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

2026-08-24 05:28:05 +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 +1,90 @@
+namespace QuanTAlib;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+/* <summary>
+ENTROPY:
+    Introduced by Claude Shannon in 1948, entropy measures the unpredictability
+    of the data, or equivalently, of its average information.
+
+Calculation:
+    P = close / Σ(close)
+    ENTROPY = Σ(-P * Log(P) / Log(base))
+
+Sources:
+    https://en.wikipedia.org/wiki/Entropy_(information_theory)
+    https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples
+
+</summary> */
+
+public class ENTROPY_Series : TSeries {
+	protected readonly int _period;
+	protected readonly bool _NaN;
+	protected readonly TSeries _data;
+	private readonly double _logbase;
+	private readonly System.Collections.Generic.List<double> _buffer = new();
+	private readonly System.Collections.Generic.List<double> _buff2 = new();
+
+	//core constructors
+	public ENTROPY_Series(int period, double logbase, bool useNaN) : base() {
+		_period = period;
+		_NaN = useNaN;
+		_logbase = logbase;
+		Name = $"ENTROPY({period})";
+	}
+	public ENTROPY_Series(TSeries source, int period, double logbase, bool useNaN) : this(period, logbase, useNaN) {
+		_data = source;
+		Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
+		_data.Pub += Sub;
+		Add(_data);
+	}
+	public ENTROPY_Series() : this(period: 0, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(int period) : this(period: period, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(TBars source) : this(source.Close, period: 0, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(TBars source, int period) : this(source.Close, period, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, logbase: 2.0, useNaN: useNaN) { }
+	public ENTROPY_Series(TSeries source) : this(source, period: 0, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(TSeries source, int period) : this(source: source, period: period, logbase: 2.0, useNaN: false) { }
+	public ENTROPY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, logbase: 2.0, useNaN: useNaN) { }
+
+	//////////////////
+	// 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);
+		}
+		BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update);
+		double _sum = _buffer.Sum();
+		double _pp = this._buffer[^1] / _sum;
+		double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase);
+		BufferTrim(_buff2, _ppp, _period, update);
+		double _entp = _buff2.Sum();
+		var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _entp);
+		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();
+		_buff2.Clear();
+	}
+}