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https://github.com/mihakralj/QuanTAlib.git
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docs: remove C# Implementation Considerations sections, clean up temp scripts, reorganize test files
- Remove 'C# Implementation Considerations' sections from 34 indicator .md files - Delete 29 temp PowerShell scripts (_fix_mojibake.ps1, _hex_scan.ps1, etc.) - Move test files into tests/ subdirectories for consistent project structure - Add trader-focused bullet points to indicator documentation
This commit is contained in:
Miha Kralj
@@ -0,0 +1,468 @@
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namespace QuanTAlib.Tests;
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public class SpearmanTests
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{
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[Fact]
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public void Constructor_PeriodOne_Throws()
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{
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Assert.Throws<ArgumentException>(() => new Spearman(1));
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}
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[Fact]
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public void Constructor_PeriodZero_Throws()
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{
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Assert.Throws<ArgumentException>(() => new Spearman(0));
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}
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[Fact]
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public void Constructor_ValidPeriod_SetsName()
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{
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var s = new Spearman(10);
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Assert.Equal("Spearman(10)", s.Name);
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}
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[Fact]
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public void SingleInput_Update_ThrowsNotSupported()
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{
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var s = new Spearman(5);
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Assert.Throws<NotSupportedException>(() => s.Update(new TValue(DateTime.UtcNow, 1.0)));
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}
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[Fact]
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public void SingleInput_UpdateTSeries_ThrowsNotSupported()
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{
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var s = new Spearman(5);
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var ts = new TSeries();
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Assert.Throws<NotSupportedException>(() => s.Update(ts));
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}
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[Fact]
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public void Prime_ThrowsNotSupported()
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{
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var s = new Spearman(5);
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Assert.Throws<NotSupportedException>(() => s.Prime(stackalloc double[] { 1, 2, 3 }));
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}
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[Fact]
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public void PerfectConcordance_ReturnsOne()
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{
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var s = new Spearman(5);
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for (int i = 1; i <= 5; i++)
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{
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s.Update((double)i, (double)i, 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 PerfectDiscordance_ReturnsMinusOne()
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{
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var s = new Spearman(5);
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for (int i = 1; i <= 5; i++)
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{
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s.Update((double)i, 6.0 - i, 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 KnownSequence_MatchesExpected()
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{
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// X = [1,2,3,4,5], Y = [1,3,2,5,4]
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// Ranks X = [1,2,3,4,5], Ranks Y = [1,3,2,5,4]
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// d = [0,-1,1,-1,1], d² = [0,1,1,1,1], Σd² = 4
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// ρ = 1 - 6*4/(5*24) = 1 - 24/120 = 1 - 0.2 = 0.8
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var s = new Spearman(5);
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double[] x = [1, 2, 3, 4, 5];
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double[] y = [1, 3, 2, 5, 4];
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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(0.8, s.Last.Value, 1e-10);
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}
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[Fact]
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public void Symmetry_RhoXY_EqualsRhoYX()
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{
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var s1 = new Spearman(5);
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var s2 = new Spearman(5);
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double[] x = [10, 20, 15, 30, 25];
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double[] y = [5, 15, 10, 25, 20];
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for (int i = 0; i < 5; i++)
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{
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s1.Update(x[i], y[i], isNew: true);
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s2.Update(y[i], x[i], isNew: true);
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}
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Assert.Equal(s1.Last.Value, s2.Last.Value, 1e-10);
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}
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[Fact]
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public void Antisymmetry_RhoXNegY_EqualsNegRhoXY()
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{
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var s1 = new Spearman(5);
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var s2 = new Spearman(5);
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double[] x = [10, 20, 15, 30, 25];
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double[] y = [5, 15, 10, 25, 20];
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for (int i = 0; i < 5; i++)
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{
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s1.Update(x[i], y[i], isNew: true);
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s2.Update(x[i], -y[i], isNew: true);
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}
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Assert.Equal(-s1.Last.Value, s2.Last.Value, 1e-10);
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}
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[Fact]
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public void ConstantSeries_ReturnsZero()
