COVAR

semver fix


VAR test fix


new: COVAR, ZSCORE, CORR, LINREG


versioning


refactoring

Source/Statistics/BIAS_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 BIAS: Rate of change between the source and a moving average.
@@ -23,17 +24,11 @@ public class BIAS_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
 
-        double _sma = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _sma += this._buffer[i]; }
-        _sma /= this._buffer.Count;
-        double _bias = (this._buffer[this._buffer.Count - 1] / ((_sma != 0) ? _sma : 1)) - 1;
+        double _sma = _buffer.Average();
+        double _bias = (_buffer[_buffer.Count - 1] / ((_sma != 0) ? _sma : 1)) - 1;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _bias);
-
-        base.Add(result, update);
+        base.Add((TValue.t, _bias), update, _NaN);
     }
 }

Source/Statistics/CORR_Series.cs

@@ -1,5 +1,7 @@
 namespace QuanTAlib;
 using System;
+using System.Collections.Generic;
+using System.Linq;
 
 /* <summary>
 CORR: Pearson's Correlation Coefficient
@@ -14,57 +16,37 @@ Sources:
 </summary> */
 
 public class CORR_Series : Pair_TSeries_Indicator
-{
-  public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN)
-  {
-    if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } }
-  }
-
-  private readonly System.Collections.Generic.List<double> _x = new();
-  private readonly System.Collections.Generic.List<double> _xx = new();
-  private readonly System.Collections.Generic.List<double> _y = new();
-  private readonly System.Collections.Generic.List<double> _yy = new();
-  private readonly System.Collections.Generic.List<double> _xy = new();
-
-  public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update)
-  {
-    if (update)
-    {
-      _x[_x.Count - 1] = TValue1.v;
-      _xx[_xx.Count - 1] = TValue1.v * TValue1.v;
-      _y[_y.Count - 1] = TValue2.v;
-      _y[_yy.Count - 1] = TValue2.v * TValue2.v;
-      _xy[_xy.Count - 1] = TValue1.v * TValue2.v;
-    }
-    else
-    {
-      _x.Add(TValue1.v);
-      _xx.Add(TValue1.v * TValue1.v);
-      _y.Add(TValue2.v);
-      _yy.Add(TValue2.v * TValue2.v);
-      _xy.Add(TValue1.v * TValue2.v);
-    }
-    if (_x.Count > this._p) { _x.RemoveAt(0); }
-    if (_xx.Count > this._p) { _xx.RemoveAt(0); }
-    if (_y.Count > this._p) { _y.RemoveAt(0); }
-    if (_yy.Count > this._p) { _yy.RemoveAt(0); }
-    if (_xy.Count > this._p) { _xy.RemoveAt(0); }
-
-    double _sumx = 0;
-    for (int i = 0; i < _x.Count; i++) { _sumx += _x[i]; }
-    double _sumxx = 0;
-    for (int i = 0; i < _xx.Count; i++) { _sumxx += _xx[i]; }
-    double _sumy = 0;
-    for (int i = 0; i < _y.Count; i++) { _sumy += _y[i]; }
-    double _sumyy = 0;
-    for (int i = 0; i < _yy.Count; i++) { _sumyy += _yy[i]; }
-    double _sumxy = 0;
-    for (int i = 0; i < _xy.Count; i++) { _sumxy += _xy[i]; }
-
-    double _div = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p);
-    double _cor = (_div != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_div) : 0.0;
-
-    var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor);
-    if (update) { base[base.Count - 1] = result; } else { base.Add(result); }
+{
+  public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN)
+  {
+    if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } }
   }
-}
+
+  private readonly System.Collections.Generic.List<double> _x = new();
+  private readonly System.Collections.Generic.List<double> _xx = new();
+  private readonly System.Collections.Generic.List<double> _y = new();
+  private readonly System.Collections.Generic.List<double> _yy = new();
+  private readonly System.Collections.Generic.List<double> _xy = new();
+
+  public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update)
+  {
+	Add_Replace_Trim(_x, TValue1.v, _p, update);
+	Add_Replace_Trim(_xx, TValue1.v * TValue1.v, _p, update);
+	Add_Replace_Trim(_y, TValue2.v, _p, update);
+	Add_Replace_Trim(_yy, TValue2.v * TValue2.v, _p, update);
+	Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update);
+
+    double _sumx = _x.Sum();
+	double _sumxx = _xx.Sum();
+	double _sumy = _y.Sum();
+	double _sumyy = _yy.Sum();
+	double _sumxy = _xy.Sum();
+
+    double _covar = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p);
+    double _cor = (_covar != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_covar) : 0.0;
+
+	var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor);
+    if (update) { base[base.Count - 1] = result; } else { base.Add(result); }
+
+    }
+}

