TR and ATR

Source/Indicators/SMA_Series.cs

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+namespace QuanTAlib;
+using System;
+
+/* <summary>
+SMA: Simple Moving Average
+    The weights are equally distributed across the period, resulting in a mean() of
+    the data within the period/
+
+Sources:
+    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
+    https://stats.stackexchange.com/a/24739
+
+Remark:
+    This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB
+    implementation, but it does allow incremental additions of inputs and real-time calculations of SMA()
+
+</summary> */
+
+public class SMA_Series : Single_TSeries_Indicator
+{
+    public SMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
+    {
+        if (base._data.Count > 0) { base.Add(base._data); }
+    }
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+
+    public override void Add((System.DateTime t, double v) d, bool update)
+    {
+        if (update) { _buffer[_buffer.Count - 1] = d.v; }
+        else { _buffer.Add(d.v); }
+        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
+
+        double _sma = 0;
+        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
+        _sma /= this._buffer.Count;
+
+        var result = (d.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _sma);
+
+        base.Add(result, update);
+    }
+}