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docs: remove C# Implementation Considerations sections, clean up temp scripts, reorganize test files
- Remove 'C# Implementation Considerations' sections from 34 indicator .md files - Delete 29 temp PowerShell scripts (_fix_mojibake.ps1, _hex_scan.ps1, etc.) - Move test files into tests/ subdirectories for consistent project structure - Add trader-focused bullet points to indicator documentation
This commit is contained in:
Miha Kralj
@@ -0,0 +1,581 @@
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namespace QuanTAlib.Tests;
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public class CmaTests
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{
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[Fact]
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public void Cma_Calc_ReturnsValue()
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{
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var cma = new Cma();
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Assert.Equal(0, cma.Last.Value);
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TValue result = cma.Update(new TValue(DateTime.UtcNow, 100));
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Assert.True(result.Value > 0);
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Assert.Equal(result.Value, cma.Last.Value);
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}
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[Fact]
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public void Cma_FirstValue_ReturnsItself()
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{
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var cma = new Cma();
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TValue result = cma.Update(new TValue(DateTime.UtcNow, 100));
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Assert.Equal(100.0, result.Value, 1e-10);
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}
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[Fact]
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public void Cma_Calc_IsNew_AcceptsParameter()
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{
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var cma = new Cma();
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cma.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
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double value1 = cma.Last.Value;
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cma.Update(new TValue(DateTime.UtcNow, 200), isNew: true);
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double value2 = cma.Last.Value;
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// Values should change with new bars
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Assert.NotEqual(value1, value2);
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}
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[Fact]
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public void Cma_Calc_IsNew_False_UpdatesValue()
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{
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var cma = new Cma();
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, 110), isNew: true);
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double beforeUpdate = cma.Last.Value;
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cma.Update(new TValue(DateTime.UtcNow, 120), isNew: false);
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double afterUpdate = cma.Last.Value;
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// Update should change the value
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Assert.NotEqual(beforeUpdate, afterUpdate);
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}
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[Fact]
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public void Cma_Reset_ClearsState()
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{
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var cma = new Cma();
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, 105));
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double valueBefore = cma.Last.Value;
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cma.Reset();
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Assert.Equal(0, cma.Last.Value);
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Assert.False(cma.IsHot);
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// After reset, should accept new values
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cma.Update(new TValue(DateTime.UtcNow, 50));
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Assert.NotEqual(0, cma.Last.Value);
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Assert.NotEqual(valueBefore, cma.Last.Value);
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}
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[Fact]
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public void Cma_Properties_Accessible()
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{
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var cma = new Cma();
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Assert.Equal(0, cma.Last.Value);
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Assert.False(cma.IsHot);
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cma.Update(new TValue(DateTime.UtcNow, 100));
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Assert.NotEqual(0, cma.Last.Value);
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Assert.True(cma.IsHot);
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}
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[Fact]
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public void Cma_IsHot_BecomesTrueAfterFirstValue()
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{
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var cma = new Cma();
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Assert.False(cma.IsHot);
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cma.Update(new TValue(DateTime.UtcNow, 100));
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Assert.True(cma.IsHot);
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}
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[Fact]
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public void Cma_CalculatesCorrectAverage()
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{
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var cma = new Cma();
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cma.Update(new TValue(DateTime.UtcNow, 10));
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Assert.Equal(10.0, cma.Last.Value, 1e-10); // (10)/1 = 10
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cma.Update(new TValue(DateTime.UtcNow, 20));
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Assert.Equal(15.0, cma.Last.Value, 1e-10); // (10+20)/2 = 15
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cma.Update(new TValue(DateTime.UtcNow, 30));
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Assert.Equal(20.0, cma.Last.Value, 1e-10); // (10+20+30)/3 = 20
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cma.Update(new TValue(DateTime.UtcNow, 40));
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Assert.Equal(25.0, cma.Last.Value, 1e-10); // (10+20+30+40)/4 = 25
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cma.Update(new TValue(DateTime.UtcNow, 50));
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Assert.Equal(30.0, cma.Last.Value, 1e-10); // (10+20+30+40+50)/5 = 30
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}
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[Fact]
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public void Cma_IncludesAllValues_NoSlidingWindow()
