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Refactor code formatting and improve consistency across various test files

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- Removed unnecessary blank lines in multiple test files to enhance readability.
- Ensured consistent spacing and formatting in the `Trima`, `Usf`, `Vidya`, `Wma`, and `Atr` test classes.
- Updated comments for clarity and consistency in the `Atr` and `Adl` classes.
- Adjusted project files for better structure and maintainability.
This commit is contained in:
Miha Kralj
2025-12-28 17:44:08 -08:00
parent ad6eebf812
commit 13d7c1215d
169 changed files with 10815 additions and 10814 deletions
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lib/statistics/beta/Beta.Tests.cs
+5 -5
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@@ -23,7 +23,7 @@ public class BetaTests
{
int period = 5;
var beta = new Beta(period);
// We need period returns.
// 1st update: initializes prev prices. No return.
// 2nd update: 1st return.
@@ -45,7 +45,7 @@ public class BetaTests
{
// Scenario: Asset returns are exactly 2x Market returns.
// We need variable market returns to have non-zero variance.
int period = 10;
var beta = new Beta(period);
@@ -66,9 +66,9 @@ public class BetaTests
marketPrice *= (1 + marketReturn);
assetPrice *= (1 + assetReturn);
TValue result = beta.Update(assetPrice, marketPrice);
if (beta.IsHot)
{
Assert.Equal(2.0, result.Value, precision: 6);
@@ -88,7 +88,7 @@ public class BetaTests
beta.Reset();
Assert.False(beta.IsHot);
// Re-initialize
beta.Update(100, 100);
Assert.False(beta.IsHot);
lib/statistics/beta/Beta.Validation.Tests.cs
+7 -7
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@@ -25,13 +25,13 @@ public sealed class BetaValidationTests : IDisposable
{
// Generate Market Data (use existing Data)
var marketQuotes = _data.Data;
// Generate Asset Data correlated to Market
// Asset Returns = 1.5 * Market Returns + Noise
var assetQuotes = new List<TBar>();
double assetPrice = 100;
double targetBeta = 1.5;
// Use GBM for noise generation (sigma=0.2 gives ~0.0006 per step noise which matches original random noise level)
var noiseGbm = new GBM(startPrice: 100, mu: 0, sigma: 0.2, seed: 777);
@@ -40,13 +40,13 @@ public sealed class BetaValidationTests : IDisposable
for (int i = 1; i < marketQuotes.Count; i++)
{
double marketReturn = (marketQuotes[i].Value - marketQuotes[i-1].Value) / marketQuotes[i-1].Value;
// Get noise from GBM return
var noiseBar = noiseGbm.Next();
double noise = (noiseBar.Close - noiseBar.Open) / noiseBar.Open;
double assetReturn = targetBeta * marketReturn + noise;
assetPrice *= (1 + assetReturn);
assetQuotes.Add(new TBar(marketQuotes[i].Time, assetPrice, assetPrice, assetPrice, assetPrice, 1000));
}
@@ -73,7 +73,7 @@ public sealed class BetaValidationTests : IDisposable
// Skip warmup period. Skender Beta needs period returns, so period+1 prices?
// Skender results align with input quotes.
// First valid value should be at index 'period'.
// We verify the last 100 values
int count = qlBeta.Count;
int skip = period + 5; // Safety margin
@@ -82,7 +82,7 @@ public sealed class BetaValidationTests : IDisposable
{
double sk = (skenderBeta[i].Beta ?? 0);
double ql = qlBeta[i];
// Skender might return null/0 for warmup.
if (Math.Abs(sk) > 1e-10)
{
lib/statistics/beta/Beta.cs
+4 -4
View File
@@ -9,14 +9,14 @@ namespace QuanTAlib;
/// <remarks>
/// Beta is calculated as the covariance of the asset's returns and the market's returns,
/// divided by the variance of the market's returns.
///
///
/// Formula:
/// Beta = Cov(Ra, Rm) / Var(Rm)
///
///
/// Where:
/// Ra = Return of Asset
/// Rm = Return of Market
///
///
/// This implementation uses the O(1) slope formula for linear regression of Ra vs Rm:
/// Beta = (N * Sum(Ra*Rm) - Sum(Ra) * Sum(Rm)) / (N * Sum(Rm^2) - Sum(Rm)^2)
/// </remarks>
@@ -25,7 +25,7 @@ public sealed class Beta : AbstractBase
{
private readonly RingBuffer _returnsAsset;
private readonly RingBuffer _returnsMarket;
private double _prevAsset;
private double _prevMarket;
private double _p_prevAsset;
lib/statistics/covariance/Covariance.Quantower.cs
+1 -1
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@@ -63,7 +63,7 @@ public sealed class CovarianceIndicator : Indicator, IWatchlistIndicator
var input1 = new TValue(time, val1);
var input2 = new TValue(time, val2);
TValue result = _cov!.Update(input1, input2, args.IsNewBar());
_series!.SetValue(result.Value, _cov.IsHot, ShowColdValues);
lib/statistics/covariance/Covariance.Simd.Tests.cs
+9 -9
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@@ -31,7 +31,7 @@ public class CovarianceSimdTests
// This will use SIMD if available and length >= 256
var simdResult = Covariance.Calculate(sourceX, sourceY, period);
// Calculate expected using scalar loop (simulating by using small chunks or manual calc,
// Calculate expected using scalar loop (simulating by using small chunks or manual calc,
// but easier to just use the streaming update which is scalar)
var scalarCov = new Covariance(period);
var expectedValues = new double[count];
@@ -56,7 +56,7 @@ public class CovarianceSimdTests
int period = 50;
var dataX = Enumerable.Range(0, count).Select(x => (double)x).ToArray();
var dataY = Enumerable.Range(0, count).Select(x => (double)x * 2).ToArray();
// Inject NaN
dataX[300] = double.NaN;
dataY[350] = double.NaN;
@@ -74,9 +74,9 @@ public class CovarianceSimdTests
// Assert
// Verify around the NaN values
// Index 300 has NaN in X. Covariance should handle it (likely treat as 0 or propagate last valid if logic dictates,
// Index 300 has NaN in X. Covariance should handle it (likely treat as 0 or propagate last valid if logic dictates,
// but current implementation replaces non-finite with 0 in scalar core).
