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https://github.com/mihakralj/QuanTAlib.git
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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.
This commit is contained in:
Miha Kralj
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namespace QuanTAlib.Tests;
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public class Sp15ValidationTests
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{
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private static TSeries MakeSeries(int count = 500)
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{
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var source = new TSeries();
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var gbm = new GBM(startPrice: 100, seed: 42);
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for (int i = 0; i < count; i++)
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{
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var bar = gbm.Next();
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source.Add(bar.C);
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}
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return source;
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}
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[Fact]
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public void BatchVsStreaming_Match()
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{
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var source = MakeSeries(100);
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// Streaming
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var sp15 = new Sp15();
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var streaming = new double[100];
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for (int i = 0; i < 100; i++)
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{
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streaming[i] = sp15.Update(source[i]).Value;
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}
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// Batch
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var batchResult = Sp15.Batch(source);
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for (int i = 0; i < 100; i++)
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{
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Assert.Equal(streaming[i], batchResult[i].Value, 1e-10);
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}
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}
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[Fact]
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public void SpanVsStreaming_Match()
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{
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var source = MakeSeries(100);
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// Streaming
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var sp15 = new Sp15();
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var streaming = new double[100];
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for (int i = 0; i < 100; i++)
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{
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streaming[i] = sp15.Update(source[i]).Value;
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}
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// Span
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double[] spanOutput = new double[100];
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Sp15.Batch(source.Values, spanOutput);
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for (int i = 0; i < 100; i++)
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{
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Assert.Equal(streaming[i], spanOutput[i], 1e-10);
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}
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}
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[Fact]
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public void LinearPolynomial_ExactFit()
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{
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var sp15 = new Sp15();
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const int total = 50;
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const double a = 5.0, b = 3.0;
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for (int i = 0; i < total; i++)
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{
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double val = a + b * i;
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sp15.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
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}
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int centerIdx = total - 1 - 7;
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double expected = a + b * centerIdx;
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Assert.Equal(expected, sp15.Last.Value, 1e-6);
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}
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[Fact]
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public void QuadraticPolynomial_ExactFit()
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{
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var sp15 = new Sp15();
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const int total = 50;
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const double a = 2.0, b = 1.5, c = 0.3;
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for (int i = 0; i < total; i++)
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{
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double val = a + b * i + c * i * i;
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sp15.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
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}
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int centerIdx = total - 1 - 7;
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double expected = a + b * centerIdx + c * centerIdx * centerIdx;
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Assert.Equal(expected, sp15.Last.Value, 1e-4);
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}
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[Fact]
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public void CubicPolynomial_ExactFit()
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{
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var sp15 = new Sp15();
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const int total = 50;
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const double a = 1.0, b = 0.5, c = 0.1, d = 0.005;
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for (int i = 0; i < total; i++)
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{
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double val = a + b * i + c * i * i + d * i * i * i;
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sp15.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
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}
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int centerIdx = total - 1 - 7;
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double expected = a + b * centerIdx + c * centerIdx * centerIdx + d * centerIdx * centerIdx * centerIdx;
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Assert.Equal(expected, sp15.Last.Value, 1.0);
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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 source = MakeSeries(50);
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var (results, indicator) = Sp15.Calculate(source);
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Assert.True(indicator.IsHot);
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Assert.Equal(50, results.Count);
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}
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[Fact]
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public void ConstantPropagation_AllModes()
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{
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const double c = 77.0;
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const int len = 30;
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// Build constant series
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var source = new TSeries();
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for (int i = 0; i < len; i++)
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{
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source.Add(new TValue(DateTime.UtcNow.AddSeconds(i), c));
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}
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// Streaming
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var sp15 = new Sp15();
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for (int i = 0; i < len; i++)
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{
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sp15.Update(source[i]);
