mirror of
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 +1,685 @@
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using Xunit;
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namespace QuanTAlib.Tests;
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public class GammadistTests
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{
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private const double Tolerance = 1e-10;
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// ─── A) Constructor validation ────────────────────────────────────────────
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[Fact]
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public void Constructor_DefaultParameters_SetsProperties()
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{
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var indicator = new Gammadist();
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Assert.Equal("Gammadist(2.00,1.00,14)", indicator.Name);
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Assert.Equal(14, indicator.WarmupPeriod);
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Assert.False(indicator.IsHot);
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}
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[Fact]
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public void Constructor_CustomParameters_SetsName()
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{
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var indicator = new Gammadist(alpha: 3.0, beta: 2.0, period: 20);
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Assert.Equal("Gammadist(3.00,2.00,20)", indicator.Name);
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Assert.Equal(20, indicator.WarmupPeriod);
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}
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[Fact]
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public void Constructor_ZeroAlpha_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(alpha: 0.0));
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Assert.Equal("alpha", ex.ParamName);
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}
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[Fact]
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public void Constructor_NegativeAlpha_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(alpha: -1.0));
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Assert.Equal("alpha", ex.ParamName);
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}
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[Fact]
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public void Constructor_ZeroBeta_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(beta: 0.0));
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Assert.Equal("beta", ex.ParamName);
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}
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[Fact]
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public void Constructor_NegativeBeta_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(beta: -0.5));
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Assert.Equal("beta", ex.ParamName);
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}
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[Fact]
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public void Constructor_PeriodOne_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(period: 1));
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Assert.Equal("period", ex.ParamName);
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}
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[Fact]
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public void Constructor_NegativePeriod_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(period: -1));
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Assert.Equal("period", ex.ParamName);
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}
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[Fact]
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public void Constructor_ZeroPeriod_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Gammadist(period: 0));
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Assert.Equal("period", ex.ParamName);
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}
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// ─── B) Basic calculation ─────────────────────────────────────────────────
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[Fact]
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public void Update_ReturnsValidTValue()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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var input = new TValue(time, 100.0);
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var result = indicator.Update(input);
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Assert.Equal(input.Time, result.Time);
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Assert.True(double.IsFinite(result.Value));
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}
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[Fact]
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public void Update_OutputInRange()
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{
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var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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Assert.True(indicator.Last.Value >= 0.0, "Output must be >= 0");
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Assert.True(indicator.Last.Value <= 1.0, "Output must be <= 1");
