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https://github.com/mihakralj/QuanTAlib.git
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Refactor T3 Moving Average Implementation and Remove Unused Tests
- Deleted DebugTulip.Tests.cs as it was no longer needed. - Refactored T3.cs to encapsulate parameters in a struct for better organization and readability. - Updated methods in T3.cs to use the new Parameters struct, improving clarity and reducing redundancy. - Enhanced T3.md documentation to provide clearer explanations of the T3 moving average and its parameters. - Removed Wma.Coverage.Tests.cs as it was obsolete. - Added new tests in IndicatorExtensions.Tests.cs to validate logic methods and ensure correct calculations. - Updated IndicatorExtensions.cs to improve method organization and add new functionality for handling chart coordinates. - Refactored mocks in TradingPlatformMocks.cs to align with new chart interface definitions.
This commit is contained in:
Miha Kralj
@@ -1,229 +0,0 @@
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using System;
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using Xunit;
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namespace QuanTAlib.Tests;
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public class SmaCoverageTests
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{
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[Fact]
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public void Sma_ResyncLogic_IsTriggeredAndCorrect()
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{
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// ResyncInterval is 1000.
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int count = 2500;
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int period = 10;
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var sma = new Sma(period);
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double constantValue = 100.0;
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for (int i = 0; i < count; i++)
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{
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sma.Update(new TValue(DateTime.UtcNow, constantValue));
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if (i >= period)
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{
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Assert.Equal(constantValue, sma.Last.Value, 1e-9);
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}
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}
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}
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[Fact]
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public void Sma_SpanCalc_LargeDataset_TriggersResync()
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{
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int count = 5000;
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int period = 10;
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double[] source = new double[count];
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double[] output = new double[count];
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for (int i = 0; i < count; i++)
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{
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source[i] = 100.0;
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}
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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for (int i = period; i < count; i++)
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{
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Assert.Equal(100.0, output[i], 1e-9);
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}
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}
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[Fact]
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public void Sma_SpanCalc_SimdThreshold_Boundary()
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{
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// SimdThreshold is 256.
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int[] lengths = { 250, 256, 260 };
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int period = 10;
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foreach (int len in lengths)
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{
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double[] source = new double[len];
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double[] output = new double[len];
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for (int i = 0; i < len; i++) source[i] = 100.0;
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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Assert.Equal(100.0, output[^1], 1e-9);
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}
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}
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[Fact]
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public void Sma_SpanCalc_Simd_WithResync()
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{
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int count = 3000;
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int period = 5;
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double[] source = new double[count];
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double[] output = new double[count];
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// Linear increase: 0, 1, 2, ...
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for (int i = 0; i < count; i++) source[i] = i;
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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// SMA(5) of x-4, x-3, x-2, x-1, x
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// = (5x - 10) / 5 = x - 2
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for (int i = period; i < count; i++)
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{
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double expected = i - 2.0;
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Assert.Equal(expected, output[i], 1e-9);
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}
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}
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[Fact]
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public void Sma_Constructor_ThrowsOnInvalidPeriod()
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{
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Assert.Throws<ArgumentException>(() => new Sma(0));
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Assert.Throws<ArgumentException>(() => new Sma(-1));
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}
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[Fact]
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public void Sma_StaticCalculate_ThrowsOnInvalidArgs()
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{
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double[] source = new double[10];
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double[] output = new double[5]; // Mismatch
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Assert.Throws<ArgumentException>(() => Sma.Calculate(source.AsSpan(), output.AsSpan(), 5));
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double[] output2 = new double[10];
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Assert.Throws<ArgumentException>(() => Sma.Calculate(source.AsSpan(), output2.AsSpan(), 0));
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}
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[Fact]
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public void Sma_Calculate_EmptyInput_DoesNothing()
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{
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Sma.Calculate(ReadOnlySpan<double>.Empty, Span<double>.Empty, 5);
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// Should not throw
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}
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[Fact]
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public void Sma_Update_WithNaN_UsesLastValid()
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{
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var sma = new Sma(5);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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sma.Update(new TValue(DateTime.UtcNow, double.NaN)); // Should use 2.0
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// Buffer: 1, 2, 2
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// SMA(3) = (1 + 2 + 2) / 3 = 5/3 = 1.666...
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Assert.Equal(5.0/3.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_Update_IsNewFalse_UpdatesLastValue()
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{
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var sma = new Sma(3);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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// Update existing with 3.0 (replaces 2.0)
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sma.Update(new TValue(DateTime.UtcNow, 3.0), isNew: false);
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// Buffer should be: 1, 3
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// SMA = (1 + 3) / 2 = 2
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Assert.Equal(2.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_TSeries_Empty_ReturnsEmpty()
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{
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var sma = new Sma(5);
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var result = sma.Update(new TSeries());
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Assert.Empty(result);
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}
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[Fact]
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public void Sma_TSeries_WithNaN_RestoresStateCorrectly()
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{
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var sma = new Sma(3);
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var series = new TSeries();
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series.Add(new TValue(DateTime.UtcNow, 1.0));
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series.Add(new TValue(DateTime.UtcNow, 2.0));
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series.Add(new TValue(DateTime.UtcNow, double.NaN));
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series.Add(new TValue(DateTime.UtcNow, 4.0));
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sma.Update(series);
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// Buffer: 2.0, 2.0 (from NaN), 4.0
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// SMA(3) = (2 + 2 + 4) / 3 = 8/3 = 2.666...
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// Let's add one more value to verify state is correct
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sma.Update(new TValue(DateTime.UtcNow, 5.0));
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// Buffer: 2.0, 4.0, 5.0
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// SMA(3) = (2 + 4 + 5) / 3 = 11/3 = 3.666...
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Assert.Equal(11.0/3.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_Reset_ClearsState()
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{
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var sma = new Sma(3);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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sma.Update(new TValue(DateTime.UtcNow, 3.0));
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sma.Reset();
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Assert.Equal(0, sma.Last.Value);
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// Start fresh
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sma.Update(new TValue(DateTime.UtcNow, 10.0));
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// Buffer: 10
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// SMA = 10
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Assert.Equal(10.0, sma.Last.Value);
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}
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[Fact]
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public void Sma_Calculate_ScalarFallback_WithNaN()
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{
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// Force scalar path by including NaN, even with large dataset
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int count = 1000;
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double[] source = new double[count];
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double[] output = new double[count];
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for (int i = 0; i < count; i++) source[i] = 1.0;
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source[500] = double.NaN; // This should trigger HasNonFiniteValues -> true
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Sma.Calculate(source.AsSpan(), output.AsSpan(), 10);
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// Check around the NaN
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// Index 500 is NaN, so it uses previous valid (1.0)
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// So effectively the stream is all 1.0s
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Assert.Equal(1.0, output[500], 1e-9);
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Assert.Equal(1.0, output[501], 1e-9);
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}
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[Fact]
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public void Sma_Constructor_WithSource_Subscribes()
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{
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var source = new Sma(10); // Just using Sma as a publisher
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var sma = new Sma(source, 5);
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source.Update(new TValue(DateTime.UtcNow, 10.0));
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Assert.Equal(10.0, sma.Last.Value);
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}
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}
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