||
var batchResults = Ccyc.Batch(source, 0.07);
|
||||
|
||||
// Span batch
|
||||
double[] values = new double[source.Count];
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
values[i] = source[i].Value;
|
||||
}
|
||||
|
||||
double[] output = new double[values.Length];
|
||||
Ccyc.Batch(values.AsSpan(), output.AsSpan(), 0.07);
|
||||
|
||||
for (int i = 0; i < source.Count; i++)
|
||||
{
|
||||
Assert.Equal(batchResults[i].Value, output[i], 6);
|
||||
}
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.03)]
|
||||
[InlineData(0.07)]
|
||||
[InlineData(0.15)]
|
||||
[InlineData(0.30)]
|
||||
public void Ccyc_AllAlphas_ProduceFiniteOutput(double alpha)
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(500, StartTime, Step);
|
||||
var ccyc = new Ccyc(alpha);
|
||||
|
||||
for (int i = 0; i < bars.Count; i++)
|
||||
{
|
||||
var r = ccyc.Update(new TValue(bars[i].Time, bars[i].Close));
|
||||
Assert.True(double.IsFinite(r.Value), $"Non-finite at bar {i} with alpha={alpha}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_ResetAndReprocess_Matches()
|
||||
{
|
||||
var gbm = new GBM(seed: 42);
|
||||
var bars = gbm.Fetch(200, StartTime, Step);
|
||||
var source = bars.Close;
|
||||
|
||||
var ccyc = new Ccyc(0.07);
|
||||
var results1 = ccyc.Update(source);
|
||||
|
||||
ccyc.Reset();
|
||||
var results2 = ccyc.Update(source);
|
||||
|
||||
Assert.Equal(results1.Count, results2.Count);
|
||||
for (int i = 0; i < results1.Count; i++)
|
||||
{
|
||||
Assert.Equal(results1[i].Value, results2[i].Value, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
#endregion
|
||||
|
||||
#region Bootstrap / Steady-State Transition
|
||||
|
||||
[Fact]
|
||||
public void Ccyc_BootstrapTransition_IsSmooth()
|
||||
{
|
||||
// The transition from bootstrap (bar < 7) to steady-state (bar >= 7) should be smooth
|
||||
var ccyc = new Ccyc(0.07);
|
||||
var results = new List<double>();
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
double value = 100 + 5 * Math.Sin(2 * Math.PI * i / 20.0);
|
||||
var r = ccyc.Update(new TValue(DateTime.UtcNow.AddMinutes(i), value), true);
|
||||
results.Add(r.Value);
|
||||
}
|
||||
|
||||
// Check that the transition at bar 7 (index 6) doesn't produce a huge jump
|
||||
double jump = Math.Abs(results[6] - results[5]);
|
||||
double avgMagnitude = 0;
|
||||
for (int i = 3; i < 10; i++)
|
||||
{
|
||||
avgMagnitude += Math.Abs(results[i]);
|
||||
}
|
||||
avgMagnitude /= 7;
|
||||
|
||||
// Jump should be within reasonable bounds (not 10x the average)
|
||||
if (avgMagnitude > 1e-10)
|
||||
{
|
||||
Assert.True(jump < 10 * avgMagnitude,
|
||||
$"Bootstrap transition jump={jump} too large vs avg magnitude={avgMagnitude}");
|
||||
}
|
||||
}
|
||||
|
||||
#endregion
|
||||
}
|
||||
@@ -0,0 +1,315 @@
|
||||
using System.Runtime.CompilerServices;
|
||||
using System.Runtime.InteropServices;
|
||||
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// CCYC: Ehlers Cyber Cycle — isolates the dominant cycle component from price data
|
||||
/// using a 4-tap FIR pre-smoother and a 2-pole high-pass IIR filter.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// From John F. Ehlers, "Cybernetic Analysis for Stocks and Futures" (Wiley, 2004), Chapter 4.
|
||||
///
|
||||
/// Algorithm:
|
||||
/// 1. 4-bar FIR smoother: smooth = (x + 2x[1] + 2x[2] + x[3]) / 6
|
||||
/// Zeros at periods 2 and 3 eliminate aliased noise.
