mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-17 10:08:05 +00:00
Update RSI_Series to check for period != 0 before calculating RSI
This commit is contained in:
Miha Kralj
@@ -1,34 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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BIAS: Rate of change between the source and a moving average.
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Bias is a statistical term which means a systematic deviation from the actual value.
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BIAS = (close - SMA) / SMA
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= (close / SMA) - 1
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Sources:
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https://en.wikipedia.org/wiki/Bias_of_an_estimator
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</summary> */
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public class BIAS_Series : Single_TSeries_Indicator
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{
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public BIAS_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sma = _buffer.Average();
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double _bias = (_buffer[_buffer.Count - 1] / ((_sma != 0) ? _sma : 1)) - 1;
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base.Add((TValue.t, _bias), update, _NaN);
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}
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}
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@@ -1,39 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Collections.Generic;
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/* <summary>
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DECAY:
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Linear decay can be modeled by a straight line with a negative slope of 1/period.
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The value decreases in a straight line from the last maximum to 0.
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Decay = Last Max - distance/period
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Exponential decay is modeled as an exponential curve with diminishing factor of
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1-1/p
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</summary> */
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public class DECAY_Series : Single_TSeries_Indicator {
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private readonly bool _exp;
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private double _pdecay, _ppdecay;
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private readonly double _dfactor;
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public DECAY_Series(TSeries source, int period = 10, bool exponential= false, bool useNaN = false) : base(source, period, false) {
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_exp = exponential;
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_dfactor = (_exp)? 1.0 - 1.0 / (double)_p : 1/(double)_p;
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_pdecay = _ppdecay = 0;
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if (source.Count > 0) { base.Add(this._data); }
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}
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public override void Add((DateTime t, double v) TValue, bool update) {
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if (update) { _pdecay = _ppdecay; }
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else { _ppdecay = _pdecay; }
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if (this.Count == 0) { _pdecay = TValue.v; }
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double _decay = Math.Max(TValue.v, Math.Max((_exp)?_pdecay*_dfactor:_pdecay-_dfactor, 0));
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_pdecay = _decay;
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base.Add((TValue.t, _decay), update, _NaN);
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}
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}
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@@ -1,44 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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ENTP: Entropy
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Introduced by Claude Shannon in 1948, entropy measures the unpredictability
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of the data, or equivalently, of its average information.
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Calculation:
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P = close / Σ(close)
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ENTP = Σ(-P * Log(P) / Log(base))
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Sources:
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https://en.wikipedia.org/wiki/Entropy_(information_theory)
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https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples
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</summary> */
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public class ENTROPY_Series : Single_TSeries_Indicator
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{
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public ENTROPY_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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private readonly double _logbase;
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private readonly System.Collections.Generic.List<double> _buffer = new();
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private readonly System.Collections.Generic.List<double> _buff2 = 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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sum = _buffer.Sum();
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double _pp = this._buffer[this._buffer.Count - 1] / _sum;
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double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase);
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Add_Replace_Trim(_buff2, _ppp, _p, update);
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double _entp = _buff2.Sum();
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base.Add((TValue.t, _entp), update, _NaN);
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}
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}
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@@ -1,57 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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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 KURTOSIS_Series : Single_TSeries_Indicator
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{
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public KURTOSIS_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _n = this._buffer.Count;
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double _avg = _buffer.Average();
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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 += (_buffer[i] - _avg) * (_buffer[i] - _avg);
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_s4 += (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_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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@@ -1,38 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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MAD: Mean Absolute Deviation
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Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation
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MAD defines the degree of variation across the series.
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Calculation:
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MAD = Σ(|close-SMA|) / period
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Sources:
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https://en.wikipedia.org/wiki/Average_absolute_deviation
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</summary> */
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public class MAD_Series : Single_TSeries_Indicator
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{
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public MAD_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sma = _buffer.Average();
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double _mad = 0;
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for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); }
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_mad /= this._buffer.Count;
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base.Add((TValue.t, _mad), update, _NaN);
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}
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}
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@@ -1,42 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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MAPE: Mean Absolute Percentage Error
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Measures the size of the error in percentage terms
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Calculation:
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MAPE = Σ(|close – SMA| / |close|) / n
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Sources:
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https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
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Remark:
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returns infinity if any of observations is 0.
