Files
QuanTAlib/lib/statistics/kurtosis/Kurtosis.Validation.Tests.cs
T
Miha Kralj b3a64f18fa 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.
2026-02-16 16:54:36 -08:00

43 lines
1.2 KiB
C#

using QuanTAlib.Tests;
using MathNet.Numerics.Statistics;
namespace QuanTAlib.Validation;
public sealed class KurtosisValidationTests : IDisposable
{
private readonly ValidationTestData _data = new();
public void Dispose()
{
_data.Dispose();
}
[Fact]
public void Kurtosis_Matches_MathNet()
{
const int period = 20;
var kurtosis = new Kurtosis(period, isPopulation: false);
var popKurtosis = new Kurtosis(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
for (int i = 0; i < input.Length; i++)
{
var val = kurtosis.Update(new TValue(quotes[i].Date, input[i]));
var popVal = popKurtosis.Update(new TValue(quotes[i].Date, input[i]));
// Validate last 100 bars
if (i >= input.Length - 100)
{
var window = input[(i - period + 1)..(i + 1)];
double expected = window.Kurtosis();
double expectedPop = window.PopulationKurtosis();
Assert.Equal(expected, val.Value, 1e-4);
Assert.Equal(expectedPop, popVal.Value, 1e-4);
}
}
}
}