TR and ATR

Docs/Comparing_w_TALIB.ipynb

@@ -15,7 +15,7 @@
     {
      "data": {
       "text/html": [
-       "<div><div></div><div></div><div><strong>Installed Packages</strong><ul><li><span>QuantLib, 1.0.7</span></li><li><span>TALib.NETCore, 0.4.4</span></li></ul></div></div>"
+       "<div><div></div><div></div><div><strong>Installed Packages</strong><ul><li><span>QuanTAlib, 0.1.10-beta</span></li><li><span>TALib.NETCore, 0.4.4</span></li></ul></div></div>"
       ]
      },
      "metadata": {},
@@ -24,7 +24,7 @@
    ],
    "source": [
     "#r \"nuget: TALib.NETCore, 0.4.4\"    \n",
-    "#r \"nuget:QuanTAlib\" \n",
+    "#r \"nuget: QuanTAlib, 0.1.10-beta\" \n",
     "\n",
     "using QuanTAlib;\n",
     "using TALib;\n"
@@ -43,13 +43,12 @@
    },
    "outputs": [
     {
-     "data": {
+     "ename": "Error",
-      "text/html": [
+     "evalue": "(1,1): error CS0246: The type or namespace name 'YAHOO_Feed' could not be found (are you missing a using directive or an assembly reference?)",
-       "<div class=\"dni-plaintext\">1394</div>"
+     "output_type": "error",
-      ]
+     "traceback": [
-     },
+      "(1,1): error CS0246: The type or namespace name 'YAHOO_Feed' could not be found (are you missing a using directive or an assembly reference?)"
-     "metadata": {},
+     ]
-     "output_type": "display_data"
     }
    ],
    "source": [

Docs/coverage.md

@@ -42,7 +42,8 @@
 | AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
 | ALMA - Arnaud Legoux Moving Average |||✔️|✔️|
 | ARIMA - Autoregressive Integrated Moving Average |||||
-| ATR - Average True Range ||✔️|✔️|✔️|
+| ATR - Average True Range |✔️|✔️|✔️|✔️|
+| ATRP - Average True Range Percent |✔️||✔️||
 | DEMA - Double EMA |✔️|✔️|✔️|✔️|
 | EMA - Exponential Moving Average |✔️|✔️|✔️|✔️|
 | EPMA - Endpoint Moving Average |||✔️||

Source/Basics/ADD_Series.cs

@@ -1,6 +1,13 @@
-// ADD - adding TSeries+TSeries together, or TSeries+double, or double+TSeries
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+ADD - adding TSeries+TSeries together, or TSeries+double, or double+TSeries
+
+Remarks: 
+    Most of scaffolding is packaged in abstracty class Pair_TSeries_Indicator.
+    
+</summary> */
 
 public class ADD_Series : Pair_TSeries_Indicator 
 {

Source/Basics/Abstracts.cs

@@ -1,6 +1,18 @@
 namespace QuanTAlib;
 using System;
+/* <summary>
+Abstract classes with all scaffolding required to build indicators.
+    All abstracts support period, NaN, and all permutations of Add() methods. 
+    Indicator classess need to implement:
+        - Chaining constructor (Abstract's constructor executes first)
+        - Default Add(value) class
+        - optional Add(series) bulk insert class (for optimization of historical analysis)
 
+    Single_TSeries_Indicator    - one single-value TSeries in, one TSeries out. 
+    Pair_TSeries_Indicator      - Two TSeries in, one TSeries out. (includes simple semaphoring)
+    Single_TBars_Indicator      - One OHLCV TBars in, one TSeries out.
+
+</summary> */
 public abstract class Single_TSeries_Indicator : TSeries
 {
     protected readonly int _p;
@@ -124,8 +136,9 @@ public abstract class Single_TBars_Indicator : TSeries
     protected readonly TBars _bars;
 
     // Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
-    protected Single_TBars_Indicator(TBars source, bool useNaN)
+    protected Single_TBars_Indicator(TBars source, int period, bool useNaN)
     {
+        this._p = period;
         this._bars = source;
         this._NaN = useNaN;
         this._bars.Close.Pub += this.Sub;

