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 +1,689 @@
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using Xunit;
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namespace QuanTAlib.Tests;
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public class WeibulldistTests
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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 Weibulldist();
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Assert.Equal("Weibulldist(1.50,1.00,14)", 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 Weibulldist(k: 2.0, lambda: 0.5, period: 20);
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Assert.Equal("Weibulldist(2.00,0.50,20)", 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_ZeroK_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Weibulldist(k: 0.0));
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Assert.Equal("k", ex.ParamName);
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}
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[Fact]
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public void Constructor_NegativeK_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Weibulldist(k: -1.0));
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Assert.Equal("k", ex.ParamName);
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}
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[Fact]
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public void Constructor_ZeroLambda_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Weibulldist(lambda: 0.0));
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Assert.Equal("lambda", ex.ParamName);
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}
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[Fact]
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public void Constructor_NegativeLambda_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Weibulldist(lambda: -1.0));
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Assert.Equal("lambda", 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 Weibulldist(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 Weibulldist(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_NegativePeriod_ThrowsArgumentException()
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{
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var ex = Assert.Throws<ArgumentException>(() => new Weibulldist(period: -1));
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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 Weibulldist(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 Weibulldist(k: 1.5, lambda: 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 Weibulldist(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 Weibulldist(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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// When current value equals window max, x=1.0 → high CDF value
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var indicator = new Weibulldist(k: 2.0, lambda: 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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// CDF(1.0, k=2, λ=1) = 1 - exp(-1) ≈ 0.6321
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Assert.True(indicator.Last.Value > 0.5, $"Expected > 0.5 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, x=0.0 → CDF(0, k, λ) = 0
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var indicator = new Weibulldist(k: 2.0, lambda: 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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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_ReturnsCdfAtHalf()
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{
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// When all values in window are identical, range=0 → x=0.5
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var indicator = new Weibulldist(k: 2.0, lambda: 1.0, 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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// CDF(0.5/1.0, k=2, λ=1) = 1 - exp(-0.5^2) = 1 - exp(-0.25)
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double expected = 1.0 - Math.Exp(-Math.Pow(0.5, 2.0));
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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 Weibulldist(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 = Weibulldist.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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Weibulldist.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 Weibulldist(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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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 Weibulldist(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 = Weibulldist.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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Weibulldist.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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Weibulldist.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_InvalidK_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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Weibulldist.Batch(src, dst, k: 0.0));
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Assert.Equal("k", ex.ParamName);
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||||
}
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||||
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[Fact]
|
||||
public void Batch_Span_NegativeK_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
|
||||
Weibulldist.Batch(src, dst, k: -0.5));
|
||||
Assert.Equal("k", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_InvalidLambda_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
|
||||
Weibulldist.Batch(src, dst, lambda: 0.0));
|
||||
Assert.Equal("lambda", ex.ParamName);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Batch_Span_NegativeLambda_ThrowsArgumentException()
|
||||
{
|
||||
double[] src = { 1.0, 2.0, 3.0 };
|
||||
double[] dst = new double[3];
|
||||
var ex = Assert.Throws<ArgumentException>(() =>
|
||||
Weibulldist.Batch(src, dst, lambda: -1.0));
|
||||
Assert.Equal("lambda", 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>(() =>
|
||||
Weibulldist.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];
|
||||
Weibulldist.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];
|
||||
Weibulldist.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];
|
||||
Weibulldist.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];
|
||||
Weibulldist.Batch(src, spanOut, period: 14);
|
||||
|
||||
var streaming = new Weibulldist(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 Weibulldist(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 Weibulldist(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 Weibulldist(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: Shape/scale parameter effects ───────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void DifferentShapes_ProduceDifferentResults()
|
||||
{
|
||||
// Verify at a non-boundary interior point (x=0.4, lambda=1.0) that different k values
|
||||
// produce provably distinct CDF outputs — no GBM needed for this mathematical property
|
||||
const double x = 0.4;
|
||||
const double lambda = 1.0;
|
||||
|
||||
double cdf05 = Weibulldist.StaticCdf(x, k: 0.5, lambda: lambda); // concave, fast rise
|
||||
double cdf15 = Weibulldist.StaticCdf(x, k: 1.5, lambda: lambda); // intermediate
|
||||
double cdf50 = Weibulldist.StaticCdf(x, k: 5.0, lambda: lambda); // sigmoidal, slow rise
|
||||
|
||||
// All in [0,1]
|
||||
Assert.InRange(cdf05, 0.0, 1.0);
|
||||
Assert.InRange(cdf15, 0.0, 1.0);
|
||||
Assert.InRange(cdf50, 0.0, 1.0);
|
||||
|
||||
// k=0.5 (concave) > k=1.5 > k=5.0 (sigmoidal) at x=0.4 < lambda: strict ordering
|
||||
Assert.True(cdf05 > cdf15 + 1e-6, $"k=0.5 ({cdf05:G10}) should exceed k=1.5 ({cdf15:G10}) at x={x}");
|
||||
Assert.True(cdf15 > cdf50 + 1e-6, $"k=1.5 ({cdf15:G10}) should exceed k=5.0 ({cdf50:G10}) at x={x}");
|
||||
}
|
||||
|
||||
[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) = Weibulldist.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 +1,355 @@
|
||||
using Xunit;
|
||||
using MathNet.Numerics.Distributions;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// WeibulldistValidationTests — validates against known mathematical properties
|
||||
/// of the Weibull CDF and cross-validates with MathNet.Numerics.Distributions.Weibull.