// Let's verify against streaming which we know uses scalar logic
// BUT: Batch implementation replaces NaN with 0, while Streaming propagates NaN.
// To compare, we must feed 0 instead of NaN to streaming.
@@ -92,7 +92,7 @@ public class CovarianceSimdTests
Assert.Equal(res.Value, result.Values[i], precision: 9);
}
}
[Fact]
public void Covariance_Simd_Resync_Check()
{
@@ -102,13 +102,13 @@ public class CovarianceSimdTests
// The SIMD loop starts at 'period' and goes up to 'simdEnd'.
// So we need length > period + 1000.
int period = 10;
int count = 2000;
int count = 2000;
// Use simple linear data to make verification easy
// y = 2x
var dataX = Enumerable.Range(0, count).Select(x => (double)x).ToArray();
var dataY = Enumerable.Range(0, count).Select(x => (double)x * 2).ToArray();
var sourceX = new TSeries();
sourceX.Add(dataX);
var sourceY = new TSeries();
@@ -123,7 +123,7 @@ public class CovarianceSimdTests
// For period 10: 0..9. Variance is constant.
// Var(0..9) = 9.16666... (Population) or 10.185... (Sample)?
// Let's just compare with scalar truth.
var scalarCov = new Covariance(period);
for (int i = 0; i < count; i++)
{
lib/statistics/covariance/Covariance.Tests.cs
+18 -18
View File
@@ -10,7 +10,7 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3, isPopulation: false);
// Act & Assert
// 1. Add (1, 2)
// MeanX = 1, MeanY = 2
@@ -50,11 +50,11 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3, isPopulation: true);
// Act & Assert
cov.Update(1, 2);
cov.Update(2, 4);
// 3. Add (3, 6)
// X: {1, 2, 3}, Y: {2, 4, 6}
// MeanX = 2, MeanY = 4
@@ -69,12 +69,12 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3);
// Act
cov.Update(1, 1);
cov.Update(2, 1);
var res = cov.Update(3, 1); // Y is constant, variance Y is 0, covariance is 0
// Assert
Assert.Equal(0, res.Value);
}
@@ -84,18 +84,18 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3);
// Act
cov.Update(1, 3);
cov.Update(2, 2);
var res = cov.Update(3, 1);
// X: {1, 2, 3}, MeanX = 2
// Y: {3, 2, 1}, MeanY = 2
// Cov = ((1-2)(3-2) + (2-2)(2-2) + (3-2)(1-2)) / 2
// = ((-1)(1) + 0 + (1)(-1)) / 2
// = (-1 - 1) / 2 = -1
// Assert
Assert.Equal(-1, res.Value);
}
@@ -105,16 +105,16 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3);
// Act
// Force many updates to trigger resync (ResyncInterval = 1000)
// We can't easily force 1000 updates in a simple test without loop,
// We can't easily force 1000 updates in a simple test without loop,
// but we can verify the logic holds for a sequence.
for (int i = 0; i < 1100; i++)
{
cov.Update(i, i * 2);
}
// Last 3: {1097, 1098, 1099}, {2194, 2196, 2198}
// This is a perfect linear relationship y = 2x
// Cov(X, 2X) = 2 * Var(X)
@@ -123,7 +123,7 @@ public class CovarianceTests
// SumSqDiff = (-1)^2 + 0 + 1^2 = 2
// Var = 2 / 2 = 1
// Cov = 2 * 1 = 2
// Assert
Assert.Equal(2, cov.Last.Value, precision: 10);
}
@@ -133,29 +133,29 @@ public class CovarianceTests
{
// Arrange
var cov = new Covariance(3);
// Act
cov.Update(1, 2);
cov.Update(2, 4);
cov.Update(3, 6); // Cov = 2
// Update last bar with new values
// Change (3, 6) to (4, 8)
// X: {1, 2, 4}, MeanX = 7/3 = 2.333...
// Y: {2, 4, 8}, MeanY = 14/3 = 4.666...