|
||||
/// 2. 2-pole high-pass IIR:
|
||||
/// cycle = c_hp * (smooth - 2*smooth[1] + smooth[2]) + c_fb1*cycle[1] + c_fb2*cycle[2]
|
||||
/// where c_hp = (1-0.5*alpha)^2, c_fb1 = 2(1-alpha), c_fb2 = -(1-alpha)^2
|
||||
/// 3. Bootstrap (bars < 7): cycle = (x - 2x[1] + x[2]) / 4
|
||||
/// 4. Trigger = cycle[1] (one-bar delay for crossover signals)
|
||||
///
|
||||
/// Properties:
|
||||
/// - O(1) per bar: 6 multiplications, 5 additions, 2 state variables
|
||||
/// - Zero allocation in hot path
|
||||
/// - Alpha controls high-pass cutoff: lower = smoother/more lag
|
||||
/// - Trigger property provides the one-bar-delayed crossover line
|
||||
/// </remarks>
|
||||
[SkipLocalsInit]
|
||||
public sealed class Ccyc : AbstractBase
|
||||
{
|
||||
private readonly double _chp; // (1 - 0.5*alpha)^2
|
||||
private readonly double _cfb1; // 2*(1 - alpha)
|
||||
private readonly double _cfb2; // -(1 - alpha)^2
|
||||
|
||||
[StructLayout(LayoutKind.Auto)]
|
||||
private record struct State(
|
||||
double Price0, double Price1, double Price2, double Price3,
|
||||
double Smooth0, double Smooth1, double Smooth2,
|
||||
double Cycle0, double Cycle1, double Cycle2,
|
||||
int Count, double LastValid);
|
||||
|
||||
private State _s;
|
||||
private State _ps;
|
||||
|
||||
/// <summary>One-bar-delayed cycle value for crossover detection.</summary>
|
||||
public double Trigger { get; private set; }
|
||||
|
||||
/// <inheritdoc />
|
||||
public override bool IsHot => _s.Count >= WarmupPeriod;
|
||||
|
||||
/// <summary>
|
||||
/// Creates a new Ccyc indicator with the specified alpha (damping factor).
|
||||
/// </summary>
|
||||
/// <param name="alpha">Damping factor controlling high-pass cutoff. Must be in (0, 1) exclusive. Default 0.07.</param>
|
||||
public Ccyc(double alpha = 0.07)
|
||||
{
|
||||
if (alpha <= 0.0 || alpha >= 1.0)
|
||||
{
|
||||
throw new ArgumentException("Alpha must be between 0 and 1 (exclusive).", nameof(alpha));
|
||||
}
|
||||
|
||||
double halfAlpha = 1.0 - 0.5 * alpha;
|
||||
_chp = halfAlpha * halfAlpha;
|
||||
double oneMinusAlpha = 1.0 - alpha;
|
||||
_cfb1 = 2.0 * oneMinusAlpha;
|
||||
_cfb2 = -(oneMinusAlpha * oneMinusAlpha);
|
||||
|
||||
Name = $"Ccyc({alpha:F2})";
|
||||
WarmupPeriod = 7;
|
||||
_s = default;
|
||||
_ps = default;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Creates a new Ccyc indicator chained to a publisher source.
|
||||
/// </summary>
|
||||
/// <param name="source">Source indicator to subscribe to.</param>
|
||||
/// <param name="alpha">Damping factor controlling high-pass cutoff. Default 0.07.</param>
|
||||
public Ccyc(ITValuePublisher source, double alpha = 0.07) : this(alpha)
|
||||
{
|
||||
ArgumentNullException.ThrowIfNull(source);
|
||||
source.Pub += HandleInput;
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
private void HandleInput(object? sender, in TValueEventArgs e)
|
||||
{
|
||||
Update(e.Value, e.IsNew);
|
||||
}
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public override TValue Update(TValue input, bool isNew = true)
|
||||
{
|
||||
// State management: save/restore for bar correction
|
||||
if (isNew)
|
||||
{
|
||||
_ps = _s;
|
||||
}
|
||||
else
|
||||
{
|
||||
_s = _ps;
|
||||
}
|
||||
|
||||
var s = _s;
|
||||
|
||||
double price = input.Value;
|
||||
|
||||
// NaN/Infinity guard: substitute last valid value
|
||||
if (!double.IsFinite(price))
|
||||
{
|
||||
price = s.LastValid;
|
||||
}
|
||||
else
|
||||
{
|
||||
s = s with { LastValid = price };
|
||||
}
|
||||
|
||||
// Increment bar count
|
||||
int count = isNew ? s.Count + 1 : s.Count;
|
||||
|
||||
// Shift price history
|
||||
double price3 = s.Price2;
|
||||
double price2 = s.Price1;
|
||||
double price1 = s.Price0;
|
||||
double price0 = price;
|
||||
|
||||
// 4-tap FIR smoother: smooth = (x + 2*x1 + 2*x2 + x3) / 6
|
||||
double smooth = (price0 + 2.0 * price1 + 2.0 * price2 + price3) / 6.0;
|
||||
|
||||
// Shift smooth history
|
||||
double smooth2 = s.Smooth1;
|
||||
double smooth1 = s.Smooth0;
|
||||
double smooth0 = smooth;
|
||||
|
||||
double cycle;
|
||||
if (count < 7)
|
||||
{
|
||||
// Bootstrap: second-difference of raw price
|
||||
cycle = (price0 - 2.0 * price1 + price2) * 0.25;
|
||||
}
|
||||
else
|
||||
{
|
||||
// Steady-state: 2-pole high-pass IIR on smoothed input
|
||||
// cycle = c_hp * (smooth - 2*smooth1 + smooth2) + c_fb1*cycle1 + c_fb2*cycle2
|
||||
double diff = smooth0 - 2.0 * smooth1 + smooth2;
|
||||
cycle = Math.FusedMultiplyAdd(_chp, diff,
|
||||
Math.FusedMultiplyAdd(_cfb1, s.Cycle1, _cfb2 * s.Cycle2));
|
||||
}
|
||||
|
||||
// Guard: if IIR diverges to non-finite, substitute zero
|
||||
if (!double.IsFinite(cycle))
|
||||
{
|
||||
cycle = 0.0;
|
||||
}
|
||||
|
||||
// Shift cycle history
|
||||
double cycle2 = s.Cycle1;
|
||||
double cycle1 = s.Cycle0;
|
||||
double cycle0 = cycle;
|
||||
|
||||
// Trigger = previous cycle value
|
||||
Trigger = cycle1;
|
||||
|
||||
_s = new State(
|
||||
price0, price1, price2, price3,
|
||||
smooth0, smooth1, smooth2,
|
||||
cycle0, cycle1, cycle2,
|
||||
count, s.LastValid);
|
||||
|
||||
Last = new TValue(input.Time, cycle);
|
||||
PubEvent(Last, isNew);
|
||||
return Last;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Processes a full TSeries, returning the cycle component for each bar.