Source/Basics/DIV_Series.cs

@@ -1,6 +1,12 @@
-// DIV - divide TSeries/TSeries , or TSeries/double, or double/TSeries
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+DIV - divide TSeries/TSeries , or TSeries/double, or double/TSeries
+
+Remarks: 
+    Most of scaffolding is packaged in abstracty class Pair_TSeries_Indicator.
+</summary> */
 
 public class DIV_Series : Pair_TSeries_Indicator 
 {

Source/Basics/MAX_Series.cs

@@ -1,20 +1,18 @@
 namespace QuanTAlib;
-/*
-MAX - Maximum value in the given period in the series.
-
-If period = 0 => period = full length of the series
-
-*/
-
 using System;
-using System.Collections.Generic;
+
+/* <summary>
+MAX - Maximum value in the given period in the series.
+    If period = 0 => period = full length of the series
+</summary> */
+
 public class MAX_Series : Single_TSeries_Indicator
 {
     public MAX_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
     {
         if (base._data.Count > 0) { base.Add(base._data); }
     }
-    private readonly List<double> _buffer = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
 
     public override void Add((DateTime t, double v) d, bool update)
     {
@@ -24,7 +22,7 @@ public class MAX_Series : Single_TSeries_Indicator
 
         double _max = d.v;
         for (int i = 0; i < this._buffer.Count; i++)
-        { _max = this._buffer[i] > _max ? this._buffer[i] : _max; }
+        { _max = (this._buffer[i] > _max) ? this._buffer[i] : _max; }
 
         var result = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _max);
 

Source/Basics/MIN_Series.cs

@@ -1,12 +1,10 @@
-/*
-MIN - Minimum value in the given period in the series.
-
-If period = 0 => period = full length of the series
-
-*/
-
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+MIN - Minimum value in the given period in the series.
+    If period = 0 => period = full length of the series
+</summary> */
 
 public class MIN_Series : Single_TSeries_Indicator
 {

Source/Basics/MUL_Series.cs

@@ -1,6 +1,10 @@
-// MUL - multiply TSeries*TSeries together, or TSeries*double, or double*TSeries
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+MUL - multiply TSeries*TSeries together, or TSeries*double, or double*TSeries
+
+</summary> */
 
 public class MUL_Series : Pair_TSeries_Indicator 
 {

Source/Basics/RND_Feed.cs

@@ -1,20 +1,28 @@
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+Random Bars generator - used for testing, validation and fun
+    Returns 'bars' number of candles that follow common market movement.
+    volatility defines how 'jumpy' is the series of
+    startvalue defines beginning closing price that then guides the rest of series
+    
+</summary> */
 
 public class RND_Feed : TBars
 {
-    public RND_Feed(int days, double volatility = 0.05, double startvalue = 100.0)
+    public RND_Feed(int bars, double volatility = 0.05, double startvalue = 100.0)
     {
         Random rnd = new();
         double c = startvalue;
-        for (int i = 0; i < days; i++)
+        for (int i = 0; i < bars; i++)
         {
             double o = Math.Round(c + c * (volatility * 0.1 * rnd.NextDouble() - 0.005), 2);
             double h = Math.Round(o + c * volatility * rnd.NextDouble(), 2);
             double l = Math.Round(o - c * volatility * rnd.NextDouble(), 2);
             c = Math.Round(l + (h - l) * rnd.NextDouble(), 2);
             double v = Math.Round(1000 * rnd.NextDouble(), 2);
-            this.Add(DateTime.Today.AddDays(i - days), o, h, l, c, v);
+            this.Add(DateTime.Today.AddDays(i - bars), o, h, l, c, v);
         }
     }
 }

Source/Basics/SUB_Series.cs

@@ -1,6 +1,11 @@
-// SUB - subtracting TSeries-TSeries, or TSeries-double, or double-TSeries
-using System;
 namespace QuanTAlib;
+using System;
+
+/* <summary>
+SUB - subtracting TSeries-TSeries, or TSeries-double, or double-TSeries
+
+</summary> */
+
 
 public class SUB_Series : Pair_TSeries_Indicator 
 {

Source/Basics/TBars.cs

@@ -1,7 +1,15 @@
 namespace QuanTAlib;
-
 using System;
 
+/* <summary>
+TBars class - includes all series for common data used in indicators and other calculations.
+    Has a bit limited overloading and casting (compared to TSeries)
+    Includes Select(int) method to simplify choosing the most optimal data source for indicators
+    Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4 
+        (it is 'cheaper' to calculate them once during data capture than each time during data analysis)
+
+</summary> */
+
 public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)>
 {
     private readonly TSeries _open = new();

