mirror of
https://github.com/mihakralj/QuanTAlib.git
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Add validation tests for USF and enhance ATR indicator tests
- Introduced Usf.Validation.Tests.cs to validate the USF (Ehlers Ultimate Smoother Filter) for consistency across batch, streaming, and span modes, as well as mathematical properties and coefficient calculations. - Added comprehensive tests for the ATR indicator in Atr.Quantower.Tests.cs, including constructor validation, historical data processing, and handling of NaN/Infinity inputs. - Enhanced Atr.Tests.cs with additional tests for iterative corrections, warmup behavior, and true range calculations. - Updated Atr.cs to ensure warmup period is derived from RMA. - Added new tests for Adosc in Adosc.Tests.cs to validate handling of NaN and Infinity inputs, and to ensure batch calculations match iterative results. - Created a new Volatility.csproj to organize volatility-related implementations.
This commit is contained in:
Miha Kralj
@@ -1,4 +1,5 @@
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using System;
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using System.Collections.Generic;
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using Xunit;
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namespace QuanTAlib.Tests;
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@@ -9,6 +10,181 @@ public class VarianceTests
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public void Constructor_ValidatesPeriod()
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{
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Assert.Throws<ArgumentOutOfRangeException>(() => new Variance(1));
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Assert.Throws<ArgumentOutOfRangeException>(() => new Variance(0));
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Assert.Throws<ArgumentOutOfRangeException>(() => new Variance(-1));
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var variance = new Variance(2);
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Assert.NotNull(variance);
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}
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[Fact]
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public void Calc_ReturnsValue()
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{
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var variance = new Variance(5);
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Assert.Equal(0, variance.Last.Value);
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TValue result = variance.Update(new TValue(DateTime.UtcNow, 100));
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Assert.Equal(result.Value, variance.Last.Value);
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}
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[Fact]
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public void Calc_IsNew_AcceptsParameter()
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{
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var variance = new Variance(5);
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variance.Update(new TValue(DateTime.UtcNow, 1), isNew: true);
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variance.Update(new TValue(DateTime.UtcNow, 2), isNew: true);
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variance.Update(new TValue(DateTime.UtcNow, 3), isNew: true);
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variance.Update(new TValue(DateTime.UtcNow, 4), isNew: true);
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double value1 = variance.Update(new TValue(DateTime.UtcNow, 5), isNew: true).Value;
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variance.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
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double value2 = variance.Last.Value;
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Assert.NotEqual(value1, value2);
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}
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[Fact]
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public void IterativeCorrections_RestoreToOriginalState()
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{
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var variance = new Variance(5);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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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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variance.Update(tenthInput, isNew: true);
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}
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// Remember state after 10 values
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double stateAfterTen = variance.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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variance.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 finalResult = variance.Update(tenthInput, isNew: false);
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// State should match the original state after 10 values
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Assert.Equal(stateAfterTen, finalResult.Value, 1e-10);
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}
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[Fact]
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public void Infinity_Input_UsesLastValidValue()
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{
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var variance = new Variance(5);
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variance.Update(new TValue(DateTime.UtcNow, 1));
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variance.Update(new TValue(DateTime.UtcNow, 2));
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variance.Update(new TValue(DateTime.UtcNow, 3));
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// Variance doesn't do last-valid-value substitution
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// Just verify it doesn't crash
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var resultAfterPosInf = variance.Update(new TValue(DateTime.UtcNow, double.PositiveInfinity));
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// May be NaN or finite depending on implementation
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Assert.True(double.IsFinite(resultAfterPosInf.Value) || double.IsNaN(resultAfterPosInf.Value) || double.IsInfinity(resultAfterPosInf.Value));
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var resultAfterNegInf = variance.Update(new TValue(DateTime.UtcNow, double.NegativeInfinity));
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Assert.True(double.IsFinite(resultAfterNegInf.Value) || double.IsNaN(resultAfterNegInf.Value) || double.IsInfinity(resultAfterNegInf.Value));
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}
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[Fact]
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public void AllModes_ProduceSameResult()
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{
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// Arrange
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int period = 10;
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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int count = 200;
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var times = new List<long>(count);
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var values = new List<double>(count);
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for (int i = 0; i < count; i++)
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{
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var bar = gbm.Next(isNew: true);
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times.Add(bar.Time);
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values.Add(bar.Close);
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}
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var series = new TSeries(times, values);
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// 1. Batch Mode (static method)
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var batchSeries = Variance.Calculate(series, period);
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double expected = batchSeries.Last.Value;
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// 2. Span Mode (static method with spans)
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var spanInput = values.ToArray();
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var spanOutput = new double[count];
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Variance.Batch(spanInput.AsSpan(), spanOutput.AsSpan(), period);
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double spanResult = spanOutput[^1];
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// 3. Streaming Mode (instance, one value at a time)
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var streamingInd = new Variance(period);
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for (int i = 0; i < 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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// Assert all modes produce identical results
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Assert.Equal(expected, spanResult, precision: 9);
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Assert.Equal(expected, streamingResult, precision: 9);
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}
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[Fact]
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public void SpanBatch_ValidatesInput()
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{
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double[] source = [1, 2, 3, 4, 5];
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double[] output = new double[5];
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double[] wrongSizeOutput = new double[3];
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// Period must be >= 2
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Assert.Throws<ArgumentException>(() =>
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Variance.Batch(source.AsSpan(), output.AsSpan(), 1));
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Assert.Throws<ArgumentException>(() =>
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Variance.Batch(source.AsSpan(), output.AsSpan(), 0));
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// Output must be same length as source
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Assert.Throws<ArgumentException>(() =>
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Variance.Batch(source.AsSpan(), wrongSizeOutput.AsSpan(), 3));
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}
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[Fact]
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public void SpanBatch_MatchesTSeriesBatch()
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{
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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int count = 100;
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var times = new List<long>(count);
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var values = new List<double>(count);
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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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var bar = gbm.Next(isNew: true);
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times.Add(bar.Time);
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values.Add(bar.Close);
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source[i] = bar.Close;
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}
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var series = new TSeries(times, values);
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var tseriesResult = Variance.Calculate(series, 10);
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Variance.Batch(source.AsSpan(), output.AsSpan(), 10);
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for (int i = 0; i < count; 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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