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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/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;