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{
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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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[Fact]
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public void BothConstant_ReturnsZero()
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{
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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, 42.0, 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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[Fact]
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public void TiedValues_HandledCorrectly()
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{
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// X = [1, 2, 2, 4, 5], Y = [5, 4, 3, 2, 1]
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// Ranks X = [1, 2.5, 2.5, 4, 5] (ties → average rank)
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// Ranks Y = [5, 4, 3, 2, 1]
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// Pearson on these ranks → negative correlation
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var s = new Spearman(5);
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double[] x = [1, 2, 2, 4, 5];
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double[] y = [5, 4, 3, 2, 1];
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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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// Should be close to -1 (strong negative monotonic relationship)
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Assert.True(s.Last.Value < -0.9);
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}
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[Fact]
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public void IsHot_RequiresAtLeastTwo()
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{
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var s = new Spearman(5);
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Assert.False(s.IsHot);
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s.Update(1.0, 2.0, isNew: true);
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Assert.False(s.IsHot);
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s.Update(2.0, 3.0, isNew: true);
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Assert.True(s.IsHot);
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}
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[Fact]
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public void SingleValue_ReturnsNaN()
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{
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var s = new Spearman(5);
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s.Update(1.0, 2.0, isNew: true);
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Assert.True(double.IsNaN(s.Last.Value));
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}
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[Fact]
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public void IsNewFalse_CorrectsBars()
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{
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var s = new Spearman(5);
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double[] x = [1, 2, 3, 4, 5];
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double[] y = [2, 4, 6, 8, 10];
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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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double original = s.Last.Value;
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// Correct with different value — break rank correlation
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s.Update(100.0, 1.0, isNew: false);
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double corrected = s.Last.Value;
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Assert.NotEqual(original, corrected);
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// Correct back to original
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s.Update(x[4], y[4], isNew: false);
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double restored = s.Last.Value;
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Assert.Equal(original, restored, 1e-10);
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}
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[Fact]
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public void NaN_SubstitutesLastValid()
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{
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var s = new Spearman(5);
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for (int i = 1; i <= 4; i++)
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{
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s.Update((double)i, (double)i, isNew: true);
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}
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// Feed NaN — should use last valid value
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s.Update(double.NaN, double.NaN, isNew: true);
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Assert.True(double.IsFinite(s.Last.Value));
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}
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[Fact]
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public void Infinity_SubstitutesLastValid()
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{
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var s = new Spearman(5);
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for (int i = 1; i <= 4; i++)
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{
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s.Update((double)i, (double)i, isNew: true);
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}
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s.Update(double.PositiveInfinity, double.NegativeInfinity, isNew: true);
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Assert.True(double.IsFinite(s.Last.Value));
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}
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[Fact]
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public void Reset_ClearsState()
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{
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var s = new Spearman(5);
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for (int i = 1; i <= 5; i++)
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{
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s.Update((double)i, (double)i, isNew: true);
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}
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Assert.True(s.IsHot);
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s.Reset();
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Assert.False(s.IsHot);
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Assert.Equal(default, s.Last);
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}
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[Fact]
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public void SlidingWindow_DropOldValues()
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{
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var s = new Spearman(3);
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// Fill window: X=[1,2,3], Y=[1,2,3] → ρ = 1.0
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s.Update(1.0, 1.0, isNew: true);
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s.Update(2.0, 2.0, isNew: true);