|
||||
/// </summary>
|
||||
public override TSeries Update(TSeries source)
|
||||
{
|
||||
if (source.Count == 0)
|
||||
{
|
||||
return [];
|
||||
}
|
||||
|
||||
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);
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
var result = Update(source[i]);
|
||||
vSpan[i] = result.Value;
|
||||
}
|
||||
source.Times.CopyTo(tSpan);
|
||||
|
||||
return new TSeries(t, v);
|
||||
}
|
||||
|
||||
/// <inheritdoc />
|
||||
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
|
||||
{
|
||||
foreach (double value in source)
|
||||
{
|
||||
Update(new TValue(DateTime.UtcNow, value));
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static batch: creates a Ccyc, processes source, returns output TSeries.
|
||||
/// </summary>
|
||||
public static TSeries Batch(TSeries source, double alpha = 0.07)
|
||||
{
|
||||
var ind = new Ccyc(alpha);
|
||||
return ind.Update(source);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static span-based batch: computes Cyber Cycle into output span.
|
||||
/// </summary>
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public static void Batch(ReadOnlySpan<double> source, Span<double> output, double alpha = 0.07)
|
||||
{
|
||||
if (source.Length != output.Length)
|
||||
{
|
||||
throw new ArgumentException("Source and output must have the same length.", nameof(output));
|
||||
}
|
||||
if (alpha <= 0.0 || alpha >= 1.0)
|
||||
{
|
||||
throw new ArgumentException("Alpha must be between 0 and 1 (exclusive).", nameof(alpha));
|
||||
}
|
||||
|
||||
int len = source.Length;
|
||||
if (len == 0)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
double halfAlpha = 1.0 - 0.5 * alpha;
|
||||
double chp = halfAlpha * halfAlpha;
|
||||
double oneMinusAlpha = 1.0 - alpha;
|
||||
double cfb1 = 2.0 * oneMinusAlpha;
|
||||
double cfb2 = -(oneMinusAlpha * oneMinusAlpha);
|
||||
|
||||
double price0 = 0, price1 = 0, price2 = 0, price3 = 0;
|
||||
double smooth0 = 0, smooth1 = 0, smooth2 = 0;
|
||||
double cycle0 = 0, cycle1 = 0, cycle2 = 0;
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
double val = source[i];
|
||||
if (!double.IsFinite(val))
|
||||
{
|
||||
val = price0; // last valid
|
||||
}
|
||||
|
||||
price3 = price2;
|
||||
price2 = price1;
|
||||
price1 = price0;
|
||||
price0 = val;
|
||||
|
||||
double smooth = (price0 + 2.0 * price1 + 2.0 * price2 + price3) / 6.0;
|
||||
smooth2 = smooth1;
|
||||
smooth1 = smooth0;
|
||||
smooth0 = smooth;
|
||||
|
||||
double cycle;
|
||||
int barNum = i + 1;
|
||||
if (barNum < 7)
|
||||
{
|
||||
cycle = (price0 - 2.0 * price1 + price2) * 0.25;
|
||||
}
|
||||
else
|
||||
{
|
||||
double diff = smooth0 - 2.0 * smooth1 + smooth2;
|
||||
cycle = Math.FusedMultiplyAdd(chp, diff,
|
||||
Math.FusedMultiplyAdd(cfb1, cycle1, cfb2 * cycle2));
|
||||
}
|
||||
|
||||
// Guard: if IIR diverges to non-finite, substitute zero
|
||||
if (!double.IsFinite(cycle))
|
||||
{
|
||||
cycle = 0.0;
|
||||
}
|
||||
|
||||
cycle2 = cycle1;
|
||||
cycle1 = cycle0;
|
||||
cycle0 = cycle;
|
||||
output[i] = cycle;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Static convenience method: returns (TSeries results, Ccyc indicator) for inspection.
