sonar fixes

2026-08-25 22:08:05 +00:00 · 2024-11-05 15:51:29 -08:00
parent 2b5c89640d
commit 0bae9ce15b
19 changed files with 801 additions and 141 deletions
@@ -0,0 +1,163 @@
+using System.Runtime.CompilerServices;
+namespace QuanTAlib;
+
+/// <summary>
+/// CORR: Correlation Coefficient
+/// A statistical measure that quantifies the strength and direction of the relationship
+/// between two variables. The correlation coefficient ranges from -1 to 1, where 1 indicates
+/// a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates
+/// no correlation.
+/// </summary>
+/// <remarks>
+/// The Correlation calculation process:
+/// 1. Calculates mean of both variables
+/// 2. Computes covariance between variables
+/// 3. Calculates standard deviation of both variables
+/// 4. Divides covariance by product of standard deviations
+///
+/// Key characteristics:
+/// - Measures linear relationship strength
+/// - Symmetric around zero
+/// - Scale-independent measure
+/// - Sensitive to outliers
+/// - Useful for portfolio diversification
+///
+/// Formula:
+/// ρ = Cov(X, Y) / (σX * σY)
+/// where:
+/// X, Y = variables
+/// Cov = covariance
+/// σ = standard deviation
+///
+/// Market Applications:
+/// - Portfolio diversification
+/// - Risk management
+/// - Pairs trading
+/// - Performance analysis
+/// - Market sentiment analysis
+///
+/// Sources:
+///     https://en.wikipedia.org/wiki/Correlation_coefficient
+///     "Modern Portfolio Theory" - Harry Markowitz
+///
+/// Note: Assumes linear relationship between variables
+/// </remarks>
+[SkipLocalsInit]
+public sealed class Corr : AbstractBase
+{
+    private readonly int Period;
+    private readonly CircularBuffer _xValues;
+    private readonly CircularBuffer _yValues;
+    private const double Epsilon = 1e-10;
+    private const int MinimumPoints = 2;
+
+    /// <param name="period">The number of points to consider for correlation calculation.</param>
+    /// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 2.</exception>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public Corr(int period)
+    {
+        if (period < MinimumPoints)
+        {
+            throw new ArgumentOutOfRangeException(nameof(period),
+                "Period must be greater than or equal to 2 for correlation calculation.");
+        }
+        Period = period;
+        WarmupPeriod = MinimumPoints;
+        _xValues = new CircularBuffer(period);
+        _yValues = new CircularBuffer(period);
+        Name = $"Corr(period={period})";
+        Init();
+    }
+
+    /// <param name="source">The data source object that publishes updates.</param>
+    /// <param name="period">The number of points to consider for correlation calculation.</param>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public Corr(object source, int period) : this(period)
+    {
+        var pubEvent = source.GetType().GetEvent("Pub");
+        pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public override void Init()
+    {
+        base.Init();
+        _xValues.Clear();
+        _yValues.Clear();
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    protected override void ManageState(bool isNew)
+    {
+        if (isNew)
+        {
+            _lastValidValue = Input.Value;
+            _index++;
+        }
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
+    private static double CalculateMean(ReadOnlySpan<double> values)
+    {
+        double sum = 0;
+        for (int i = 0; i < values.Length; i++)
+        {
+            sum += values[i];
+        }
+        return sum / values.Length;
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
+    private static double CalculateCovariance(ReadOnlySpan<double> xValues, ReadOnlySpan<double> yValues, double xMean, double yMean)
+    {
+        double covariance = 0;
+        for (int i = 0; i < xValues.Length; i++)
+        {
+            covariance += (xValues[i] - xMean) * (yValues[i] - yMean);
+        }
+        return covariance / xValues.Length;
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
+    private static double CalculateStandardDeviation(ReadOnlySpan<double> values, double mean)
+    {
+        double sumSquaredDeviations = 0;
+        for (int i = 0; i < values.Length; i++)
+        {
+            double deviation = values[i] - mean;
+            sumSquaredDeviations += deviation * deviation;
+        }
+        return Math.Sqrt(sumSquaredDeviations / values.Length);
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
+    protected override double Calculation()
+    {
+        ManageState(Input.IsNew);
+
+        _xValues.Add(Input.Value, Input.IsNew);
+        _yValues.Add(Input2.Value, Input.IsNew);
+
+        double correlation = 0;
+        if (_xValues.Count >= MinimumPoints && _yValues.Count >= MinimumPoints)
+        {
+            ReadOnlySpan<double> xValues = _xValues.GetSpan();
+            ReadOnlySpan<double> yValues = _yValues.GetSpan();
+
+            double xMean = CalculateMean(xValues);
+            double yMean = CalculateMean(yValues);
+
+            double covariance = CalculateCovariance(xValues, yValues, xMean, yMean);
+            double xStdDev = CalculateStandardDeviation(xValues, xMean);
+            double yStdDev = CalculateStandardDeviation(yValues, yMean);
+
+            if (xStdDev > Epsilon && yStdDev > Epsilon)
+            {
+                correlation = covariance / (xStdDev * yStdDev);
+            }
+        }
+
+        IsHot = _xValues.Count >= Period && _yValues.Count >= Period;
+        return correlation;
+    }
+}