mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-23 13:08:04 +00:00
Implement ZTEST: One-Sample t-Test Statistic with validation tests
- Added Ztest class to compute the one-sample t-statistic using sample standard deviation with Bessel correction. - Implemented validation tests for Ztest to ensure accuracy against manual calculations and PineScript. - Updated documentation for Ztest, detailing its mathematical foundation, performance profile, and common pitfalls. - Adjusted NDepend badges to reflect changes in code metrics after implementation. - Updated missing indicators report to reflect the completion of statistical indicators, including ZTEST.
This commit is contained in:
Miha Kralj
@@ -0,0 +1,204 @@
|
||||
using Xunit;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// Validation tests for JB — self-consistency and mathematical properties.
|
||||
/// No external library implements rolling Jarque-Bera, so validation is based
|
||||
/// on known mathematical properties and analytical results.
|
||||
/// </summary>
|
||||
public class JbValidationTests
|
||||
{
|
||||
[Fact]
|
||||
public void ConstantSeries_JbIsZero()
|
||||
{
|
||||
var jb = new Jb(20);
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
jb.Update(new TValue(DateTime.UtcNow, 100.0));
|
||||
}
|
||||
Assert.Equal(0.0, jb.Last.Value, 10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void SymmetricData_SkewnessTermIsZero()
|
||||
{
|
||||
// Symmetric data around mean → skewness ≈ 0
|
||||
// JB should be driven entirely by excess kurtosis term
|
||||
var jb = new Jb(11);
|
||||
for (int i = -5; i <= 5; i++)
|
||||
{
|
||||
jb.Update(new TValue(DateTime.UtcNow, i));
|
||||
}
|
||||
// For uniform-like data, excess kurtosis ≈ -1.2, so JB > 0
|
||||
Assert.True(jb.Last.Value >= 0.0);
|
||||
Assert.True(double.IsFinite(jb.Last.Value));
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void LinearSequence_KnownJb()
|
||||
{
|
||||
// Window of {1,...,20}: uniform distribution
|
||||
// Population skewness ≈ 0, excess kurtosis ≈ -1.2
|
||||
// JB = (20/6) * (S² + EK²/4) ≈ 1.212 (exact depends on FP rounding in moment sums)
|
||||
var jb = new Jb(20);
|
||||
for (int i = 1; i <= 20; i++)
|
||||
{
|
||||
jb.Update(new TValue(DateTime.UtcNow, i));
|
||||
}
|
||||
// Verify JB is in expected range for uniform-like data
|
||||
Assert.True(jb.Last.Value > 1.0 && jb.Last.Value < 1.5,
|
||||
$"JB for linear sequence {1..20} expected ~1.2, got {jb.Last.Value}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void SkewedData_LargerJb()
|
||||
{
|
||||
// Right-skewed data should produce larger JB than symmetric
|
||||
var jbSymmetric = new Jb(10);
|
||||
for (int i = -5; i <= 4; i++)
|
||||
{
|
||||
jbSymmetric.Update(new TValue(DateTime.UtcNow, i));
|
||||
}
|
||||
|
||||
var jbSkewed = new Jb(10);
|
||||
double[] skewed = [1, 1, 1, 2, 2, 3, 5, 10, 20, 100];
|
||||
for (int i = 0; i < skewed.Length; i++)
|
||||
{
|
||||
jbSkewed.Update(new TValue(DateTime.UtcNow, skewed[i]));
|
||||
}
|
||||
|
||||
Assert.True(jbSkewed.Last.Value > jbSymmetric.Last.Value,
|
||||
$"Skewed JB ({jbSkewed.Last.Value}) should exceed symmetric JB ({jbSymmetric.Last.Value})");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Deterministic_SameInputSameOutput()
|
||||
{
|
||||
int period = 10;
|
||||
var jb1 = new Jb(period);
|
||||
var jb2 = new Jb(period);
|
||||
|
||||
var rng1 = new GBM(seed: 42);
|
||||