Source/Basics/TR_Series.cs

@@ -0,0 +1,38 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+TR: True Range
+    True Range was introduced by J. Welles Wilder in his book New Concepts in Technical Trading Systems.
+    It measures the daily range plus any gap from the closing price of the preceding day.
+
+Calculation:
+    d1 = ABS(High - Low)
+    d2 = ABS(High - Previous close)
+    d3 = ABS(Previous close - Low)
+    TR = MAX(d1,d2,d3)
+
+Sources:
+     https://www.macroption.com/true-range/
+
+</summary> */
+
+public class TR_Series : Single_TBars_Indicator
+{
+    private double _cm1 = double.NaN;
+    public TR_Series(TBars source, bool useNaN = false) : base(source, period:0, useNaN:useNaN) {
+        if (this._bars.Count > 0) { base.Add(this._bars); }
+    }
+
+    public override void Add((DateTime t, double o, double h, double l, double c, double v) TValue, bool update = false)
+    {
+        if (_cm1 is double.NaN) { _cm1 = TValue.c; }
+
+        double d1 = Math.Abs(TValue.h - TValue.l);
+        double d2 = Math.Abs(_cm1 - TValue.h);
+        double d3 = Math.Abs(_cm1 - TValue.l);
+        var ret = (TValue.t, (base.Count==0 && base._NaN) ? double.NaN : Math.Max(d1,Math.Max(d2,d3)) );
+        base.Add(ret, update);
+        _cm1 = TValue.c;
+    }
+}

Source/Basics/TSeries.cs

@@ -1,8 +1,18 @@
 namespace QuanTAlib;
-
 using System;
 using System.Linq;
 
+/* <summary>
+TSeries is the cornerstone of all QuanTAlib classess. 
+    TSeries is a single List of tuples (time, value) and contains several operators, casts, overloads 
+    and other helpers that simplify usage of library. 
+    Think of TSeries as an equivalent of Numpy array.
+    
+        - includes Length property (to mimic array's method)
+        - includes publishing and subscribing methods that attach to events
+        - uses Linq only for two transmutations - needs to be refactored out eventually (for speed)
+
+</summary> */
 public class TSeries : System.Collections.Generic.List<(DateTime t, double v)>
 {
     // when asked for a (t,v) tuple, return the last (t,v) on the List

Source/Indicators/ATRP_Series.cs

@@ -0,0 +1,64 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+ATR: wildeR Moving Average
+    The average true range (ATR) is a price volatility indicator
+    showing the average price variation of assets within a given time period.
+
+Sources:
+    https://en.wikipedia.org/wiki/Average_true_range
+    https://www.tradingview.com/wiki/Average_True_Range_(ATR)
+    https://www.investopedia.com/terms/a/atr.asp
+
+</summary> */
+
+
+public class ATR_Series : Single_TBars_Indicator
+{
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+    private readonly double _k, _k1m;
+    private double _lastema, _lastlastema, _lastcm1;
+    private double _cm1 = double.NaN;
+
+    public ATR_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
+    {
+        this._k = 1.0 / (double)(this._p);
+        this._k1m = 1.0 - this._k;
+        this._lastema = this._lastlastema = double.NaN;
+        if (_bars.Count > 0) { base.Add(_bars); }
+    }
+
+    public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false)
+    {
+        if (update) { 
+            this._lastema = this._lastlastema; 
+            this._cm1 = this._lastcm1;
+        }
+
+        if (_cm1 is double.NaN) { _cm1 = TBar.c; }
+        double d1 = Math.Abs(TBar.h - TBar.l);
+        double d2 = Math.Abs(_cm1 - TBar.h);
+        double d3 = Math.Abs(_cm1 - TBar.l);
+        (DateTime t, double v)d = (TBar.t, Math.Max(d1,Math.Max(d2,d3))); //TR value for RMA below
+        _lastcm1 = _cm1;
+        _cm1 = TBar.c;
+
+        double _ema = 0;
+        if (this.Count < this._p)
+        {
+            if (update) { _buffer[_buffer.Count - 1] = d.v; }
+                else { _buffer.Add(d.v); }
+            if (_buffer.Count > this._p) { _buffer.RemoveAt(0); }
+            for (int i = 0; i < _buffer.Count; i++) { _ema += _buffer[i]; }
+            _ema /= this._buffer.Count;
+        }
+        else { _ema = d.v * _k + _lastema * _k1m; }
+
+        this._lastlastema = this._lastema;
+        this._lastema = _ema;
+
+        var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema);
+        base.Add(ret, update);
+    }
+}