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}
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[Fact]
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public void Last_IsAccessible_AfterUpdate()
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{
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var indicator = new Gammadist(period: 3);
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var time = DateTime.UtcNow;
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indicator.Update(new TValue(time, 50.0));
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Assert.NotEqual(default, indicator.Last);
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}
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[Fact]
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public void Name_ContainsGammadist()
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{
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var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 14);
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Assert.Contains("Gammadist", indicator.Name, StringComparison.OrdinalIgnoreCase);
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}
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[Fact]
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public void IsHot_Property_ReflectsWarmup()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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for (int i = 0; i < 4; i++)
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{
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indicator.Update(new TValue(time.AddMinutes(i), 100.0 + i));
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Assert.False(indicator.IsHot);
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}
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indicator.Update(new TValue(time.AddMinutes(4), 104.0));
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Assert.True(indicator.IsHot);
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}
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[Fact]
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public void Update_AtMaxOfWindow_ReturnsNearOne()
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{
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var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 101.0, 110.0 }; // 110 is max
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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// When x=1.0 → xGamma=10.0, Gamma CDF well above 0.9 for α=2,β=1
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Assert.True(indicator.Last.Value > 0.9, $"Expected near 1 but got {indicator.Last.Value}");
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}
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[Fact]
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public void Update_AtMinOfWindow_ReturnsZero()
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{
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var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 110.0, 102.0, 98.0, 101.0, 90.0 }; // 90 is min
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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Assert.Equal(0.0, indicator.Last.Value, Tolerance);
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}
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// ─── C) State + bar correction ────────────────────────────────────────────
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[Fact]
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public void Update_IsNewTrue_AdvancesState()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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double first = indicator.Last.Value;
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indicator.Update(new TValue(time, 110.0));
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double second = indicator.Last.Value;
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Assert.NotEqual(first, second, Tolerance);
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}
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[Fact]
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public void Update_IsNewFalse_RewritesLastBar()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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// New bar with value A
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indicator.Update(new TValue(time, 110.0), true);
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double valueA = indicator.Last.Value;
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// Correct same bar with value B
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indicator.Update(new TValue(time, 90.0), false);
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double valueB = indicator.Last.Value;
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Assert.NotEqual(valueA, valueB, Tolerance);
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}
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[Fact]
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public void Update_IterativeCorrection_RestoresState()
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{
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var time = DateTime.UtcNow;
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 72001);
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var bars = gbm.Fetch(20, time.Ticks, TimeSpan.FromMinutes(1));
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// Streaming without corrections