|
||||
/// StaticCdf tests call Weibulldist.StaticCdf directly (bypassing windowing)
|
||||
/// so results are exact closed-form comparisons.
|
||||
/// </summary>
|
||||
public class WeibulldistValidationTests
|
||||
{
|
||||
private const double Tolerance = 1e-9;
|
||||
private const double LooseTolerance = 1e-6;
|
||||
|
||||
// ─── Known-value tests via StaticCdf static method ───────────────────────
|
||||
// F(x; k, λ) = 1 - exp(-(x/λ)^k), closed-form.
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.0, 1.5, 1.0, 0.0)] // F(0; k, λ) = 0 always
|
||||
[InlineData(1.0, 1.0, 1.0, 0.6321205588285578)] // k=1: exponential, F(1;1,1) = 1-1/e
|
||||
[InlineData(1.0, 2.0, 1.0, 0.6321205588285578)] // F(λ; k, λ) = 1-1/e for any k (x=λ=1)
|
||||
[InlineData(1.0, 1.5, 1.0, 0.6321205588285578)] // F(λ; k, λ) = 1-1/e (x=λ=1)
|
||||
[InlineData(1.0, 3.0, 1.0, 0.6321205588285578)] // F(λ; k, λ) = 1-1/e (x=λ=1)
|
||||
[InlineData(2.0, 2.0, 2.0, 0.6321205588285578)] // F(λ=2; k=2, λ=2) = 1-1/e
|
||||
[InlineData(0.5, 1.0, 1.0, 0.3934693402873666)] // k=1: F(0.5;1,1)=1-exp(-0.5)
|
||||
[InlineData(1.0, 2.0, 2.0, 0.2211992169285951)] // F(1;2,2)=1-exp(-0.25)
|
||||
[InlineData(2.0, 1.0, 1.0, 0.8646647167633873)] // k=1: F(2;1,1)=1-exp(-2)
|
||||
public void StaticCdf_KnownValues(double x, double k, double lambda, double expected)
|
||||
{
|
||||
double actual = Weibulldist.StaticCdf(x, k, lambda);
|
||||
Assert.Equal(expected, actual, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Boundary conditions ─────────────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.5, 1.0)]
|
||||
[InlineData(2.0, 2.0)]
|
||||
[InlineData(5.0, 0.5)]
|
||||
[InlineData(0.5, 3.0)]
|
||||
public void StaticCdf_AtZero_IsAlwaysZero(double k, double lambda)
|
||||
{
|
||||
Assert.Equal(0.0, Weibulldist.StaticCdf(0.0, k, lambda), Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.5, 1.0)]
|
||||
[InlineData(2.0, 0.5)]
|
||||
[InlineData(0.5, 2.0)]
|
||||
public void StaticCdf_AtNegative_IsAlwaysZero(double k, double lambda)
|
||||
{
|
||||
Assert.Equal(0.0, Weibulldist.StaticCdf(-1.0, k, lambda), Tolerance);
|
||||
Assert.Equal(0.0, Weibulldist.StaticCdf(-100.0, k, lambda), Tolerance);
|
||||
}
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.5, 1.0)]
|
||||
[InlineData(2.0, 2.0)]
|
||||
[InlineData(0.5, 0.5)]
|
||||
public void StaticCdf_AtLargeX_ApproachesOne(double k, double lambda)
|
||||
{
|
||||
double cdf = Weibulldist.StaticCdf(1000.0, k, lambda);
|
||||
Assert.Equal(1.0, cdf, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Characteristic life property: F(λ; k, λ) = 1 - 1/e for any k ───────
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.5, 0.5)]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(1.5, 1.0)]
|
||||
[InlineData(2.0, 2.0)]
|
||||
[InlineData(3.6, 0.5)]
|
||||
[InlineData(5.0, 3.0)]
|
||||
public void StaticCdf_AtCharacteristicLife_Is1MinusInvE(double k, double lambda)
|
||||
{
|
||||
// CDF(lambda, k, lambda) = 1 - exp(-(lambda/lambda)^k) = 1 - exp(-1) for any k
|
||||
double expected = 1.0 - Math.Exp(-1.0); // ≈ 0.6321205588285578
|
||||
double actual = Weibulldist.StaticCdf(lambda, k, lambda);
|
||||
Assert.Equal(expected, actual, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── k=1 reduces to Exponential distribution ─────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.5, 1.0)]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(2.0, 2.0)]
|
||||
[InlineData(0.3, 0.5)]
|
||||
public void StaticCdf_KEquals1_MatchesExponential(double x, double lambda)
|
||||
{
|
||||
// Weibull(k=1, λ) = Exponential(rate=1/λ)