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{
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var cma = new Cma();
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// Add 10 values: 10, 20, 30, ..., 100
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for (int i = 1; i <= 10; i++)
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{
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cma.Update(new TValue(DateTime.UtcNow, i * 10));
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}
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// CMA of 10,20,30,40,50,60,70,80,90,100 = 550/10 = 55
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Assert.Equal(55.0, cma.Last.Value, 1e-10);
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// Add one more value
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cma.Update(new TValue(DateTime.UtcNow, 110));
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// CMA now includes ALL 11 values: (550 + 110)/11 = 660/11 = 60
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Assert.Equal(60.0, cma.Last.Value, 1e-10);
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}
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[Fact]
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public void Cma_IterativeCorrections_RestoreToOriginalState()
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{
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var cma = new Cma();
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
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// Feed 10 new values
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TValue tenthInput = default;
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for (int i = 0; i < 10; i++)
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{
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var bar = gbm.Next(isNew: true);
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tenthInput = new TValue(bar.Time, bar.Close);
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cma.Update(tenthInput, isNew: true);
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}
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// Remember CMA state after 10 values
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double cmaAfterTen = cma.Last.Value;
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// Generate 9 corrections with isNew=false (different values)
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for (int i = 0; i < 9; i++)
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{
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var bar = gbm.Next(isNew: false);
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cma.Update(new TValue(bar.Time, bar.Close), isNew: false);
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}
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// Feed the remembered 10th input again with isNew=false
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TValue finalCma = cma.Update(tenthInput, isNew: false);
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// CMA should match the original state after 10 values
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Assert.Equal(cmaAfterTen, finalCma.Value, 1e-10);
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}
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[Fact]
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public void Cma_BatchCalc_MatchesIterativeCalc()
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{
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var cmaIterative = new Cma();
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var cmaBatch = new Cma();
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
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// Generate data
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var series = new TSeries();
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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series.Add(bar.Time, bar.Close);
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}
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Assert.True(series.Count > 0);
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// Calculate iteratively
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var iterativeResults = new TSeries();
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foreach (var item in series)
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{
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iterativeResults.Add(cmaIterative.Update(item));
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}
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// Calculate batch
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var batchResults = cmaBatch.Update(series);
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// Compare
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Assert.Equal(iterativeResults.Count, batchResults.Count);
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for (int i = 0; i < iterativeResults.Count; i++)
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{
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Assert.Equal(iterativeResults[i].Value, batchResults[i].Value, 1e-10);
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Assert.Equal(iterativeResults[i].Time, batchResults[i].Time);
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}
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}
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[Fact]
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public void Cma_NaN_Input_UsesLastValidValue()
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{
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var cma = new Cma();
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// Feed some valid values
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, 110));
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// Feed NaN - should use last valid value (110)
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var resultAfterNaN = cma.Update(new TValue(DateTime.UtcNow, double.NaN));
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// Result should be finite (not NaN)
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Assert.True(double.IsFinite(resultAfterNaN.Value));
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Assert.NotEqual(0, resultAfterNaN.Value);
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}
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[Fact]
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public void Cma_Infinity_Input_UsesLastValidValue()
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{
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var cma = new Cma();
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// Feed some valid values
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, 110));
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// Feed positive infinity - should use last valid value
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var resultAfterPosInf = cma.Update(new TValue(DateTime.UtcNow, double.PositiveInfinity));
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Assert.True(double.IsFinite(resultAfterPosInf.Value));
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// Feed negative infinity - should use last valid value
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var resultAfterNegInf = cma.Update(new TValue(DateTime.UtcNow, double.NegativeInfinity));
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Assert.True(double.IsFinite(resultAfterNegInf.Value));
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}
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[Fact]
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public void Cma_MultipleNaN_ContinuesWithLastValid()