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}
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Assert.Equal(c, sp15.Last.Value, 1e-10);
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// Batch
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var batch = Sp15.Batch(source);
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for (int i = 15; i < len; i++)
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{
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Assert.Equal(c, batch[i].Value, 1e-10);
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}
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// Span
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double[] spanOut = new double[len];
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Sp15.Batch(source.Values, spanOut);
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for (int i = 15; i < len; i++)
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{
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Assert.Equal(c, spanOut[i], 1e-10);
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}
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}
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[Fact]
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public void WeightSymmetry_ForwardReverse()
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{
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// Symmetric weights: reversing input gives same center value for linear input
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var sp15Fwd = new Sp15();
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var sp15Rev = new Sp15();
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double[] forward = new double[15];
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double[] reverse = new double[15];
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for (int i = 0; i < 15; i++)
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{
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forward[i] = 10.0 + 2.0 * i;
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reverse[i] = 10.0 + 2.0 * (14 - i);
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}
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TValue fwdResult = default;
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TValue revResult = default;
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for (int i = 0; i < 15; i++)
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{
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fwdResult = sp15Fwd.Update(new TValue(DateTime.UtcNow.AddSeconds(i), forward[i]));
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revResult = sp15Rev.Update(new TValue(DateTime.UtcNow.AddSeconds(i), reverse[i]));
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}
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// For linear input centered at i=7: forward center = 10+14=24, reverse center = 10+14=24
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// Both should give the same result for symmetric weights applied to symmetric-about-center linear data
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double expected = 2.0 * (10.0 + 2.0 * 7.0);
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Assert.Equal(expected, fwdResult.Value + revResult.Value, 1e-6);
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}
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[Fact]
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public void Period4_Sinusoid_Suppressed()
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{
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// Spencer filter zeros out period-4 signals
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var sp15 = new Sp15();
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const int n = 60;
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for (int i = 0; i < n; i++)
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{
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// Pure period-4 sinusoid centered at 100
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double val = 100.0 + 10.0 * Math.Sin(2.0 * Math.PI * i / 4.0);
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sp15.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
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}
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// After warmup, the output should be ~100 (sinusoid suppressed)
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Assert.Equal(100.0, sp15.Last.Value, 0.5);
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}
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[Fact]
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public void Period5_Sinusoid_Suppressed()
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{
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// Spencer filter zeros out period-5 signals
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var sp15 = new Sp15();
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const int n = 60;
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for (int i = 0; i < n; i++)
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{
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double val = 100.0 + 10.0 * Math.Sin(2.0 * Math.PI * i / 5.0);
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sp15.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
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}
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Assert.Equal(100.0, sp15.Last.Value, 0.5);
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}
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[Fact]
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public void DifferentSeeds_ProduceDifferentResults()
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{
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var source1 = new TSeries();
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var gbm1 = new GBM(startPrice: 100, seed: 42);
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for (int i = 0; i < 30; i++)
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{
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source1.Add(gbm1.Next().C);
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}
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var source2 = new TSeries();
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var gbm2 = new GBM(startPrice: 100, seed: 99);
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for (int i = 0; i < 30; i++)
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{
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source2.Add(gbm2.Next().C);
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}
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var batch1 = Sp15.Batch(source1);
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var batch2 = Sp15.Batch(source2);
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// At least one value should differ
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bool anyDifferent = false;
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for (int i = 15; i < 30; i++)
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{
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if (Math.Abs(batch1[i].Value - batch2[i].Value) > 1e-6)
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{
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anyDifferent = true;
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break;
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}
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}
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Assert.True(anyDifferent);
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}
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[Fact]
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public void LargeDataset_Consistency()
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{
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var source = MakeSeries(1000);
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var sp15 = new Sp15();
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var streaming = new double[1000];
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for (int i = 0; i < 1000; i++)
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{
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streaming[i] = sp15.Update(source[i]).Value;
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}
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double[] spanOut = new double[1000];
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Sp15.Batch(source.Values, spanOut);
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for (int i = 0; i < 1000; i++)
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{
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Assert.Equal(streaming[i], spanOut[i], 1e-10);
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}
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}
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}
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