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Add Close-to-Close Volatility (CCV) implementation and validation tests
- Implemented CCV class for calculating annualized log return volatility using SMA, EMA, and WMA smoothing methods. - Added comprehensive unit tests for CCV to validate mathematical correctness, consistency across methods, and edge cases. - Created documentation for CCV detailing its mathematical foundation, smoothing methods, and performance metrics.
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Miha Kralj
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namespace QuanTAlib.Tests;
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using Xunit;
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/// <summary>
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/// Validation tests for BBWP (Bollinger Band Width Percentile).
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/// BBWP is a proprietary indicator, so we validate against internal consistency
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/// and mathematical properties rather than external libraries.
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/// </summary>
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public class BbwpValidationTests
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{
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private static TBarSeries GenerateTestData(int count = 500)
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{
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var gbm = new GBM(seed: 42);
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return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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}
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[Fact]
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public void BBWP_OutputRange_AlwaysValid()
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{
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var bars = GenerateTestData(500);
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var bbwp = new Bbwp(20, 2.0, 100);
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]));
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Assert.True(result.Value >= 0.0, $"BBWP at {i} should be >= 0, got {result.Value}");
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Assert.True(result.Value <= 1.0, $"BBWP at {i} should be <= 1, got {result.Value}");
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}
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}
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[Fact]
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public void BBWP_StreamingVsBatch_Match()
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{
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var bars = GenerateTestData(200);
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var times = bars.Times;
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var close = bars.CloseValues;
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// Streaming calculation
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var bbwpStream = new Bbwp(10, 2.0, 50);
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var streamResults = new List<double>();
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwpStream.Update(new TValue(times[i], close[i]));
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streamResults.Add(result.Value);
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}
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// Batch calculation
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var ts = new TSeries();
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for (int i = 0; i < bars.Count; i++)
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{
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ts.Add(new TValue(times[i], close[i]));
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}
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var batchResults = Bbwp.Calculate(ts, 10, 2.0, 50);
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// Compare results (should be identical)
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for (int i = 0; i < bars.Count; i++)
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{
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Assert.Equal(streamResults[i], batchResults.Values[i], 1e-10);
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}
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}
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[Fact]
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public void BBWP_DifferentPeriods_ProduceValidResults()
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{
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var bars = GenerateTestData(300);
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int[] periods = { 5, 10, 20, 50 };
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foreach (int period in periods)
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{
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var bbwp = new Bbwp(period, 2.0, 100);
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]));
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Assert.True(double.IsFinite(result.Value), $"Period {period} at {i} should be finite");
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Assert.True(result.Value >= 0.0 && result.Value <= 1.0, $"Period {period} at {i} should be in [0,1]");
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}
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}
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}
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[Fact]
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public void BBWP_DifferentLookbacks_ProduceValidResults()
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{
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var bars = GenerateTestData(300);
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int[] lookbacks = { 20, 50, 100, 200 };
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foreach (int lookback in lookbacks)
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{
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var bbwp = new Bbwp(20, 2.0, lookback);
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]));
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Assert.True(double.IsFinite(result.Value), $"Lookback {lookback} at {i} should be finite");
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Assert.True(result.Value >= 0.0 && result.Value <= 1.0, $"Lookback {lookback} at {i} should be in [0,1]");
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}
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}
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}
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[Fact]
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public void BBWP_DifferentMultipliers_ProduceValidResults()
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{
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var bars = GenerateTestData(200);
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double[] multipliers = { 1.0, 1.5, 2.0, 2.5, 3.0 };
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foreach (double mult in multipliers)
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{
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var bbwp = new Bbwp(20, mult, 100);
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]));
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Assert.True(double.IsFinite(result.Value), $"Multiplier {mult} at {i} should be finite");
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Assert.True(result.Value >= 0.0 && result.Value <= 1.0, $"Multiplier {mult} at {i} should be in [0,1]");