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var straight = new Gammadist(period: 5);
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for (int i = 0; i < bars.Close.Count; i++)
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{
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straight.Update(bars.Close[i]);
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}
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double finalStraight = straight.Last.Value;
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// With corrections (wrong → corrected)
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var corrected = new Gammadist(period: 5);
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for (int i = 0; i < bars.Close.Count; i++)
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{
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corrected.Update(new TValue(bars.Close[i].Time, 999.0), true);
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corrected.Update(bars.Close[i], false);
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}
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Assert.Equal(finalStraight, corrected.Last.Value, Tolerance);
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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 indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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Assert.True(indicator.IsHot);
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indicator.Reset();
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Assert.False(indicator.IsHot);
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Assert.Equal(default, indicator.Last);
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}
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// ─── D) Warmup / convergence ──────────────────────────────────────────────
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[Fact]
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public void IsHot_FlipsAtPeriod()
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{
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int period = 10;
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var indicator = new Gammadist(period: period);
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var time = DateTime.UtcNow;
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for (int i = 0; i < period - 1; i++)
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{
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indicator.Update(new TValue(time.AddMinutes(i), 100.0 + i));
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Assert.False(indicator.IsHot, $"Should not be hot at bar {i + 1}");
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}
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indicator.Update(new TValue(time.AddMinutes(period - 1), 100.0 + period));
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Assert.True(indicator.IsHot, "Should be hot after period bars");
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}
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[Fact]
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public void WarmupPeriod_EqualsPeriod()
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{
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var indicator = new Gammadist(period: 25);
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Assert.Equal(25, indicator.WarmupPeriod);
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}
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// ─── E) Robustness ────────────────────────────────────────────────────────
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[Fact]
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public void Update_NaN_UsesLastValidValue()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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double before = indicator.Last.Value;
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indicator.Update(new TValue(time, double.NaN));
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Assert.Equal(before, indicator.Last.Value, Tolerance);
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}
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[Fact]
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public void Update_PositiveInfinity_UsesLastValidValue()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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double before = indicator.Last.Value;
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indicator.Update(new TValue(time, double.PositiveInfinity));
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Assert.Equal(before, indicator.Last.Value, Tolerance);
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}
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[Fact]
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public void Update_NegativeInfinity_UsesLastValidValue()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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indicator.Update(new TValue(time, p));
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time = time.AddMinutes(1);
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}
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double before = indicator.Last.Value;
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indicator.Update(new TValue(time, double.NegativeInfinity));
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Assert.Equal(before, indicator.Last.Value, Tolerance);
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}
