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Add Kahan-Babuška Summation Algorithm and Enhance Variance Indicator Tests
- Introduced a new `Sum` class implementing the Kahan-Babuška algorithm for high-precision rolling summation. - Added comprehensive documentation for the `Sum` class, detailing its mathematical foundation, performance profile, and use cases. - Refactored `VarianceIndicator` tests to improve clarity and coverage, including checks for different source types and the ability to change properties. - Enhanced `UsfIndicator` tests to validate initialization, processing of updates, and property changes. - Updated `UsfIndicator` implementation to simplify source handling and improve short name generation. - Modified Qodana configuration to exclude unused auto property accessor warnings.
This commit is contained in:
Miha Kralj
@@ -1,126 +1,517 @@
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using QuanTAlib.Tests;
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using Skender.Stock.Indicators;
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using TALib;
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using Tulip;
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using OoplesFinance.StockIndicators;
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using OoplesFinance.StockIndicators.Models;
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using OoplesFinance.StockIndicators.Enums;
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using MathNet.Numerics.Statistics;
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namespace QuanTAlib.Validation;
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namespace QuanTAlib.Tests;
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public class StdDevValidationTests
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public sealed class StdDevValidationTests : IDisposable
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{
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private readonly ValidationTestData _data = new();
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private readonly ValidationTestData _testData;
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private bool _disposed;
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public StdDevValidationTests()
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{
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_testData = new ValidationTestData();
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}
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public void Dispose()
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{
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Dispose(true);
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}
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private void Dispose(bool disposing)
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{
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if (_disposed) return;
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_disposed = true;
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if (disposing) _testData?.Dispose();
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}
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#region Skender Validation
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[Fact]
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public void StdDev_Matches_Skender()
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public void StdDev_Matches_Skender_Batch()
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{
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// Skender StdDev uses Population Standard Deviation (N)
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: true);
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var skenderStdDev = _data.SkenderQuotes.GetStdDev(period);
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int[] periods = { 5, 10, 20, 50, 100 };
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var skenderList = skenderStdDev.ToList();
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var quotes = _data.SkenderQuotes.ToList();
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for (int i = 0; i < quotes.Count; i++)
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foreach (var period in periods)
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{
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var tValue = stdDev.Update(new TValue(quotes[i].Date, (double)quotes[i].Close));
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var skenderVal = skenderList[i].StdDev;
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var stdDev = new StdDev(period, isPopulation: true);
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var qResult = stdDev.Update(_testData.Data);
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if (i >= period && skenderVal.HasValue)
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{
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Assert.Equal(skenderVal.Value, tValue.Value, ValidationHelper.DefaultTolerance);
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}
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var sResult = _testData.SkenderQuotes.GetStdDev(period).ToList();
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ValidationHelper.VerifyData(qResult, sResult, (s) => s.StdDev);
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}
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}
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[Fact]
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public void StdDev_Matches_Talib()
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public void StdDev_Matches_Skender_Streaming()
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{
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// Skender StdDev uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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foreach (var period in periods)
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{
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var stdDev = new StdDev(period, isPopulation: true);
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var qResults = new List<double>();
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foreach (var item in _testData.Data)
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{
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qResults.Add(stdDev.Update(item).Value);
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}
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var sResult = _testData.SkenderQuotes.GetStdDev(period).ToList();
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ValidationHelper.VerifyData(qResults, sResult, (s) => s.StdDev);
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}
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}
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[Fact]
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public void StdDev_Matches_Skender_Span()
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{
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// Skender StdDev uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] sourceData = _testData.RawData.ToArray();