Source/Indicators/ATR_Series.cs

@@ -0,0 +1,63 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+ATRP: Average True Range Percent
+    Average True Range Percent is (ATR/Close Price)*100. 
+    This normalizes so it can be compared to other stocks.
+
+Sources:
+    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp
+
+</summary> */
+
+public class ATRP_Series : Single_TBars_Indicator
+{
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+    private readonly double _k, _k1m;
+    private double _lastema, _lastlastema, _lastcm1;
+    private double _cm1 = double.NaN;
+
+    public ATRP_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
+    {
+        this._k = 1.0 / (double)(this._p);
+        this._k1m = 1.0 - this._k;
+        this._lastema = this._lastlastema = double.NaN;
+        if (_bars.Count > 0) { base.Add(_bars); }
+    }
+
+    public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false)
+    {
+        if (update) { 
+            this._lastema = this._lastlastema; 
+            this._cm1 = this._lastcm1;
+        }
+
+        if (_cm1 is double.NaN) { _cm1 = TBar.c; }
+        double d1 = Math.Abs(TBar.h - TBar.l);
+        double d2 = Math.Abs(_cm1 - TBar.h);
+        double d3 = Math.Abs(_cm1 - TBar.l);
+        (DateTime t, double v)d = (TBar.t, Math.Max(d1,Math.Max(d2,d3))); //TR value for RMA below
+        _lastcm1 = _cm1;
+        _cm1 = TBar.c;
+
+        double _ema = 0;
+        if (this.Count < this._p)
+        {
+            if (update) { _buffer[_buffer.Count - 1] = d.v; }
+                else { _buffer.Add(d.v); }
+            if (_buffer.Count > this._p) { _buffer.RemoveAt(0); }
+            for (int i = 0; i < _buffer.Count; i++) { _ema += _buffer[i]; }
+            _ema /= this._buffer.Count;
+        }
+        else { _ema = d.v * _k + _lastema * _k1m; }
+
+        this._lastlastema = this._lastema;
+        this._lastema = _ema;
+
+        double _atrp = 100 * (_ema / TBar.c);
+
+        var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atrp);
+        base.Add(ret, update);
+    }
+}

Source/Indicators/DEMA_Series.cs

@@ -1,8 +1,9 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 DEMA: Double Exponential Moving Average
-DEMA uses EMA(EMA()) to calculate smoother Exponential moving average.
+    DEMA uses EMA(EMA()) to calculate smoother Exponential moving average.
 
 Sources:
     https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/
@@ -11,13 +12,12 @@ Remark:
     ema1 = EMA(close, length)
     ema2 = EMA(ema1, length)
     DEMA = 2 * ema1 - ema2
-**/
 
-using System;
+</summary> */
-using System.Collections.Generic;
+
 public class DEMA_Series : Single_TSeries_Indicator
 {
-    private readonly List<double> _buffer = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
     private readonly double _k, _k1m;
     private double _lastema1, _lastlastema1;
     private double _lastema2, _lastlastema2;

Source/Indicators/EMA_Series.cs

@@ -1,26 +1,27 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 EMA: Exponential Moving Average
+    EMA needs very short history buffer and calculates the EMA value using just the
+    previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
 
-EMA needs very short history buffer and calculates the EMA value using just the
-previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
 Sources:
     https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
     https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
     https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA
+
 Issues:
     There is no consensus what the first EMA value should be - a zero, a first
-datapoint, or an average of the initial Period bars. All three starting methods
+    datapoint, or an average of the initial Period bars. All three starting methods
-converge within 20+ bars to the same moving average. Most implementations (including this one)
+    converge within 20+ bars to the same moving average. Most implementations (including this one)
-use SMA() for the first Period bars as a seeding value for EMA.
+    use SMA() for the first Period bars as a seeding value for EMA.
-**/
+
+</summary> */
 
-using System;
-using System.Collections.Generic;
 public class EMA_Series : Single_TSeries_Indicator
 {
-    private readonly List<double> _buffer = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
     private readonly double _k, _k1m;
     private double _lastema, _lastlastema;
 

Source/Indicators/HEMA_Series.cs

@@ -1,16 +1,17 @@
-using System;
+namespace QuanTAlib;
-namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 HEMA: Hull-EMA Moving Average
-Modified HUll Moving Average; instead of using WMA (Weighted MA) for a
+    Modified HUll Moving Average; instead of using WMA (Weighted MA) for acalculation, 
-calculation, HEMA uses EMA for Hull's formula:
+    HEMA uses EMA for Hull's formula:
 
 EMA1 = EMA(n/2) of price - where k = 4/(n/2 +1)
 EMA2 = EMA(n) of price - where k = 3/(n+1)
 Raw HMA = (2 * EMA1) - EMA2
 EMA3 = EMA(sqrt(n)) of Raw HMA - where k = 2/(sqrt(n)+1)
-**/
+
+</summary> */
 
 public class HEMA_Series : Single_TSeries_Indicator
 {

Source/Indicators/HMA_Series.cs

@@ -1,19 +1,21 @@
-using System;
+namespace QuanTAlib;
-namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 HMA: Hull Moving Average
-Developed by Alan Hull, an extremely fast and smooth moving average; almost
+    Developed by Alan Hull, an extremely fast and smooth moving average; almost
-eliminates lag altogether and manages to improve smoothing at the same time.
+    eliminates lag altogether and manages to improve smoothing at the same time.
 