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{
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var cma = new Cma();
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// Feed valid values
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, 110));
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cma.Update(new TValue(DateTime.UtcNow, 120));
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// Feed multiple NaN values
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var r1 = cma.Update(new TValue(DateTime.UtcNow, double.NaN));
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var r2 = cma.Update(new TValue(DateTime.UtcNow, double.NaN));
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var r3 = cma.Update(new TValue(DateTime.UtcNow, double.NaN));
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// All results should be finite
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Assert.True(double.IsFinite(r1.Value));
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Assert.True(double.IsFinite(r2.Value));
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Assert.True(double.IsFinite(r3.Value));
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}
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[Fact]
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public void Cma_BatchCalc_HandlesNaN()
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{
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var cma = new Cma();
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// Create series with NaN values interspersed
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var series = new TSeries();
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series.Add(DateTime.UtcNow.Ticks, 100);
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series.Add(DateTime.UtcNow.Ticks + 1, 110);
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series.Add(DateTime.UtcNow.Ticks + 2, double.NaN);
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series.Add(DateTime.UtcNow.Ticks + 3, 120);
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series.Add(DateTime.UtcNow.Ticks + 4, double.PositiveInfinity);
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series.Add(DateTime.UtcNow.Ticks + 5, 130);
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var results = cma.Update(series);
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// All results should be finite
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foreach (var result in results)
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{
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Assert.True(double.IsFinite(result.Value), $"Expected finite value but got {result.Value}");
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}
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}
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[Fact]
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public void Cma_Reset_ClearsLastValidValue()
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{
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var cma = new Cma();
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// Feed values including NaN
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cma.Update(new TValue(DateTime.UtcNow, 100));
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cma.Update(new TValue(DateTime.UtcNow, double.NaN));
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// Reset
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cma.Reset();
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// After reset, first valid value should establish new baseline
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var result = cma.Update(new TValue(DateTime.UtcNow, 50));
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Assert.Equal(50.0, result.Value, 1e-10);
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}
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[Fact]
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public void Cma_StaticBatch_Works()
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{
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var series = new TSeries();
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series.Add(DateTime.UtcNow.Ticks, 10);
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series.Add(DateTime.UtcNow.Ticks + 1, 20);
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series.Add(DateTime.UtcNow.Ticks + 2, 30);
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series.Add(DateTime.UtcNow.Ticks + 3, 40);
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series.Add(DateTime.UtcNow.Ticks + 4, 50);
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var results = Cma.Batch(series);
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Assert.Equal(5, results.Count);
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// CMA for last value: (10+20+30+40+50)/5 = 30
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Assert.Equal(30.0, results.Last.Value, 1e-10);
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}
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[Fact]
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public void Cma_FlatLine_ReturnsSameValue()
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{
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var cma = new Cma();
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for (int i = 0; i < 20; i++)
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{
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cma.Update(new TValue(DateTime.UtcNow, 100));
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}
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Assert.Equal(100.0, cma.Last.Value, 1e-10);
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}
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// ============== Span API Tests ==============
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[Fact]
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public void Cma_SpanBatch_ValidatesInput()
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{
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double[] source = [1, 2, 3, 4, 5];
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double[] wrongSizeOutput = new double[3];
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// Output must be same length as source
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Assert.Throws<ArgumentException>(() => Cma.Batch(source.AsSpan(), wrongSizeOutput.AsSpan()));
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}
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[Fact]
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public void Cma_SpanBatch_MatchesTSeriesBatch()
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{
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var series = new TSeries();
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double[] source = new double[100];
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double[] output = new double[100];
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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source[i] = bar.Close;
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series.Add(bar.Time, bar.Close);
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}
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// Calculate with TSeries API
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var tseriesResult = Cma.Batch(series);
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// Calculate with Span API
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Cma.Batch(source.AsSpan(), output.AsSpan());
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// Compare results
