Codacy cleanup

.github/workflows/main_automation.yml

@@ -103,6 +103,7 @@ jobs:
           --skip-duplicate
 
     - name: Push  package to nuget.org
+      if: ${{ github.ref == 'refs/heads/main' }}
       run: dotnet nuget push '.\Source\bin\Release\QuanTAlib.*.nupkg'
           --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }}
           --source https://api.nuget.org/v3/index.json

Source/Basics/MAX_Series.cs

@@ -23,7 +23,6 @@ public class MAX_Series : Single_TSeries_Indicator
         double _max = TValue.v;
         for (int i = 0; i < this._buffer.Count; i++)
         { 
-            //_max = (this._buffer[i] > _max) ? this._buffer[i] : _max; 
             _max = Math.Max(this._buffer[i], _max);
         }
 

Source/Basics/MIDPOINT_Series.cs

@@ -1,44 +1,44 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-MIDPOINT: Midpoint value (max+min)/2 in the given period in the series.
-    If period = 0 => period = full length of the series
-
-Sources:
-    https://thefaqblog.com/what-is-the-midpoint-in-statistics/
-
-</summary> */
-
-public class MIDPOINT_Series : Single_TSeries_Indicator
-{
-	public MIDPOINT_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((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 _max = TValue.v;
-		double _min = TValue.v;
-		for (int i = 0; i < this._buffer.Count; i++)
-		{
-			_max = Math.Max(this._buffer[i], _max);
-			_min = Math.Min(this._buffer[i], _min);
-		}
-		double _mid = (_max + _min) * 0.5;
-
-		var result = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _mid);
-
-		base.Add(result, update);
-	}
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+MIDPOINT: Midpoint value (max+min)/2 in the given period in the series.
+    If period = 0 => period = full length of the series
+
+Sources:
+    https://thefaqblog.com/what-is-the-midpoint-in-statistics/
+
+</summary> */
+
+public class MIDPOINT_Series : Single_TSeries_Indicator
+{
+	public MIDPOINT_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((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 _max = TValue.v;
+		double _min = TValue.v;
+		for (int i = 0; i < this._buffer.Count; i++)
+		{
+			_max = Math.Max(this._buffer[i], _max);
+			_min = Math.Min(this._buffer[i], _min);
+		}
+		double _mid = (_max + _min) * 0.5;
+
+		var result = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _mid);
+
+		base.Add(result, update);
+	}
 }

Source/Basics/MIDPRICE_Series.cs

@@ -1,50 +1,50 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-MIDPRICE: Midpoint price (highhest high + lowest low)/2 in the given period in the series.
-    If period = 0 => period = full length of the series
-
-</summary> */
-
-public class MIDPRICE_Series : Single_TBars_Indicator
-{
-	public MIDPRICE_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
-	{
-		if (base._bars.Count > 0)
-		{ base.Add(base._bars); }
-	}
-	private readonly System.Collections.Generic.List<double> _bufferhi = new();
-	private readonly System.Collections.Generic.List<double> _bufferlo = new();
-
-	public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
-	{
-		if (update)
-		{
-			this._bufferhi[this._bufferhi.Count - 1] = TBar.h;
-			this._bufferlo[this._bufferlo.Count - 1] = TBar.l;
-		}
-		else
-		{
-			this._bufferhi.Add(TBar.h);
-			this._bufferlo.Add(TBar.l);
-		}
-		if (this._bufferhi.Count > this._p && this._p != 0)
-		{ this._bufferhi.RemoveAt(0); }
-		if (this._bufferlo.Count > this._p && this._p != 0)
-		{ this._bufferlo.RemoveAt(0); }
-
-		double _max = TBar.h;
-		double _min = TBar.l;
-		for (int i = 0; i < this._bufferhi.Count; i++)
-		{
-			_max = Math.Max(this._bufferhi[i], _max);
-			_min = Math.Min(this._bufferlo[i], _min);
-		}
-		double _mid = (_max + _min) * 0.5;
-
-		var result = (TBar.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _mid);
-
-		base.Add(result, update);
-	}
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+MIDPRICE: Midpoint price (highhest high + lowest low)/2 in the given period in the series.
+    If period = 0 => period = full length of the series
+
+</summary> */
+
+public class MIDPRICE_Series : Single_TBars_Indicator
+{
+	public MIDPRICE_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
+	{
+		if (base._bars.Count > 0)
+		{ base.Add(base._bars); }
+	}
+	private readonly System.Collections.Generic.List<double> _bufferhi = new();
+	private readonly System.Collections.Generic.List<double> _bufferlo = new();
+
+	public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
+	{
+		if (update)
+		{
+			this._bufferhi[this._bufferhi.Count - 1] = TBar.h;
+			this._bufferlo[this._bufferlo.Count - 1] = TBar.l;
+		}
+		else
+		{
+			this._bufferhi.Add(TBar.h);
+			this._bufferlo.Add(TBar.l);
+		}
+		if (this._bufferhi.Count > this._p && this._p != 0)
+		{ this._bufferhi.RemoveAt(0); }
+		if (this._bufferlo.Count > this._p && this._p != 0)
+		{ this._bufferlo.RemoveAt(0); }
+
+		double _max = TBar.h;
+		double _min = TBar.l;
+		for (int i = 0; i < this._bufferhi.Count; i++)
+		{
+			_max = Math.Max(this._bufferhi[i], _max);
+			_min = Math.Min(this._bufferlo[i], _min);
+		}
+		double _mid = (_max + _min) * 0.5;
+
+		var result = (TBar.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _mid);
+
+		base.Add(result, update);
+	}
 }