 Sources:
     https://alanhull.com/hull-moving-average
     https://school.stockcharts.com/doku.php?id=technical_indicators:hull_moving_average
+
 WMA1 = WMA(n/2) of price
 WMA2 = WMA(n) of price
 Raw HMA = (2 * WMA1) - WMA2
 HMA = WMA(sqrt(n)) of Raw HMA
-**/
+
+</summary> */
 
 public class HMA_Series : TSeries
 {

Source/Indicators/JMA_Series.cs

@@ -1,11 +1,11 @@
-using System;
+namespace QuanTAlib;
-namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 JMA: Jurik Moving Average
-Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the
+    Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the
-underlying activity. It has extremely low lag, is very smooth and is responsive
+    underlying activity. It has extremely low lag, is very smooth and is responsive
-to market gaps.
+    to market gaps.
 
 Sources:
     https://c.mql5.com/forextsd/forum/164/jurik_1.pdf
@@ -13,10 +13,12 @@ Sources:
 
 Issues:
     Real JMA algorithm is not published and this formula is derived through
-deduction and reverse analysis of JMA behavior. It is really close, but not
+    deduction and reverse analysis of JMA behavior. It is really close, but not
-exact - published JMA tests against JMA.CSV fail with small deviation. The
+    exact - published JMA tests against JMA.CSV fail with small deviation. The
-original algo is slightly different, yet this approximation is close enough.
+    original algo is slightly different, yet this approximation is close enough.
-**/
+
+</summary> */
+
 public class JMA_Series : Single_TSeries_Indicator
 {
     private readonly System.Collections.Generic.List<double> vbuffer10;

Source/Indicators/RMA_Series.cs

@@ -1,12 +1,13 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 RMA: wildeR Moving Average
+    J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is
+    set as 1/period, giving less weight to the new data compared to EMA. 
 
-J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is
+Sources:
-set as 1/period, giving less weight to the new data compared to EMA. Sources:
     https://archive.org/details/newconceptsintec00wild/page/23/mode/2up
-
 	https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
     https://www.incrediblecharts.com/indicators/wilder_moving_average.php
 
@@ -15,13 +16,11 @@ Issues:
 	pandas.ewm().mean() and returns incorrect first (period) of bars compared to 
 	published formula. This implementation passess the validation test in Wilder's book.
 
-**/
+</summary> */
 
-using System;
-using System.Collections.Generic;
 public class RMA_Series : Single_TSeries_Indicator
 {
-    private readonly List<double> _buffer = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
     private readonly double _k, _k1m;
     private double _lastema, _lastlastema;
 

Source/Indicators/SMA_Series.cs

@@ -1,17 +1,20 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 SMA: Simple Moving Average
-The weights are equally distributed across the period, resulting in a mean() of
+    The weights are equally distributed across the period, resulting in a mean() of
-the data within the period/
+    the data within the period/
 
 Sources:
     https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
     https://stats.stackexchange.com/a/24739
 
 Remark:
-    This calc doesn't use LINQ or SUM() or any of iterative methods.
+    This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB
-**/
+    implementation, but it does allow incremental additions of inputs and real-time calculations of SMA()
+
+</summary> */
 
 public class SMA_Series : Single_TSeries_Indicator
 {

Source/Indicators/TEMA_Series.cs

@@ -1,8 +1,9 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 TEMA: Triple Exponential Moving Average
-TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average.
+    TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average.
 
 Sources:
     https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
@@ -12,13 +13,12 @@ Remark:
     ema2 = EMA(ema1, length)
     ema3 = EMA(ema2, length)
     TEMA = 3 * (ema1 - ema2) + ema3
-**/
 
-using System;
+</summary> */
-using System.Collections.Generic;
+
 public class TEMA_Series : Single_TSeries_Indicator
 {
-    private readonly List<double> _buffer = new();
+    private readonly System.Collections.Generic.List<double> _buffer = new();
     private readonly double _k, _k1m;
     private double _lastema1, _lastlastema1;
     private double _lastema2, _lastlastema2;

Source/Indicators/WMA_Series.cs

@@ -1,14 +1,16 @@
 namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 WMA: (linearly) Weighted Moving Average
-The weights are linearly decreasing over the period and the most recent data has
+    The weights are linearly decreasing over the period and the most recent data has
-the heaviest weight.
+    the heaviest weight.
 
