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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 +1,671 @@
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using Xunit;
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namespace QuanTAlib.Tests;
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public class FdistTests
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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 Fdist();
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Assert.Equal("Fdist(1,1,14)", 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 Fdist(d1: 5, d2: 10, period: 20);
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Assert.Equal("Fdist(5,10,20)", 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_D1Zero_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(d1: 0));
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Assert.Equal("d1", ex.ParamName);
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}
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[Fact]
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public void Constructor_D1Negative_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(d1: -1));
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Assert.Equal("d1", ex.ParamName);
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}
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[Fact]
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public void Constructor_D2Zero_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(d2: 0));
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Assert.Equal("d2", ex.ParamName);
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}
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[Fact]
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public void Constructor_D2Negative_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(d2: -1));
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Assert.Equal("d2", 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 Fdist(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_PeriodZero_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(period: 0));
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Assert.Equal("period", ex.ParamName);
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}
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[Fact]
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public void Constructor_PeriodNegative_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Fdist(period: -5));
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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 Fdist(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 Fdist(d1: 5, d2: 5, 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 Fdist(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 IsHot_Property_ReflectsWarmup()
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{
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var indicator = new Fdist(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_ReturnsHighValue()
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{
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// When current value equals window max, xNorm=1, xF=10 → F-CDF near 1
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var indicator = new Fdist(d1: 5, d2: 5, 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 };
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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 > 0.9, $"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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// When current value equals window min, xNorm=0, xF=0 → F-CDF(0) = 0
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var indicator = new Fdist(d1: 5, d2: 5, 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 };
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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 Fdist(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 Fdist(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 (min of window → 0)
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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: 65001);
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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 Fdist(d1: 5, d2: 5, 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 Fdist(d1: 5, d2: 5, 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 Fdist(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 Fdist(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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// ─── 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 Fdist(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 Fdist(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 Fdist(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 Fdist(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_ReturnsStableValue()
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{
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// All identical values → range=0 → xNorm=0.5 → xF=5 → F-CDF(5; d1, d2)
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var indicator = new Fdist(d1: 5, d2: 5, 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 = Fdist.FCdf(5.0, 5, 5);
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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: 65002);
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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 Fdist(d1: 5, d2: 5, 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 = Fdist.Batch(source, d1: 5, d2: 5, 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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Fdist.Batch(rawValues, spanOutput, d1: 5, d2: 5, 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 Fdist(eventSource, d1: 5, d2: 5, 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: 65003);
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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 Fdist(d1: 3, d2: 7, 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 = Fdist.Batch(source, d1: 3, d2: 7, 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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||||
Fdist.Batch([], Array.Empty<double>()));
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||||
Assert.Equal("source", ex.ParamName);
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}
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||||
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[Fact]
|
||||
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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Fdist.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]
|
||||
public void Batch_Span_InvalidD1_ThrowsArgumentException()
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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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Fdist.Batch(src, dst, d1: 0));
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||||
Assert.Equal("d1", ex.ParamName);
|
||||
}
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||||
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||||
[Fact]
|
||||
public void Batch_Span_InvalidD2_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
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||||
Fdist.Batch(src, dst, d2: 0));
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||||
Assert.Equal("d2", 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>(() =>
|
||||
Fdist.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: 65004);
|
||||
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];
|
||||
Fdist.Batch(src, dst, d1: 5, d2: 5, 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];
|
||||
Fdist.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];
|
||||
Fdist.Batch(src, dst, d1: 5, d2: 5, 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: 65005);
|
||||
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];
|
||||
Fdist.Batch(src, spanOut, d1: 5, d2: 5, period: 14);
|
||||
|
||||
var streaming = new Fdist(d1: 5, d2: 5, 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 Fdist(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 Fdist(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 Fdist(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: DoF parameter effects ──────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void DifferentDoF_ProduceDifferentResults()
|
||||
{
|
||||
int count = 60;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65006);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var ind1 = new Fdist(d1: 1, d2: 1, period: 20);
|
||||
var ind2 = new Fdist(d1: 5, d2: 5, period: 20);
|
||||
var ind3 = new Fdist(d1: 10, d2: 2, period: 20);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
ind1.Update(bars.Close[i]);
|
||||
ind2.Update(bars.Close[i]);
|
||||
ind3.Update(bars.Close[i]);
|
||||
}
|
||||
|
||||
// Different DoFs produce different CDFs
|
||||
Assert.False(
|
||||
Math.Abs(ind1.Last.Value - ind2.Last.Value) < 1e-6 &&
|
||||
Math.Abs(ind2.Last.Value - ind3.Last.Value) < 1e-6,
|
||||
"Different DoFs should produce at least one distinct result");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Calculate_StaticMethod_ReturnsTuple()
|
||||
{
|
||||
int count = 50;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65007);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var (results, instance) = Fdist.Calculate(bars.Close, d1: 5, d2: 5, period: 20);
|
||||
|
||||
Assert.Equal(count, results.Count);
|
||||
Assert.True(instance.IsHot);
|
||||
Assert.Equal(results[^1].Value, instance.Last.Value, Tolerance);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,298 @@
|
||||
using Xunit;
|
||||
using MathNet.Numerics.Distributions;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// FdistValidationTests — validates against known mathematical properties
|
||||
/// of the F-Distribution CDF and against MathNet.Numerics FisherSnedecor.