// This is harder to calc manually, let's use the property that it should match adding (4, 8) directly
var res = cov.Update(4, 8, isNew: false);
var cov2 = new Covariance(3);
cov2.Update(1, 2);
cov2.Update(2, 4);
var expected = cov2.Update(4, 8);
// Assert
Assert.Equal(expected.Value, res.Value, precision: 10);
}
[Fact]
public void Covariance_Throws_On_Single_Input()
{
lib/statistics/covariance/Covariance.Validation.Tests.cs
+8 -8
View File
@@ -13,7 +13,7 @@ public class CovarianceValidationTests
var cov = new Covariance(period, isPopulation: false);
var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
var gbmY = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 456);
double[] x = new double[100];
double[] y = new double[100];
for (int i = 0; i < 100; i++)
@@ -21,7 +21,7 @@ public class CovarianceValidationTests
x[i] = gbmX.Next().Close;
y[i] = gbmY.Next().Close;
cov.Update(x[i], y[i]);
if (i >= period - 1)
{
// Manual calculation for last 'period' items
@@ -34,13 +34,13 @@ public class CovarianceValidationTests
}
double meanX = sumX / period;
double meanY = sumY / period;
double sumProd = 0;
for (int j = 0; j < period; j++)
{
sumProd += (x[i - j] - meanX) * (y[i - j] - meanY);
}
double expected = sumProd / (period - 1);
Assert.Equal(expected, cov.Last.Value, precision: 8);
}
@@ -55,7 +55,7 @@ public class CovarianceValidationTests
var cov = new Covariance(period, isPopulation: true);
var gbmX = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 456);
var gbmY = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 789);
double[] x = new double[100];
double[] y = new double[100];
for (int i = 0; i < 100; i++)
@@ -63,7 +63,7 @@ public class CovarianceValidationTests
x[i] = gbmX.Next().Close;
y[i] = gbmY.Next().Close;
cov.Update(x[i], y[i]);
if (i >= period - 1)
{
// Manual calculation for last 'period' items
@@ -76,13 +76,13 @@ public class CovarianceValidationTests
}
double meanX = sumX / period;
double meanY = sumY / period;
double sumProd = 0;
for (int j = 0; j < period; j++)
{
sumProd += (x[i - j] - meanX) * (y[i - j] - meanY);
}
double expected = sumProd / period;
Assert.Equal(expected, cov.Last.Value, precision: 8);
}
lib/statistics/linreg/LinReg.Validation.Tests.cs
+2 -2
View File
@@ -29,7 +29,7 @@ public sealed class LinRegValidationTests : IDisposable
{
var period = 14;
var skender = _data.SkenderQuotes.GetSlope(period).ToList();
var linreg = new LinReg(period);
var slopeSeries = new TSeries();
foreach (var item in _data.Data)
@@ -47,7 +47,7 @@ public sealed class LinRegValidationTests : IDisposable
{
var period = 14;
var skender = _data.SkenderQuotes.GetSlope(period).ToList();
var linreg = new LinReg(period);
var r2Series = new TSeries();
foreach (var item in _data.Data)
lib/statistics/linreg/LinReg.cs
+2 -2
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@@ -166,7 +166,7 @@ public sealed class LinReg : AbstractBase
_state.SumY = _buffer.Sum;
_state.SumXY = 0;
var span = _buffer.GetSpan();
// Vectorized SumY2
_state.SumY2 = span.DotProduct(span);
@@ -248,7 +248,7 @@ public sealed class LinReg : AbstractBase
// R2 = (n * sum_xy - sum_x * sum_y)^2 / ( (n * sum_x2 - sum_x^2) * (n * sum_y2 - sum_y^2) )
double numerator = Math.FusedMultiplyAdd(n, _state.SumXY, -sx * _state.SumY);
double term2 = Math.FusedMultiplyAdd(n, _state.SumY2, -_state.SumY * _state.SumY);
RSquared = Math.Abs(term2) < MinDenominator
? 1.0 // All y are same
: numerator * numerator / (denom * term2);
lib/statistics/median/Median.Tests.cs
+9 -9
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@@ -9,7 +9,7 @@ public class MedianTests
{
// Arrange
var median = new Median(3);
// Act
median.Update(new TValue(DateTime.MinValue, 10));
median.Update(new TValue(DateTime.MinValue, 30));
@@ -25,7 +25,7 @@ public class MedianTests
{
// Arrange
var median = new Median(4);
// Act
median.Update(new TValue(DateTime.MinValue, 10));
median.Update(new TValue(DateTime.MinValue, 40));
@@ -42,13 +42,13 @@ public class MedianTests
{
// Arrange
var median = new Median(3);
// Act
median.Update(new TValue(DateTime.MinValue, 10));
median.Update(new TValue(DateTime.MinValue, 20));
// Update with 30 (isNew=true)
var r1 = median.Update(new TValue(DateTime.MinValue, 30));
var r1 = median.Update(new TValue(DateTime.MinValue, 30));
// Window: [10, 20, 30] -> Median 20
Assert.Equal(20, r1.Value);
@@ -79,7 +79,7 @@ public class MedianTests
var medianBatch = Median.Batch(source, period);
var medianStream = new Median(period);
var streamResults = new List<double>();
foreach (var val in source)
{
streamResults.Add(medianStream.Update(val).Value);
@@ -91,7 +91,7 @@ public class MedianTests
Assert.Equal(medianBatch.Values[i], streamResults[i], 1e-9);
}
}
[Fact]
public void Median_StaticBatch_Matches_ClassBatch()
{
@@ -99,11 +99,11 @@ public class MedianTests
int period = 5;
double[] data = new double[20];
for(int i=0; i<data.Length; i++) data[i] = i;
// Act
double[] output = new double[data.Length];
Median.Batch(data, output, period);
var series = new TSeries();
for(int i=0; i<data.Length; i++) series.Add(new TValue(DateTime.MinValue, data[i]));
var batchSeries = Median.Batch(series, period);
lib/statistics/median/Median.Validation.Tests.cs
+1 -1
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@@ -17,7 +17,7 @@ public sealed class MedianValidationTests : IDisposable
_data.Dispose();
}
// Note: Standard TA libraries (Skender, TA-Lib, Tulip, Ooples) do not provide a
// Note: Standard TA libraries (Skender, TA-Lib, Tulip, Ooples) do not provide a
// "Rolling Median" indicator. They typically provide "Median Price" which is (High+Low)/2.
