mirror of
https://github.com/mihakralj/QuanTAlib.git
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This commit is contained in:
Miha Kralj
@@ -1,62 +1,62 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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KURT: Kurtosis of population
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Kurtosis characterizes the relative peakedness or flatness of a distribution
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compared with the normal distribution. Positive kurtosis indicates a relatively
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peaked distribution. Negative kurtosis indicates a relatively flat distribution.
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The normal curve is called Mesokurtic curve. If the curve of a distribution is
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more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then
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it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or
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lighter-tailed) than a normal curve, it is called as a platykurtic curve.
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Calculation:
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sum4 = Σ(close-SMA)^4
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sum2 = (Σ(close-SMA)^2)^2
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KURT = length * (sum4/sum2)
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Sources:
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https://en.wikipedia.org/wiki/Kurtosis
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https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/
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</summary> */
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public class KURT_Series : Single_TSeries_Indicator
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{
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public KURT_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
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{
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this._logbase = logbase;
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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protected double _logbase;
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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double _n = this._buffer.Count;
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double _avg = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _avg += this._buffer[i]; }
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_avg /= _n;
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double _s2 = 0;
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double _s4 = 0;
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for (int i = 0; i < this._buffer.Count; i++)
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{
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_s2 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
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_s4 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
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}
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double _Vx = _s2 / (_n - 1);
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double _kurt = (_n > 3) ? ((((_n * (_n + 1)) / (((_n - 1) * (_n - 2)) * (_n - 3))) * (_s4 / (_Vx * _Vx))) - (3 * (((_n - 1) * (_n - 1)) / ((_n - 2) * (_n - 3))))) : Double.NaN;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? Double.NaN : _kurt);
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base.Add(result, update);
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}
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namespace QuanTAlib;
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using System;
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/* <summary>
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KURT: Kurtosis of population
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Kurtosis characterizes the relative peakedness or flatness of a distribution
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compared with the normal distribution. Positive kurtosis indicates a relatively
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peaked distribution. Negative kurtosis indicates a relatively flat distribution.
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The normal curve is called Mesokurtic curve. If the curve of a distribution is
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more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then
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it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or
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lighter-tailed) than a normal curve, it is called as a platykurtic curve.
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Calculation:
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sum4 = Σ(close-SMA)^4
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sum2 = (Σ(close-SMA)^2)^2
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KURT = length * (sum4/sum2)
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Sources:
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https://en.wikipedia.org/wiki/Kurtosis
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https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/
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</summary> */
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public class KURT_Series : Single_TSeries_Indicator
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{
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public KURT_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
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{
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this._logbase = logbase;
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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protected double _logbase;
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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double _n = this._buffer.Count;
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double _avg = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _avg += this._buffer[i]; }
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_avg /= _n;
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double _s2 = 0;
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double _s4 = 0;
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for (int i = 0; i < this._buffer.Count; i++)
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{
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_s2 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
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_s4 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
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}
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double _Vx = _s2 / (_n - 1);
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double _kurt = (_n > 3) ? ((((_n * (_n + 1)) / (((_n - 1) * (_n - 2)) * (_n - 3))) * (_s4 / (_Vx * _Vx))) - (3 * (((_n - 1) * (_n - 1)) / ((_n - 2) * (_n - 3))))) : Double.NaN;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? Double.NaN : _kurt);
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base.Add(result, update);
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}
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}
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@@ -1,47 +1,47 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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MED - Median value
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Median of numbers is the middlemost value of the given set of numbers.
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It separates the higher half and the lower half of a given data sample.
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At least half of the observations are smaller than or equal to median
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and at least half of the observations are greater than or equal to the median.
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If the number of values is odd, the middlemost observation of the sorted
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list is the median of the given data. If the number of values is even,
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median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list.
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If period = 0 => period is max
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Sources:
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https://corporatefinanceinstitute.com/resources/knowledge/other/median/
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https://en.wikipedia.org/wiki/Median
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</summary> */
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public class MED_Series : Single_TSeries_Indicator
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{
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public MED_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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System.Collections.Generic.List<double> _s = new(this._buffer);
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_s.Sort();
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int _p1 = _s.Count / 2;
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int _p2 = Math.Max(0, (_s.Count / 2) - 1);
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double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _med);
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base.Add(result, update);
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}
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}
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namespace QuanTAlib;
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using System;
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/* <summary>
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MED - Median value
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Median of numbers is the middlemost value of the given set of numbers.