|
||||
/// </summary>
|
||||
public static (TSeries Results, Ccyc Indicator) Calculate(TSeries source, double alpha = 0.07)
|
||||
{
|
||||
var ind = new Ccyc(alpha);
|
||||
var results = ind.Update(source);
|
||||
return (results, ind);
|
||||
}
|
||||
|
||||
/// <inheritdoc />
|
||||
public override void Reset()
|
||||
{
|
||||
_s = default;
|
||||
_ps = default;
|
||||
Last = default;
|
||||
Trigger = 0;
|
||||
}
|
||||
}
|
||||
@@ -133,6 +133,28 @@ function CCYC(source, alpha):
|
||||
- **Cycle crosses below trigger**: potential cycle peak (sell signal)
|
||||
- **Both near zero**: minimal cyclic energy; trend-dominated regime
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count | Cost (cycles) | Subtotal |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| ADD/SUB | 5 | 1 | 5 |
|
||||
| MUL | 6 | 3 | 18 |
|
||||
| FMA | 2 | 4 | 8 |
|
||||
| **Total** | **13** | — | **~31 cycles** |
|
||||
|
||||
O(1) per bar. The 4-tap FIR smoother uses 3 MUL + 2 ADD; the 2-pole IIR high-pass uses 2 FMA + 1 MUL. Bootstrap path (bars < 7) is even cheaper: 2 MUL + 1 SUB.
|
||||
|
||||
### Quality Metrics
|
||||
|
||||
| Metric | Score | Notes |
|
||||
| :--- | :---: | :--- |
|
||||
| **Accuracy** | 9/10 | IIR filter faithfully isolates cycle component |
|
||||
| **Timeliness** | 9/10 | Only 7-bar warmup; 2 state variables converge fast |
|
||||
| **Smoothness** | 8/10 | 4-tap FIR + 2-pole IIR suppresses aliased noise |
|
||||
| **Memory** | 10/10 | O(1) state: 12 scalar values in record struct |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cybernetic Analysis for Stocks and Futures*. Wiley, 2004. Chapter 4: "Cyber Cycle."
|
||||
|
||||
@@ -73,6 +73,28 @@ function CG(source, period):
|
||||
| Zero crossing down | Momentum shifting bearish |
|
||||
| Hanging at extremes | Strong trend in progress |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count | Cost (cycles) | Subtotal |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| ADD/SUB | 2×N | 1 | 2N |
|
||||
| MUL | N | 3 | 3N |
|
||||
| DIV | 1 | 15 | 15 |
|
||||
| **Total** | **~3N+1** | — | **~5N+15** |
|
||||
|
||||
The `RecalculateSums()` loop iterates over the full buffer each bar, making this O(N) per bar. For default $N = 10$: ~65 cycles. A periodic resync every 1000 bars maintains numerical stability.
|
||||
|
||||
### Quality Metrics
|
||||
|
||||
| Metric | Score | Notes |
|
||||
| :--- | :---: | :--- |
|
||||
| **Accuracy** | 10/10 | Exact weighted center-of-mass calculation |
|
||||
| **Timeliness** | 9/10 | Leads price movement by construction |
|
||||
| **Smoothness** | 7/10 | Raw oscillator; no internal smoothing |
|
||||
| **Memory** | 9/10 | O(N) ring buffer + 2 running sums |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cybernetic Analysis for Stocks and Futures*. Wiley, 2002.
|
||||
|
||||
@@ -87,6 +87,29 @@ function DSP(source, period):
|
||||
| Zero crossing | Cycle phase transition point |
|
||||
| Divergence from price | Cycle energy waning; potential trend exhaustion |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count | Cost (cycles) | Subtotal |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| ADD/SUB | 3 | 1 | 3 |
|
||||
| MUL | 4 | 3 | 12 |
|
||||
| FMA | 2 | 4 | 8 |
|
||||
| DIV | 2 | 15 | 30 |
|
||||
| **Total** | **11** | — | **~53 cycles** |
|
||||
|
||||
O(1) per bar. Two EMA updates (fast + slow) using FMA, plus warmup bias-correction divisions. After warmup completes, the DIV cost drops to zero, reducing steady-state to ~23 cycles.
|
||||
|
||||
### Quality Metrics
|
||||
|
||||
| Metric | Score | Notes |
|
||||
| :--- | :---: | :--- |
|
||||
| **Accuracy** | 9/10 | Bias-corrected EMAs eliminate warmup distortion |
|
||||
| **Timeliness** | 8/10 | Quarter-cycle EMA responds quickly; half-cycle provides reference |
|
||||
| **Smoothness** | 8/10 | Dual EMA differencing inherently smooths noise |
|
||||
| **Memory** | 10/10 | O(1) state: 6 scalar values in record struct |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cybernetic Analysis for Stocks and Futures*. Wiley, 2004.
|
||||
|
||||
@@ -110,6 +110,34 @@ function EACP(source, minPeriod, maxPeriod, enhance):
|
||||
| Rapidly changing value | Market transitioning between regimes |
|
||||
| Pegged at maxPeriod | No clear cycle detected; likely trending |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| HP filter (2-pole IIR) | ~8 | Pre-processing trend removal |
|
||||
| Super-Smoother (2-pole IIR) | ~6 | Anti-aliasing low-pass |
|
||||
| Pearson autocorrelation | ~5M | Mean, variance, cross-product over M samples per lag |
|
||||
| Autocorrelation loop (N lags) | ~5NM | Nested: N lags × M-sample windows |
|
||||
| DFT cosine transform | ~3NM | N periods × M cosine multiply-accumulates |
|
||||
| Cosine evaluation | NM | `Math.Cos` calls (expensive transcendental) |
|
||||
| Exponential smoothing | ~2N | FMA per period bin |
|
||||
| Cubic enhancement | ~2N | Two multiplies per bin (when enabled) |
|
||||
| AGC normalization | ~2N | Max scan + N divides |
|
||||
| Center-of-gravity | ~3N | Weighted sum + division |
|
||||
| **Total (default N=41, M=48)** | **~16,000** | **Dominated by autocorrelation + DFT** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | Partially: inner DFT cosine loops vectorizable; autocorrelation outer loop sequential |
|
||||
| Bottleneck | Pearson autocorrelation: N×M multiply-accumulates with data-dependent means |
|
||||
| Parallelism | DFT accumulation per period is independent; `Vector<double>` applicable to inner sums |
|
||||
| Memory | O(N) power arrays + O(M) circular buffer for SSF history |
|
||||
| Throughput | ~100-200× slower than O(1) IIR indicators; most expensive cycle indicator |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cycle Analytics for Traders*. Wiley, 2013.