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}
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}
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}
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[Fact]
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public void BBWP_ConstantInput_ProducesZeroPercentile()
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{
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var bbwp = new Bbwp(10, 2.0, 50);
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// Feed constant values - BBW will be 0, and percentile of 0 among 0s is 0
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for (int i = 0; i < 100; i++)
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{
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var result = bbwp.Update(new TValue(DateTime.UtcNow.Ticks + i, 100.0));
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Assert.True(double.IsFinite(result.Value));
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Assert.True(result.Value >= 0.0 && result.Value <= 1.0);
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}
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// With constant input, BBW=0 always, so percentile should be 0 (nothing below 0)
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Assert.Equal(0.0, bbwp.Last.Value, 1e-10);
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}
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[Fact]
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public void BBWP_HighVolatilitySpike_ProducesHighPercentile()
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{
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var bbwp = new Bbwp(5, 2.0, 20);
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// Feed low volatility data first
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for (int i = 0; i < 25; i++)
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{
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bbwp.Update(new TValue(DateTime.UtcNow.Ticks + i, 100.0 + (i % 2) * 0.1));
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}
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// Then introduce a high volatility spike
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bbwp.Update(new TValue(DateTime.UtcNow.Ticks + 25, 100.0));
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bbwp.Update(new TValue(DateTime.UtcNow.Ticks + 26, 110.0)); // Big move
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bbwp.Update(new TValue(DateTime.UtcNow.Ticks + 27, 105.0));
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// After high volatility, percentile should be elevated
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Assert.True(bbwp.Last.Value > 0.3, $"High volatility should produce elevated percentile, got {bbwp.Last.Value}");
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}
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[Fact]
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public void BBWP_PercentileDistribution_Reasonable()
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{
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var bars = GenerateTestData(500);
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var bbwp = new Bbwp(20, 2.0, 100);
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var results = new List<double>();
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for (int i = 0; i < bars.Count; i++)
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{
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var result = bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]));
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if (i >= 120) // After warmup
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{
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results.Add(result.Value);
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}
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}
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// Percentile values should be distributed - check quartiles
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results.Sort();
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int q1Idx = results.Count / 4;
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int q3Idx = 3 * results.Count / 4;
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double q1 = results[q1Idx];
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double q3 = results[q3Idx];
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// Should have meaningful spread
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Assert.True(q3 - q1 > 0.1, $"Percentile spread should be meaningful, Q1={q1:F3}, Q3={q3:F3}");
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}
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[Fact]
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public void BBWP_BarCorrection_Works()
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{
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var bbwp = new Bbwp(10, 2.0, 30);
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var bars = GenerateTestData(50);
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// Process all bars
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for (int i = 0; i < bars.Count; i++)
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{
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bbwp.Update(new TValue(bars.Times[i], bars.CloseValues[i]), isNew: true);
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}
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double originalValue = bbwp.Last.Value;
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// Correct the last bar with different value
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bbwp.Update(new TValue(bars.Times[bars.Count - 1], bars.CloseValues[bars.Count - 1] * 2), isNew: false);
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// Restore original value
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var restored = bbwp.Update(new TValue(bars.Times[bars.Count - 1], bars.CloseValues[bars.Count - 1]), isNew: false);
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Assert.Equal(originalValue, restored.Value, 1e-10);
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}
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[Fact]
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public void BBWP_SpanBatch_MatchesStreaming()
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{
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var bars = GenerateTestData(100);
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var close = bars.CloseValues.ToArray();
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// Streaming
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var bbwpStream = new Bbwp(10, 2.0, 30);
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for (int i = 0; i < close.Length; i++)
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{
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bbwpStream.Update(new TValue(DateTime.UtcNow.Ticks + i, close[i]));
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}
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// Batch via span
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var output = new double[close.Length];
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Bbwp.Batch(close, output, 10, 2.0, 30);
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Assert.Equal(bbwpStream.Last.Value, output[output.Length - 1], 1e-10);
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}
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}
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