mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-19 11:08:05 +00:00
Miha Kralj
b3a64f18fa
- Added Ztest class to compute the one-sample t-statistic using sample standard deviation with Bessel correction. - Implemented validation tests for Ztest to ensure accuracy against manual calculations and PineScript. - Updated documentation for Ztest, detailing its mathematical foundation, performance profile, and common pitfalls. - Adjusted NDepend badges to reflect changes in code metrics after implementation. - Updated missing indicators report to reflect the completion of statistical indicators, including ZTEST.
108 lines
3.2 KiB
C#
108 lines
3.2 KiB
C#
namespace QuanTAlib.Validation;
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public sealed class SpearmanValidationTests
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{
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[Fact]
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public void PerfectLinear_RhoEqualsOne()
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{
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// Perfect linear relationship: Y = 2X + 5
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// Ranks of X and Y are identical → ρ = 1.0
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var s = new Spearman(10);
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for (int i = 1; i <= 10; i++)
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{
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s.Update((double)i, 2.0 * i + 5.0, isNew: true);
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}
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Assert.Equal(1.0, s.Last.Value, 1e-10);
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}
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[Fact]
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public void PerfectNonlinearMonotonic_RhoEqualsOne()
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{
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// Perfect monotonic but nonlinear: Y = X³
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// Ranks are identical → ρ = 1.0 (Spearman captures monotonic, not just linear)
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var s = new Spearman(10);
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for (int i = 1; i <= 10; i++)
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{
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double x = i;
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s.Update(x, x * x * x, isNew: true);
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}
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Assert.Equal(1.0, s.Last.Value, 1e-10);
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}
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[Fact]
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public void BatchAndStreaming_ProduceSameResults()
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{
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var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 777);
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var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 888);
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var seriesX = new TSeries();
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var seriesY = new TSeries();
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for (int i = 0; i < 50; i++)
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{
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var barX = gbmX.Next();
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var barY = gbmY.Next();
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seriesX.Add(new TValue(barX.Time, barX.Close));
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seriesY.Add(new TValue(barY.Time, barY.Close));
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}
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TSeries batch = Spearman.Batch(seriesX, seriesY, 10);
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var streaming = new Spearman(10);
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for (int i = 0; i < 50; i++)
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{
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streaming.Update(seriesX[i], seriesY[i], isNew: true);
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Assert.Equal(streaming.Last.Value, batch[i].Value, 1e-10);
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}
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}
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[Fact]
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public void KnownRanks_NoTies_MatchesSimplifiedFormula()
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{
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// Without ties: ρ = 1 - 6·Σd²/(n(n²-1))
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// X = [10,20,30,40,50], Y = [50,30,10,40,20]
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// Ranks X = [1,2,3,4,5], Ranks Y = [5,3,1,4,2]
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// d = [-4,-1,2,0,3], d² = [16,1,4,0,9], Σd² = 30
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// ρ = 1 - 6*30 / (5*24) = 1 - 180/120 = 1 - 1.5 = -0.5
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var s = new Spearman(5);
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double[] x = [10, 20, 30, 40, 50];
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double[] y = [50, 30, 10, 40, 20];
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for (int i = 0; i < 5; i++)
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{
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s.Update(x[i], y[i], isNew: true);
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}
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Assert.Equal(-0.5, s.Last.Value, 1e-10);
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}
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[Fact]
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public void SpearmanVsKendall_BothDetectMonotonic()
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{
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// Both Spearman and Kendall should be +1 for perfectly concordant data
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var spearman = new Spearman(5);
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var kendall = new Kendall(5);
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for (int i = 1; i <= 5; i++)
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{
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spearman.Update((double)i, (double)i, isNew: true);
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kendall.Update(new TValue(DateTime.UtcNow, i), new TValue(DateTime.UtcNow, i), isNew: true);
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}
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Assert.Equal(1.0, spearman.Last.Value, 1e-10);
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Assert.Equal(1.0, kendall.Last.Value, 1e-10);
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}
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[Fact]
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public void BoundaryValues_AllTied()
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{
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// All X values identical → zero variance in ranks → ρ = 0
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var s = new Spearman(5);
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for (int i = 0; i < 5; i++)
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{
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s.Update(42.0, (double)(i + 1), isNew: true);
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}
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Assert.Equal(0.0, s.Last.Value, 1e-10);
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}
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}
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