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foreach (var period in periods)
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{
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double[] qOutput = new double[sourceData.Length];
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StdDev.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period, isPopulation: true);
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var sResult = _testData.SkenderQuotes.GetStdDev(period).ToList();
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ValidationHelper.VerifyData(qOutput, sResult, (s) => s.StdDev);
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}
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}
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#endregion
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#region TA-Lib Validation
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[Fact]
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public void StdDev_Matches_Talib_Batch()
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{
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// TA-Lib STDDEV uses Population Standard Deviation (N)
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: true);
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] tData = _testData.RawData.ToArray();
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double[] output = new double[tData.Length];
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var quotes = _data.SkenderQuotes.ToList();
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double[] input = quotes.Select(q => (double)q.Close).ToArray();
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double[] output = new double[input.Length];
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// TA-Lib calculation
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// STDDEV(real, timeperiod=5, nbdev=1)
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var retCode = TALib.Functions.StdDev(input, 0..^0, output, out var outRange, period, 1.0);
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Assert.Equal(TALib.Core.RetCode.Success, retCode);
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for (int i = 0; i < quotes.Count; i++)
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foreach (var period in periods)
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{
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var tValue = stdDev.Update(new TValue(quotes[i].Date, (double)quotes[i].Close));
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var stdDev = new StdDev(period, isPopulation: true);
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var qResult = stdDev.Update(_testData.Data);
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if (i >= outRange.Start.Value)
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{
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double talibVal = output[i - outRange.Start.Value];
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Assert.Equal(talibVal, tValue.Value, ValidationHelper.DefaultTolerance);
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}
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var retCode = TALib.Functions.StdDev(tData, 0..^0, output, out var outRange, period, 1.0);
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Assert.Equal(TALib.Core.RetCode.Success, retCode);
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int lookback = TALib.Functions.StdDevLookback(period);
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ValidationHelper.VerifyData(qResult, output, outRange, lookback);
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}
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}
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[Fact]
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public void StdDev_Matches_Tulip()
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public void StdDev_Matches_Talib_Streaming()
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{
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// TA-Lib STDDEV uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] tData = _testData.RawData.ToArray();
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double[] output = new double[tData.Length];
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foreach (var period in periods)
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{
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var stdDev = new StdDev(period, isPopulation: true);
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var qResults = new List<double>();
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foreach (var item in _testData.Data)
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{
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qResults.Add(stdDev.Update(item).Value);
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}
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var retCode = TALib.Functions.StdDev(tData, 0..^0, output, out var outRange, period, 1.0);
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Assert.Equal(TALib.Core.RetCode.Success, retCode);
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int lookback = TALib.Functions.StdDevLookback(period);
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ValidationHelper.VerifyData(qResults, output, outRange, lookback);
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}
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}
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[Fact]
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public void StdDev_Matches_Talib_Span()
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{
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// TA-Lib STDDEV uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] sourceData = _testData.RawData.ToArray();
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double[] output = new double[sourceData.Length];
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foreach (var period in periods)
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{
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double[] qOutput = new double[sourceData.Length];
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StdDev.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period, isPopulation: true);
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var retCode = TALib.Functions.StdDev(sourceData, 0..^0, output, out var outRange, period, 1.0);
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Assert.Equal(TALib.Core.RetCode.Success, retCode);
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int lookback = TALib.Functions.StdDevLookback(period);
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ValidationHelper.VerifyData(qOutput, output, outRange, lookback);
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}
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}
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#endregion
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#region Tulip Validation
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[Fact]
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public void StdDev_Matches_Tulip_Batch()
