LinReg

Docs/coverage.md

@@ -20,7 +20,7 @@
 | BIAS - Bias |✔️|||✔️|
 | ENTR - Entropy |✔️|||✔️|
 | KUR - Kurtosis |✔️|||✔️|
-| LINREG - Linear Regression ||✔️|✔️||
+| LINREG - Linear Regression |✔️|✔️|✔️||
 | MAD - Mean Absolute Deviation |✔️||✔️|✔️|
 | MAPE - Mean Absolute Percent Error |✔️||✔️||
 | MAX - Max value |✔️|✔️|||
@@ -30,9 +30,7 @@
 | PSDEV - Population Standard Deviation |✔️||||
 | PVAR - Population Variance |✔️||||
 | QUANTILE ||||✔️|
-| RS - R-Squared Coefficient |||✔️||
 | SKEW - Skewness ||||✔️|
-| SLOPE - Slope |||✔️||
 | SMAPE - Symmetric Mean Absolute Percent Error |✔️||||
 | SDEV - Sample Standard Deviation |✔️|✔️|✔️|✔️|
 | VAR - Sample Variance |✔️|||✔️|

Source/Statistics/LINREG_Series.cs

@@ -0,0 +1,93 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+LINREG: Linear Regression (using Least Square Method)
+  Linear Regression provides a slope of a straight line that is the best approximation of the given set of data.
+  The method of least squares is a standard approach in linear regression analysis to approximate the solution
+  by minimizing the sum of the squares of the residuals made in the results of each individual equation.
+
+Additional outputs provided by LINREG:
+  .Intercept - y-intercept point of the best fit line
+  .RSquared - R-Squared (R²), Coefficient of Determination
+  .StdDev - Standard Deviation of data over given periods
+
+  y = Slope * x + Intercept
+
+Sources:
+  https://en.wikipedia.org/wiki/Least_squares
+
+</summary> */
+
+public class LINREG_Series : Single_TSeries_Indicator
+{
+    public readonly TSeries Intercept = new();
+    public readonly TSeries RSquared = new();
+    public readonly TSeries StdDev = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+
+    public LINREG_Series(TSeries source, int period, bool useNaN = false)
+        : base(source, period, useNaN)
+    {
+        if (this._data.Count > 0) { base.Add(this._data); }
+    }
+
+    public override void Add((System.DateTime t, double v) TValue, bool update)
+    {
+        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
+        else { this._buffer.Add(TValue.v); }
+        if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
+
+        int _len = this._buffer.Count;
+
+        // get averages for period
+        double sumX = 0;
+        double sumY = 0;
+
+        for (int p = 0; p < _len; p++)
+        {
+            sumX += this.Count - _len + 2 + p;
+            sumY += _buffer[p];
+        }
+        double avgX = sumX / _len;
+        double avgY = sumY / _len;
+
+        // least squares method
+        double sumSqX = 0;
+        double sumSqY = 0;
+        double sumSqXY = 0;
+
+        for (int p = 0; p < _len; p++)
+        {
+            double devX = this.Count - _len + 2 + p - avgX;
+            double devY = _buffer[p] - avgY;
+
+            sumSqX += devX * devX;
+            sumSqY += devY * devY;
+            sumSqXY += devX * devY;
+        }
+
+        double _slope = sumSqXY / sumSqX;
+        double _intercept = avgY - (_slope * avgX);
+
+        // calculate Standard Deviation and R-Squared
+        double stdDevX = Math.Sqrt((double)sumSqX / _len);
+        double stdDevY = Math.Sqrt((double)sumSqY / _len);
+        double _StdDev = stdDevY;
+
+        double arrr = (stdDevX * stdDevY != 0) ? (double)sumSqXY / (stdDevX * stdDevY) / _len : 0;
+        double _RSquared = arrr * arrr;
+
+        var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _slope);
+        base.Add(ret, update);
+
+        ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _intercept);
+        Intercept.Add(ret, update);
+
+        ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _StdDev);
+        StdDev.Add(ret, update);
+
+        ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _RSquared);
+        RSquared.Add(ret, update);
+    }
+}

Tests/Statistics/LINREG_Test.cs

@@ -0,0 +1,33 @@
+using Xunit;
+using System;
+using QuanTAlib;
+
+namespace Statistics;
+public class LINREG_Test
+{
+	[Fact]
+	public void Add_Test()
+	{
+		TSeries a = new() { 0, 1, 2, 3, 4, 5 };
+		LINREG_Series c = new(a, 3);
+		Assert.Equal(6, c.Count);
+		a.Add(5);
+		Assert.Equal(a.Count, c.Count);
+		a.Add(0, update: true);
+		Assert.Equal(a.Count, c.Count);
+	}
+
+	[Fact]
+	public void Edge_Test()
+	{
+		TSeries a = new() { double.NaN, double.Epsilon, double.PositiveInfinity, double.MaxValue };
+		LINREG_Series c = new(a, 3);
+		Assert.Equal(a.Count, c.Count);
+		a.Add(double.NaN);
+		Assert.Equal(a.Count, c.Count);
+		a.Add(double.PositiveInfinity);
+		Assert.Equal(a.Count, c.Count);
+
+	}
+
+}

Tests/Validations/Skender_Stock.cs

@@ -161,4 +161,16 @@ public class Skender_Stock
 
 	    Assert.Equal(Math.Round((double)SK.Last().Alma!, 8), Math.Round(QL.Last().v, 8));
     }
+
+    [Fact]
+    public void LINREG()
+    {
+	    LINREG_Series QL = new(this.bars.Close, this.period, useNaN: false);
+	    var SK = this.quotes.GetSlope(this.period);
+
+	    Assert.Equal(Math.Round((double)SK.Last().Slope!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Intercept!, 8), Math.Round(QL.Intercept.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().RSquared!, 8), Math.Round(QL.RSquared.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().StdDev!, 8), Math.Round(QL.StdDev.Last().v, 8));
+	}
 }