LinReg

2026-08-09 06:27:45 +00:00 · 2022-04-29 17:40:46 -07:00
parent a6ed7c78eb
commit 6b0408741a
4 changed files with 139 additions and 3 deletions
@@ -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);
+    }
+}