|
||||
/// Known-value tests call Fdist.FCdf directly (bypassing windowing) so results
|
||||
/// are exact closed-form comparisons with tolerance 1e-9.
|
||||
/// </summary>
|
||||
public class FdistValidationTests
|
||||
{
|
||||
private const double Tolerance = 1e-9;
|
||||
private const double LooseTolerance = 1e-6;
|
||||
|
||||
// ─── Known-value tests via FCdf static method vs MathNet ─────────────────
|
||||
// F(x; d1, d2) = I(d1*x/(d1*x+d2), d1/2, d2/2)
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.0, 1, 1)] // F(0; d1, d2) = 0 always
|
||||
[InlineData(0.0, 5, 5)]
|
||||
[InlineData(0.0, 10, 2)]
|
||||
public void FCdf_AtZero_IsAlwaysZero(double x, int d1, int d2)
|
||||
{
|
||||
Assert.Equal(0.0, Fdist.FCdf(x, d1, d2), Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(-0.1, 1, 1)]
|
||||
[InlineData(-1.0, 5, 5)]
|
||||
[InlineData(-100.0, 2, 3)]
|
||||
public void FCdf_Negative_IsAlwaysZero(double x, int d1, int d2)
|
||||
{
|
||||
Assert.Equal(0.0, Fdist.FCdf(x, d1, d2), Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(100.0, 1, 1, 0.90)] // F(1,1) is heavy-tailed; F(100) ≈ 0.936
|
||||
[InlineData(100.0, 5, 5, 0.99)]
|
||||
[InlineData(100.0, 10, 2, 0.99)]
|
||||
public void FCdf_AtLargeX_ApproachesOne(double x, int d1, int d2, double minExpected)
|
||||
{
|
||||
double cdf = Fdist.FCdf(x, d1, d2);
|
||||
Assert.True(cdf > minExpected, $"F({x}; {d1},{d2}) = {cdf} should be > {minExpected}");
|
||||
}
|
||||
|
||||
// ─── MathNet.Numerics cross-validation ───────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1, 1)]
|
||||
[InlineData(2.0, 1, 1)]
|
||||
[InlineData(0.5, 2, 3)]
|
||||
[InlineData(1.5, 5, 5)]
|
||||
[InlineData(0.8, 10, 2)]
|
||||
[InlineData(3.0, 3, 7)]
|
||||
[InlineData(0.25, 2, 10)]
|
||||
[InlineData(5.0, 5, 10)]
|
||||
[InlineData(0.1, 1, 5)]
|
||||
[InlineData(2.5, 8, 4)]
|
||||
public void FCdf_VsMathNet_KnownValues(double x, int d1, int d2)
|
||||
{
|
||||
var dist = new FisherSnedecor(d1, d2);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Fdist.FCdf(x, d1, d2);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.0, 1, 1)]
|
||||
[InlineData(2.0, 5, 5)]
|
||||
[InlineData(0.5, 2, 3)]
|
||||
[InlineData(1.5, 10, 10)]
|
||||
[InlineData(0.8, 3, 7)]
|
||||
public void StaticCdf_VsMathNet_KnownValues(double x, int d1, int d2)
|
||||
{
|
||||
var dist = new FisherSnedecor(d1, d2);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Fdist.StaticCdf(x, d1, d2);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
// ─── Monotonicity ─────────────────────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1, 1)]
|
||||
[InlineData(5, 5)]
|
||||
[InlineData(2, 10)]
|
||||
[InlineData(10, 3)]
|
||||
public void FCdf_MonotonicIncreasing(int d1, int d2)
|
||||
{
|
||||
double prev = -1.0;
|
||||
|
||||
for (int i = 0; i <= 30; i++)
|
||||
{
|
||||
double x = i * 0.2;
|
||||
double cdf = Fdist.FCdf(x, d1, d2);
|
||||
Assert.True(cdf >= prev - LooseTolerance,
|
||||
$"CDF not monotonic at x={x} (d1={d1}, d2={d2}): got {cdf}, prev={prev}");
|
||||
prev = cdf;
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Output bounded [0, 1] ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void FdistCdf_OutputBounded_Zero_To_One()
|
||||
{
|
||||
int count = 200;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 66001);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Fdist(d1: 5, d2: 5, 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 → F-CDF at 5.0 (xNorm=0.5, xF=5) ────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1, 1)]
|
||||
[InlineData(5, 5)]
|
||||
[InlineData(2, 3)]
|
||||
[InlineData(10, 5)]
|
||||
public void FdistCdf_FlatRange_ReturnsCdfAtFive(int d1, int d2)
|
||||
{
|
||||
var ind = new Fdist(d1, d2, period: 20);
|
||||
var time = DateTime.UtcNow;
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
ind.Update(new TValue(time.AddSeconds(i), 100.0));