 Sources:
     https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/weighted-moving-average-wma/
     https://www.technicalindicators.net/indicators-technical-analysis/83-moving-averages-simple-exponential-weighted
-**/
+
+</summary> */
 
 public class WMA_Series : Single_TSeries_Indicator
 {

Source/Indicators/ZLEMA_Series.cs

@@ -1,22 +1,23 @@
-using System;
+namespace QuanTAlib;
-namespace QuanTAlib;
+using System;
 
-/**
+/* <summary>
 ZLEMA: Zero Lag Exponential Moving Average
-
+    The Zero lag exponential moving average (ZLEMA) indicator was created by John
-The Zero lag exponential moving average (ZLEMA) indicator was created by John
+    Ehlers and Ric Way.
-Ehlers and Ric Way.
 
 The formula for a given N-Day period and for a given Data series is:
-Lag = (Period-1)/2
+    Lag = (Period-1)/2
-Ema Data = {Data+(Data-Data(Lag days ago))
+    Ema Data = {Data+(Data-Data(Lag days ago))
-ZLEMA = EMA (EmaData,Period)
+    ZLEMA = EMA (EmaData,Period)
 
-The idea is do a regular exponential moving average (EMA) calculation but on a
+Remark:
-de-lagged data instead of doing it on the regular data. Data is de-lagged by
+    The idea is do a regular exponential moving average (EMA) calculation but on a
-removing the data from "lag" days ago thus removing (or attempting to remove)
+    de-lagged data instead of doing it on the regular data. Data is de-lagged by
-the cumulative lag effect of the moving average.
+    removing the data from "lag" days ago thus removing (or attempting to remove)
-**/
+    the cumulative lag effect of the moving average.
+
+</summary> */
 
 public class ZLEMA_Series : Single_TSeries_Indicator
 {

Source/QuanTAlib.csproj

@@ -1,6 +1,6 @@
 <Project Sdk="Microsoft.NET.Sdk">
   <PropertyGroup>
-    <Version>0.1.10-beta</Version>
+    <Version>0.1.11</Version>
     <releaseNotes></releaseNotes>
     <Title>QuanTAlib</Title>
     <Product>Library of Technical Indicators for .NET</Product>

Source/Statistics/BIAS_Series.cs

@@ -1,18 +1,17 @@
-/**
+namespace QuanTAlib;
-  BIAS: Rate of change between the source and a moving average.
+using System;
 
-  Bias is a statistical term which means a systematic deviation from the actual value.
+/* <summary>
+BIAS: Rate of change between the source and a moving average.
+    Bias is a statistical term which means a systematic deviation from the actual value.
 
-  BIAS = (close - SMA) / SMA
+BIAS = (close - SMA) / SMA
        = (close / SMA) - 1
 
 Sources:
-  https://en.wikipedia.org/wiki/Bias_of_an_estimator
+	https://en.wikipedia.org/wiki/Bias_of_an_estimator
 
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class BIAS_Series : Single_TSeries_Indicator
 {

Source/Statistics/ENTP_Series.cs

@@ -1,8 +1,10 @@
-/**
+namespace QuanTAlib;
-ENTP: Entropy
+using System;
 
-Introduced by Claude Shannon in 1948, entropy measures the unpredictability
+/* <summary>
-of the data, or equivalently, of its average information.
+ENTP: Entropy
+    Introduced by Claude Shannon in 1948, entropy measures the unpredictability
+    of the data, or equivalently, of its average information.
 
 Calculation:
     P = close / Σ(close)
@@ -12,9 +14,8 @@ Sources:
     https://en.wikipedia.org/wiki/Entropy_(information_theory)
     https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples
 
-**/
+</summary> */
-namespace QuanTAlib;
+
-using System;
 public class ENTP_Series : Single_TSeries_Indicator
 {
     public ENTP_Series(TSeries source, int period, double logbase = 2.0, bool useNaN = false) : base(source, period, useNaN)

Source/Statistics/KURT_Series.cs

@@ -1,6 +1,8 @@
-/**
+namespace QuanTAlib;
-KURT: Kurtosis of population
+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.
@@ -19,12 +21,7 @@ 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> */
-
-using System;
-namespace QuanTAlib;
-
-// https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/
 
 public class KURT_Series : Single_TSeries_Indicator
 {

Source/Statistics/MAD_Series.cs

@@ -1,8 +1,10 @@
-/**
+namespace QuanTAlib;
-MAD: Mean Absolute Deviation
+using System;
 
-Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation
+/* <summary>
-MAD defines the degree of variation across the series.
+MAD: Mean Absolute Deviation
+  Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation
+  MAD defines the degree of variation across the series.
 
