Update RSI_Series to check for period != 0 before calculating RSI

2026-08-23 13:08:04 +00:00 · 2023-04-27 22:23:50 -07:00
parent c80f57ddab
commit abdcbf5f20
145 changed files with 4826 additions and 4207 deletions
@@ -0,0 +1,114 @@
+namespace QuanTAlib;
+using System;
+using System.Collections.Generic;
+
+/* <summary>
+KAMA: Kaufman's Adaptive Moving Average
+    Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as
+    Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972,
+    it was not until the popular book titled "Trading Systems and Methods" that it was made widely
+    available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive
+    Moving Average, considers market volatility apart from price fluctuations.
+
+    KAMA[i] = KAMA[i-1] + SC * ( price - KAMA[i-1] )
+
+Sources:
+    https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work
+    https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/
+    https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average
+
+Remark:
+    If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards.
+    Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields
+    slightly different results for the first 50 bars - and then converges with the other one.
+
+</summary> */
+
+public class KAMA_Series : TSeries {
+	private readonly System.Collections.Generic.List<double> _buffer = new();
+	private double _lastkama, _lastlastkama;
+	private int _len;
+	private readonly double _scFast, _scSlow;
+	protected readonly int _period;
+	protected readonly bool _NaN;
+	protected readonly TSeries _data;
+
+	//core constructors
+	public KAMA_Series(int period, int fast, int slow, bool useNaN) : base() {
+		_period = period;
+		_NaN = useNaN;
+		_len = 0;
+		_scFast = 2.0 / (((period < fast) ? period : fast) + 1);
+		_scSlow = 2.0 / (slow + 1);
+		_lastkama = _lastlastkama = 0;
+		Name = $"KAMA({period})";
+	}
+	public KAMA_Series(TSeries source, int period, int fast, int slow, bool useNaN) : this(period, fast, slow, useNaN) {
+		_data = source;
+		Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
+		_data.Pub += Sub;
+		Add(_data);
+	}
+	public KAMA_Series() : this(period: 0, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(int period) : this(period: period, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(TBars source) : this(source.Close, period: 0, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(TBars source, int period) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: useNaN) { }
+	public KAMA_Series(TSeries source) : this(source, period: 0, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(TSeries source, int period) : this(source: source, period: period, fast: 2, slow: 30, useNaN: false) { }
+	public KAMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, fast: 2, slow: 30, useNaN: useNaN) { }
+	public KAMA_Series(TSeries source, int period, int fast, int slow) : this(source: source, period: period, fast: fast, slow: slow, useNaN: false) { }
+
+
+	//////////////////
+	// core Add() algo
+	public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
+		if (double.IsNaN(TValue.v)) {
+			return base.Add((TValue.t, Double.NaN), update);
+		}
+
+		if (update) { _lastkama = _lastlastkama; }
+		else { _lastlastkama = _lastkama; }
+		BufferTrim(buffer: _buffer, value: TValue.v, period: _period + 1, update: update);
+
+		double _kama = 0;
+		if (this.Count < _period) { _kama = TValue.v; }
+		else {
+			double _change = Math.Abs(_buffer[^1] - _buffer[(_buffer.Count > _period + 1) ? 1 : 0]);
+			double _sumpv = 0;
+			for (int i = 1; i < _buffer.Count; i++) { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); }
+			double _er = (_sumpv == 0) ? 0 : _change / _sumpv;
+			double _sc = (_er * (_scFast - _scSlow)) + _scSlow;
+			_kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama)));
+		}
+		_len++;
+		_lastkama = _kama;
+		var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kama);
+		return base.Add(res, update);
+	}
+
+	public override (DateTime t, double v) Add(TSeries data) {
+		if (data == null) { return (DateTime.Today, Double.NaN); }
+		foreach (var item in data) { Add(item, false); }
+		return _data.Last;
+	}
+	public new (DateTime t, double v) Add((DateTime t, double v) TValue) {
+		return Add(TValue, false);
+	}
+	public (DateTime t, double v) Add(bool update) {
+		return this.Add(TValue: _data.Last, update: update);
+	}
+	public (DateTime t, double v) Add() {
+		return Add(TValue: _data.Last, update: false);
+	}
+	private new void Sub(object source, TSeriesEventArgs e) {
+		Add(TValue: _data.Last, update: e.update);
+	}
+
+	//reset calculation
+	public override void Reset() {
+		_buffer.Clear();
+		_len = 0;
+		_lastkama = _lastlastkama = 0;
+	}
+}