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[Fact]
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public void Update_BatchNaN_Stable()
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{
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var indicator = new Gammadist(period: 5);
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var time = DateTime.UtcNow;
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double[] prices = { 100.0, double.NaN, 102.0, double.NaN, 98.0, 105.0, 103.0 };
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foreach (var p in prices)
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{
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var result = indicator.Update(new TValue(time, p));
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Assert.True(double.IsFinite(result.Value), "Output must always be finite");
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time = time.AddMinutes(1);
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}
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}
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[Fact]
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public void Update_FlatRange_ReturnsCdfAtFive()
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{
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// When all values in window are identical, range=0 → xNorm=0.5 → xGamma=5.0
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double alpha = 2.0;
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double beta = 1.0;
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var indicator = new Gammadist(alpha: alpha, beta: beta, period: 5);
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var time = DateTime.UtcNow;
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for (int i = 0; i < 10; i++)
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{
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indicator.Update(new TValue(time.AddMinutes(i), 100.0));
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}
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double expected = Gammadist.GammaCdf(5.0, alpha, beta);
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Assert.Equal(expected, indicator.Last.Value, 1e-6);
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}
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// ─── F) Consistency: batch == streaming == span == eventing ──────────────
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[Fact]
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public void AllModes_ConsistencyCheck()
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{
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int count = 100;
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int period = 20;
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 72002);
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var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var source = bars.Close;
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// Streaming
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var streaming = new Gammadist(period: period);
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for (int i = 0; i < source.Count; i++)
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{
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streaming.Update(source[i]);
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}
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// Batch (TSeries)
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var batch = Gammadist.Batch(source, period: period);
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// Span
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var rawValues = new double[source.Count];
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for (int i = 0; i < source.Count; i++)
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{
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rawValues[i] = source[i].Value;
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}
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var spanOutput = new double[source.Count];
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Gammadist.Batch(rawValues, spanOutput, period: period);
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// Eventing
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var eventResults = new List<double>();
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var eventSource = new TSeries();
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var eventIndicator = new Gammadist(eventSource, period: period);
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eventIndicator.Pub += (object? s, in TValueEventArgs e) => eventResults.Add(e.Value.Value);
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for (int i = 0; i < source.Count; i++)
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{
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eventSource.Add(source[i], true);
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}
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// Verify last value matches across all modes
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double streamingLast = streaming.Last.Value;
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double batchLast = batch[source.Count - 1].Value;
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double spanLast = spanOutput[source.Count - 1];
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double eventLast = eventResults[^1];
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Assert.Equal(streamingLast, batchLast, Tolerance);