|
||||
|
||||
@@ -111,6 +111,31 @@ function EBSW(source, hpLength, ssfLength):
|
||||
| Zero crossing down | Bearish phase transition |
|
||||
| Railing at $\pm 1$ | Strong directional move overwhelming cycle |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| High-pass filter | ~4 | 1 SUB + 1 MUL + 1 FMA |
|
||||
| Super-Smoother (2-pole IIR) | ~5 | 1 ADD + 2 FMA + 1 MUL |
|
||||
| Wave (3-bar average) | ~3 | 2 ADD + 1 MUL |
|
||||
| Power (3-bar RMS²) | ~5 | 3 MUL + 2 ADD |
|
||||
| SQRT normalization | ~4 | 1 SQRT + 1 DIV + 1 branch |
|
||||
| Clamp | ~2 | 2 comparisons |
|
||||
| State shift | ~4 | 4 register moves |
|
||||
| **Total** | **~27** | **O(1) fixed; no loops or allocations** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: HP and SSF are recursive IIR filters with sequential dependencies |
|
||||
| Bottleneck | `Math.Sqrt` in AGC normalization (~15 cycles per call) |
|
||||
| Parallelism | None: each bar depends on previous bar's filter state |
|
||||
| Memory | O(1): 6 scalar state variables + 2 previous filter values |
|
||||
| Throughput | Very fast; comparable to single EMA despite 3-stage pipeline |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cycle Analytics for Traders*. Wiley, 2013.
|
||||
|
||||
@@ -118,6 +118,34 @@ function HOMOD(source, minPeriod, maxPeriod):
|
||||
| Period drifting to maxPeriod | Trending market; cycle measurement unreliable |
|
||||
| Rapidly fluctuating period | Noisy or transitioning market regime |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| 4-bar WMA | ~5 | 3 MUL + 1 ADD + 1 DIV (precomputed as ×0.1) |
|
||||
| Hilbert FIR (detrender) | ~7 | 4-tap FIR: 4 MUL + 3 ADD |
|
||||
| Hilbert FIR (Q1) | ~7 | Same 4-tap structure on det buffer |
|
||||
| Hilbert FIR (jI, jQ) | ~14 | Two additional 4-tap Hilbert passes |
|
||||
| Phasor EMA (I2, Q2) | ~8 | 2 SUB/ADD + 4 FMA |
|
||||
| Homodyne mixing | ~8 | 4 MUL + 2 ADD/SUB per Re/Im |
|
||||
| Homodyne EMA smoothing | ~4 | 2 FMA for Re, Im |
|
||||
| ATAN2 | ~20 | `Math.Atan2` transcendental (~15-20 cycles) |
|
||||
| Period clamp + EMA | ~4 | 2 comparisons + 1 FMA |
|
||||
| Buffer management | ~8 | 4 circular buffer writes + index updates |
|
||||
| **Total** | **~85** | **O(1) fixed; dominated by ATAN2** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: cascaded IIR filters and Hilbert FIR with sequential state dependencies |
|
||||
| Bottleneck | `Math.Atan2` transcendental (~20 cycles); Hilbert FIR circular buffer lookups |
|
||||
| Parallelism | None: each bar depends on previous bar's I2, Q2, Re, Im state |
|
||||
| Memory | O(1): ~7-element circular buffers × 4 + 6 scalar EMA states (~300 bytes) |
|
||||
| Throughput | Moderate; ~3× slower than simple EMA due to multi-stage Hilbert pipeline |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Rocket Science for Traders*. Wiley, 2001.