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Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE
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</summary> */
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public class MAPE_Series : Single_TSeries_Indicator
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{
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public MAPE_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sma = _buffer.Average();
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double _mape = 0;
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for (int i = 0; i < _buffer.Count; i++) {
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_mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity;
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}
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_mape /= (_buffer.Count>0) ? _buffer.Count : 1;
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base.Add((TValue.t, _mape), update, _NaN);
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}
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}
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@@ -1,44 +0,0 @@
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namespace QuanTAlib;
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using System;
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using static System.Net.Mime.MediaTypeNames;
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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 MEDIAN_Series : Single_TSeries_Indicator
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{
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public MEDIAN_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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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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base.Add((TValue.t, _med), update, _NaN);
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}
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}
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@@ -1,33 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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MSE: Mean Square Error
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Defined as a Mean (Average) of the Square of the difference between actual and estimated values.
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Sources:
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https://en.wikipedia.org/wiki/Mean_squared_error
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</summary> */
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public class MSE_Series : Single_TSeries_Indicator
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{
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public MSE_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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sma = _buffer.Average();
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double _mse = 0;
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for (int i = 0; i < _buffer.Count; i++) { _mse += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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_mse /= this._buffer.Count;
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base.Add((TValue.t, _mse), update, _NaN);
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}
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}
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@@ -1,39 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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SDEV: Population Standard Deviation
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Population Standard Deviation is the square root of the biased variance, also knons as
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Uncorrected Sample Standard Deviation
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Sources:
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https://en.wikipedia.org/wiki/Standard_deviation#Uncorrected_sample_standard_deviation
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Remark:
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SDEV (Population Standard Deviation) is also known as a biased/uncorrected Standard Deviation.
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For unbiased version that uses Bessel's correction, use SDEV instead.
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</summary> */
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public class SDEV_Series : Single_TSeries_Indicator
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{
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public SDEV_Series(TSeries source, int period=0, 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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Add_Replace_Trim(_buffer, TValue.v, _p, update);
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double _sma = _buffer.Average();
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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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double _psdev = Math.Sqrt(_pvar);
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base.Add((TValue.t, _psdev), update, _NaN);
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}
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}
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@@ -1,33 +0,0 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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SMAPE: Symmetric Mean Absolute Percentage Error
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Measures the size of the error in percentage terms
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Sources:
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https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error
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</summary> */
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public class SMAPE_Series : Single_TSeries_Indicator
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{
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public SMAPE_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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|
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public override void Add((System.DateTime t, double v) TValue, bool update)
|
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{
|
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Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
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double _sma = _buffer.Average();
|
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|
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double _smape = 0;
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for (int i = 0; i < _buffer.Count; i++) { _smape += Math.Abs(_buffer[i] - _sma) / (Math.Abs(_buffer[i]) + Math.Abs(_sma)); }
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_smape /= this._buffer.Count;
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base.Add((TValue.t, _smape), update, _NaN);
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}
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}
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@@ -1,39 +0,0 @@
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namespace QuanTAlib;
|
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using System;
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using System.Linq;
|
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|
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/* <summary>
|
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SSDEV: (Corrected) Sample Standard Deviation
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Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance.
|
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Sources:
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https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation
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Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
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Remark:
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SSDEV (Sample Standard Deviation) is also known as a unbiased/corrected Standard Deviation.
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For a population/biased/uncorrected Standard Deviation, use PSDEV instead
|
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</summary> */
|
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|
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public class SSDEV_Series : Single_TSeries_Indicator
|
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{
|
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public SSDEV_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
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{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
||||
double _sma = _buffer.Average();
|
||||
|
||||
double _svar = 0;
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||||
for (int i = 0; i < this._buffer.Count; i++) { _svar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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||||
_svar /= (_buffer.Count > 1) ? _buffer.Count - 1 : 1; // Bessel's correction
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||||
double _ssdev = Math.Sqrt(_svar);
|
||||
|
||||
base.Add((TValue.t, _ssdev), update, _NaN);
|
||||
}
|
||||
}
|
||||
@@ -1,38 +0,0 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Linq;
|
||||
|
||||
/* <summary>
|
||||
SVAR: Sample Variance
|
||||
Sample variance uses Bessel's correction to correct the bias in the estimation of population variance.
|
||||
|
||||
Sources:
|
||||
https://en.wikipedia.org/wiki/Variance
|
||||
Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
|
||||
|
||||
Remark:
|
||||
SVAR is also known as the Unbiased Sample Variance, while VAR (Population Variance) is known as
|
||||
the Biased Sample Variance.