|
||||
double weibull = Weibulldist.StaticCdf(x, 1.0, lambda);
|
||||
double exponential = 1.0 - Math.Exp(-x / lambda);
|
||||
Assert.Equal(exponential, weibull, Tolerance);
|
||||
}
|
||||
|
||||
// ─── Monotonicity ────────────────────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.5)]
|
||||
[InlineData(1.0)]
|
||||
[InlineData(2.0)]
|
||||
[InlineData(5.0)]
|
||||
public void StaticCdf_MonotonicIncreasing(double k)
|
||||
{
|
||||
double lambda = 1.0;
|
||||
double prev = -1.0;
|
||||
|
||||
for (int i = 0; i <= 30; i++)
|
||||
{
|
||||
double x = i * 0.1;
|
||||
double cdf = Weibulldist.StaticCdf(x, k, lambda);
|
||||
Assert.True(cdf >= prev - LooseTolerance,
|
||||
$"CDF not monotonic at x={x}, k={k}: got {cdf}, prev={prev}");
|
||||
prev = cdf;
|
||||
}
|
||||
}
|
||||
|
||||
// ─── MathNet cross-validation ─────────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(0.5, 1.5, 1.0)]
|
||||
[InlineData(1.0, 1.0, 1.0)]
|
||||
[InlineData(1.0, 2.0, 1.0)]
|
||||
[InlineData(0.5, 2.0, 0.5)]
|
||||
[InlineData(2.0, 0.5, 2.0)]
|
||||
[InlineData(1.5, 3.0, 1.5)]
|
||||
[InlineData(3.0, 1.5, 2.0)]
|
||||
[InlineData(0.1, 5.0, 1.0)]
|
||||
[InlineData(0.9, 2.0, 1.0)]
|
||||
[InlineData(2.5, 1.5, 2.0)]
|
||||
public void StaticCdf_MatchesMathNet(double x, double k, double lambda)
|
||||
{
|
||||
// MathNet Weibull(shape, scale) = Weibull(k, lambda) — same parameterization
|
||||
var dist = new Weibull(k, lambda);
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Weibulldist.StaticCdf(x, k, lambda);
|
||||
Assert.Equal(expected, actual, Tolerance);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StaticCdf_MathNet_ExtensiveComparison()
|
||||
{
|
||||
double[] kValues = { 0.5, 1.0, 1.5, 2.0, 3.6, 5.0 };
|
||||
double[] lambdaValues = { 0.5, 1.0, 2.0 };
|
||||
double[] xValues = { 0.0, 0.1, 0.5, 1.0, 1.5, 2.0, 5.0, 10.0 };
|
||||
|
||||
foreach (double k in kValues)
|
||||
{
|
||||
foreach (double lambda in lambdaValues)
|
||||
{
|
||||
var dist = new Weibull(k, lambda);
|
||||
foreach (double x in xValues)
|
||||
{
|
||||
double expected = dist.CumulativeDistribution(x);
|
||||
double actual = Weibulldist.StaticCdf(x, k, lambda);
|
||||
// MathNet uses internal Taylor approximations; tolerance 1e-8 covers its rounding
|
||||
Assert.Equal(expected, actual, LooseTolerance);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Flat range → F(0.5; k, λ) ───────────────────────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(1.5, 1.0)]
|
||||
[InlineData(2.0, 0.5)]
|
||||
[InlineData(1.0, 1.0)]
|
||||
[InlineData(3.0, 2.0)]
|
||||
public void WeibulldistCdf_FlatRange_ReturnsCdfAtHalf(double k, double lambda)
|
||||
{
|
||||
var ind = new Weibulldist(k, lambda, 20);
|
||||
var time = DateTime.UtcNow;
|
||||
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
ind.Update(new TValue(time.AddSeconds(i), 100.0));
|
||||
}
|
||||
|
||||
// Streaming normalizes to [0,1] then multiplies by invLambda before pow
|
||||
// Equivalent: 1 - exp(-(0.5 * (1/lambda))^k)
|
||||
double expectedDirect = 1.0 - Math.Exp(-Math.Pow(0.5 * (1.0 / lambda), k));
|
||||
Assert.Equal(expectedDirect, ind.Last.Value, LooseTolerance);
|
||||
}
|
||||
|
||||
// ─── Output bounded [0, 1] ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void WeibulldistCdf_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 Weibulldist(k: 1.5, lambda: 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]");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── 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 = Weibulldist.Batch(bars.Close, period: 30);