Source/Basics/MIN_Series.cs

@@ -23,7 +23,6 @@ public class MIN_Series : Single_TSeries_Indicator
         double _min = TValue.v;
         for (int i = 0; i < this._buffer.Count; i++)
         { 
-            //_min = (this._buffer[i] < _min) ? this._buffer[i] : _min; 
             _min = Math.Min(this._buffer[i], _min);
         }
 

Source/Basics/SUM_Series.cs

@@ -1,35 +1,35 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-SUM: Cumulative Sum (aka Running Total)
-    SUM across a period provides a rolling sum of all values across the period.
-    If SUM values would be divided with period, the output would be SMA()
-
-Sources:
-    https://en.wikipedia.org/wiki/CUSUM
-
-</summary> */
-
-public class SUM_Series : Single_TSeries_Indicator
-{
-    public SUM_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) { _buffer[_buffer.Count - 1] = TValue.v; }
-        else { _buffer.Add(TValue.v); }
-        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
-
-        double _sum = 0;
-        for (int i = 0; i < _buffer.Count; i++) { _sum += _buffer[i]; }
-
-        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _sum);
-
-        base.Add(result, update);
-    }
-}
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+SUM: Cumulative Sum (aka Running Total)
+    SUM across a period provides a rolling sum of all values across the period.
+    If SUM values would be divided with period, the output would be SMA()
+
+Sources:
+    https://en.wikipedia.org/wiki/CUSUM
+
+</summary> */
+
+public class SUM_Series : Single_TSeries_Indicator
+{
+    public SUM_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) { _buffer[_buffer.Count - 1] = TValue.v; }
+        else { _buffer.Add(TValue.v); }
+        if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
+
+        double _sum = 0;
+        for (int i = 0; i < _buffer.Count; i++) { _sum += _buffer[i]; }
+
+        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _sum);
+
+        base.Add(result, update);
+    }
+}

Source/Feeds/Alphavantage_Feed.cs

@@ -8,8 +8,6 @@ Alphavantage - Free API to collect 100 recent daily quotes. It requires a (free)
     Parameters:
         Symbol: stock ("AAPL"),
         APIkey: unique Alphavantage API key
-    Usage:
-        Alphavantage_Feed ticker = new("MSFT", APIkey:"xxxxxxx");
 
 </summary> */
 
@@ -47,9 +45,10 @@ public class Alphavantage_Feed : TBars
                 case "3b. low (USD)": l = Convert.ToDouble(val.Value.ToString()); break;
                 case "4. close": c = Convert.ToDouble(val.Value.ToString()); break;
                 case "4b. close (USD)": c = Convert.ToDouble(val.Value.ToString()); break;
-                //case "5. adjusted close": c = Convert.ToDouble(val.Value.ToString()); break;
+                case "5. adjusted close": c = Convert.ToDouble(val.Value.ToString()); break;
                 case "5. volume": v = Convert.ToDouble(val.Value.ToString()); break;
                 case "6. volume": v = Convert.ToDouble(val.Value.ToString()); break;
+                default: o = 0; h = 0; l = 0; c = 0; v = 0; break;
             }
         }
         return (date, o, h, l, c, v);

Source/Feeds/Yahoo_Feed.cs

@@ -8,16 +8,14 @@ Yahoo Finance - Free API feed to collect daily market quotes
         Symbol: stock symbol (default: "IBM")
         Period: number of days of collected history (default: 252)
     Usage:
-        Yahoo_Feed ticker = new("MSFT", 20);
+        Yahoo_Feed ticker = new("MSFT", 20)
     
 </summary> */
 
 public class Yahoo_Feed : TBars
 {
-    private static string requestUrl;
-
     public Yahoo_Feed(string Symbol = "IBM", int Period =  252) {
-        requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/"+
+        string requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/"+
             Symbol+"?interval=1d&period1="+
             (int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period+1)).ToUnixTimeSeconds()+"&period2="+
             (int)new DateTimeOffset(DateTime.UtcNow).ToUnixTimeSeconds();

Source/Statistics/LINREG_Series.cs

@@ -71,11 +71,11 @@ public class LINREG_Series : Single_TSeries_Indicator
         double _intercept = avgY - (_slope * avgX);
 
         // calculate Standard Deviation and R-Squared
-        double stdDevX = Math.Sqrt((double)sumSqX / _len);
-        double stdDevY = Math.Sqrt((double)sumSqY / _len);
+        double stdDevX = Math.Sqrt(sumSqX / _len);
+        double stdDevY = Math.Sqrt(sumSqY / _len);
         double _StdDev = stdDevY;
 