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for (int i = 0; i < 100; i++)
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{
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Assert.Equal(tseriesResult[i].Value, output[i], 1e-10);
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}
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}
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[Fact]
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public void Cma_SpanBatch_CalculatesCorrectly()
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{
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double[] source = [10, 20, 30, 40, 50];
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double[] output = new double[5];
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Cma.Batch(source.AsSpan(), output.AsSpan());
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Assert.Equal(10.0, output[0], 1e-10); // 10/1 = 10
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Assert.Equal(15.0, output[1], 1e-10); // (10+20)/2 = 15
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Assert.Equal(20.0, output[2], 1e-10); // (10+20+30)/3 = 20
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Assert.Equal(25.0, output[3], 1e-10); // (10+20+30+40)/4 = 25
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Assert.Equal(30.0, output[4], 1e-10); // (10+20+30+40+50)/5 = 30
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}
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[Fact]
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public void Cma_SpanBatch_ZeroAllocation()
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{
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double[] source = new double[10000];
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double[] output = new double[10000];
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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for (int i = 0; i < source.Length; i++)
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{
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source[i] = gbm.Next().Close;
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}
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// Warm up
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Cma.Batch(source.AsSpan(), output.AsSpan());
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// This test verifies the method runs without throwing
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Assert.True(double.IsFinite(output[^1]));
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}
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[Fact]
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public void Cma_SpanBatch_HandlesNaN()
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{
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double[] source = [100, 110, double.NaN, 120, 130];
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double[] output = new double[5];
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Cma.Batch(source.AsSpan(), output.AsSpan());
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// All outputs should be finite
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foreach (var val in output)
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{
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Assert.True(double.IsFinite(val), $"Expected finite value but got {val}");
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}
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}
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[Fact]
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public void Cma_AllModes_ProduceSameResult()
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{
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// Arrange
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var series = bars.Close;
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// 1. Batch Mode
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var batchSeries = Cma.Batch(series);
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double expected = batchSeries.Last.Value;
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// 2. Span Mode
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var tValues = series.Values.ToArray();
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var spanInput = new ReadOnlySpan<double>(tValues);
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var spanOutput = new double[tValues.Length];
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Cma.Batch(spanInput, spanOutput);
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double spanResult = spanOutput[^1];
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// 3. Streaming Mode
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var streamingInd = new Cma();
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for (int i = 0; i < series.Count; i++)
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{
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streamingInd.Update(series[i]);
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}
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double streamingResult = streamingInd.Last.Value;
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// 4. Eventing Mode
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var pubSource = new TSeries();
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var eventingInd = new Cma(pubSource);
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for (int i = 0; i < series.Count; i++)
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{
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pubSource.Add(series[i]);
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}
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double eventingResult = eventingInd.Last.Value;
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// Assert
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Assert.Equal(expected, spanResult, precision: 9);
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Assert.Equal(expected, streamingResult, precision: 9);
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Assert.Equal(expected, eventingResult, precision: 9);
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}
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[Fact]
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public void Chainability_Works()
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{
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var source = new TSeries();
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var cma = new Cma(source);
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source.Add(new TValue(DateTime.UtcNow, 100));
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Assert.Equal(100, cma.Last.Value);
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}
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[Fact]
|
||||
public void WarmupPeriod_IsSetCorrectly()
|
||||
{
|
||||
var cma = new Cma();
|
||||
Assert.Equal(1, cma.WarmupPeriod);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Prime_SetsStateCorrectly()
|
||||
{
|
||||
var cma = new Cma();
|
||||
double[] history = [10, 20, 30, 40, 50]; // CMA = 30
|
||||
|
||||
cma.Prime(history);
|
||||
|
||||
Assert.True(cma.IsHot);
|
||||
Assert.Equal(30.0, cma.Last.Value, 1e-10);
|
||||
|
||||
// Verify it continues correctly
|
||||
cma.Update(new TValue(DateTime.UtcNow, 60)); // (10+20+30+40+50+60)/6 = 35
|
||||
Assert.Equal(35.0, cma.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Prime_HandlesNaN_InHistory()
|
||||
{
|
||||
var cma = new Cma();
|
||||
double[] history = [10, 20, double.NaN, 40];
|
||||
// 10 -> 10
|
||||
// 10, 20 -> 15
|
||||
// 10, 20, 20 (NaN replaced by 20) -> 16.666...