 Calculation:
   MAD = Σ(|close-SMA|) / period
@@ -10,10 +12,7 @@ Calculation:
 Sources:
   https://en.wikipedia.org/wiki/Average_absolute_deviation
    
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class MAD_Series : Single_TSeries_Indicator
 {

Source/Statistics/MAPE_Series.cs

@@ -1,7 +1,9 @@
-/**
+namespace QuanTAlib;
-MAPE: Mean Absolute Percentage Error
+using System;
 
-Measures the size of the error in percentage terms
+/* <summary>
+MAPE: Mean Absolute Percentage Error
+  Measures the size of the error in percentage terms
 
 Calculation:
   MAPE = Σ(|close – SMA| / |close|) / n
@@ -9,13 +11,11 @@ Calculation:
 Sources:
   https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
 
-Remark: returns infinity if any of observations is 0. 
+Remark: 
-Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE
+  returns infinity if any of observations is 0. 
+  Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE
     
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class MAPE_Series : Single_TSeries_Indicator
 {

Source/Statistics/MED_Series.cs

@@ -1,25 +1,24 @@
-/*
+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.
 
-Median of numbers is the middlemost value of the given set of numbers. 
+    If the number of values is odd, the middlemost observation of the sorted
-It separates the higher half and the lower half of a given data sample. 
+    list is the median of the given data.  If the number of values is even, 
-At least half of the observations are smaller than or equal to median 
+    median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list.
-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
+    If period = 0 => period is max
-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> */
-
-using System;
-namespace QuanTAlib;
 
 public class MED_Series : Single_TSeries_Indicator
 {

Source/Statistics/MSE_Series.cs

@@ -1,17 +1,14 @@
-/**
+namespace QuanTAlib;
-MSE: Mean Square Error 
+using System;
 
-Defined as a Mean (Average) of the Square of the difference between actual and estimated values.
+/* <summary>
+MSE: Mean Square Error 
+    Defined as a Mean (Average) of the Square of the difference between actual and estimated values.
 
 Sources:
   https://en.wikipedia.org/wiki/Mean_squared_error
 
-Remark:
+</summary> */
-    
-**/
-
-using System;
-namespace QuanTAlib;
 
 public class MSE_Series : Single_TSeries_Indicator
 {

Source/Statistics/PSDEV_Series.cs

@@ -1,8 +1,10 @@
-/**
+namespace QuanTAlib;
-PSDEV: Population Standard Deviation
+using System;
 
-Population Standard Deviation is the square root of the biased variance, also knons as
+/* <summary>
-Uncorrected Sample Standard Deviation
+PSDEV: Population Standard Deviation
+  Population Standard Deviation is the square root of the biased variance, also knons as
+  Uncorrected Sample Standard Deviation
 
 Sources:
   https://en.wikipedia.org/wiki/Standard_deviation#Uncorrected_sample_standard_deviation
@@ -11,10 +13,7 @@ Remark:
   PSDEV (Population Standard Deviation) is also known as a biased/uncorrected Standard Deviation.
   For unbiased version that uses Bessel's correction, use SDEV instead.
     
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class PSDEV_Series : Single_TSeries_Indicator
 {

Source/Statistics/PVAR_Series.cs

@@ -1,7 +1,9 @@
-/**
+namespace QuanTAlib;
-PVAR: Population Variance
+using System;
 
-Population variance....
+/* <summary>
+PVAR: Population Variance
+  Population variance without Bessel's correction
 
 Sources:
   https://en.wikipedia.org/wiki/Variance
@@ -11,10 +13,7 @@ Remark:
   PVAR (Population Variance) is also known as a biased Sample Variance. For unbiased
   sample variance use SVAR instead.
     
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class PVAR_Series : Single_TSeries_Indicator
 {

Source/Statistics/SDEV_Series.cs

@@ -1,21 +1,19 @@
-/**
+namespace QuanTAlib;
-SDEV: (Corrected) Sample Standard Deviation
+using System;
 
-Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance.
+/* <summary>
+SDEV: (Corrected) Sample Standard Deviation
+  Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance.
 
 Sources:
   https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation
   Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction
 
 Remark:
-
   SSDEV (Sample Standard Deviation) is also known as a unbiased/corrected Standard Deviation.
   For a population/biased/uncorrected Standard Deviation, use PSDEV instead
       
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class SDEV_Series : Single_TSeries_Indicator
 {

Source/Statistics/SMAPE_Series.cs

@@ -1,15 +1,14 @@
-/**
+namespace QuanTAlib;
-SMAPE: Symmetric Mean Absolute Percentage Error
+using System;
 
-Measures the size of the error in percentage terms
+/* <summary>
+SMAPE: Symmetric Mean Absolute Percentage Error
+    Measures the size of the error in percentage terms
 
 Sources:
   https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error
 
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class SMAPE_Series : Single_TSeries_Indicator
 {

Source/Statistics/VAR_Series.cs

@@ -1,7 +1,9 @@
-/**
+namespace QuanTAlib;
-VAR: Sample Variance
+using System;
 
-Sample variance uses Bessel's correction to correct the bias in the estimation of population variance.
+/* <summary>
+VAR: 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
@@ -11,10 +13,7 @@ Remark:
   VAR is also known as the Unbiased Sample Variance, while PVAR (Population Variance) is known as
   the Biased Sample Variance. 
     