|
||||
|
||||
</summary> */
|
||||
|
||||
public class SVAR_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public SVAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
||||
double _sma = _buffer.Average();
|
||||
|
||||
double _svar = 0;
|
||||
for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); }
|
||||
_svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction
|
||||
|
||||
base.Add((TValue.t, _svar), update, _NaN);
|
||||
}
|
||||
}
|
||||
@@ -1,38 +0,0 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Linq;
|
||||
|
||||
/* <summary>
|
||||
VAR: Population Variance
|
||||
Population variance without Bessel's correction
|
||||
|
||||
Sources:
|
||||
https://en.wikipedia.org/wiki/Variance
|
||||
Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
|
||||
|
||||
Remark:
|
||||
VAR (Population Variance) is also known as a biased Sample Variance. For unbiased
|
||||
sample variance use SVAR instead.
|
||||
|
||||
</summary> */
|
||||
|
||||
public class VAR_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public VAR_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
||||
double _sma = _buffer.Average();
|
||||
|
||||
double _pvar = 0;
|
||||
for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
|
||||
_pvar /= this._buffer.Count;
|
||||
|
||||
base.Add((TValue.t, _pvar), update, _NaN);
|
||||
}
|
||||
}
|
||||
@@ -1,40 +0,0 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Linq;
|
||||
|
||||
/* <summary>
|
||||
WMAPE: Weighted Mean Absolute Percentage Error
|
||||
Measures the size of the error in percentage terms. Improves problems with MAPE
|
||||
when there are zero or close-to-zero values because there would be a division by zero
|
||||
or values of MAPE tending to infinity.
|
||||
|
||||
Sources:
|
||||
https://en.wikipedia.org/wiki/WMAPE
|
||||
|
||||
</summary> */
|
||||
|
||||
public class WMAPE_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public WMAPE_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
||||
double _sma = _buffer.Average();
|
||||
|
||||
double _div = 0;
|
||||
double _wmape = 0;
|
||||
for (int i = 0; i < _buffer.Count; i++)
|
||||
{
|
||||
_wmape += Math.Abs(_buffer[i] - _sma);
|
||||
_div += Math.Abs(_buffer[i]);
|
||||
}
|
||||
_wmape = (_div!=0) ? _wmape/_div : double.PositiveInfinity;
|
||||
|
||||
base.Add((TValue.t, _wmape), update, _NaN);
|
||||
}
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
namespace QuanTAlib;
|
||||
using System;
|
||||
using System.Linq;
|
||||
|
||||
/* <summary>
|
||||
ZSCORE: number of standard deviations from SMA
|
||||
Z-score describes a value's relationship to the mean of a series, as measured in
|
||||
terms of standard deviations from the mean. If a Z-score is 0, it indicates that
|
||||
the data point's score is identical to the mean score. A Z-score of 1.0 would
|
||||
indicate a value that is one standard deviation from the mean. Z-scores may be
|
||||
positive or negative, with a positive value indicating the score is above the
|
||||
mean and a negative score indicating it is below the mean.
|
||||
|
||||
Sources:
|
||||
https://en.wikipedia.org/wiki/Z-score
|
||||
https://www.investopedia.com/terms/z/zscore.asp
|
||||
|
||||
Calculation:
|
||||
std = std * STDEV(close, length)
|
||||
mean = SMA(close, length)
|
||||
ZSCORE = (close - mean) / std
|
||||
|
||||
</summary> */
|
||||
|
||||
public class ZSCORE_Series : Single_TSeries_Indicator
|
||||
{
|
||||
public ZSCORE_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
|
||||
{
|
||||
if (base._data.Count > 0) { base.Add(base._data); }
|
||||
}
|
||||
private readonly System.Collections.Generic.List<double> _buffer = new();
|
||||
|
||||
public override void Add((System.DateTime t, double v) TValue, bool update)
|
||||
{
|
||||
Add_Replace_Trim(_buffer, TValue.v, _p, update);
|
||||
double _sma = _buffer.Average();
|
||||
|
||||
double _pvar = 0;
|
||||
for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
|
||||
_pvar /= this._buffer.Count;
|
||||
double _psdev = Math.Sqrt(_pvar);
|
||||
double _zscore = (_psdev == 0) ? double.NaN : (TValue.v - _sma) / _psdev;
|
||||
|
||||
base.Add((TValue.t, _zscore), update, _NaN);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user