|
||||
// 10, 20, 20, 40 -> 22.5
|
||||
|
||||
cma.Prime(history);
|
||||
|
||||
Assert.True(cma.IsHot);
|
||||
Assert.Equal(22.5, cma.Last.Value, 1e-9);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Calculate_ReturnsCorrectResultsAndHotIndicator()
|
||||
{
|
||||
var series = new TSeries();
|
||||
for (int i = 1; i <= 10; i++)
|
||||
{
|
||||
series.Add(DateTime.UtcNow, i * 10);
|
||||
}
|
||||
// 10, 20, 30, 40, 50, 60, 70, 80, 90, 100
|
||||
|
||||
var (results, indicator) = Cma.Calculate(series);
|
||||
|
||||
// Check results
|
||||
Assert.Equal(10, results.Count);
|
||||
Assert.Equal(30.0, results[4].Value, 1e-10); // CMA after 5 values = 30
|
||||
Assert.Equal(55.0, results.Last.Value, 1e-10); // CMA of all 10 = 55
|
||||
|
||||
// Check indicator state
|
||||
Assert.True(indicator.IsHot);
|
||||
Assert.Equal(55.0, indicator.Last.Value, 1e-10);
|
||||
Assert.Equal(1, indicator.WarmupPeriod);
|
||||
|
||||
// Verify indicator continues correctly
|
||||
indicator.Update(new TValue(DateTime.UtcNow, 110));
|
||||
// CMA now = (550 + 110)/11 = 60
|
||||
Assert.Equal(60.0, indicator.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Cma_NumericalStability_LargeDataset()
|
||||
{
|
||||
// Test that CMA remains stable over a large number of values
|
||||
var cma = new Cma();
|
||||
double expectedSum = 0;
|
||||
|
||||
for (int i = 1; i <= 100000; i++)
|
||||
{
|
||||
cma.Update(new TValue(DateTime.UtcNow, 100.0)); // All same value
|
||||
expectedSum += 100.0;
|
||||
}
|
||||
|
||||
// CMA of 100000 values all equal to 100 should be exactly 100
|
||||
Assert.Equal(100.0, cma.Last.Value, 1e-9);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Cma_NumericalStability_VaryingValues()
|
||||
{
|
||||
// Test with alternating values
|
||||
var cma = new Cma();
|
||||
|
||||
for (int i = 0; i < 10000; i++)
|
||||
{
|
||||
double value = (i % 2 == 0) ? 100.0 : 200.0;
|
||||
cma.Update(new TValue(DateTime.UtcNow, value));
|
||||
}
|
||||
|
||||
// CMA of alternating 100, 200 should converge to 150
|
||||
Assert.Equal(150.0, cma.Last.Value, 1e-9);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,318 @@
|
||||
using Xunit.Abstractions;
|
||||
|
||||
namespace QuanTAlib.Tests;
|
||||
|
||||
/// <summary>
|
||||
/// Validation tests for CMA (Cumulative Moving Average).
|
||||
/// CMA is not commonly found in standard TA libraries (like TA-Lib, Skender, etc.)
|
||||
/// as it's a fundamental statistical concept rather than a trading indicator.
|
||||
/// These tests validate against known mathematical results.
|
||||
/// </summary>
|
||||
public sealed class CmaValidationTests : IDisposable
|
||||
{
|
||||
private readonly ValidationTestData _testData;
|
||||
private readonly ITestOutputHelper _output;
|
||||
private bool _disposed;
|
||||
|
||||
public CmaValidationTests(ITestOutputHelper output)
|
||||
{
|
||||
_output = output;
|
||||
_testData = new ValidationTestData();
|
||||
}
|
||||
|
||||
public void Dispose()
|
||||
{
|
||||
Dispose(true);
|
||||
}
|
||||
|
||||
private void Dispose(bool disposing)
|
||||
{
|
||||
if (_disposed)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
_disposed = true;
|
||||
|
||||
if (disposing)
|
||||
{
|
||||
_testData?.Dispose();
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_MathematicalCorrectness_Batch()
|
||||
{
|
||||
// Calculate QuanTAlib CMA (batch TSeries)
|
||||
var cma = new Cma();
|
||||
var qResult = cma.Update(_testData.Data);
|
||||
|
||||
// Calculate expected CMA manually using running sum
|
||||
double runningSum = 0;
|
||||
int count = 0;
|
||||
|
||||
foreach (var item in _testData.Data)
|
||||
{
|
||||
count++;
|
||||
runningSum += item.Value;
|
||||
double expectedCma = runningSum / count;
|
||||
|
||||
// Get corresponding QuanTAlib result
|
||||
double qValue = qResult[count - 1].Value;
|
||||
|
||||
Assert.True(
|
||||
Math.Abs(qValue - expectedCma) <= ValidationHelper.DefaultTolerance,
|
||||
$"Mismatch at index {count - 1}: QuanTAlib={qValue:G17}, Expected={expectedCma:G17}");
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA Batch(TSeries) validated successfully against manual calculation");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_MathematicalCorrectness_Streaming()
|
||||
{
|
||||
// Calculate QuanTAlib CMA (streaming)
|
||||
var cma = new Cma();
|
||||
var qResults = new List<double>();
|
||||
foreach (var item in _testData.Data)
|
||||
{
|
||||
qResults.Add(cma.Update(item).Value);
|
||||
}
|
||||
|
||||
// Calculate expected CMA manually
|
||||
double runningSum = 0;
|
||||
|
||||
for (int i = 0; i < _testData.Data.Count; i++)
|
||||
{
|
||||