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{
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// Tulip STDDEV uses Population Standard Deviation (N)
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: true);
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] tData = _testData.RawData.ToArray();
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var quotes = _data.SkenderQuotes.ToList();
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double[] input = quotes.Select(q => (double)q.Close).ToArray();
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// Tulip calculation
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var stdDevInd = Tulip.Indicators.stddev;
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double[][] inputs = { input };
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double[] options = { period };
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double[][] outputs = { new double[input.Length - stdDevInd.Start(options)] };
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stdDevInd.Run(inputs, options, outputs);
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double[] output = outputs[0];
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int lookback = stdDevInd.Start(options);
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for (int i = 0; i < quotes.Count; i++)
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foreach (var period in periods)
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{
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var tValue = stdDev.Update(new TValue(quotes[i].Date, (double)quotes[i].Close));
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var stdDev = new StdDev(period, isPopulation: true);
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var qResult = stdDev.Update(_testData.Data);
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if (i >= lookback)
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{
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double tulipVal = output[i - lookback];
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Assert.Equal(tulipVal, tValue.Value, ValidationHelper.DefaultTolerance);
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}
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var stdDevInd = Tulip.Indicators.stddev;
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double[][] inputs = { tData };
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double[] options = { period };
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int lookback = stdDevInd.Start(options);
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double[][] outputs = { new double[tData.Length - lookback] };
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stdDevInd.Run(inputs, options, outputs);
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var tResult = outputs[0];
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ValidationHelper.VerifyData(qResult, tResult, lookback);
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}
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}
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[Fact]
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public void StdDev_Matches_MathNet()
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public void StdDev_Matches_Tulip_Streaming()
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{
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// Tulip STDDEV uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] tData = _testData.RawData.ToArray();
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foreach (var period in periods)
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{
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var stdDev = new StdDev(period, isPopulation: true);
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var qResults = new List<double>();
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foreach (var item in _testData.Data)
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{
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qResults.Add(stdDev.Update(item).Value);
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}
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var stdDevInd = Tulip.Indicators.stddev;
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double[][] inputs = { tData };
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double[] options = { period };
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int lookback = stdDevInd.Start(options);
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double[][] outputs = { new double[tData.Length - lookback] };
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stdDevInd.Run(inputs, options, outputs);
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var tResult = outputs[0];
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ValidationHelper.VerifyData(qResults, tResult, lookback);
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}
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}
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[Fact]
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public void StdDev_Matches_Tulip_Span()
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{
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// Tulip STDDEV uses Population Standard Deviation (N)
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int[] periods = { 5, 10, 20, 50, 100 };
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double[] sourceData = _testData.RawData.ToArray();
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foreach (var period in periods)
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{
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double[] qOutput = new double[sourceData.Length];
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StdDev.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period, isPopulation: true);
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var stdDevInd = Tulip.Indicators.stddev;
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double[][] inputs = { sourceData };
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double[] options = { period };
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int lookback = stdDevInd.Start(options);
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double[][] outputs = { new double[sourceData.Length - lookback] };
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stdDevInd.Run(inputs, options, outputs);
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var tResult = outputs[0];
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ValidationHelper.VerifyData(qOutput, tResult, lookback);
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}
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}
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#endregion
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#region MathNet Validation
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[Fact]
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public void StdDev_Matches_MathNet_Sample()
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{
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: false);
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var popStdDev = new StdDev(period, isPopulation: true);