|
||||
double[] spanResult = new double[count];
|
||||
Weibulldist.Batch(rawValues, spanResult, period: 30);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
Assert.Equal(tseriesResult[i].Value, spanResult[i], Tolerance);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Streaming convergence ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void WeibulldistCdf_HighPeriod_StillConverges()
|
||||
{
|
||||
int period = 200;
|
||||
var indicator = new Weibulldist(k: 2.0, lambda: 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}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void WeibulldistCdf_ExtremePrices_StillInRange()
|
||||
{
|
||||
var indicator = new Weibulldist(k: 1.5, lambda: 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}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Parameter combos all produce output in range ─────────────────────────
|
||||
|
||||
[Theory]
|
||||
[InlineData(5, 0.5, 1.0)]
|
||||
[InlineData(14, 1.5, 1.0)]
|
||||
[InlineData(50, 2.0, 0.5)]
|
||||
[InlineData(20, 3.6, 2.0)]
|
||||
[InlineData(30, 5.0, 1.0)]
|
||||
public void WeibulldistCdf_ParameterCombos_OutputBounded(int period, double k, double lambda)
|
||||
{
|
||||
int count = period + 50;
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 73004 + period);
|
||||
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
var indicator = new Weibulldist(k, lambda, 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} (k={k}, lambda={lambda}, period={period})");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Large dataset: stable ────────────────────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void WeibulldistCdf_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 Weibulldist(k: 1.5, lambda: 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}");
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Survival function: F(x) + S(x) = 1 ─────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void StaticCdf_PlusSurvival_IsOne()
|
||||
{
|
||||
double[] kValues = { 0.5, 1.0, 2.0, 5.0 };
|
||||
double[] lambdaValues = { 0.5, 1.0, 2.0 };
|
||||
double[] xs = { 0.1, 0.5, 1.0, 2.0 };
|
||||
|
||||
foreach (double k in kValues)
|
||||
{
|
||||
foreach (double lambda in lambdaValues)
|
||||
{
|
||||
foreach (double x in xs)
|
||||
{
|
||||
double cdf = Weibulldist.StaticCdf(x, k, lambda);
|
||||
double survival = Math.Exp(-Math.Pow(x / lambda, k));
|
||||
Assert.Equal(1.0, cdf + survival, LooseTolerance);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ─── Streaming vs MathNet cross-validation ────────────────────────────────
|
||||
|
||||
[Fact]
|
||||
public void WeibulldistCdf_StreamingOutput_MatchesMathNetOnKnownData()
|
||||
{
|
||||
// Feed known values so streaming result is predictable via MathNet
|
||||
// Period=3, strictly ascending: first 3 bars warm up, then check bar 3
|
||||
var indicator = new Weibulldist(k: 2.0, lambda: 1.0, period: 3);
|
||||
var time = DateTime.UtcNow;
|
||||
|
||||
// Values: 100, 102, 104 → x = (104-100)/(104-100) = 1.0
|
||||
indicator.Update(new TValue(time, 100.0));
|
||||
indicator.Update(new TValue(time.AddMinutes(1), 102.0));
|
||||
indicator.Update(new TValue(time.AddMinutes(2), 104.0));
|
||||
|
||||
// After 3 bars: window = [100,102,104], min=100, max=104, range=4
|
||||
// Current (104-100)/4 = 1.0 → x=1.0, CDF(1/1.0, k=2) = 1-exp(-1)
|
||||
double expected = 1.0 - Math.Exp(-Math.Pow(1.0, 2.0)); // = 1 - exp(-1) ≈ 0.6321
|
||||
Assert.Equal(expected, indicator.Last.Value, LooseTolerance);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user