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Assert.Equal(streamingLast, spanLast, Tolerance);
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Assert.Equal(streamingLast, eventLast, Tolerance);
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}
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[Fact]
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public void Streaming_VsBatch_AllValues_Match()
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{
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int count = 80;
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int period = 15;
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var gbm = new GBM(startPrice: 50, mu: 0.0, sigma: 0.3, seed: 72003);
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var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var source = bars.Close;
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var streaming = new Gammadist(period: period);
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var streamingVals = new double[count];
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for (int i = 0; i < count; i++)
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{
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streaming.Update(source[i]);
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streamingVals[i] = streaming.Last.Value;
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}
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var batch = Gammadist.Batch(source, period: period);
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for (int i = 0; i < count; i++)
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{
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Assert.Equal(streamingVals[i], batch[i].Value, Tolerance);
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}
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}
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// ─── G) Span API tests ────────────────────────────────────────────────────
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[Fact]
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public void Batch_Span_EmptySource_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() =>
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Gammadist.Batch([], Array.Empty<double>()));
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Assert.Equal("source", ex.ParamName);
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}
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[Fact]
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public void Batch_Span_OutputTooShort_ThrowsArgumentException()
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||||
{
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double[] src = { 1.0, 2.0, 3.0 };
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||||
double[] dst = new double[2];
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var ex = Assert.Throws<ArgumentException>(() =>
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Gammadist.Batch(src, dst));
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Assert.Equal("output", ex.ParamName);
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}
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||||
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[Fact]
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public void Batch_Span_ZeroAlpha_ThrowsArgumentException()
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{
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double[] src = { 1.0, 2.0, 3.0 };
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||||
double[] dst = new double[3];
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||||
var ex = Assert.Throws<ArgumentException>(() =>
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Gammadist.Batch(src, dst, alpha: 0.0));
|
||||
Assert.Equal("alpha", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_ZeroBeta_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
|
||||
Gammadist.Batch(src, dst, beta: 0.0));
|
||||
Assert.Equal("beta", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_InvalidPeriod_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
|
||||
Gammadist.Batch(src, dst, period: 1));
|
||||
Assert.Equal("period", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_OutputInRange()
|
||||
{
|
||||
int count = 100;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 72004);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
double[] src = new double[count];
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
src[i] = bars.Close[i].Value;
|
||||
}
|
||||
|
||||
double[] dst = new double[count];
|
||||
Gammadist.Batch(src, dst, period: 20);
|
||||
|
||||
foreach (double v in dst)
|
||||
{
|
||||
Assert.True(v >= 0.0 && v <= 1.0, $"Output {v} out of [0,1] range");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_HandlesNaN()
|
||||
{
|
||||
double[] src = { 100.0, double.NaN, 102.0, 98.0, 105.0, 103.0 };
|
||||
double[] dst = new double[src.Length];
|
||||
Gammadist.Batch(src, dst, period: 5);
|
||||
|
||||
foreach (double v in dst)
|
||||
{
|
||||
Assert.True(double.IsFinite(v), "Span output should always be finite");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_NoStackOverflow_LargeData()
|
||||
{
|
||||
int count = 5000;
|
||||
double[] src = new double[count];
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
src[i] = 100.0 + Math.Sin(i * 0.1) * 10.0;
|
||||
}
|
||||
|
||||
double[] dst = new double[count];
|
||||
Gammadist.Batch(src, dst, period: 300);
|
||||
|
||||
foreach (double v in dst)