// Therefore, we validate against a robust LINQ-based reference implementation and MathNet.
lib/statistics/skew/Skew.Tests.cs
+15 -15
View File
@@ -21,7 +21,7 @@ public class SkewTests
// Variance (Sample) = 2.5
// StdDev (Sample) = 1.58113883
// Skewness (Sample) = 0 (Symmetric)
var skew = new Skew(5, isPopulation: false);
skew.Update(new TValue(DateTime.UtcNow, 1));
skew.Update(new TValue(DateTime.UtcNow, 2));
@@ -38,7 +38,7 @@ public class SkewTests
// Test data: 1, 1, 1, 10
// Mean = 3.25
// Skewness should be positive (right tail)
var skew = new Skew(4, isPopulation: false);
skew.Update(new TValue(DateTime.UtcNow, 1));
skew.Update(new TValue(DateTime.UtcNow, 1));
@@ -54,7 +54,7 @@ public class SkewTests
// Test data: 10, 10, 10, 1
// Mean = 7.75
// Skewness should be negative (left tail)
var skew = new Skew(4, isPopulation: false);
skew.Update(new TValue(DateTime.UtcNow, 10));
skew.Update(new TValue(DateTime.UtcNow, 10));
@@ -68,16 +68,16 @@ public class SkewTests
public void Update_HandlesUpdates_IsNewFalse()
{
var skew = new Skew(5);
// 1, 2, 3, 4
skew.Update(new TValue(DateTime.UtcNow, 1));
skew.Update(new TValue(DateTime.UtcNow, 2));
skew.Update(new TValue(DateTime.UtcNow, 3));
skew.Update(new TValue(DateTime.UtcNow, 4));
// Add 5
skew.Update(new TValue(DateTime.UtcNow, 5), isNew: true);
// Update 5 to 10
var res2 = skew.Update(new TValue(DateTime.UtcNow, 10), isNew: false);
@@ -97,10 +97,10 @@ public class SkewTests
{
var skew = new Skew(5);
for (int i = 0; i < 5; i++) skew.Update(new TValue(DateTime.UtcNow, i));
skew.Reset();
Assert.False(skew.IsHot);
// Should behave like new
skew.Update(new TValue(DateTime.UtcNow, 1));
Assert.Equal(0, skew.Last.Value); // Not enough data
@@ -111,7 +111,7 @@ public class SkewTests
{
var data = new double[] { 1, 2, 3, 4, 5, 10, 1, 2, 3 };
int period = 5;
// Streaming
var skew = new Skew(period);
var streamingResults = new System.Collections.Generic.List<double>();
@@ -139,7 +139,7 @@ public class SkewTests
// StdDev (Pop) = sqrt(2/3)
// M3 (Pop) = ((1-2)^3 + (2-2)^3 + (3-2)^3) / 3 = 0
// Skew (Pop) = 0
var skew = new Skew(3, isPopulation: true);
skew.Update(new TValue(DateTime.UtcNow, 1));
skew.Update(new TValue(DateTime.UtcNow, 2));
@@ -166,7 +166,7 @@ public class SkewTests
skew.Update(new TValue(DateTime.UtcNow, 1));
skew.Update(new TValue(DateTime.UtcNow, 2));
skew.Update(new TValue(DateTime.UtcNow, double.NaN)); // Should be treated as 0 or handled gracefully
var result = skew.Last.Value;
Assert.True(double.IsNaN(result) || result == 0);
}
@@ -177,12 +177,12 @@ public class SkewTests
// Run for > 1000 updates to trigger Resync
var skew = new Skew(10);
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
for (int i = 0; i < 1100; i++)
{
skew.Update(new TValue(DateTime.UtcNow, gbm.Next().Close));
}
Assert.True(double.IsFinite(skew.Last.Value));
}
@@ -195,10 +195,10 @@ public class SkewTests
for (int i = 0; i < count; i++) data[i] = (double)i;
var series = new TSeries(new System.Collections.Generic.List<long>(new long[count]), new System.Collections.Generic.List<double>(data));
// Batch calculation
var batchResult = Skew.Calculate(series, 10);
// Verify last value against streaming
var skew = new Skew(10);
double lastStreaming = 0;
lib/statistics/skew/Skew.Validation.Tests.cs
+2 -2
View File
@@ -22,7 +22,7 @@ public sealed class SkewValidationTests : IDisposable
int period = 20;
var skew = new Skew(period, isPopulation: false);
var popSkew = new Skew(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
@@ -37,7 +37,7 @@ public sealed class SkewValidationTests : IDisposable
var window = input[(i - period + 1)..(i + 1)];
double expected = Statistics.Skewness(window);
double expectedPop = Statistics.PopulationSkewness(window);
Assert.Equal(expected, val.Value, 1e-6);
Assert.Equal(expectedPop, popVal.Value, 1e-6);
}
lib/statistics/skew/Skew.cs
+19 -19
View File
@@ -80,7 +80,7 @@ public sealed class Skew : AbstractBase
{
double oldNewest = _buffer.Newest;
_buffer.UpdateNewest(input.Value);
double val = input.Value;