Source/Statistics/COVAR_Series.cs

@@ -0,0 +1,40 @@
+namespace QuanTAlib;
+using System;
+using System.Linq;
+
+/* <summary>
+COVAR: Covariance
+   Covariance is defined as the expected value (or mean) of the product
+   of their deviations from their individual expected values.
+
+Sources:
+  https://en.wikipedia.org/wiki/Covariance
+
+</summary> */
+
+public class COVAR_Series : Pair_TSeries_Indicator
+{
+  public COVAR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN)
+  {
+    if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } }
+  }
+
+  private readonly System.Collections.Generic.List<double> _x = new();
+  private readonly System.Collections.Generic.List<double> _y = new();
+  private readonly System.Collections.Generic.List<double> _xy = new();
+
+  public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update)
+  {
+    Add_Replace_Trim(_x, TValue1.v, _p, update);
+    Add_Replace_Trim(_y, TValue2.v, _p, update);
+    Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update);
+
+    double _avgx = _x.Average();
+    double _avgy = _y.Average();
+    double _avgxy = _xy.Average();
+    double _covar = _avgxy - (_avgx * _avgy);
+
+    var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _covar);
+    if (update) { base[base.Count - 1] = result; } else { base.Add(result); }
+    }
+}

Source/Statistics/ENTROPY_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 ENTP: Entropy
@@ -16,9 +17,9 @@ Sources:
 
 </summary> */
 
-public class ENTP_Series : Single_TSeries_Indicator
+public class ENTROPY_Series : Single_TSeries_Indicator
 {
-    public ENTP_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
+    public ENTROPY_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
     {
         this._logbase = logbase;
         if (base._data.Count > 0) { base.Add(base._data); }
@@ -29,24 +30,15 @@ public class ENTP_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
-
-        double _sum = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _sum += this._buffer[i]; }
-
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+        double _sum = _buffer.Sum();
+        
         double _pp = this._buffer[this._buffer.Count - 1] / _sum;
         double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase);
 
-        if (update) { this._buff2[this._buff2.Count - 1] = _ppp; }
-        else { this._buff2.Add(_ppp); }
-        if (this._buff2.Count > this._p && this._p != 0) { this._buff2.RemoveAt(0); }
+        Add_Replace_Trim(_buff2, _ppp, _p, update);
+        double _entp = _buff2.Sum();
 
-        double _entp = 0;
-        for (int i = 0; i < this._buff2.Count; i++) { _entp += this._buff2[i]; }
-
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _entp);
-        base.Add(result, update);
+        base.Add((TValue.t, _entp), update, _NaN);
     }
 }

Source/Statistics/KURTOSIS_Series.cs

@@ -1,62 +1,57 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-KURT: Kurtosis of population
-  Kurtosis characterizes the relative peakedness or flatness of a distribution
-  compared with the normal distribution. Positive kurtosis indicates a relatively
-  peaked distribution. Negative kurtosis indicates a relatively flat distribution.
-
-  The normal curve is called Mesokurtic curve. If the curve of a distribution is
-  more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then
-  it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or
-  lighter-tailed) than a normal curve, it is called as a platykurtic curve.
-
-Calculation:
-    sum4 = Σ(close-SMA)^4
-    sum2 = (Σ(close-SMA)^2)^2
-    KURT = length * (sum4/sum2)
-
-Sources:
-  https://en.wikipedia.org/wiki/Kurtosis
-  https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/
-
-</summary> */
-
-public class KURT_Series : Single_TSeries_Indicator
-{
-    public KURT_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
-    {
-        this._logbase = logbase;
-        if (base._data.Count > 0) { base.Add(base._data); }
-    }
-    protected double _logbase;
-    private readonly System.Collections.Generic.List<double> _buffer = new();
-
-    public override void Add((System.DateTime t, double v) TValue, bool update)
-    {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
-
-        double _n = this._buffer.Count;
-
-        double _avg = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _avg += this._buffer[i]; }
-        _avg /= _n;
-
-        double _s2 = 0;
-        double _s4 = 0;
-        for (int i = 0; i < this._buffer.Count; i++)
-        {
-            _s2 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
-            _s4 += (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg) * (this._buffer[i] - _avg);
-        }
-
-        double _Vx = _s2 / (_n - 1);
-        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;
-
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? Double.NaN : _kurt);
-        base.Add(result, update);
-    }
+namespace QuanTAlib;
+using System;
+using System.Linq;
+
+/* <summary>
+KURT: Kurtosis of population
+  Kurtosis characterizes the relative peakedness or flatness of a distribution
+  compared with the normal distribution. Positive kurtosis indicates a relatively
+  peaked distribution. Negative kurtosis indicates a relatively flat distribution.
+
+  The normal curve is called Mesokurtic curve. If the curve of a distribution is
+  more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then
+  it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or
+  lighter-tailed) than a normal curve, it is called as a platykurtic curve.
+
+Calculation:
+    sum4 = Σ(close-SMA)^4
+    sum2 = (Σ(close-SMA)^2)^2
+    KURT = length * (sum4/sum2)
+
+Sources:
+  https://en.wikipedia.org/wiki/Kurtosis
+  https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/
+
+</summary> */
+
+public class KURTOSIS_Series : Single_TSeries_Indicator
+{
+    public KURTOSIS_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)
+    {
+        this._logbase = logbase;
+        if (base._data.Count > 0) { base.Add(base._data); }
+    }
+    protected double _logbase;
+    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 _n = this._buffer.Count;
+        double _avg = _buffer.Average();
+
+        double _s2 = 0;
+        double _s4 = 0;
+        for (int i = 0; i < this._buffer.Count; i++)
+        {
+            _s2 += (_buffer[i] - _avg) * (_buffer[i] - _avg);
+            _s4 += (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg);
+        }
+
+        double _Vx = _s2 / (_n - 1);
+        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;
+
+        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? Double.NaN : _kurt);
+        base.Add(result, update);
+    }
 }