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s.Update(3.0, 3.0, isNew: true);
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Assert.Equal(1.0, s.Last.Value, 1e-10);
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// Push to window: X=[2,3,100], Y=[2,3,-100] → mixed correlation
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s.Update(100.0, -100.0, isNew: true);
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// Window now [2,3,100] vs [2,3,-100]: ranks X=[1,2,3], Y=[2,3,1] → not perfect
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Assert.True(s.Last.Value < 1.0);
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}
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[Fact]
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public void BatchTSeries_MatchesStreaming()
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{
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var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 99);
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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 BatchSpan_MatchesStreaming()
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{
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var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 99);
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double[] xValues = new double[50];
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double[] yValues = new double[50];
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for (int i = 0; i < 50; i++)
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{
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xValues[i] = gbmX.Next().Close;
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yValues[i] = gbmY.Next().Close;
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}
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double[] output = new double[50];
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Spearman.Batch(xValues.AsSpan(), yValues.AsSpan(), output.AsSpan(), 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(xValues[i], yValues[i], isNew: true);
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Assert.Equal(streaming.Last.Value, output[i], 1e-10);
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}
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}
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[Fact]
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public void BatchSpan_MismatchedLengths_Throws()
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{
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double[] x = new double[10];
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double[] y = new double[5];
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double[] output = new double[10];
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Assert.Throws<ArgumentException>(() =>
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Spearman.Batch(x.AsSpan(), y.AsSpan(), output.AsSpan(), 3));
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}
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[Fact]
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public void BatchSpan_MismatchedOutput_Throws()
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{
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double[] x = new double[10];
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double[] y = new double[10];
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double[] output = new double[5];
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Assert.Throws<ArgumentException>(() =>
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Spearman.Batch(x.AsSpan(), y.AsSpan(), output.AsSpan(), 3));
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}
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[Fact]
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public void BatchSpan_InvalidPeriod_Throws()
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{
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double[] x = new double[10];
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double[] y = new double[10];
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double[] output = new double[10];
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Assert.Throws<ArgumentException>(() =>
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Spearman.Batch(x.AsSpan(), y.AsSpan(), output.AsSpan(), 1));
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}
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[Fact]
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public void BatchTSeries_MismatchedLengths_Throws()
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{
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var sx = new TSeries();
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var sy = new TSeries();
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sx.Add(new TValue(DateTime.UtcNow, 1.0));
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Assert.Throws<ArgumentException>(() => Spearman.Batch(sx, sy, 3));
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}
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[Fact]
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public void Calculate_ReturnsTupleWithResults()
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{
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var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 99);
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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 < 30; 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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var (results, indicator) = Spearman.Calculate(seriesX, seriesY, 10);
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Assert.Equal(30, results.Count);
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Assert.NotNull(indicator);
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}
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[Fact]
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public void OutputBounded_BetweenMinusOneAndPlusOne()
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{
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var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 99);
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var s = new Spearman(10);
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for (int i = 0; i < 100; 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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s.Update(barX.Close, barY.Close, isNew: true);
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if (double.IsFinite(s.Last.Value))
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{
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Assert.InRange(s.Last.Value, -1.0, 1.0);
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}
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}
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}
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[Fact]
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public void MonotonicTransform_PreservesCorrelation()
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{
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// Spearman measures monotonic association — applying a strictly increasing
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// transform to either series should not change ρ
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var s1 = new Spearman(5);
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var s2 = new Spearman(5);
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double[] x = [1, 2, 3, 4, 5];