|
||||
|
||||
@@ -102,6 +102,35 @@ function HT_DCPERIOD(source):
|
||||
| `period` $\approx 30$-$50$ | Long-cycle or trending; period drifting toward upper bound suggests trend |
|
||||
| Stable value | Regular cyclical market, ideal for oscillator-based strategies |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| 4-bar WMA | ~5 | 3 MUL + 1 ADD + 1 MUL(×0.1) |
|
||||
| Hilbert FIR (detrender) | ~7 | 4-tap FIR with period-adaptive coefficients |
|
||||
| Hilbert FIR (Q1) | ~7 | Same structure applied to detrender buffer |
|
||||
| Hilbert FIR (jI) | ~7 | Applied to I1 history buffer |
|
||||
| Hilbert FIR (jQ) | ~7 | Applied to Q1 history buffer |
|
||||
| Phasor EMA (I2, Q2) | ~8 | 2 SUB/ADD + 4 FMA |
|
||||
| Homodyne mixing + EMA | ~12 | 4 MUL + 2 ADD/SUB + 2 FMA |
|
||||
| ATAN | ~15 | `Math.Atan` transcendental |
|
||||
| Period division (2π/θ) | ~2 | 1 DIV |
|
||||
| Clamp + EMA smoothing | ~4 | 2 comparisons + 1 FMA |
|
||||
| Buffer management | ~10 | 4 circular buffer writes + index arithmetic |
|
||||
| **Total** | **~84** | **O(1) fixed; identical pipeline to HOMOD** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: full Hilbert cascade is sequentially dependent IIR chain |
|
||||
| Bottleneck | `Math.Atan` transcendental + 4 Hilbert FIR passes per bar |
|
||||
| Parallelism | None: each bar's phasor depends on previous bar's EMA state |
|
||||
| Memory | O(1): 4 circular buffers (7 elements each) + 6 scalar EMA states (~280 bytes) |
|
||||
| Throughput | Moderate; ~3× slower than simple EMA; matches HOMOD performance |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Rocket Science for Traders*. Wiley, 2001.
|
||||
|
||||
@@ -99,6 +99,30 @@ function HT_DCPHASE(source):
|
||||
| Rapid phase change | Potential reversal imminent |
|
||||
| Discontinuity ($315° \to -45°$) | One cycle complete, new cycle begins |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Hilbert cascade (WMA + 4×FIR + phasor + homodyne) | ~84 | Same as HT_DCPERIOD pipeline |
|
||||
| DFT sin/cos evaluation | 2P | `Math.Sin` + `Math.Cos` per iteration (~15-20 cycles each) |
|
||||
| DFT multiply-accumulate | 2P | realPart/imagPart FMA per iteration |
|
||||
| ATAN phase extraction | ~15 | `Math.Atan` transcendental |
|
||||
| Phase adjustment + wrapping | ~5 | 2 ADD + 2 comparisons + 1 conditional ADD |
|
||||
| **Total (P=20 typical)** | **~184** | **O(P) dominated by DFT sin/cos loop** |
|
||||
| **Total (P=50 worst case)** | **~384** | **Upper bound when period near maximum** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | Partially: DFT inner loop sin/cos accumulation is vectorizable with precomputed twiddle factors |
|
||||
| Bottleneck | DFT loop: P transcendental calls per bar; Hilbert cascade is sequential |
|
||||
| Parallelism | DFT accumulation independent per frequency bin; `Vector<double>` applicable to sin/cos MACs |
|
||||
| Memory | O(P): ~50-element smooth price circular buffer + Hilbert state (~1.2 KB) |
|
||||
| Throughput | ~2-4× slower than O(1) Hilbert-only indicators (HOMOD, HT_DCPERIOD) due to variable-length DFT |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Rocket Science for Traders*. Wiley, 2001.
|
||||
|
||||
@@ -89,6 +89,31 @@ function HT_PHASOR(source):
|
||||
| `InPhase` | unbounded | Cycle component aligned with price |
|
||||
| `Quadrature` | unbounded | Rate of change (velocity) of cycle |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| 4-bar WMA | ~5 | 3 MUL + 1 ADD + 1 MUL(×0.1) |
|
||||
| Hilbert FIR (detrender) | ~7 | 4-tap FIR: 4 MUL + 3 ADD |
|
||||
| Hilbert FIR (Q1) | ~7 | Same 4-tap structure on det buffer |
|
||||
| Hilbert FIR (jI) | ~7 | 4-tap on I1 history |
|
||||
| Hilbert FIR (jQ) | ~7 | 4-tap on Q1 history |
|
||||
| Phasor EMA (I2, Q2) | ~8 | 2 SUB/ADD + 4 FMA |
|
||||
| Buffer management | ~10 | 4 circular buffer writes + index arithmetic |
|
||||
| **Total** | **~51** | **O(1) fixed; no transcendentals (no period/phase extraction)** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: cascaded IIR EMA smoothing creates sequential dependencies |
|
||||
| Bottleneck | Circular buffer indexed lookups for 4 Hilbert FIR passes |
|
||||
| Parallelism | None: each bar's phasor depends on previous bar's EMA state |
|
||||
| Memory | O(1): 4 circular buffers (7 elements each) + 2 scalar EMA states (~240 bytes) |
|
||||
| Throughput | Fastest of the HT family; no transcendental calls (no ATAN/SIN/COS) |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Rocket Science for Traders*. Wiley, 2001.