runningSum += _testData.Data[i].Value;
|
||||
double expectedCma = runningSum / (i + 1);
|
||||
|
||||
Assert.True(
|
||||
Math.Abs(qResults[i] - expectedCma) <= ValidationHelper.DefaultTolerance,
|
||||
$"Mismatch at index {i}: QuanTAlib={qResults[i]:G17}, Expected={expectedCma:G17}");
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA Streaming validated successfully against manual calculation");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_MathematicalCorrectness_Span()
|
||||
{
|
||||
// Prepare data for Span API
|
||||
double[] sourceData = _testData.RawData.ToArray();
|
||||
double[] qOutput = new double[sourceData.Length];
|
||||
|
||||
// Calculate QuanTAlib CMA (Span API)
|
||||
Cma.Batch(sourceData.AsSpan(), qOutput.AsSpan());
|
||||
|
||||
// Calculate expected CMA manually
|
||||
double runningSum = 0;
|
||||
|
||||
for (int i = 0; i < sourceData.Length; i++)
|
||||
{
|
||||
runningSum += sourceData[i];
|
||||
double expectedCma = runningSum / (i + 1);
|
||||
|
||||
Assert.True(
|
||||
Math.Abs(qOutput[i] - expectedCma) <= ValidationHelper.DefaultTolerance,
|
||||
$"Mismatch at index {i}: QuanTAlib={qOutput[i]:G17}, Expected={expectedCma:G17}");
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA Span validated successfully against manual calculation");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_WelfordAlgorithm_Stability()
|
||||
{
|
||||
// Test numerical stability with large values
|
||||
// Welford's algorithm should handle this without overflow
|
||||
var cma = new Cma();
|
||||
double[] largeValues = new double[1000];
|
||||
const double baseValue = 1e10;
|
||||
|
||||
for (int i = 0; i < largeValues.Length; i++)
|
||||
{
|
||||
largeValues[i] = baseValue + i;
|
||||
}
|
||||
|
||||
// Calculate CMA
|
||||
foreach (var val in largeValues)
|
||||
{
|
||||
cma.Update(new TValue(DateTime.UtcNow, val));
|
||||
}
|
||||
|
||||
// Expected: average of 1e10, 1e10+1, ..., 1e10+999
|
||||
// = 1e10 + average of 0,1,2,...,999
|
||||
// = 1e10 + 499.5
|
||||
double expectedMean = baseValue + 499.5;
|
||||
|
||||
Assert.Equal(expectedMean, cma.Last.Value, 1e-6);
|
||||
_output.WriteLine($"CMA Welford stability test passed: {cma.Last.Value:G17}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_WelfordAlgorithm_SmallDifferences()
|
||||
{
|
||||
// Test with values that have small differences (challenges precision)
|
||||
var cma = new Cma();
|
||||
double[] values = new double[10000];
|
||||
double baseValue = 1e8;
|
||||
|
||||
for (int i = 0; i < values.Length; i++)
|
||||
{
|
||||
values[i] = baseValue + (i % 2 == 0 ? 0.1 : -0.1);
|
||||
}
|
||||
|
||||
foreach (var val in values)
|
||||
{
|
||||
cma.Update(new TValue(DateTime.UtcNow, val));
|
||||
}
|
||||
|
||||
// With alternating +0.1 and -0.1, the average offset is 0
|
||||
Assert.Equal(baseValue, cma.Last.Value, 1e-7);
|
||||
_output.WriteLine($"CMA small differences test passed: {cma.Last.Value:G17}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_AgainstNaiveSum_ShortSequence()
|
||||
{
|
||||
// For short sequences, compare against naive sum/count
|
||||
double[] values = [100, 200, 150, 175, 125, 180, 160, 140, 190, 170];
|
||||
var cma = new Cma();
|
||||
|
||||
double sum = 0;
|
||||
for (int i = 0; i < values.Length; i++)
|
||||
{
|
||||
sum += values[i];
|
||||
cma.Update(new TValue(DateTime.UtcNow, values[i]));
|
||||
|
||||
double naiveMean = sum / (i + 1);
|
||||
Assert.Equal(naiveMean, cma.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA validated against naive sum for short sequence");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_AgainstNaiveSum_LongSequence()
|
||||
{
|
||||
// For longer sequences, verify the final value
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.0, sigma: 0.1, seed: 42);
|
||||
int count = 50000;
|
||||
double sum = 0;
|
||||
var cma = new Cma();
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
double value = gbm.Next().Close;
|
||||
sum += value;
|
||||
cma.Update(new TValue(DateTime.UtcNow, value));
|
||||
}
|
||||
|
||||
double naiveMean = sum / count;
|
||||
double welfordMean = cma.Last.Value;
|
||||
|
||||
// Both should be very close
|
||||
Assert.True(
|
||||
Math.Abs(naiveMean - welfordMean) < 1e-8,
|
||||
$"Naive={naiveMean:G17}, Welford={welfordMean:G17}, Diff={Math.Abs(naiveMean - welfordMean):G17}");
|
||||
|
||||
_output.WriteLine($"CMA long sequence: Naive={naiveMean:G10}, Welford={welfordMean:G10}");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_KnownSequence_ArithmeticProgression()
|
||||
{
|
||||
// Arithmetic progression: 1, 2, 3, ..., n
|
||||
// CMA at each point: 1, 1.5, 2, 2.5, 3, ...
