mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-08 05:57:43 +00:00
Miha Kralj
86 lines
2.9 KiB
C#
86 lines
2.9 KiB
C#
namespace QuanTAlib;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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/* <summary>
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CCI: Commodity Channel Index
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Commodity Channel Index is a momentum oscillator used to primarily identify overbought
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and oversold levels relative to a mean. CCI measures the current price level relative
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to an average price level over a given period of time:
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- CCI is relatively high when prices are far above their average.
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- CCI is relatively low when prices are far below their average.
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Using this method, CCI can be used to identify overbought and oversold levels.
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Sources:
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https://www.investopedia.com/terms/c/commoditychannelindex.asp
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https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cci
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</summary> */
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public class CCI_Series : TSeries {
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protected readonly int _period;
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protected readonly bool _NaN;
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protected readonly TBars _data;
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private readonly System.Collections.Generic.List<double> _tp = new();
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//core constructors
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public CCI_Series(int period, bool useNaN) {
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_period = period;
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_NaN = useNaN;
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Name = $"CCI({period})";
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}
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public CCI_Series(TBars source, int period, bool useNaN) : this(period, useNaN) {
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_data = source;
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Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
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_data.Pub += Sub;
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Add(data: _data);
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}
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public CCI_Series() : this(period: 2, useNaN: false) { }
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public CCI_Series(int period) : this(period: period, useNaN: false) { }
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public CCI_Series(TBars source) : this(source, period: 2, useNaN: false) { }
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public CCI_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { }
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//////////////////
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// core Add() algo
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public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) {
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double _tpItem = (TBar.h + TBar.l + TBar.c) / 3.0;
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if (update) {
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this._tp[this._tp.Count - 1] = _tpItem;
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}
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else {
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this._tp.Add(_tpItem);
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}
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if (this._tp.Count > this._period) { this._tp.RemoveAt(0); }
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// average TP over _tp buffer
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double _avgTp = _tp.Average();
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// average Deviation over _tp buffer
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double _avgDv = 0;
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for (int i = 0; i < this._tp.Count; i++) { _avgDv += Math.Abs(_avgTp - this._tp[i]); }
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_avgDv /= this._tp.Count;
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double _cci = (_avgDv == 0) ? 0 : (this._tp[this._tp.Count - 1] - _avgTp) / (0.015 * _avgDv);
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var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _cci);
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return base.Add(res, update);
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}
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public new void Add(TBars data) {
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foreach (var item in data) { Add(item, false); }
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}
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public (DateTime t, double v) Add(bool update) {
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return this.Add(TBar: _data.Last, update: update);
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}
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public (DateTime t, double v) Add() {
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return Add(TBar: _data.Last, update: false);
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}
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private new void Sub(object source, TSeriesEventArgs e) {
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Add(TBar: _data.Last, update: e.update);
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}
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//reset calculation
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public override void Reset() {
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_tp.Clear();
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}
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} |