mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
65 lines
2.9 KiB
C#
65 lines
2.9 KiB
C#
namespace QuanTAlib;
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using System;
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/* <summary>
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KAMA: Kaufman's Adaptive Moving Average
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Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as
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Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972,
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it was not until the popular book titled "Trading Systems and Methods" that it was made widely
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available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive
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Moving Average, considers market volatility apart from price fluctuations.
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KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
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Sources:
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https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work
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https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/
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https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average
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Remark:
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If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards.
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Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields
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slightly different results for the first 50 bars - and then converges with the other one.
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</summary> */
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public class KAMA_Series : Single_TSeries_Indicator
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{
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private readonly double _scFast, _scSlow;
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private readonly System.Collections.Generic.List<double> _buffer = new();
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private double _lastkama = double.NaN;
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private double _lastlastkama;
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public KAMA_Series(TSeries source, int period, int fast = 2, int slow= 30, bool useNaN = false) : base(source, period, useNaN) {
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_scFast = 2.0 / (fast+1);
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_scSlow = 2.0 / (slow+1);
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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public override void Add((System.DateTime t, double v) TValue, bool update)
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{
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if (update){
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_buffer[_buffer.Count - 1] = TValue.v;
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this._lastkama = this._lastlastkama;
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} else {
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_buffer.Add(TValue.v);
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}
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if (_buffer.Count > _p + 1) { _buffer.RemoveAt(0); }
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double _kama = 0;
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if (this.Count < this._p) {
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for (int i = 0; i < this._buffer.Count; i++) { _kama += this._buffer[i]; }
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_kama /= this._buffer.Count;
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} else {
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double _change = Math.Abs(_buffer[_buffer.Count - 1] - _buffer[(_buffer.Count > _p + 1) ? 1 : 0]);
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double _sumpv = 0;
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for (int i = 1; i < _buffer.Count; i++)
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{ _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); }
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double _er = (_sumpv == 0) ? 0 : _change / _sumpv;
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double _sc = (_er * (_scFast - _scSlow)) + _scSlow;
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_kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama)));
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}
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_lastlastkama = _lastkama;
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_lastkama = _kama;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _kama);
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base.Add(result, update);
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}
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} |