_sum = _sum - oldNewest + val;
_sumSq = _sumSq - (oldNewest * oldNewest) + (val * val);
@@ -92,7 +92,7 @@ public sealed class Skew : AbstractBase
{
double n = _buffer.Count;
double mean = _sum / n;
// Calculate 2nd moment (Variance)
// m2 = Sum((x-mean)^2) / n = (SumSq - Sum^2/n) / n
double m2Numerator = _sumSq - (_sum * _sum) / n;
@@ -106,7 +106,7 @@ public sealed class Skew : AbstractBase
// = SumCu - 3*mean*SumSq + 3*mean^2*Sum - n*mean^3
// Since Sum = n*mean:
// = SumCu - 3*mean*SumSq + 2*n*mean^3
double m3Numerator = _sumCu - 3 * mean * _sumSq + 2 * n * mean * mean * mean;
double m3 = m3Numerator / n;
@@ -232,7 +232,7 @@ public sealed class Skew : AbstractBase
double sum = 0;
double sumSq = 0;
double sumCu = 0;
int i = 0;
// Warmup phase
@@ -259,11 +259,11 @@ public sealed class Skew : AbstractBase
double oldVal = source[i - period];
if (!double.IsFinite(oldVal)) oldVal = 0;
sum = sum - oldVal + val;
sumSq = sumSq - (oldVal * oldVal) + (val * val);
sumCu = sumCu - (oldVal * oldVal * oldVal) + (val * val * val);
output[i] = CalculateSkewFromSums(sum, sumSq, sumCu, period, isPopulation);
tickCount++;
@@ -293,7 +293,7 @@ public sealed class Skew : AbstractBase
private static double CalculateSkewFromSums(double sum, double sumSq, double sumCu, double n, bool isPopulation)
{
double mean = sum / n;
double m2Numerator = sumSq - (sum * sum) / n;
if (m2Numerator < Epsilon) return 0;
double m2 = m2Numerator / n;
@@ -326,7 +326,7 @@ public sealed class Skew : AbstractBase
sum += val;
sumSq += val * val;
sumCu += val * val * val;
double n = i + 1;
Unsafe.Add(ref outRef, i) = (n >= 3) ? CalculateSkewFromSums(sum, sumSq, sumCu, n, isPopulation) : 0;
}
@@ -383,34 +383,34 @@ public sealed class Skew : AbstractBase
var vShift1 = Avx2.Permute4x64(vDelta.AsUInt64(), 0b_10_01_00_00).AsDouble(); // skipcq: CS-R1131
vShift1 = Avx.Blend(vZero, vShift1, 0b_1110);
var vP1 = Avx.Add(vDelta, vShift1);
// Shift 2: [0, 0, d0, d0+d1]
var vShift2 = Avx2.Permute4x64(vP1.AsUInt64(), 0b_01_00_00_00).AsDouble(); // skipcq: CS-R1131
vShift2 = Avx.Blend(vZero, vShift2, 0b_1100);
var vP2 = Avx.Add(vP1, vShift2);
var vSums = Avx.Add(Vector256.Create(sum), vP2);
// Prefix sum for SumSq
var vShiftSq1 = Avx2.Permute4x64(vDeltaSq.AsUInt64(), 0b_10_01_00_00).AsDouble(); // skipcq: CS-R1131
vShiftSq1 = Avx.Blend(vZero, vShiftSq1, 0b_1110);
var vP1Sq = Avx.Add(vDeltaSq, vShiftSq1);
var vShiftSq2 = Avx2.Permute4x64(vP1Sq.AsUInt64(), 0b_01_00_00_00).AsDouble(); // skipcq: CS-R1131
vShiftSq2 = Avx.Blend(vZero, vShiftSq2, 0b_1100);
var vP2Sq = Avx.Add(vP1Sq, vShiftSq2);
var vSumSqs = Avx.Add(Vector256.Create(sumSq), vP2Sq);
// Prefix sum for SumCu
var vShiftCu1 = Avx2.Permute4x64(vDeltaCu.AsUInt64(), 0b_10_01_00_00).AsDouble(); // skipcq: CS-R1131
vShiftCu1 = Avx.Blend(vZero, vShiftCu1, 0b_1110);
var vP1Cu = Avx.Add(vDeltaCu, vShiftCu1);
var vShiftCu2 = Avx2.Permute4x64(vP1Cu.AsUInt64(), 0b_01_00_00_00).AsDouble(); // skipcq: CS-R1131
vShiftCu2 = Avx.Blend(vZero, vShiftCu2, 0b_1100);
var vP2Cu = Avx.Add(vP1Cu, vShiftCu2);
var vSumCus = Avx.Add(Vector256.Create(sumCu), vP2Cu);
// Calculate Skewness
@@ -440,13 +440,13 @@ public sealed class Skew : AbstractBase
// g1 = m3 / (m2 * sqrt(m2))
var vM2Sqrt = Avx.Sqrt(vM2);
var vDenom = Avx.Multiply(vM2, vM2Sqrt);
// Check for small m2
var vMask = Avx.Compare(vM2, vEpsilon, FloatComparisonMode.OrderedGreaterThanNonSignaling);
var vG1 = Avx.Divide(vM3, vDenom);
var vSkew = Avx.Multiply(vG1, vCorrection);
// Apply mask
vSkew = Avx.BlendVariable(vZero, vSkew, vMask);
@@ -481,11 +481,11 @@ public sealed class Skew : AbstractBase
{
double val = Unsafe.Add(ref srcRef, i);
double oldVal = Unsafe.Add(ref srcRef, i - period);
sum = sum - oldVal + val;
sumSq = sumSq - (oldVal * oldVal) + (val * val);
sumCu = sumCu - (oldVal * oldVal * oldVal) + (val * val * val);
Unsafe.Add(ref outRef, i) = CalculateSkewFromSums(sum, sumSq, sumCu, n, isPopulation);
}
}
lib/statistics/stddev/StdDev.Tests.cs
+5 -5
View File
@@ -25,7 +25,7 @@ public class StdDevTests
// Sample StdDev: Sqrt(4.571428...) = 2.1380899...