Source/Statistics/LINREG_Series.cs

@@ -34,9 +34,7 @@ public class LINREG_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
 
         int _len = this._buffer.Count;
 
@@ -79,7 +77,7 @@ public class LINREG_Series : Single_TSeries_Indicator
         double _RSquared = arrr * arrr;
 
         var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _slope);
-        base.Add(ret, update);
+        base.Add(ret, update, _NaN);
 
         ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _intercept);
         Intercept.Add(ret, update);

Source/Statistics/MAD_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 MAD: Mean Absolute Deviation
@@ -24,19 +25,14 @@ public class MAD_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
 
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        double _sma = _buffer.Average();
 
         double _mad = 0;
         for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); }
         _mad /= this._buffer.Count;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _mad);
-        base.Add(result, update);
+        base.Add((TValue.t, _mad), update, _NaN);
     }
 }

Source/Statistics/MAPE_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 MAPE: Mean Absolute Percentage Error
@@ -27,19 +28,15 @@ public class MAPE_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+        double _sma = _buffer.Average();
 
         double _mape = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity; }
-        _mape /= this._buffer.Count;
+        for (int i = 0; i < _buffer.Count; i++) { 
+            _mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity; 
+        }
+        _mape /= (_buffer.Count>0) ? _buffer.Count : 1;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _mape);
-        base.Add(result, update);
+        base.Add((TValue.t, _mape), update, _NaN);
     }
 }

Source/Statistics/MEDIAN_Series.cs

@@ -1,47 +1,44 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-MED - Median value
-    Median of numbers is the middlemost value of the given set of numbers. 
-    It separates the higher half and the lower half of a given data sample. 
-    At least half of the observations are smaller than or equal to median 
-    and at least half of the observations are greater than or equal to the median.
-
-    If the number of values is odd, the middlemost observation of the sorted
-    list is the median of the given data.  If the number of values is even, 
-    median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list.
-
-    If period = 0 => period is max
-
-Sources:
-    https://corporatefinanceinstitute.com/resources/knowledge/other/median/
-    https://en.wikipedia.org/wiki/Median
-
-</summary> */
-
-public class MED_Series : Single_TSeries_Indicator
-{
-    public MED_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)
-    {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
-
-        System.Collections.Generic.List<double> _s = new(this._buffer);
-        _s.Sort();
-        int _p1 = _s.Count / 2;
-        int _p2 = Math.Max(0, (_s.Count / 2) - 1);
-        double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2;
-
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _med);
-
-        base.Add(result, update);
-    }
-}
+namespace QuanTAlib;
+using System;
+using static System.Net.Mime.MediaTypeNames;
+
+/* <summary>
+MED - Median value
+    Median of numbers is the middlemost value of the given set of numbers. 
+    It separates the higher half and the lower half of a given data sample. 
+    At least half of the observations are smaller than or equal to median 
+    and at least half of the observations are greater than or equal to the median.
+
+    If the number of values is odd, the middlemost observation of the sorted
+    list is the median of the given data.  If the number of values is even, 
+    median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list.
+
+    If period = 0 => period is max
+
+Sources:
+    https://corporatefinanceinstitute.com/resources/knowledge/other/median/
+    https://en.wikipedia.org/wiki/Median
+
+</summary> */
+
+public class MEDIAN_Series : Single_TSeries_Indicator
+{
+    public MEDIAN_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);
+
+        System.Collections.Generic.List<double> _s = new(this._buffer);
+        _s.Sort();
+        int _p1 = _s.Count / 2;
+        int _p2 = Math.Max(0, (_s.Count / 2) - 1);
+        double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2;
+
+        base.Add((TValue.t, _med), update, _NaN);
+    }
+}