|
||||
{
|
||||
Assert.True(double.IsFinite(v));
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_MatchesStreaming()
|
||||
{
|
||||
int count = 60;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.25, seed: 72005);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
double[] src = new double[count];
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
src[i] = bars.Close[i].Value;
|
||||
}
|
||||
|
||||
double[] spanOut = new double[count];
|
||||
Gammadist.Batch(src, spanOut, period: 14);
|
||||
|
||||
var streaming = new Gammadist(period: 14);
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
streaming.Update(bars.Close[i]);
|
||||
Assert.Equal(streaming.Last.Value, spanOut[i], Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── H) Chainability ──────────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void Pub_EventFires()
|
||||
{
|
||||
var indicator = new Gammadist(period: 3);
|
||||
int count = 0;
|
||||
indicator.Pub += (object? sender, in TValueEventArgs args) => count++;
|
||||
|
||||
var time = DateTime.UtcNow;
|
||||
indicator.Update(new TValue(time, 100.0));
|
||||
indicator.Update(new TValue(time.AddMinutes(1), 102.0));
|
||||
indicator.Update(new TValue(time.AddMinutes(2), 98.0));
|
||||
|
||||
Assert.Equal(3, count);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Chaining_Constructor_Works()
|
||||
{
|
||||
int period = 5;
|
||||
var source = new TSeries();
|
||||
var indicator = new Gammadist(source, period: period);
|
||||
|
||||
var time = DateTime.UtcNow;
|
||||
double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
|
||||
|
||||
foreach (var p in prices)
|
||||
{
|
||||
source.Add(new TValue(time, p), true);
|
||||
time = time.AddMinutes(1);
|
||||
}
|
||||
|
||||
Assert.True(indicator.IsHot);
|
||||
Assert.True(indicator.Last.Value >= 0.0 && indicator.Last.Value <= 1.0);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Pub_EventValue_MatchesLast()
|
||||
{
|
||||
var indicator = new Gammadist(period: 5);
|
||||
TValue? lastEvent = null;
|
||||
indicator.Pub += (object? s, in TValueEventArgs e) => lastEvent = e.Value;
|
||||
|
||||
var time = DateTime.UtcNow;
|
||||
double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
|
||||
|
||||
foreach (var p in prices)
|
||||
{
|
||||
indicator.Update(new TValue(time, p));
|
||||
time = time.AddMinutes(1);
|
||||
}
|
||||
|
||||
Assert.NotNull(lastEvent);
|
||||
Assert.Equal(indicator.Last.Value, lastEvent.Value.Value, Tolerance);
|
||||
}
|
||||
|
||||
// ─── Additional: Alpha/Beta parameter effects ─────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void DifferentAlpha_ProduceDifferentResults()
|
||||
{
|
||||
int count = 60;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 72006);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var ind1 = new Gammadist(alpha: 1.0, beta: 1.0, period: 20);
|
||||
var ind2 = new Gammadist(alpha: 2.0, beta: 1.0, period: 20);
|
||||
var ind3 = new Gammadist(alpha: 5.0, beta: 1.0, period: 20);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
ind1.Update(bars.Close[i]);
|
||||
ind2.Update(bars.Close[i]);
|
||||
ind3.Update(bars.Close[i]);
|
||||
}
|
||||
|
||||
// All outputs must be in [0, 1]
|
||||
Assert.True(ind1.Last.Value >= 0.0 && ind1.Last.Value <= 1.0);
|
||||
Assert.True(ind2.Last.Value >= 0.0 && ind2.Last.Value <= 1.0);
|
||||
Assert.True(ind3.Last.Value >= 0.0 && ind3.Last.Value <= 1.0);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Calculate_StaticMethod_ReturnsTuple()
|
||||
{
|
||||
int count = 50;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 72007);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var (results, instance) = Gammadist.Calculate(bars.Close, period: 20);
|
||||
|
||||
Assert.Equal(count, results.Count);
|
||||
Assert.True(instance.IsHot);
|
||||
Assert.Equal(results[^1].Value, instance.Last.Value, Tolerance);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,376 @@
|
||||
using Xunit;
|
||||
using MathNet.Numerics.Distributions;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// GammadistValidationTests — validates against known mathematical properties
|
||||
/// of the Gamma Distribution CDF and against MathNet.Numerics Gamma.
|
||||
/// Known-value tests call Gammadist.GammaCdf / StaticCdf directly (bypassing windowing)
|
||||
/// so results are exact closed-form comparisons with tolerance 1e-9.
|
||||
/// Note: MathNet Gamma(shape, rate) uses rate = 1/scale, so rate = 1/beta.
|
||||
/// </summary>
|
||||
public class GammadistValidationTests
|
||||
{
|
||||
private const double Tolerance = 1e-9;
|
||||
private const double LooseTolerance = 1e-6;
|
||||
|
||||
// ─── Boundary: F(0; α, β) = 0 always ────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(2.0, 1.0)]
|
||||
[InlineData(0.5, 2.0)]
|
||||
[InlineData(5.0, 3.0)]
|
||||
public void GammaCdf_AtZero_IsAlwaysZero(double alpha, double beta)
|
||||
{
|
||||
Assert.Equal(0.0, Gammadist.GammaCdf(0.0, alpha, beta), Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(-0.1, 1.0, 1.0)]
|
||||
[InlineData(-1.0, 2.0, 1.0)]
|
||||
[InlineData(-100.0, 5.0, 2.0)]
|
||||
public void GammaCdf_Negative_IsAlwaysZero(double x, double alpha, double beta)
|
||||
{
|
||||
Assert.Equal(0.0, Gammadist.GammaCdf(x, alpha, beta), Tolerance);
|
||||
}
|
||||
|
||||
// ─── Boundary: F(+∞; α, β) → 1 ──────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0, 0.9999)]
|
||||
[InlineData(2.0, 1.0, 0.9999)]
|
||||
[InlineData(5.0, 2.0, 0.999)]
|
||||
public void GammaCdf_AtLargeX_ApproachesOne(double alpha, double beta, double minExpected)
|
||||
{
|
||||
double cdf = Gammadist.GammaCdf(1000.0, alpha, beta);