var data = new double[] { 2, 4, 4, 4, 5, 5, 7, 9 };
// Test Population StdDev
var popStd = new StdDev(8, isPopulation: true);
foreach (var val in data)
@@ -48,7 +48,7 @@ public class StdDevTests
{
int period = 5;
var stdDev = new StdDev(period);
for (int i = 0; i < period; i++)
{
Assert.False(stdDev.IsHot);
@@ -79,7 +79,7 @@ public class StdDevTests
int count = 1000;
var data = new double[count];
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
for (int i = 0; i < count; i++)
{
data[i] = gbm.Next().Close;
@@ -104,7 +104,7 @@ public class StdDevTests
Assert.Equal(iterativeResults[i], batchResults[i], precision: 6);
}
}
[Fact]
public void Update_TSeries_Matches_Iterative()
{
@@ -112,7 +112,7 @@ public class StdDevTests
int count = 1000;
var data = new TSeries();
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
for (int i = 0; i < count; i++)
{
var bar = gbm.Next();
lib/statistics/stddev/StdDev.Validation.Tests.cs
+8 -8
View File
@@ -27,7 +27,7 @@ public class StdDevValidationTests
var skenderList = skenderStdDev.ToList();
var quotes = _data.SkenderQuotes.ToList();
for (int i = 0; i < quotes.Count; i++)
{
var tValue = stdDev.Update(new TValue(quotes[i].Date, (double)quotes[i].Close));
@@ -46,7 +46,7 @@ public class StdDevValidationTests
// TA-Lib STDDEV uses Population Standard Deviation (N)
int period = 20;
var stdDev = new StdDev(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
double[] output = new double[input.Length];
@@ -74,18 +74,18 @@ public class StdDevValidationTests
// Tulip STDDEV uses Population Standard Deviation (N)
int period = 20;
var stdDev = new StdDev(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
// Tulip calculation
var stdDevInd = Tulip.Indicators.stddev;
double[][] inputs = { input };
double[] options = { period };
double[][] outputs = { new double[input.Length - stdDevInd.Start(options)] };
stdDevInd.Run(inputs, options, outputs);
double[] output = outputs[0];
int lookback = stdDevInd.Start(options);
@@ -107,7 +107,7 @@ public class StdDevValidationTests
int period = 20;
var stdDev = new StdDev(period, isPopulation: false);
var popStdDev = new StdDev(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
@@ -121,7 +121,7 @@ public class StdDevValidationTests
var window = input[(i - period + 1)..(i + 1)];
double expected = Statistics.StandardDeviation(window);
double expectedPop = Statistics.PopulationStandardDeviation(window);
Assert.Equal(expected, val.Value, ValidationHelper.DefaultTolerance);
Assert.Equal(expectedPop, popVal.Value, ValidationHelper.DefaultTolerance);
}
lib/statistics/stddev/StdDev.cs
+9 -9
View File
@@ -14,10 +14,10 @@ namespace QuanTAlib;
/// </summary>
/// <remarks>
/// Standard Deviation is the square root of Variance.
///
///
/// Formula:
/// StdDev = Sqrt(Variance)
///
///
/// This implementation wraps the optimized Variance indicator and applies a square root.
/// </remarks>
[SkipLocalsInit]
@@ -47,7 +47,7 @@ public sealed class StdDev : AbstractBase
public override TValue Update(TValue input, bool isNew = true)
{
TValue varResult = _variance.Update(input, isNew);
// Sqrt(Variance)
// Handle potential negative zero or extremely small negative noise from Variance
double val = varResult.Value;
@@ -73,12 +73,12 @@ public sealed class StdDev : AbstractBase
// 1. Calculate Variance
Variance.Batch(source.Values, vSpan, _period, _isPopulation);
// 2. Calculate Sqrt in-place
SqrtSpan(vSpan);
source.Times.CopyTo(tSpan);
// Prime the state
// We need to feed the last 'period' values into the _variance instance
// so that subsequent streaming updates work correctly.
@@ -122,7 +122,7 @@ public sealed class StdDev : AbstractBase
{
// 1. Calculate Variance
Variance.Batch(source, output, period, isPopulation);
// 2. Sqrt
SqrtSpan(output);
}
@@ -139,7 +139,7 @@ public sealed class StdDev : AbstractBase
const int VectorWidth = 8;
int simdEnd = len - (len % VectorWidth);
ref double dataRef = ref MemoryMarshal.GetReference(data);
for (; i < simdEnd; i += VectorWidth)
{
var v = Vector512.LoadUnsafe(ref Unsafe.Add(ref dataRef, i));
@@ -153,7 +153,7 @@ public sealed class StdDev : AbstractBase
const int VectorWidth = 4;
int simdEnd = len - (len % VectorWidth);
ref double dataRef = ref MemoryMarshal.GetReference(data);
for (; i < simdEnd; i += VectorWidth)
{
var v = Vector256.LoadUnsafe(ref Unsafe.Add(ref dataRef, i));
@@ -167,7 +167,7 @@ public sealed class StdDev : AbstractBase
const int VectorWidth = 2;
int simdEnd = len - (len % VectorWidth);
ref double dataRef = ref MemoryMarshal.GetReference(data);
for (; i < simdEnd; i += VectorWidth)
{
var v = Vector128.LoadUnsafe(ref Unsafe.Add(ref dataRef, i));
lib/statistics/variance/Variance.Tests.cs
+11 -11
View File
@@ -23,7 +23,7 @@ public class VarianceTests
// Sample Variance (N-1=7): 32 / 7 = 4.571428...