var rng2 = new GBM(seed: 42);
|
||||
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
var bar1 = rng1.Next();
|
||||
var bar2 = rng2.Next();
|
||||
jb1.Update(new TValue(bar1.Time, bar1.Close));
|
||||
jb2.Update(new TValue(bar2.Time, bar2.Close));
|
||||
}
|
||||
|
||||
Assert.Equal(jb1.Last.Value, jb2.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void BatchVsStreaming_Match()
|
||||
{
|
||||
int period = 10;
|
||||
int bars = 100;
|
||||
var rng = new GBM();
|
||||
var source = new TSeries();
|
||||
for (int i = 0; i < bars; i++)
|
||||
{
|
||||
var bar = rng.Next();
|
||||
source.Add(new TValue(bar.Time, bar.Close));
|
||||
}
|
||||
|
||||
var streaming = new Jb(period);
|
||||
double lastStreaming = 0;
|
||||
for (int i = 0; i < bars; i++)
|
||||
{
|
||||
streaming.Update(source[i]);
|
||||
lastStreaming = streaming.Last.Value;
|
||||
}
|
||||
|
||||
var batchSeries = Jb.Batch(source, period);
|
||||
Assert.Equal(lastStreaming, batchSeries[bars - 1].Value, 1e-8);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void SpanVsStreaming_Match()
|
||||
{
|
||||
int period = 10;
|
||||
int bars = 100;
|
||||
var rng = new GBM();
|
||||
var source = new TSeries();
|
||||
for (int i = 0; i < bars; i++)
|
||||
{
|
||||
var bar = rng.Next();
|
||||
source.Add(new TValue(bar.Time, bar.Close));
|
||||
}
|
||||
|
||||
var streaming = new Jb(period);
|
||||
var streamResults = new double[bars];
|
||||
for (int i = 0; i < bars; i++)
|
||||
{
|
||||
streaming.Update(source[i]);
|
||||
streamResults[i] = streaming.Last.Value;
|
||||
}
|
||||
|
||||
var spanOutput = new double[bars];
|
||||
Jb.Batch(source.Values, spanOutput.AsSpan(), period);
|
||||
|
||||
for (int i = period - 1; i < bars; i++)
|
||||
{
|
||||
Assert.Equal(streamResults[i], spanOutput[i], 1e-8);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void CalculateBridge_ReturnsIndicatorAndResults()
|
||||
{
|
||||
int period = 10;
|
||||
var rng = new GBM();
|
||||
var source = new TSeries();
|
||||
for (int i = 0; i < 50; i++)
|
||||
{
|
||||
var bar = rng.Next();
|
||||
source.Add(new TValue(bar.Time, bar.Close));
|
||||
}
|
||||
|
||||
var (results, indicator) = Jb.Calculate(source, period);
|
||||
Assert.Equal(50, results.Count);
|
||||
Assert.True(indicator.IsHot);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void JbNonNegative_ForAllInputs()
|
||||
{
|
||||
var jb = new Jb(20);
|
||||
var rng = new GBM();
|
||||
for (int i = 0; i < 200; i++)
|
||||
{
|
||||
var bar = rng.Next();
|
||||
jb.Update(new TValue(bar.Time, bar.Close));
|
||||
Assert.True(jb.Last.Value >= 0.0, $"JB negative at bar {i}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void OutlierIncreases_Jb()
|
||||
{
|
||||
// Adding outlier to normal-ish data should increase JB
|
||||
var jb = new Jb(10);
|
||||
for (int i = 1; i <= 9; i++)
|
||||
{
|
||||
jb.Update(new TValue(DateTime.UtcNow, 50.0 + i));
|
||||
}
|
||||
jb.Update(new TValue(DateTime.UtcNow, 55.0));
|
||||
double normalJb = jb.Last.Value;
|
||||
|
||||
var jbOutlier = new Jb(10);
|
||||
for (int i = 1; i <= 9; i++)
|
||||
{
|
||||
jbOutlier.Update(new TValue(DateTime.UtcNow, 50.0 + i));
|
||||
}
|
||||
jbOutlier.Update(new TValue(DateTime.UtcNow, 500.0));
|
||||
double outlierJb = jbOutlier.Last.Value;
|
||||
|
||||
Assert.True(outlierJb > normalJb,
|
||||
$"Outlier JB ({outlierJb}) should exceed normal JB ({normalJb})");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user