|
||||
|
||||
@@ -92,6 +92,31 @@ function HT_SINE(source):
|
||||
| `Sine` | $[-1, +1]$ | Current cycle phase position |
|
||||
| `LeadSine` | $[-1, +1]$ | 45° advanced cycle phase (early warning) |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Hilbert cascade (WMA + 4×FIR + phasor + homodyne) | ~84 | Same pipeline as HT_DCPERIOD |
|
||||
| DFT sin/cos accumulation | ~4P | P sin + P cos evaluations + 2P FMA |
|
||||
| Phase ATAN extraction | ~15 | `Math.Atan` transcendental |
|
||||
| Phase adjustment + unwrapping | ~5 | Quadrant correction + wrapping |
|
||||
| Final SIN (sine) | ~15 | `Math.Sin` transcendental |
|
||||
| Final SIN (leadSine) | ~15 | `Math.Sin(φ + π/4)` transcendental |
|
||||
| **Total (P=20 typical)** | **~214** | **O(P) dominated by DFT + 3 transcendentals** |
|
||||
| **Total (P=50 worst case)** | **~454** | **Heaviest of the HT family** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | Partially: DFT inner loop vectorizable; final sin calls are scalar |
|
||||
| Bottleneck | DFT loop (P sin/cos calls) + 3 final transcendentals per bar |
|
||||
| Parallelism | DFT accumulation independent; dual sin output trivially parallel |
|
||||
| Memory | O(P): ~50-element smooth price buffer + ~44-element det buffer + Hilbert state (~1.3 KB) |
|
||||
| Throughput | Slowest HT variant; ~2.5× HT_DCPHASE due to extra sin evaluations |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Rocket Science for Traders*. Wiley, 2001.
|
||||
|
||||
@@ -114,6 +114,31 @@ function LUNAR(timestamp):
|
||||
| $k \approx 0.5$ (falling) | Last Quarter |
|
||||
| $k$ falling, $< 0.5$ | Waning Crescent |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Julian date conversion | ~4 | 1 DIV + 1 ADD + 1 SUB + 1 DIV |
|
||||
| Horner polynomial (5 elements) | ~25 | 5 FMA chains (3-4 deep each) |
|
||||
| Modular reduction (5 elements) | ~5 | 5 `mod 360` operations |
|
||||
| SIN evaluations (perturbations) | ~48 | 6 `Math.Sin` calls (~8 cycles each) |
|
||||
| Perturbation sum | ~11 | 6 MUL + 5 ADD |
|
||||
| Solar longitude (Horner + 2 SIN) | ~20 | 2 FMA + 2 `Math.Sin` + 2 FMA |
|
||||
| Phase angle + COS | ~10 | 1 SUB + 1 `Math.Cos` + 1 SUB + 1 MUL |
|
||||
| **Total** | **~123** | **O(1) pure arithmetic; no state, no buffers** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | Yes: fully stateless; each timestamp independent; `Vector<double>` applicable to Horner chains |
|
||||
| Bottleneck | 8 transcendental calls (6 SIN + 1 SIN + 1 COS); ~64 cycles total |
|
||||
| Parallelism | Full: no inter-bar dependencies; ideal for `Vector<double>` batch processing |
|
||||
| Memory | O(0): zero state; pure function of timestamp |
|
||||
| Throughput | Very fast; bulk evaluation benefits from SIMD Horner + vectorized sin/cos |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Meeus, J.** *Astronomical Algorithms*. 2nd ed., Willmann-Bell, 1998.
|
||||
|
||||
@@ -121,6 +121,31 @@ function SINE(source, hpPeriod, ssfPeriod):
|
||||
| Zero crossing down | Bearish phase transition |
|
||||
| Erratic output | Strong trend overwhelming cycle extraction |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| High-pass filter | ~4 | 1 SUB + 1 MUL + 1 FMA |
|
||||
| Super-Smoother (2-pole IIR) | ~5 | 1 ADD + 2 FMA + 1 MUL |
|
||||
| Hilbert FIR (quadrature) | ~7 | 4-tap FIR: 4 MUL + 3 ADD |
|
||||
| I² + Q² (power) | ~3 | 2 MUL + 1 ADD |
|
||||
| SQRT + normalization | ~4 | 1 SQRT + 1 DIV + 1 branch |
|
||||
| Buffer management | ~3 | 1 circular buffer write + index update |
|
||||
| State shift | ~4 | 4 register moves |
|
||||
| **Total** | **~30** | **O(1) fixed; single SQRT is only transcendental** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: HP and SSF are recursive IIR with sequential state dependencies |
|
||||
| Bottleneck | `Math.Sqrt` in power normalization (~15 cycles); rest is pure arithmetic |
|
||||
| Parallelism | None: each bar's HP/SSF output depends on previous bar |
|
||||
| Memory | O(1): 8-element ring buffer + 4 scalar state variables (~96 bytes) |
|
||||
| Throughput | Very fast; slightly faster than EBSW (no 3-bar averaging, no clamp) |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cybernetic Analysis for Stocks and Futures*. Wiley, 2004.