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double[] y = [10, 20, 15, 30, 25];
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for (int i = 0; i < 5; i++)
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{
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s1.Update(x[i], y[i], isNew: true);
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// Apply f(x) = x³ (strictly increasing)
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s2.Update(x[i] * x[i] * x[i], y[i], isNew: true);
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}
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Assert.Equal(s1.Last.Value, s2.Last.Value, 1e-10);
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}
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[Fact]
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public void EventChaining_Fires()
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{
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var s = new Spearman(3);
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int eventCount = 0;
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s.Pub += (object? _, in TValueEventArgs _) => eventCount++;
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for (int i = 1; i <= 5; i++)
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{
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s.Update((double)i, (double)i, isNew: true);
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}
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Assert.Equal(5, eventCount);
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}
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[Fact]
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public void BatchSpan_NaN_HandledSafely()
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{
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double[] x = [1, 2, double.NaN, 4, 5];
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double[] y = [5, 4, 3, 2, 1];
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double[] output = new double[5];
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Spearman.Batch(x.AsSpan(), y.AsSpan(), output.AsSpan(), 3);
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for (int i = 0; i < 5; i++)
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{
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Assert.True(double.IsFinite(output[i]) || double.IsNaN(output[i]));
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}
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}
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[Fact]
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public void LargePeriod_NoStackOverflow()
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{
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// Test with period > StackallocThreshold (256)
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var s = new Spearman(300);
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for (int i = 1; i <= 300; i++)
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{
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s.Update((double)i, (double)i, isNew: true);
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||||
}
|
||||
Assert.Equal(1.0, s.Last.Value, 1e-10);
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||||
}
|
||||
}
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||||
@@ -0,0 +1,127 @@
|
||||
|
||||
using OoplesFinance.StockIndicators;
|
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using OoplesFinance.StockIndicators.Models;
|
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namespace QuanTAlib.Validation;
|
||||
|
||||
public sealed class SpearmanValidationTests
|
||||
{
|
||||
[Fact]
|
||||
public void PerfectLinear_RhoEqualsOne()
|
||||
{
|
||||
// Perfect linear relationship: Y = 2X + 5
|
||||
// Ranks of X and Y are identical → ρ = 1.0
|
||||
var s = new Spearman(10);
|
||||
for (int i = 1; i <= 10; i++)
|
||||
{
|
||||
s.Update((double)i, 2.0 * i + 5.0, isNew: true);
|
||||
}
|
||||
Assert.Equal(1.0, s.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void PerfectNonlinearMonotonic_RhoEqualsOne()
|
||||
{
|
||||
// Perfect monotonic but nonlinear: Y = X³
|
||||
// Ranks are identical → ρ = 1.0 (Spearman captures monotonic, not just linear)
|
||||
var s = new Spearman(10);
|
||||
for (int i = 1; i <= 10; i++)
|
||||
{
|
||||
double x = i;
|
||||
s.Update(x, x * x * x, isNew: true);
|
||||
}
|
||||
Assert.Equal(1.0, s.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void BatchAndStreaming_ProduceSameResults()
|
||||
{
|
||||
var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 777);
|
||||
var gbmY = new GBM(startPrice: 100, mu: 0.03, sigma: 0.15, seed: 888);
|
||||
|
||||
var seriesX = new TSeries();
|
||||
var seriesY = new TSeries();
|
||||
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
var barX = gbmX.Next();
|
||||
var barY = gbmY.Next();
|
||||
seriesX.Add(new TValue(barX.Time, barX.Close));
|
||||
seriesY.Add(new TValue(barY.Time, barY.Close));
|
||||
}
|
||||
|
||||
TSeries batch = Spearman.Batch(seriesX, seriesY, 10);
|
||||
|
||||
var streaming = new Spearman(10);
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
streaming.Update(seriesX[i], seriesY[i], isNew: true);
|
||||
Assert.Equal(streaming.Last.Value, batch[i].Value, 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void KnownRanks_NoTies_MatchesSimplifiedFormula()
|
||||
{
|
||||
// Without ties: ρ = 1 - 6·Σd²/(n(n²-1))
|
||||
// X = [10,20,30,40,50], Y = [50,30,10,40,20]
|
||||
// Ranks X = [1,2,3,4,5], Ranks Y = [5,3,1,4,2]
|
||||
// d = [-4,-1,2,0,3], d² = [16,1,4,0,9], Σd² = 30
|
||||
// ρ = 1 - 6*30 / (5*24) = 1 - 180/120 = 1 - 1.5 = -0.5
|
||||
var s = new Spearman(5);
|
||||
double[] x = [10, 20, 30, 40, 50];
|
||||
double[] y = [50, 30, 10, 40, 20];
|
||||
|
||||
for (int i = 0; i < 5; i++)
|
||||
{
|
||||
s.Update(x[i], y[i], isNew: true);
|
||||
}
|
||||
|
||||
Assert.Equal(-0.5, s.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void SpearmanVsKendall_BothDetectMonotonic()
|
||||
{
|
||||
// Both Spearman and Kendall should be +1 for perfectly concordant data
|
||||
var spearman = new Spearman(5);
|
||||
var kendall = new Kendall(5);
|
||||
|
||||
for (int i = 1; i <= 5; i++)
|
||||
{
|
||||
spearman.Update((double)i, (double)i, isNew: true);
|
||||
kendall.Update(new TValue(DateTime.UtcNow, i), new TValue(DateTime.UtcNow, i), isNew: true);
|
||||
}
|
||||
|
||||
Assert.Equal(1.0, spearman.Last.Value, 1e-10);
|
||||
Assert.Equal(1.0, kendall.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void BoundaryValues_AllTied()
|
||||
{
|
||||
// All X values identical → zero variance in ranks → ρ = 0
|
||||
var s = new Spearman(5);
|
||||
for (int i = 0; i < 5; i++)
|
||||
{
|
||||
s.Update(42.0, (double)(i + 1), isNew: true);
|
||||
}
|
||||
Assert.Equal(0.0, s.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Spearman_MatchesOoples_Structural()
|
||||
{
|
||||
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.15, seed: 42);
|
||||
var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
var ooplesData = bars.Select(b => new TickerData
|
||||
{
|
||||
Date = new DateTime(b.Time, DateTimeKind.Utc),
|
||||
Open = b.Open, High = b.High, Low = b.Low,
|
||||
Close = b.Close, Volume = b.Volume
|
||||
}).ToList();
|
||||
var result = new StockData(ooplesData).CalculateEhlersSpearmanRankIndicator();
|
||||
var values = result.CustomValuesList;
|
||||
int finiteCount = values.Count(v => double.IsFinite(v));
|
||||
Assert.True(finiteCount > 100, $"Expected >100 finite values, got {finiteCount}");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user