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It separates the higher half and the lower half of a given data sample.
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At least half of the observations are smaller than or equal to median
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and at least half of the observations are greater than or equal to the median.
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If the number of values is odd, the middlemost observation of the sorted
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list is the median of the given data. If the number of values is even,
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median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list.
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If period = 0 => period is max
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Sources:
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https://corporatefinanceinstitute.com/resources/knowledge/other/median/
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https://en.wikipedia.org/wiki/Median
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</summary> */
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public class MED_Series : Single_TSeries_Indicator
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{
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public MED_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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System.Collections.Generic.List<double> _s = new(this._buffer);
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_s.Sort();
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int _p1 = _s.Count / 2;
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int _p2 = Math.Max(0, (_s.Count / 2) - 1);
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double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _med);
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base.Add(result, update);
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}
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}
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@@ -1,43 +1,43 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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SVAR: Sample Variance
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Sample variance uses Bessel's correction to correct the bias in the estimation of population variance.
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Sources:
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https://en.wikipedia.org/wiki/Variance
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Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
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Remark:
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SVAR is also known as the Unbiased Sample Variance, while VAR (Population Variance) is known as
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the Biased Sample Variance.
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</summary> */
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public class SVAR_Series : Single_TSeries_Indicator
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{
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public SVAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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double _sma = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _sma += this._buffer[i]; }
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_sma /= this._buffer.Count;
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double _svar = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); }
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_svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _svar);
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base.Add(result, update);
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}
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namespace QuanTAlib;
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using System;
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/* <summary>
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SVAR: Sample Variance
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Sample variance uses Bessel's correction to correct the bias in the estimation of population variance.
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Sources:
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https://en.wikipedia.org/wiki/Variance
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Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
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Remark:
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SVAR is also known as the Unbiased Sample Variance, while VAR (Population Variance) is known as
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the Biased Sample Variance.
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</summary> */
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public class SVAR_Series : Single_TSeries_Indicator
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{
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public SVAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { this._buffer[this._buffer.Count - 1] = TValue.v; }
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else { this._buffer.Add(TValue.v); }
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if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
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double _sma = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _sma += this._buffer[i]; }
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_sma /= this._buffer.Count;
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double _svar = 0;
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for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); }
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_svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _svar);
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base.Add(result, update);
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}
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}
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@@ -1,43 +1,43 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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VAR: Population Variance
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Population variance without Bessel's correction
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Sources:
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https://en.wikipedia.org/wiki/Variance
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Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
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Remark:
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VAR (Population Variance) is also known as a biased Sample Variance. For unbiased
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sample variance use SVAR instead.
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</summary> */
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public class VAR_Series : Single_TSeries_Indicator
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{
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public VAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { _buffer[_buffer.Count - 1] = TValue.v; }
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else { _buffer.Add(TValue.v); }
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if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
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double _sma = 0;
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for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
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_sma /= this._buffer.Count;
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double _pvar = 0;
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for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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_pvar /= this._buffer.Count;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _pvar);
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base.Add(result, update);
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}
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namespace QuanTAlib;
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using System;
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/* <summary>
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VAR: Population Variance
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Population variance without Bessel's correction
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Sources:
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https://en.wikipedia.org/wiki/Variance
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Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
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Remark:
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VAR (Population Variance) is also known as a biased Sample Variance. For unbiased
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sample variance use SVAR instead.
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</summary> */
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public class VAR_Series : Single_TSeries_Indicator
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{
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public VAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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) { _buffer[_buffer.Count - 1] = TValue.v; }
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else { _buffer.Add(TValue.v); }
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if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
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double _sma = 0;
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for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
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_sma /= this._buffer.Count;
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double _pvar = 0;
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for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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_pvar /= this._buffer.Count;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _pvar);
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base.Add(result, update);
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}
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}
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