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KAMA w. SMA warmup

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Miha Kralj
2022-04-24 13:45:17 -07:00
parent 24793f432f
commit 6b9aa85aa1
3 changed files with 110 additions and 64 deletions
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Source/Indicators/KAMA_Series.cs
+66 -62
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@@ -1,63 +1,67 @@
namespace QuanTAlib;
using System;
/* <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.
KAMAi = KAMAi - 1 + SC * ( price - KAMAi-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 : Single_TSeries_Indicator
{
private static double _scFast, _scSlow;
private readonly System.Collections.Generic.List<double> _buffer = new();
private double _lastkama = double.NaN;
private double _lastlastkama;
public KAMA_Series(TSeries source, int period, int fast = 2, int slow= 30, bool useNaN = false) : base(source, period, useNaN) {
_scFast = 2.0 / (fast+1);
_scSlow = 2.0 / (slow+1);
if (base._data.Count > 0) { base.Add(base._data); }
}
public override void Add((System.DateTime t, double v) TValue, bool update) {
if (update) {
_buffer[_buffer.Count - 1] = TValue.v;
this._lastkama = this._lastlastkama;
}
else {
_buffer.Add(TValue.v);
}
if (_buffer.Count>_p+1) { _buffer.RemoveAt(0); }
double _kama = TValue.v;
double _change = Math.Abs( _buffer[_buffer.Count-1] - _buffer[(_buffer.Count>_p+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;
if (_buffer.Count==1) { _lastkama = _buffer[0]; }
_kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama)));
_lastlastkama = _lastkama;
_lastkama = _kama;
var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _kama);
base.Add(result, update);
}
namespace QuanTAlib;
using System;
/* <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.
KAMAi = KAMAi - 1 + SC * ( price - KAMAi-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 : Single_TSeries_Indicator
{
private static double _scFast, _scSlow;
private readonly System.Collections.Generic.List<double> _buffer = new();
private double _lastkama = double.NaN;
private double _lastlastkama;
public KAMA_Series(TSeries source, int period, int fast = 2, int slow= 30, bool useNaN = false) : base(source, period, useNaN) {
_scFast = 2.0 / (fast+1);
_scSlow = 2.0 / (slow+1);
if (base._data.Count > 0) { base.Add(base._data); }
}
public override void Add((System.DateTime t, double v) TValue, bool update)
{
if (update){
_buffer[_buffer.Count - 1] = TValue.v;
this._lastkama = this._lastlastkama;
} else {
_buffer.Add(TValue.v);
}
if (_buffer.Count > _p + 1) { _buffer.RemoveAt(0); }
double _kama = TValue.v;
if (this.Count < this._p) {
for (int i = 0; i < this._buffer.Count; i++) { _kama += this._buffer[i]; }
_kama /= this._buffer.Count;
} else {
double _change = Math.Abs(_buffer[_buffer.Count - 1] - _buffer[(_buffer.Count > _p + 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)));
}
_lastlastkama = _lastkama;
_lastkama = _kama;
var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _kama);
base.Add(result, update);
}
}
Tests/MovingAvg/KAMA_Test.cs
+33
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@@ -0,0 +1,33 @@
using Xunit;
using System;
using QuanTAlib;
namespace MovingAvg;
public class KAMA_Test
{
[Fact]
public void Add_Test()
{
TSeries a = new() { 0, 1, 2, 3, 4, 5 };
KAMA_Series c = new(a, 3);
Assert.Equal(6, c.Count);
a.Add(5);
Assert.Equal(a.Count, c.Count);
a.Add(0, update: true);
Assert.Equal(a.Count, c.Count);
}
[Fact]
public void Edge_Test()
{
TSeries a = new() { double.NaN, double.Epsilon, double.PositiveInfinity, double.MaxValue };
KAMA_Series c = new(a, 3);
Assert.Equal(a.Count, c.Count);
a.Add(double.NaN);
Assert.Equal(a.Count, c.Count);
a.Add(double.PositiveInfinity);
Assert.Equal(a.Count, c.Count);
}
}
Tests/Validations/Skender_Stock.cs
+11 -2
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@@ -1,7 +1,7 @@
using Xunit;
using System;
using Skender.Stock.Indicators;
using QuanTAlib;
using Skender.Stock.Indicators;
using Xunit;
namespace Validation;
@@ -108,4 +108,13 @@ public class Skender_Stock
Assert.Equal(Math.Round((double)SK.Last().Atrp!, 8), Math.Round(QL.Last().v, 8));
}
[Fact]
public void KAMA()
{
KAMA_Series QL = new(this.bars.Close, this.period, useNaN: false);
var SK = this.quotes.GetKama(this.period);
Assert.Equal(Math.Round((double)SK.Last().Kama!, 8), Math.Round(QL.Last().v, 8));
}
}