var data = new double[] { 2, 4, 4, 4, 5, 5, 7, 9 };
// Test Population Variance
var popVar = new Variance(8, isPopulation: true);
foreach (var val in data)
@@ -46,7 +46,7 @@ public class VarianceTests
{
int period = 5;
var variance = new Variance(period);
for (int i = 0; i < period; i++)
{
Assert.False(variance.IsHot);
@@ -75,18 +75,18 @@ public class VarianceTests
{
// Test differential update
var variance = new Variance(3, isPopulation: true);
// Add 1, 2, 3. Mean=2. Var = ((1-2)^2 + (2-2)^2 + (3-2)^2)/3 = (1+0+1)/3 = 2/3 = 0.666...
variance.Update(new TValue(DateTime.UtcNow, 1));
variance.Update(new TValue(DateTime.UtcNow, 2));
variance.Update(new TValue(DateTime.UtcNow, 3));
Assert.Equal(2.0/3.0, variance.Last.Value, precision: 6);
// Update last value from 3 to 6.
// Data: 1, 2, 6. Mean=3. Var = ((1-3)^2 + (2-3)^2 + (6-3)^2)/3 = (4+1+9)/3 = 14/3 = 4.666...
variance.Update(new TValue(DateTime.UtcNow, 6), isNew: false);
Assert.Equal(14.0/3.0, variance.Last.Value, precision: 6);
}
@@ -97,7 +97,7 @@ public class VarianceTests
int count = 1000;
var data = new double[count];
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
for (int i = 0; i < count; i++)
{
data[i] = gbm.Next().Close;
@@ -144,7 +144,7 @@ public class VarianceTests
variance.Update(new TValue(DateTime.UtcNow, 1));
variance.Update(new TValue(DateTime.UtcNow, 2));
variance.Update(new TValue(DateTime.UtcNow, double.NaN));
var result = variance.Last.Value;
Assert.True(double.IsNaN(result));
}
@@ -155,12 +155,12 @@ public class VarianceTests
// Run for > 1000 updates to trigger Resync
var variance = new Variance(10);
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
for (int i = 0; i < 1100; i++)
{
variance.Update(new TValue(DateTime.UtcNow, gbm.Next().Close));
}
Assert.True(double.IsFinite(variance.Last.Value));
Assert.True(variance.Last.Value >= 0);
}
@@ -174,10 +174,10 @@ public class VarianceTests
for (int i = 0; i < count; i++) data[i] = (double)i;
var series = new TSeries(new System.Collections.Generic.List<long>(new long[count]), new System.Collections.Generic.List<double>(data));
// Batch calculation
var batchResult = Variance.Calculate(series, 10);
// Verify last value against streaming
var variance = new Variance(10);
double lastStreaming = 0;
lib/statistics/variance/Variance.Validation.Tests.cs
+9 -9
View File
@@ -23,14 +23,14 @@ public class VarianceValidationTests
// Skender StdDev uses Population Standard Deviation (N) for calculation,
// despite documentation often implying Sample (N-1).
// Variance(isPopulation: true) should match StdDev^2.
int period = 20;
var variance = new Variance(period, isPopulation: true);
var skenderStdDev = _data.SkenderQuotes.GetStdDev(period);
var skenderList = skenderStdDev.ToList();
var quotes = _data.SkenderQuotes.ToList();
for (int i = 0; i < quotes.Count; i++)
{
var tValue = variance.Update(new TValue(quotes[i].Date, (double)quotes[i].Close));
@@ -50,7 +50,7 @@ public class VarianceValidationTests
// TA-Lib VAR uses Population Variance (N)
int period = 20;
var variance = new Variance(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
double[] output = new double[input.Length];
@@ -78,18 +78,18 @@ public class VarianceValidationTests
// Tulip VAR uses Population Variance (N)
int period = 20;
var variance = new Variance(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
// Tulip calculation
var varInd = Tulip.Indicators.var;
double[][] inputs = { input };
double[] options = { period };
double[][] outputs = { new double[input.Length - varInd.Start(options)] };
varInd.Run(inputs, options, outputs);
double[] output = outputs[0];
int lookback = varInd.Start(options);
@@ -111,7 +111,7 @@ public class VarianceValidationTests
int period = 20;
var variance = new Variance(period, isPopulation: false);
var popVariance = new Variance(period, isPopulation: true);
var quotes = _data.SkenderQuotes.ToList();
double[] input = quotes.Select(q => (double)q.Close).ToArray();
@@ -125,7 +125,7 @@ public class VarianceValidationTests
var window = input[(i - period + 1)..(i + 1)];
double expected = Statistics.Variance(window);
double expectedPop = Statistics.PopulationVariance(window);
Assert.Equal(expected, val.Value, ValidationHelper.DefaultTolerance);
Assert.Equal(expectedPop, popVal.Value, ValidationHelper.DefaultTolerance);
}
lib/statistics/variance/Variance.cs
+23 -23
View File
@@ -13,11 +13,11 @@ namespace QuanTAlib;
/// </summary>
/// <remarks>
/// Variance is calculated as the average of the squared differences from the Mean.
///
///
/// Formula:
/// Population Variance = Sum((x - Mean)^2) / N
/// Sample Variance = Sum((x - Mean)^2) / (N - 1)
///
///
/// This implementation uses the O(1) running sum of squares formula:
/// Variance = (SumSq - (Sum * Sum) / N) / (N - 1) (for Sample)
/// </remarks>
@@ -76,7 +76,7 @@ public sealed class Variance : AbstractBase
// Differential update
double oldNewest = _buffer.Newest;
_buffer.UpdateNewest(input.Value);
// Reconstruct SumSq from previous state is safer/cleaner than differential on current
// But we updated buffer already.