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var quotes = _data.SkenderQuotes.ToList();
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double[] input = quotes.Select(q => (double)q.Close).ToArray();
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double[] input = _testData.RawData.ToArray();
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for (int i = 0; i < input.Length; i++)
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{
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var val = stdDev.Update(new TValue(DateTime.UtcNow, input[i]));
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var popVal = popStdDev.Update(new TValue(DateTime.UtcNow, input[i]));
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if (i >= input.Length - 100)
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if (i >= period - 1)
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{
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var window = input[(i - period + 1)..(i + 1)];
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double expected = Statistics.StandardDeviation(window);
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double expectedPop = Statistics.PopulationStandardDeviation(window);
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double expected = MathNet.Numerics.Statistics.Statistics.StandardDeviation(window);
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Assert.Equal(expected, val.Value, ValidationHelper.DefaultTolerance);
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Assert.Equal(expectedPop, popVal.Value, ValidationHelper.DefaultTolerance);
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}
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}
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}
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[Fact]
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public void StdDev_Matches_MathNet_Population()
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{
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: true);
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double[] input = _testData.RawData.ToArray();
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for (int i = 0; i < input.Length; i++)
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{
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var val = stdDev.Update(new TValue(DateTime.UtcNow, input[i]));
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if (i >= period - 1)
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{
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var window = input[(i - period + 1)..(i + 1)];
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double expected = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(window);
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Assert.Equal(expected, val.Value, ValidationHelper.DefaultTolerance);
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}
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}
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}
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#endregion
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#region Comprehensive Tests
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[Fact]
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public void StdDev_AllModes_ProduceIdenticalResults()
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{
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// Critical validation: All 3 API modes must produce identical results
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int[] periods = { 5, 10, 20, 50 };
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foreach (var period in periods)
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{
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// Test both population and sample
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foreach (bool isPopulation in new[] { true, false })
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{
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// 1. Batch Mode (TSeries)
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var batchStdDev = new StdDev(period, isPopulation);
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var batchResult = batchStdDev.Update(_testData.Data);
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// 2. Span Mode
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double[] sourceData = _testData.RawData.ToArray();
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double[] spanOutput = new double[sourceData.Length];
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StdDev.Batch(sourceData.AsSpan(), spanOutput.AsSpan(), period, isPopulation);
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// 3. Streaming Mode
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var streamingStdDev = new StdDev(period, isPopulation);
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var streamingResults = new List<double>();
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foreach (var item in _testData.Data)
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{
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streamingResults.Add(streamingStdDev.Update(item).Value);
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}
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// Compare all modes (allow 1e-8 tolerance for accumulated floating-point errors)
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for (int i = 0; i < _testData.Data.Count; i++)
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{
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Assert.Equal(batchResult[i].Value, spanOutput[i], 1e-8);
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Assert.Equal(batchResult[i].Value, streamingResults[i], 1e-8);
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}
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}
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}
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}
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[Fact]
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public void StdDev_Matches_SqrtVariance()
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{
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// StdDev = Sqrt(Variance)
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// Validate this relationship holds
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int[] periods = { 5, 10, 20, 50, 100 };
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foreach (var period in periods)
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{
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foreach (bool isPopulation in new[] { true, false })
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{
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var stdDev = new StdDev(period, isPopulation);
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var variance = new Variance(period, isPopulation);
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for (int i = 0; i < _testData.Data.Count; i++)
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{
