mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-13 08:08:05 +00:00
- 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.
133 lines
3.3 KiB
C#
133 lines
3.3 KiB
C#
namespace QuanTAlib.Tests;
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using Xunit;
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public class IlrsValidationTests
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{
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private const int DataCount = 5000;
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private readonly TSeries _data;
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public IlrsValidationTests()
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{
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 123);
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_data = gbm.Fetch(DataCount, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1)).Close;
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}
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[Fact]
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public void Batch_Matches_Streaming()
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{
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const int period = 14;
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var batchResult = Ilrs.Batch(_data, period);
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var ilrs = new Ilrs(period);
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for (int i = 0; i < _data.Count; i++)
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{
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ilrs.Update(_data[i]);
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Assert.Equal(batchResult.Values[i], ilrs.Last.Value, 1e-6);
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}
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}
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[Fact]
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public void Span_Matches_Streaming()
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{
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const int period = 14;
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var spanOutput = new double[_data.Count];
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Ilrs.Batch(_data.Values, spanOutput, period);
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var ilrs = new Ilrs(period);
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for (int i = 0; i < _data.Count; i++)
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{
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double expected = ilrs.Update(_data[i]).Value;
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Assert.Equal(expected, spanOutput[i], 1e-6);
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}
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}
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[Theory]
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[InlineData(2)]
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[InlineData(7)]
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[InlineData(14)]
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[InlineData(50)]
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public void DifferentPeriods_ProduceValidResults(int period)
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{
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var ilrs = new Ilrs(period);
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for (int i = 0; i < _data.Count; i++)
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{
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var result = ilrs.Update(_data[i]);
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Assert.True(double.IsFinite(result.Value), $"Non-finite at bar {i}, period {period}");
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}
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Assert.True(ilrs.IsHot);
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}
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[Fact]
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public void ConstantInput_ConvergesToConstant()
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{
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const int period = 14;
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const double price = 50.0;
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var ilrs = new Ilrs(period);
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for (int i = 0; i < 200; i++)
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{
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ilrs.Update(new TValue(DateTime.UtcNow, price));
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}
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Assert.Equal(price, ilrs.Last.Value, 1e-6);
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}
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[Fact]
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public void Calculate_ReturnsHotIndicator()
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{
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var (results, indicator) = Ilrs.Calculate(_data, 14);
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Assert.True(indicator.IsHot);
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Assert.Equal(_data.Count, results.Count);
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}
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[Fact]
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public void BarCorrection_Consistency()
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{
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const int period = 7;
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var ilrs = new Ilrs(period);
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for (int i = 0; i < 20; i++)
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{
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ilrs.Update(_data[i]);
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}
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var baseline = ilrs.Last.Value;
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// Apply correction then revert
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ilrs.Update(new TValue(DateTime.UtcNow, 999.0), isNew: false);
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Assert.NotEqual(baseline, ilrs.Last.Value);
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ilrs.Update(_data[19], isNew: false);
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Assert.Equal(baseline, ilrs.Last.Value, 1e-6);
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}
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[Fact]
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public void SubsetStability()
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{
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const int period = 14;
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// Run on first 100 bars
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var ilrs1 = new Ilrs(period);
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for (int i = 0; i < 100; i++)
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{
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ilrs1.Update(_data[i]);
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}
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double val100 = ilrs1.Last.Value;
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// Run on first 200 bars, check the output at bar 99 matches
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var ilrs2 = new Ilrs(period);
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double val100_from200 = 0;
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for (int i = 0; i < 200; i++)
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{
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ilrs2.Update(_data[i]);
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if (i == 99)
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{
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val100_from200 = ilrs2.Last.Value;
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}
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}
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Assert.Equal(val100, val100_from200, 1e-9);
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}
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}
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