// _sumSq currently includes oldNewest^2.
@@ -93,12 +93,12 @@ public sealed class Variance : AbstractBase
// Var = (SumSq - 2*Mean*(N*Mean) + N*Mean^2) / ...
// Var = (SumSq - 2*N*Mean^2 + N*Mean^2) / ...
// Var = (SumSq - N*Mean^2) / ...
// Using Sum:
// Var = (SumSq - (Sum*Sum)/N) / ...
double numerator = _sumSq - (_buffer.Sum * _buffer.Sum) / n;
// Handle floating point noise
if (numerator < 0) numerator = 0;
@@ -221,14 +221,14 @@ public sealed class Variance : AbstractBase
int len = source.Length;
double sum = 0;
double sumSq = 0;
// We need a buffer to handle the sliding window removal
// For scalar path, we can use a simple array or stackalloc
const int StackAllocThreshold = 256;
Span<double> buffer = period <= StackAllocThreshold
? stackalloc double[period]
: new double[period];
int bufferIndex = 0;
int i = 0;
@@ -265,11 +265,11 @@ public sealed class Variance : AbstractBase
if (!double.IsFinite(val)) val = 0; // Fallback
double oldVal = buffer[bufferIndex];
sum = sum - oldVal + val;
sumSq = Math.FusedMultiplyAdd(-oldVal, oldVal, sumSq);
sumSq = Math.FusedMultiplyAdd(val, val, sumSq);
buffer[bufferIndex] = val;
bufferIndex++;
if (bufferIndex >= period) bufferIndex = 0;
@@ -300,7 +300,7 @@ public sealed class Variance : AbstractBase
double val = Unsafe.Add(ref srcRef, i);
sum += val;
sumSq = Math.FusedMultiplyAdd(val, val, sumSq);
double n = i + 1;
if (n > 1)
{
@@ -382,9 +382,9 @@ public sealed class Variance : AbstractBase
var vSumSquared = Avx512F.Multiply(vSums, vSums);
var vMeanTerm = Avx512F.Multiply(vSumSquared, vInvN);
var vNumerator = Avx512F.Subtract(vSumSqs, vMeanTerm);
vNumerator = Avx512F.Max(vZero, vNumerator);
var vResult = Avx512F.Multiply(vNumerator, vInvDenom);
Vector512.StoreUnsafe(vResult, ref Unsafe.Add(ref outRef, i));
@@ -414,11 +414,11 @@ public sealed class Variance : AbstractBase
{
double val = Unsafe.Add(ref srcRef, i);
double oldVal = Unsafe.Add(ref srcRef, i - period);
sum = sum - oldVal + val;
sumSq = Math.FusedMultiplyAdd(-oldVal, oldVal, sumSq);
sumSq = Math.FusedMultiplyAdd(val, val, sumSq);
double numerator = sumSq - (sum * sum) * invN;
if (numerator < 0) numerator = 0;
Unsafe.Add(ref outRef, i) = numerator * invDenom;
@@ -479,9 +479,9 @@ public sealed class Variance : AbstractBase
var vSumSquared = AdvSimd.Arm64.Multiply(vSums, vSums);
var vMeanTerm = AdvSimd.Arm64.Multiply(vSumSquared, vInvN);
var vNumerator = AdvSimd.Arm64.Subtract(vSumSqs, vMeanTerm);
vNumerator = AdvSimd.Arm64.Max(vZero, vNumerator);
var vResult = AdvSimd.Arm64.Multiply(vNumerator, vInvDenom);
Vector128.StoreUnsafe(vResult, ref Unsafe.Add(ref outRef, i));
@@ -511,11 +511,11 @@ public sealed class Variance : AbstractBase
{
double val = Unsafe.Add(ref srcRef, i);
double oldVal = Unsafe.Add(ref srcRef, i - period);
sum = sum - oldVal + val;
sumSq = Math.FusedMultiplyAdd(-oldVal, oldVal, sumSq);
sumSq = Math.FusedMultiplyAdd(val, val, sumSq);
double numerator = sumSq - (sum * sum) * invN;
if (numerator < 0) numerator = 0;
Unsafe.Add(ref outRef, i) = numerator * invDenom;
@@ -593,10 +593,10 @@ public sealed class Variance : AbstractBase
var vSumSquared = Avx.Multiply(vSums, vSums);
var vMeanTerm = Avx.Multiply(vSumSquared, vInvN);
var vNumerator = Avx.Subtract(vSumSqs, vMeanTerm);
// Max(0, numerator) to handle floating point noise
vNumerator = Avx.Max(vZero, vNumerator);
var vResult = Avx.Multiply(vNumerator, vInvDenom);
Vector256.StoreUnsafe(vResult, ref Unsafe.Add(ref outRef, i));
@@ -628,11 +628,11 @@ public sealed class Variance : AbstractBase
{
double val = Unsafe.Add(ref srcRef, i);
double oldVal = Unsafe.Add(ref srcRef, i - period);
sum = sum - oldVal + val;
sumSq = Math.FusedMultiplyAdd(-oldVal, oldVal, sumSq);
sumSq = Math.FusedMultiplyAdd(val, val, sumSq);
double numerator = sumSq - (sum * sum) * invN;
if (numerator < 0) numerator = 0;
Unsafe.Add(ref outRef, i) = numerator * invDenom;