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var input = _testData.Data[i];
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var s = stdDev.Update(input);
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var v = variance.Update(input);
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double expected = Math.Sqrt(Math.Max(0, v.Value));
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Assert.Equal(expected, s.Value, 1e-10);
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}
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}
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}
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}
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[Fact]
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public void StdDev_FlatLine_ProducesZero()
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{
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// Flat price should produce zero standard deviation
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var stdDev = new StdDev(10);
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for (int i = 0; i < 50; i++)
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{
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stdDev.Update(new TValue(DateTime.UtcNow, 100));
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}
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// After sufficient warmup, flat line should produce StdDev ≈ 0
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Assert.True(Math.Abs(stdDev.Last.Value) < 1e-10,
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$"Expected StdDev ≈ 0 for flat line, got {stdDev.Last.Value}");
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}
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[Fact]
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public void StdDev_LargeDataset_MaintainsPrecision()
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{
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// Test with large dataset to ensure no drift
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int period = 20;
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var stdDev = new StdDev(period, isPopulation: true);
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var variance = new Variance(period, isPopulation: true);
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 42);
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var bars = gbm.Fetch(10000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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for (int i = 0; i < bars.Close.Count; i++)
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{
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var input = bars.Close[i];
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var s = stdDev.Update(input);
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var v = variance.Update(input);
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// Every 1000th point, verify precision
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if (i % 1000 == 0 && i > period)
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{
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double expected = Math.Sqrt(Math.Max(0, v.Value));
|
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Assert.Equal(expected, s.Value, 1e-9);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StdDev_PopulationVsSample_Difference()
|
||||
{
|
||||
// Population and Sample StdDev should differ
|
||||
int period = 10;
|
||||
var popStdDev = new StdDev(period, isPopulation: true);
|
||||
var sampStdDev = new StdDev(period, isPopulation: false);
|
||||
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 123);
|
||||
var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
foreach (var bar in bars.Close)
|
||||
{
|
||||
popStdDev.Update(bar);
|
||||
sampStdDev.Update(bar);
|
||||
}
|
||||
|
||||
// Sample StdDev should be larger than Population StdDev (divides by N-1 instead of N)
|
||||
Assert.True(sampStdDev.IsHot && popStdDev.IsHot);
|
||||
Assert.True(sampStdDev.Last.Value > popStdDev.Last.Value,
|
||||
$"Sample StdDev ({sampStdDev.Last.Value}) should be > Population StdDev ({popStdDev.Last.Value})");
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StdDev_BatchSpan_HandlesNaN_InMiddle()
|
||||
{
|
||||
double[] data = new double[100];
|
||||
var gbm = new GBM(startPrice: 100, seed: 42);
|
||||
|
||||
for (int i = 0; i < 100; i++)
|
||||
{
|
||||
data[i] = gbm.Next().Close;
|
||||
}
|
||||
|
||||
// Insert NaN in the middle
|
||||
data[50] = double.NaN;
|
||||
|
||||
double[] output = new double[100];
|
||||
StdDev.Batch(data.AsSpan(), output.AsSpan(), 10);
|
||||
|
||||
// All outputs should be finite
|
||||
foreach (var value in output)
|
||||
{
|
||||
Assert.True(double.IsFinite(value), $"Expected finite value, got {value}");
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StdDev_Convergence_AfterWarmup()
|
||||
{
|
||||
// After warmup period, indicator should be "hot"
|
||||
int[] periods = { 5, 10, 20, 50 };
|
||||
|
||||
foreach (var period in periods)
|
||||
{
|
||||
var stdDev = new StdDev(period);
|
||||
|
||||
Assert.False(stdDev.IsHot);
|
||||
|
||||
// Feed period number of bars
|
||||
for (int i = 0; i < period - 1; i++)
|
||||
{
|
||||
stdDev.Update(_testData.Data[i]);
|
||||
Assert.False(stdDev.IsHot);
|
||||
}
|
||||
|
||||
stdDev.Update(_testData.Data[period - 1]);
|
||||
Assert.True(stdDev.IsHot);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StdDev_DifferentPeriods_ProduceDifferentSensitivity()
|
||||
{
|
||||
// Shorter periods should be more sensitive to price changes
|
||||
var stdDev5 = new StdDev(5);
|
||||
var stdDev20 = new StdDev(20);
|
||||
var stdDev50 = new StdDev(50);
|
||||
|
||||
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 123);
|
||||
var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
|
||||
|
||||
foreach (var bar in bars.Close)
|
||||
{
|
||||
stdDev5.Update(bar);
|
||||
stdDev20.Update(bar);
|
||||
stdDev50.Update(bar);
|
||||
}
|
||||
|
||||
// All periods should produce finite numeric results
|
||||
Assert.True(double.IsFinite(stdDev5.Last.Value));
|
||||
Assert.True(double.IsFinite(stdDev20.Last.Value));
|
||||
Assert.True(double.IsFinite(stdDev50.Last.Value));
|
||||
|
||||
// All should be hot
|
||||
Assert.True(stdDev5.IsHot && stdDev20.IsHot && stdDev50.IsHot);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public void StdDev_EdgeCase_Period2()
|
||||
{
|
||||
// Period=2 is minimum (constructor throws on period=1)
|
||||
var stdDev = new StdDev(2);
|
||||
|
||||
stdDev.Update(new TValue(DateTime.UtcNow, 100));
|
||||
stdDev.Update(new TValue(DateTime.UtcNow, 100));
|
||||
|
||||
// Two identical values should produce StdDev = 0
|
||||
Assert.Equal(0, stdDev.Last.Value, 1e-10);
|
||||
|
||||
stdDev.Update(new TValue(DateTime.UtcNow, 110));
|
||||
// 100, 110: mean = 105, deviations = -5, 5, squared = 25, 25, sum = 50
|
||||
// Population variance = 50/2 = 25, StdDev = 5
|
||||
// Sample variance = 50/1 = 50, StdDev = 7.071...
|
||||
|
||||
// Default is sample (isPopulation=false)
|
||||
Assert.Equal(Math.Sqrt(50), stdDev.Last.Value, 1e-10);
|
||||
}
|
||||
|
||||
#endregion
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user