|
||||
}
|
||||
|
||||
double expected = Fdist.FCdf(5.0, d1, d2);
|
||||
Assert.Equal(expected, ind.Last.Value, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Streaming vs MathNet on raw (unnormalized) values ───────────────────
|
||||
|
||||
[Fact]
|
||||
public void FCdf_MultiplePoints_AllMatchMathNet()
|
||||
{
|
||||
int d1 = 5, d2 = 5;
|
||||
var dist = new FisherSnedecor(d1, d2);
|
||||
|
||||
double[] testX = { 0.0, 0.1, 0.5, 1.0, 2.0, 5.0, 10.0 };
|
||||
|
||||
foreach (double x in testX)
|
||||
{
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Fdist.FCdf(x, d1, d2);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── 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: 66002);
|
||||
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 = Fdist.Batch(bars.Close, d1: 5, d2: 5, period: 30);
|
||||
double[] spanResult = new double[count];
|
||||
Fdist.Batch(rawValues, spanResult, d1: 5, d2: 5, period: 30);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
Assert.Equal(tseriesResult[i].Value, spanResult[i], Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Streaming convergence ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void FdistCdf_HighPeriod_StillConverges()
|
||||
{
|
||||
int period = 200;
|
||||
var indicator = new Fdist(d1: 5, d2: 5, period: period);
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 66003);
|
||||
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, 1, 5)]
|
||||
[InlineData(2, 3, 14)]
|
||||
[InlineData(5, 5, 20)]
|
||||
[InlineData(10, 2, 30)]
|
||||
[InlineData(1, 10, 10)]
|
||||
public void FdistCdf_ParameterCombos_OutputBounded(int d1, int d2, int period)
|
||||
{
|
||||
int count = period + 50;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 66004 + d1 * 100 + d2);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Fdist(d1, d2, 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} (d1={d1}, d2={d2}, period={period})");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Large dataset stable ─────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void FdistCdf_LargeDataset_Stable()
|
||||
{
|
||||
int count = 2000;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 66005);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Fdist(d1: 5, d2: 5, 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}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Complementary property F(x;d1,d2) = 1 - G(1/x;d2,d1) ──────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.5, 5, 5)]
|
||||
[InlineData(1.0, 3, 7)]
|
||||
[InlineData(2.0, 2, 4)]
|
||||
[InlineData(0.25, 4, 8)]
|
||||
public void FCdf_ComplementaryProperty(double x, int d1, int d2)
|
||||
{
|
||||
// F(x; d1, d2) = 1 - F(1/x; d2, d1) — the reciprocal (swapped-DoF) relation
|
||||
double direct = Fdist.FCdf(x, d1, d2);
|
||||
// Use local variables to avoid S2234 name-order false positive when intentionally swapping d1/d2
|
||||
double xRecip = 1.0 / x;
|
||||
int swappedD1 = d2;
|
||||
int swappedD2 = d1;
|
||||
double reciprocal = Fdist.FCdf(xRecip, swappedD1, swappedD2);
|
||||
Assert.Equal(1.0, direct + reciprocal, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Symmetric case (d1=d2=n) median near 1 ──────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1)]
|
||||
[InlineData(5)]
|
||||
[InlineData(10)]
|
||||
public void FCdf_SymmetricDoF_MedianIsOne(int n)
|
||||
{
|
||||
// When d1==d2, the F distribution median is 1.0 (approx) → CDF(1) ≈ 0.5
|
||||
double cdf = Fdist.FCdf(1.0, n, n);
|
||||
Assert.Equal(0.5, cdf, 1e-6);
|
||||
}
|
||||
|
||||
// ─── Extreme prices don't blow up ─────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void FdistCdf_ExtremePrices_StillInRange()
|
||||
{
|
||||
var indicator = new Fdist(d1: 5, d2: 5, 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}");
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user