|
||||
Assert.True(cdf > minExpected,
|
||||
$"Gamma({alpha},{beta}) CDF at large x={cdf} should be > {minExpected}");
|
||||
}
|
||||
|
||||
// ─── Known value: Gamma(1,1) = Exp(1), F(1;1,1) = 1 - e^(-1) ≈ 0.6321 ──
|
||||
|
||||
[Fact]
|
||||
public void GammaCdf_Alpha1_Beta1_AtOne_EqualsExpDist()
|
||||
{
|
||||
// Gamma(α=1, β=1) = Exponential(λ=1): F(1) = 1 - e^(-1)
|
||||
double expected = 1.0 - Math.Exp(-1.0); // ≈ 0.63212055882856
|
||||
double actual = Gammadist.GammaCdf(1.0, 1.0, 1.0);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void GammaCdf_Alpha1_Beta2_AtTwo_EqualsExpDist()
|
||||
{
|
||||
// Gamma(α=1, β=2) = Exponential(λ=0.5): F(2) = 1 - e^(-2/2) = 1 - e^(-1)
|
||||
double expected = 1.0 - Math.Exp(-1.0);
|
||||
double actual = Gammadist.GammaCdf(2.0, 1.0, 2.0);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
// ─── MathNet.Numerics cross-validation ───────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0, 1.0)]
|
||||
[InlineData(2.0, 2.0, 1.0)]
|
||||
[InlineData(0.5, 0.5, 0.5)]
|
||||
[InlineData(3.0, 2.0, 1.0)]
|
||||
[InlineData(1.0, 5.0, 2.0)]
|
||||
[InlineData(5.0, 3.0, 1.0)]
|
||||
[InlineData(0.1, 1.0, 1.0)]
|
||||
[InlineData(10.0, 4.0, 2.0)]
|
||||
[InlineData(2.0, 1.5, 0.5)]
|
||||
[InlineData(8.0, 2.0, 3.0)]
|
||||
public void GammaCdf_VsMathNet_KnownValues(double x, double alpha, double beta)
|
||||
{
|
||||
// MathNet Gamma(shape, rate) where rate = 1/scale = 1/beta
|
||||
var dist = new MathNet.Numerics.Distributions.Gamma(alpha, 1.0 / beta);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Gammadist.GammaCdf(x, alpha, beta);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0, 1.0)]
|
||||
[InlineData(2.0, 2.0, 1.0)]
|
||||
[InlineData(3.0, 3.0, 1.0)]
|
||||
[InlineData(5.0, 2.0, 2.0)]
|
||||
[InlineData(0.5, 1.5, 0.5)]
|
||||
public void StaticCdf_VsMathNet_KnownValues(double x, double alpha, double beta)
|
||||
{
|
||||
var dist = new MathNet.Numerics.Distributions.Gamma(alpha, 1.0 / beta);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Gammadist.StaticCdf(x, alpha, beta);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
// ─── Monotonicity ─────────────────────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(2.0, 1.0)]
|
||||
[InlineData(0.5, 1.0)]
|
||||
[InlineData(5.0, 2.0)]
|
||||
[InlineData(2.0, 0.5)]
|
||||
public void GammaCdf_MonotonicIncreasing(double alpha, double beta)
|
||||
{
|
||||
double prev = -1.0;
|
||||
|
||||
for (int i = 0; i <= 30; i++)
|
||||
{
|
||||
double x = i * 0.5;
|
||||
double cdf = Gammadist.GammaCdf(x, alpha, beta);
|
||||
Assert.True(cdf >= prev - LooseTolerance,
|
||||
$"CDF not monotonic at x={x} (α={alpha}, β={beta}): got {cdf}, prev={prev}");
|
||||
prev = cdf;
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Output bounded [0, 1] with streaming indicator ──────────────────────
|
||||
|
||||
[Fact]
|
||||
public void GammadistCdf_OutputBounded_Zero_To_One()
|
||||
{
|
||||
int count = 200;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 73001);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 20);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
indicator.Update(bars.Close[i]);
|
||||
double v = indicator.Last.Value;
|
||||
Assert.True(v >= 0.0 && v <= 1.0, $"Output {v} at bar {i} out of [0,1]");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Flat range → CDF at xGamma = 5.0 / beta ─────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(2.0, 1.0)]
|
||||
[InlineData(2.0, 0.5)]
|
||||
[InlineData(5.0, 2.0)]
|
||||
public void GammadistCdf_FlatRange_ReturnsCdfAtFive(double alpha, double beta)
|
||||
{
|
||||
var ind = new Gammadist(alpha, beta, period: 20);
|
||||
var time = DateTime.UtcNow;
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
ind.Update(new TValue(time.AddSeconds(i), 100.0));
|
||||
}
|
||||
|
||||
// xNorm=0.5 → xGamma=5.0 → x/beta = 5/beta
|
||||
double expected = Gammadist.GammaCdf(5.0, alpha, beta);
|
||||
Assert.Equal(expected, ind.Last.Value, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Mean: E[Gamma(α,β)] = α*β; median CDF check ─────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0)] // mean = 1
|
||||
[InlineData(2.0, 2.0)] // mean = 4
|
||||
[InlineData(3.0, 1.0)] // mean = 3
|
||||
public void GammaCdf_AtMean_IsNearExpected(double alpha, double beta)
|
||||
{
|
||||
double mean = alpha * beta;
|
||||
// For alpha >= 1, CDF at mean is between 0.5 and 1 (shifted right of median)
|
||||
double cdf = Gammadist.GammaCdf(mean, alpha, beta);
|
||||
Assert.True(cdf > 0.3 && cdf < 1.0,
|
||||
$"CDF at mean ({cdf}) should be in (0.3,1) for α={alpha}, β={beta}");
|
||||
}
|
||||
|
||||
// ─── Shape shift: larger α shifts CDF right ───────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 5.0)]
|
||||
[InlineData(2.0, 5.0)]
|
||||
[InlineData(5.0, 5.0)]
|
||||
public void GammaCdf_LargerAlpha_ShiftsCdfRight(double x, double beta)
|
||||
{
|
||||
// At the same x, larger α → lower CDF (mass shifted right)
|
||||
double cdf1 = Gammadist.GammaCdf(x, 1.0, beta);
|
||||
double cdf2 = Gammadist.GammaCdf(x, 3.0, beta);
|
||||
double cdf3 = Gammadist.GammaCdf(x, 7.0, beta);
|
||||
|
||||
Assert.True(cdf1 >= cdf2 - LooseTolerance,
|
||||
$"α=1 CDF={cdf1} should be >= α=3 CDF={cdf2} at x={x}");
|
||||
Assert.True(cdf2 >= cdf3 - LooseTolerance,
|
||||
$"α=3 CDF={cdf2} should be >= α=7 CDF={cdf3} at x={x}");
|
||||
}
|
||||
|
||||
// ─── Scale shift: larger β stretches CDF right (same relative shape) ─────
|
||||
|
||||
[Fact]
|
||||
public void GammaCdf_ScaleIdentity_Gamma_AlphaBeta_VsMathNet()
|
||||
{
|
||||
// F(x; α, β) = F(x/β; α, 1) — scaling identity
|
||||