|
||||
|
||||
@@ -107,6 +107,31 @@ function SOLAR(timestamp):
|
||||
| $Solar = 0$ (falling) | Autumn equinox crossing |
|
||||
| Southern Hemisphere | Negate the output |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Julian date conversion | ~4 | 1 DIV + 1 ADD + 1 SUB + 1 DIV |
|
||||
| Horner polynomial (L0) | ~5 | 2 FMA + 1 mod |
|
||||
| Horner polynomial (M) | ~5 | 2 FMA + 1 mod |
|
||||
| SIN evaluations (equation of center) | ~24 | 3 `Math.Sin` calls (~8 cycles each) |
|
||||
| Equation of center arithmetic | ~8 | 3 FMA chains + 2 ADD |
|
||||
| True longitude addition | ~1 | 1 ADD |
|
||||
| Final SIN (seasonal index) | ~10 | 1 degree-to-radian MUL + 1 `Math.Sin` |
|
||||
| **Total** | **~57** | **O(1) pure arithmetic; simpler than LUNAR** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | Yes: fully stateless; each timestamp independent; `Vector<double>` applicable |
|
||||
| Bottleneck | 4 transcendental calls (3 SIN for equation of center + 1 final SIN); ~32 cycles |
|
||||
| Parallelism | Full: no inter-bar dependencies; ideal for `Vector<double>` batch processing |
|
||||
| Memory | O(0): zero state; pure function of timestamp |
|
||||
| Throughput | Fastest cycle indicator; ~2× faster than LUNAR (fewer perturbation terms) |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Meeus, J.** *Astronomical Algorithms*. 2nd ed., Willmann-Bell, 1998.
|
||||
|
||||
@@ -118,6 +118,29 @@ function SSFDSP(source, period):
|
||||
| Divergence with price | Cycle energy waning; trend exhaustion |
|
||||
| Amplitude shrinking | Cycle losing dominance; transition to trend |
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Input averaging | ~2 | 1 ADD + 1 MUL(×0.5) |
|
||||
| Fast SSF (2-pole IIR) | ~5 | 1 MUL(c1f) + 2 FMA(c2f, c3f) |
|
||||
| Slow SSF (2-pole IIR) | ~5 | 1 MUL(c1s) + 2 FMA(c2s, c3s) |
|
||||
| Subtraction (output) | ~1 | 1 SUB |
|
||||
| State shift | ~5 | 5 register moves |
|
||||
| **Total** | **~18** | **O(1) fixed; pure FMA arithmetic, zero transcendentals** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: both SSF filters are recursive 2-pole IIR with sequential state dependencies |
|
||||
| Bottleneck | None significant; pure multiply-accumulate with precomputed coefficients |
|
||||
| Parallelism | None: each bar depends on two previous bars' filter state |
|
||||
| Memory | O(1): 4 scalar filter states + 1 previous price (~40 bytes) |
|
||||
| Throughput | Among fastest cycle indicators; comparable to dual-EMA DSP; no transcendentals at runtime |
|
||||
|
||||
## Resources
|
||||
|
||||
- **Ehlers, J.F.** *Cybernetic Analysis for Stocks and Futures*. Wiley, 2004.
|
||||
|
||||
@@ -142,6 +142,37 @@ Setting $k \approx f/2$ targets the half-cycle of the MACD's dominant frequency,
|
||||
|
||||
The recursive EMA dependencies and sequential min/max ring buffer updates prevent SIMD vectorization of the streaming path. The `Calculate(Span)` path can parallelize independent MACD computations but must serialize the double-Stochastic pipeline.
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
| Operation | Count per bar | Notes |
|
||||
|-----------|--------------|-------|
|
||||
| Fast EMA | ~3 | 1 FMA + 1 MUL |
|
||||
| Slow EMA | ~3 | 1 FMA + 1 MUL |
|
||||
| MACD subtraction | ~1 | 1 SUB |
|
||||
| Ring buffer add (MACD) | ~1 | 1 write + index update |
|
||||
| Min/Max scan (MACD buf) | ~2k | Linear scan of k elements × 2 (min + max) |
|
||||
| First Stochastic (%K₁) | ~4 | 1 SUB + 1 DIV + 1 MUL + 1 branch |
|
||||
| First EMA smoothing (%D₁) | ~3 | 1 FMA + 1 MUL |
|
||||
| Ring buffer add (%D₁) | ~1 | 1 write + index update |
|
||||
| Min/Max scan (%D₁ buf) | ~2k | Linear scan of k elements × 2 |
|
||||
| Second Stochastic (%K₂) | ~4 | 1 SUB + 1 DIV + 1 MUL + 1 branch |
|
||||
| Final smoothing (EMA) | ~3 | 1 FMA + 1 MUL |
|
||||
| Clamp | ~2 | 2 comparisons |
|
||||
| **Total (k=10 default)** | **~65** | **O(k) dominated by dual min/max scans** |
|
||||
| **Total (k=50 worst)** | **~225** | **Linear growth with kPeriod** |
|
||||
|
||||
### Batch Mode (SIMD Analysis)
|
||||
|
||||
| Aspect | Assessment |
|
||||
|--------|------------|
|
||||
| SIMD vectorizable | No: recursive EMAs + sequential ring buffer min/max prevent vectorization |
|
||||
| Bottleneck | Dual min/max scans over ring buffers (2×k comparisons per bar) |
|
||||
| Parallelism | MACD EMA computation is independent of Stochastic pipeline but still sequential IIR |
|
||||
| Memory | O(k): two ring buffers of kPeriod doubles + 6 scalar EMA states (~200 bytes at k=10) |
|
||||
| Throughput | Moderate; faster than HT family (no transcendentals) but slower than pure IIR (min/max scans) |
|
||||
|
||||
## Resources
|
||||
|
||||
- Schaff, D. — "Schaff Trend Cycle" (currency trading methodology, 1990s)
|
||||
|
||||
Reference in New Issue
Block a user