Auto stash before rebase of "origin/main"

2026-08-16 09:38:05 +00:00 · 2022-09-03 12:32:01 -07:00
parent 31a92331d4
commit ade6322657
7 changed files with 515 additions and 513 deletions
@@ -1,68 +1,68 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-ALMA: Arnaud Legoux Moving Average
-    The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
-    can be shifted from 0 to 1. This allows regulating the smoothness and high
-    sensitivity of the indicator. Sigma is another parameter that is responsible for
-    the shape of the curve coefficients. This moving average reduces lag of the data
-    in conjunction with smoothing to reduce noise.
-
-
-Sources:
-    https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
-    https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
-
-</summary> */
-
-public class ALMA_Series : Single_TSeries_Indicator
-{
-    private readonly System.Collections.Generic.List<double> _buffer = new();
-    private readonly double[] _weight;
-    private double _norm;
-    private readonly double _offset, _sigma;
-
-    public ALMA_Series(TSeries source, int period, double offset = 0.85, double sigma = 6.0, bool useNaN = false)
-        : base(source, period, useNaN)
-    {
-        _offset = offset;
-        _sigma = sigma;
-        _weight = new double[period];
-
-        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); }
-
-        if (this._buffer.Count <= _p) { calc_weights(); }
-
-        double _weightedSum = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _weightedSum += _weight[i] * _buffer[i]; }
-        double _alma = _weightedSum / _norm;
-
-        var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _alma);
-        base.Add(ret, update);
-    }
-
-    private void calc_weights()
-    {
-        int _len = this._buffer.Count;
-        _norm = 0;
-        double _m = _offset * (_len - 1);
-        double _s = _len / _sigma;
-        for (int i = 0; i < _len; i++)
-        {
-            double _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
-            _weight[i] = _wt;
-            _norm += _wt;
-        }
-    }
-
-}
-
-
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+ALMA: Arnaud Legoux Moving Average
+    The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
+    can be shifted from 0 to 1. This allows regulating the smoothness and high
+    sensitivity of the indicator. Sigma is another parameter that is responsible for
+    the shape of the curve coefficients. This moving average reduces lag of the data
+    in conjunction with smoothing to reduce noise.
+
+
+Sources:
+    https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
+    https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
+
+</summary> */
+
+public class ALMA_Series : Single_TSeries_Indicator
+{
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+    private readonly double[] _weight;
+    private double _norm;
+    private readonly double _offset, _sigma;
+
+    public ALMA_Series(TSeries source, int period, double offset = 0.85, double sigma = 6.0, bool useNaN = false)
+        : base(source, period, useNaN)
+    {
+        _offset = offset;
+        _sigma = sigma;
+        _weight = new double[period];
+
+        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); }
+
+        if (this._buffer.Count <= _p) { calc_weights(); }
+
+        double _weightedSum = 0;
+        for (int i = 0; i < this._buffer.Count; i++) { _weightedSum += _weight[i] * _buffer[i]; }
+        double _alma = _weightedSum / _norm;
+
+        var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _alma);
+        base.Add(ret, update);
+    }
+
+    private void calc_weights()
+    {
+        int _len = this._buffer.Count;
+        _norm = 0;
+        double _m = _offset * (_len - 1);
+        double _s = _len / _sigma;
+        for (int i = 0; i < _len; i++)
+        {
+            double _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
+            _weight[i] = _wt;
+            _norm += _wt;
+        }
+    }
+
+}
+
+