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class VAR_Series : Single_TSeries_Indicator
 {

Source/Statistics/WMAPE_Series.cs

@@ -1,15 +1,14 @@
-/**
+namespace QuanTAlib;
-WMAPE: Weighted Mean Absolute Percentage Error
+using System;
 
-Measures the size of the error in percentage terms
+/* <summary>
+WMAPE: Weighted Mean Absolute Percentage Error
+    Measures the size of the error in percentage terms
 
 Sources:
   https://en.wikipedia.org/wiki/WMAPE
 
-**/
+</summary> */
-
-using System;
-namespace QuanTAlib;
 
 public class WMAPE_Series : Single_TSeries_Indicator
 {

Tests/Validations/Skender_Stock.cs

@@ -90,4 +90,22 @@ public class Skender_Stock
 		Assert.Equal(Math.Round((double)SK.Last().Mape!, 8), Math.Round(QL.Last().v, 8));
 	}
 
+	[Fact]
+	public void ATR()
+	{
+		ATR_Series QL = new(this.bars, this.period, false);
+		var SK = this.quotes.GetAtr(this.period);
+
+		Assert.Equal(Math.Round((double)SK.Last().Atr!, 8), Math.Round(QL.Last().v, 8));
+	}
+
+
+	[Fact]
+	public void ATRP()
+	{
+		ATRP_Series QL = new(this.bars, this.period, false);
+		var SK = this.quotes.GetAtr(this.period);
+
+		Assert.Equal(Math.Round((double)SK.Last().Atrp!, 8), Math.Round(QL.Last().v, 8));
+	}
 }

Tests/Validations/TA_LIB.cs

@@ -10,14 +10,22 @@ public class TA_LIB
 	private readonly Random rnd = new();
 	private readonly int period;
 	private readonly double[] TALIB;
-	private readonly double[] input;
+	private readonly double[] inopen;
+	private readonly double[] inhigh;
+	private readonly double[] inlow;
+	private readonly double[] inclose;
+	private readonly double[] involume;
 
 	public TA_LIB()
 	{
 		this.bars = new(1000);
 		this.period = this.rnd.Next(28) + 3;
 		this.TALIB = new double[this.bars.Count];
-		this.input = this.bars.Close.v.ToArray();
+		this.inopen = this.bars.Open.v.ToArray();
+		this.inhigh = this.bars.High.v.ToArray();
+		this.inlow = this.bars.Low.v.ToArray();
+		this.inclose = this.bars.Close.v.ToArray();
+		this.involume = this.bars.Volume.v.ToArray();
 	}
 
 /////////////////////////////////////////
@@ -26,7 +34,7 @@ public class TA_LIB
 	public void SMA()
 	{
 		SMA_Series QL = new(this.bars.Close, this.period, false);
-		Core.Sma(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Sma(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -35,7 +43,7 @@ public class TA_LIB
 	public void EMA()
 	{
 		EMA_Series QL = new(this.bars.Close, this.period, false);
-		Core.Ema(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Ema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -44,7 +52,7 @@ public class TA_LIB
 	public void WMA()
 	{
 		WMA_Series QL = new(this.bars.Close, this.period, false);
-		Core.Wma(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Wma(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -53,7 +61,7 @@ public class TA_LIB
 	public void DEMA()
 	{
 		DEMA_Series QL = new(this.bars.Close, this.period, false);
-		Core.Dema(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Dema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -62,7 +70,7 @@ public class TA_LIB
 	public void TEMA()
 	{
 		TEMA_Series QL = new(this.bars.Close, this.period, false);
-		Core.Tema(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Tema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -71,7 +79,7 @@ public class TA_LIB
 	public void MAX()
 	{
 		MAX_Series QL = new(this.bars.Close, this.period, false);
-		Core.Max(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Max(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
@@ -80,9 +88,17 @@ public class TA_LIB
 	public void MIN()
 	{
 		MIN_Series QL = new(this.bars.Close, this.period, false);
-		Core.Min(this.input, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+		Core.Min(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
 		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
 	}
 
+	[Fact]
+	public void ATR()
+	{
+		ATR_Series QL = new(this.bars, this.period, false);
+		Core.Atr(this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+	}
 }