|
||||
// Formula: CMA_n = (n+1)/2
|
||||
|
||||
var cma = new Cma();
|
||||
|
||||
for (int n = 1; n <= 100; n++)
|
||||
{
|
||||
cma.Update(new TValue(DateTime.UtcNow, n));
|
||||
double expected = (n + 1.0) / 2.0;
|
||||
Assert.Equal(expected, cma.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA validated for arithmetic progression");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_KnownSequence_GeometricProgression()
|
||||
{
|
||||
// Geometric progression: r, r^2, r^3, ..., r^n
|
||||
// Sum = r * (r^n - 1) / (r - 1)
|
||||
// CMA = Sum / n
|
||||
|
||||
double r = 1.1;
|
||||
var cma = new Cma();
|
||||
|
||||
for (int n = 1; n <= 50; n++)
|
||||
{
|
||||
double value = Math.Pow(r, n);
|
||||
cma.Update(new TValue(DateTime.UtcNow, value));
|
||||
|
||||
// Sum of geometric series: a * (r^n - 1) / (r - 1) where a = r
|
||||
double sum = r * (Math.Pow(r, n) - 1) / (r - 1);
|
||||
double expected = sum / n;
|
||||
|
||||
Assert.Equal(expected, cma.Last.Value, 1e-9);
|
||||
}
|
||||
|
||||
_output.WriteLine("CMA validated for geometric progression");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_ConstantSequence()
|
||||
{
|
||||
// CMA of constant sequence should be the constant
|
||||
double constant = 42.5;
|
||||
var cma = new Cma();
|
||||
|
||||
for (int i = 0; i < 10000; i++)
|
||||
{
|
||||
cma.Update(new TValue(DateTime.UtcNow, constant));
|
||||
}
|
||||
|
||||
Assert.Equal(constant, cma.Last.Value, 1e-10);
|
||||
_output.WriteLine("CMA validated for constant sequence");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void Validate_AllModes_Consistency()
|
||||
{
|
||||
// Verify all three calculation modes produce identical results
|
||||
var sourceData = _testData.RawData.ToArray();
|
||||
|
||||
// Mode 1: TSeries Batch
|
||||
var cma1 = new Cma();
|
||||
var batchResult = cma1.Update(_testData.Data);
|
||||
|
||||
// Mode 2: Streaming
|
||||
var cma2 = new Cma();
|
||||
var streamingResults = new List<double>();
|
||||
foreach (var item in _testData.Data)
|
||||
{
|
||||
streamingResults.Add(cma2.Update(item).Value);
|
||||
}
|
||||
|
||||
// Mode 3: Span
|
||||
var spanOutput = new double[sourceData.Length];
|
||||
Cma.Batch(sourceData.AsSpan(), spanOutput.AsSpan());
|
||||
|
||||
// Compare all three
|
||||
for (int i = 0; i < sourceData.Length; i++)
|
||||
{
|
||||
double batchVal = batchResult[i].Value;
|
||||
double streamVal = streamingResults[i];
|
||||
double spanVal = spanOutput[i];
|
||||
|
||||
Assert.Equal(batchVal, streamVal, 1e-10);
|
||||
Assert.Equal(batchVal, spanVal, 1e-10);
|
||||
}
|
||||
|
||||
_output.WriteLine("All CMA calculation modes produce consistent results");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user