Source/Statistics/MSE_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 MSE: Mean Square Error 
@@ -20,19 +21,13 @@ public class MSE_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+        double _sma = _buffer.Average();
 
         double _mse = 0;
         for (int i = 0; i < _buffer.Count; i++) { _mse += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
         _mse /= this._buffer.Count;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _mse);
-        base.Add(result, update);
+        base.Add((TValue.t, _mse), update, _NaN);
     }
 }

Source/Statistics/SDEV_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 SDEV: Population Standard Deviation
@@ -25,20 +26,14 @@ public class SDEV_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        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);
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _psdev);
-        base.Add(result, update);
+        base.Add((TValue.t, _psdev), update, _NaN);
     }
 }

Source/Statistics/SMAPE_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 SMAPE: Symmetric Mean Absolute Percentage Error
@@ -20,19 +21,13 @@ public class SMAPE_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+        double _sma = _buffer.Average();
 
         double _smape = 0;
         for (int i = 0; i < _buffer.Count; i++) { _smape += Math.Abs(_buffer[i] - _sma) / (Math.Abs(_buffer[i]) + Math.Abs(_sma)); }
         _smape /= this._buffer.Count;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _smape);
-        base.Add(result, update);
+        base.Add((TValue.t, _smape), update, _NaN);
     }
 }

Source/Statistics/SSDEV_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 SSDEV: (Corrected) Sample Standard Deviation
@@ -25,20 +26,14 @@ public class SSDEV_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _sma += this._buffer[i]; }
-        _sma /= this._buffer.Count;
+        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
+        for (int i = 0; i < this._buffer.Count; i++) { _svar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
+        _svar /= (_buffer.Count > 1) ? _buffer.Count - 1 : 1; // Bessel's correction
         double _ssdev = Math.Sqrt(_svar);
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _ssdev);
-        base.Add(result, update);
+        base.Add((TValue.t, _ssdev), update, _NaN);
     }
 }

Source/Statistics/SVAR_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 SVAR: Sample Variance
@@ -25,19 +26,13 @@ public class SVAR_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p && this._p != 0) { this._buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _sma += this._buffer[i]; }
-        _sma /= this._buffer.Count;
+        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
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _svar);
-        base.Add(result, update);
+        base.Add((TValue.t, _svar), update, _NaN);
     }
 }

Source/Statistics/VAR_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 VAR: Population Variance
@@ -25,19 +26,13 @@ public class VAR_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        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;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _pvar);
-        base.Add(result, update);
+        base.Add((TValue.t, _pvar), update, _NaN);
     }
 }

Source/Statistics/WMAPE_Series.cs

@@ -1,9 +1,12 @@
 namespace QuanTAlib; 
 using System;
+using System.Linq;
 
 /* <summary>
 WMAPE: Weighted Mean Absolute Percentage Error
-    Measures the size of the error in percentage terms
+    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
@@ -20,13 +23,8 @@ public class WMAPE_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+        double _sma = _buffer.Average();
 
         double _div = 0;
         double _wmape = 0;
@@ -35,9 +33,8 @@ public class WMAPE_Series : Single_TSeries_Indicator
             _wmape += Math.Abs(_buffer[i] - _sma);
             _div += Math.Abs(_buffer[i]);
         }
-        _wmape /= _div;
+        _wmape = (_div!=0) ? _wmape/_div : double.PositiveInfinity;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _wmape);
-        base.Add(result, update);
+        base.Add((TValue.t, _wmape), update, _NaN);
     }
 }

Source/Statistics/ZSCORE_Series.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib;
 using System;
+using System.Linq;
 
 /* <summary>
 ZSCORE: number of standard deviations from SMA
@@ -31,13 +32,8 @@ public class ZSCORE_Series : Single_TSeries_Indicator
 
     public override void Add((System.DateTime t, double v) TValue, bool update)
     {
-        if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sma = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
-        _sma /= this._buffer.Count;
+        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); }
@@ -45,7 +41,6 @@ public class ZSCORE_Series : Single_TSeries_Indicator
         double _psdev = Math.Sqrt(_pvar);
         double _zscore = (_psdev == 0) ? double.NaN : (TValue.v - _sma) / _psdev;
 
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _zscore);
-        base.Add(result, update);
+        base.Add((TValue.t, _zscore), update, _NaN);
     }
 }