double alpha = 3.0, beta = 2.0, x = 6.0;
|
||||
double direct = Gammadist.GammaCdf(x, alpha, beta);
|
||||
double scaled = Gammadist.GammaCdf(x / beta, alpha, 1.0);
|
||||
Assert.Equal(direct, scaled, Tolerance);
|
||||
}
|
||||
|
||||
// ─── LnGamma internal correctness ────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 0.0)] // Γ(1) = 1 → ln(1) = 0
|
||||
[InlineData(2.0, 0.0)] // Γ(2) = 1! = 1 → ln(1) = 0
|
||||
[InlineData(3.0, 0.6931471805599453)] // Γ(3) = 2! = 2 → ln(2)
|
||||
[InlineData(4.0, 1.791759469228327)] // Γ(4) = 3! = 6 → ln(6)
|
||||
[InlineData(5.0, 3.178053830347946)] // Γ(5) = 4! = 24 → ln(24)
|
||||
public void LnGamma_IntegerArguments_MatchKnownValues(double z, double expected)
|
||||
{
|
||||
double actual = Gammadist.LnGamma(z);
|
||||
Assert.Equal(expected, actual, 1e-10);
|
||||
}
|
||||
|
||||
// ─── Span batch consistency ───────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_MatchesTSeries()
|
||||
{
|
||||
int count = 150;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.25, seed: 73002);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
double[] rawValues = new double[count];
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
rawValues[i] = bars.Close[i].Value;
|
||||
}
|
||||
|
||||
var tseriesResult = Gammadist.Batch(bars.Close, alpha: 2.0, beta: 1.0, period: 30);
|
||||
double[] spanResult = new double[count];
|
||||
Gammadist.Batch(rawValues, spanResult, alpha: 2.0, beta: 1.0, period: 30);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
Assert.Equal(tseriesResult[i].Value, spanResult[i], Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Streaming convergence ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void GammadistCdf_HighPeriod_StillConverges()
|
||||
{
|
||||
int period = 200;
|
||||
var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: period);
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 73003);
|
||||
var bars = gbm.Fetch(period + 50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
for (int i = 0; i < bars.Close.Count; i++)
|
||||
{
|
||||
indicator.Update(bars.Close[i]);
|
||||
Assert.True(double.IsFinite(indicator.Last.Value),
|
||||
$"Non-finite output at bar {i}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Parameter combos all within [0,1] ────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0, 5)]
|
||||
[InlineData(2.0, 1.0, 14)]
|
||||
[InlineData(0.5, 0.5, 10)]
|
||||
[InlineData(5.0, 2.0, 20)]
|
||||
[InlineData(3.0, 0.5, 30)]
|
||||
public void GammadistCdf_ParameterCombos_OutputBounded(double alpha, double beta, int period)
|
||||
{
|
||||
int count = period + 50;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 73004 + (int)(alpha * 100));
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Gammadist(alpha, beta, period);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
indicator.Update(bars.Close[i]);
|
||||
double v = indicator.Last.Value;
|
||||
Assert.True(v >= 0.0 && v <= 1.0,
|
||||
$"Out of [0,1] at bar {i}: {v} (α={alpha}, β={beta}, period={period})");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Large dataset stable ─────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void GammadistCdf_LargeDataset_Stable()
|
||||
{
|
||||
int count = 2000;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 73005);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 50);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
indicator.Update(bars.Close[i]);
|
||||
double v = indicator.Last.Value;
|
||||
Assert.True(double.IsFinite(v) && v >= 0.0 && v <= 1.0,
|
||||
$"Invalid output {v} at bar {i}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Extreme prices don't blow up ─────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void GammadistCdf_ExtremePrices_StillInRange()
|
||||
{
|
||||
var indicator = new Gammadist(alpha: 2.0, beta: 1.0, period: 20);
|
||||
var time = DateTime.UtcNow;
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
double price = (i % 2 == 0) ? 1e10 : 1e-10;
|
||||
indicator.Update(new TValue(time.AddMinutes(i), price));
|
||||
double v = indicator.Last.Value;
|
||||
Assert.True(v >= 0.0 && v <= 1.0, $"Out of range at {i}: {v}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Multiple points all match MathNet ───────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void GammaCdf_MultiplePoints_AllMatchMathNet()
|
||||
{
|
||||
double alpha = 2.0, beta = 1.0;
|
||||
var dist = new MathNet.Numerics.Distributions.Gamma(alpha, 1.0 / beta);
|
||||
|
||||
double[] testX = { 0.0, 0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0 };
|
||||
|
||||
foreach (double x in testX)
|
||||
{
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Gammadist.GammaCdf(x, alpha, beta);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── RegularizedIncompleteGamma internal tests ────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void RegularizedIncompleteGamma_AtZero_IsZero()
|
||||
{
|
||||
double lnGammaA = Gammadist.LnGamma(2.0);
|
||||
double result = Gammadist.RegularizedIncompleteGamma(2.0, 0.0, lnGammaA);
|
||||
Assert.Equal(0.0, result, Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1.0)] // P(1, 1) = 1 - e^(-1)
|
||||
[InlineData(2.0, 2.0)] // vs MathNet
|
||||
[InlineData(3.0, 1.5)] // vs MathNet
|
||||
public void RegularizedIncompleteGamma_VsMathNet(double a, double x)
|
||||
{
|
||||
var dist = new MathNet.Numerics.Distributions.Gamma(a, 1.0);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double lnGammaA = Gammadist.LnGamma(a);
|
||||
double actual = Gammadist.RegularizedIncompleteGamma(a, x, lnGammaA);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user