Files
QuanTAlib/lib/trends_FIR/ilrs/Ilrs.Validation.Tests.cs
T
Miha Kralj 7253f61299 Add TRAMA implementation and comprehensive tests
- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic.
- Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks.
- Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations).
- Enhanced documentation for TRAMA, including performance profiles and quality metrics.
- Updated workspace configuration by removing unnecessary folder references.
2026-02-21 20:45:38 -08:00

133 lines
3.3 KiB
C#

namespace QuanTAlib.Tests;
using Xunit;
public class IlrsValidationTests
{
private const int DataCount = 5000;
private readonly TSeries _data;
public IlrsValidationTests()
{
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 123);
_data = gbm.Fetch(DataCount, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)).Close;
}
[Fact]
public void Batch_Matches_Streaming()
{
const int period = 14;
var batchResult = Ilrs.Batch(_data, period);
var ilrs = new Ilrs(period);
for (int i = 0; i < _data.Count; i++)
{
ilrs.Update(_data[i]);
Assert.Equal(batchResult.Values[i], ilrs.Last.Value, 1e-6);
}
}
[Fact]
public void Span_Matches_Streaming()
{
const int period = 14;
var spanOutput = new double[_data.Count];
Ilrs.Batch(_data.Values, spanOutput, period);
var ilrs = new Ilrs(period);
for (int i = 0; i < _data.Count; i++)
{
double expected = ilrs.Update(_data[i]).Value;
Assert.Equal(expected, spanOutput[i], 1e-6);
}
}
[Theory]
[InlineData(2)]
[InlineData(7)]
[InlineData(14)]
[InlineData(50)]
public void DifferentPeriods_ProduceValidResults(int period)
{
var ilrs = new Ilrs(period);
for (int i = 0; i < _data.Count; i++)
{
var result = ilrs.Update(_data[i]);
Assert.True(double.IsFinite(result.Value), $"Non-finite at bar {i}, period {period}");
}
Assert.True(ilrs.IsHot);
}
[Fact]
public void ConstantInput_ConvergesToConstant()
{
const int period = 14;
const double price = 50.0;
var ilrs = new Ilrs(period);
for (int i = 0; i < 200; i++)
{
ilrs.Update(new TValue(DateTime.UtcNow, price));
}
Assert.Equal(price, ilrs.Last.Value, 1e-6);
}
[Fact]
public void Calculate_ReturnsHotIndicator()
{
var (results, indicator) = Ilrs.Calculate(_data, 14);
Assert.True(indicator.IsHot);
Assert.Equal(_data.Count, results.Count);
}
[Fact]
public void BarCorrection_Consistency()
{
const int period = 7;
var ilrs = new Ilrs(period);
for (int i = 0; i < 20; i++)
{
ilrs.Update(_data[i]);
}
var baseline = ilrs.Last.Value;
// Apply correction then revert
ilrs.Update(new TValue(DateTime.UtcNow, 999.0), isNew: false);
Assert.NotEqual(baseline, ilrs.Last.Value);
ilrs.Update(_data[19], isNew: false);
Assert.Equal(baseline, ilrs.Last.Value, 1e-6);
}
[Fact]
public void SubsetStability()
{
const int period = 14;
// Run on first 100 bars
var ilrs1 = new Ilrs(period);
for (int i = 0; i < 100; i++)
{
ilrs1.Update(_data[i]);
}
double val100 = ilrs1.Last.Value;
// Run on first 200 bars, check the output at bar 99 matches
var ilrs2 = new Ilrs(period);
double val100_from200 = 0;
for (int i = 0; i < 200; i++)
{
ilrs2.Update(_data[i]);
if (i == 99)
{
val100_from200 = ilrs2.Last.Value;
}
}
Assert.Equal(val100, val100_from200, 1e-9);
}
}