-        double arrr = (stdDevX * stdDevY != 0) ? (double)sumSqXY / (stdDevX * stdDevY) / _len : 0;
+        double arrr = (stdDevX * stdDevY != 0) ? sumSqXY / (stdDevX * stdDevY) / _len : 0;
         double _RSquared = arrr * arrr;
 
         var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _slope);

Source/Volume/OBV_Series.cs

@@ -47,9 +47,6 @@ public class OBV_Series : Single_TBars_Indicator
     if (TBar.c > this._lastclose) { _obv += TBar.v; }
     if (TBar.c < this._lastclose) { _obv -= TBar.v; }
 
-    // Unclear what the first value in OBV series is - currently set to volume[0]
-    // if (this.Count == 0) { _obv = 0; }
-
     this._lastlastobv = this._lastobv;
     this._lastobv = _obv;
 

Tests/Validations/Pandas_TA.cs

@@ -7,10 +7,10 @@ using Python.Included;
 namespace Validations;
 public class PandasTA : IDisposable
 {
-  private GBM_Feed bars;
-  private Random rnd = new();
-  private int period;
-  private string OStype;
+  private readonly GBM_Feed bars;
+  private readonly Random rnd = new();
+  private readonly int period;
+  private readonly string OStype;
   private dynamic np;
   private dynamic ta;
   private dynamic df;
@@ -23,14 +23,19 @@ public class PandasTA : IDisposable
     // Checking the host OS and setting PythonDLL accordingly
     OStype = Environment.OSVersion.ToString();
     if (OStype == "Unix 13.1.0")
-      OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
-    else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
+    {
+	    OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
+    }
+    else
+    {
+	    OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
+    }
 
     Installer.InstallPath = Path.GetFullPath(".");
     Installer.SetupPython().Wait();
     Installer.TryInstallPip();
     Installer.PipInstallModule("pandas-ta");
-    //Installer.PipInstallModule("git+https://github.com/twopirllc/pandas-ta@development");
+    //alternative: git+https://github.com/twopirllc/pandas-ta
 
     Runtime.PythonDLL = OStype;
     PythonEngine.Initialize();
@@ -74,35 +79,98 @@ public class PandasTA : IDisposable
   {
     var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
     Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(bars.OHLC4.Last().v, 7));
-  }
-
-  [Fact]
+  }
+
+	[Fact]
+	void MEDIAN()
+	{
+		MED_Series QL = new(bars.Close, period);
+		var pta = df.ta.median(close: df.close, length: period);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void VARIANCE()
+	{
+		VAR_Series QL = new(bars.Close, period);
+		var pta = df.ta.variance(close: df.close, length: period, ddof:0);
+		Assert.Equal(Math.Round((double)pta.tail(1), 5), Math.Round(QL.Last().v, 5));
+	}
+
+	[Fact]
+	void SVARIANCE()
+	{
+		SVAR_Series QL = new(bars.Close, period);
+		var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
+		Assert.Equal(Math.Round((double)pta.tail(1), 5), Math.Round(QL.Last().v, 5));
+	}
+
+	[Fact]
+	void ADL()
+	{
+		ADL_Series QL = new(bars);
+		var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void ADOSC()
+	{
+		ADOSC_Series QL = new(bars);
+		var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void TR()
+	{
+		TR_Series QL = new(bars);
+		var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void ATR()
+	{
+		ATR_Series QL = new(bars, period);
+		var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void RSI()
+	{
+		RSI_Series QL = new(bars.Close, period);
+		var pta = df.ta.rsi(close: df.close, length: period);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void TRIMA()
+	{
+    //TODO: return length to variable length (period) when Pandas-TA fixes trima
+		TRIMA_Series QL = new(bars.Close, 11);
+		var pta = df.ta.trima(close: df.close, length: 11);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
   void KAMA()
   {
     KAMA_Series QL = new(bars.Close, period);
     var pta = df.ta.kama(close: df.close, length: period);
     Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
-  }
-
-  /*
-  [Fact]
-  void ALMA()
-  {
-    ALMA_Series QL = new(bars.Close, period: period, offset: 0.85, sigma: 6.0, false);
-    var pta = df.ta.alma(close: df.close, length: period, distribution_offset: 0.85, sigma: 6.0);
-    Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
-  }
-  */
-
-  [Fact]
+  }
+
+	[Fact]
   void HMA()
   {
     HMA_Series QL = new(bars.Close, period, false);
     var pta = df.ta.hma(close: df.close, length: period);
     Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
-  }
-
-  [Fact]
+  }
+
+	[Fact]
   void SMA()
   {
     SMA_Series QL = new(bars.Close, period, false);
@@ -140,9 +208,25 @@ public class PandasTA : IDisposable
     WMA_Series QL = new(bars.Close, period, false);
     var pta = df.ta.wma(close: df.close, length: period);
     Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
-  }
-
-  [Fact]
+  }
+
+	[Fact]
+	void RMA()
+	{
+		RMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.rma(close: df.close, length: period);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
+	void ZLEMA()
+	{
+		ZLEMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.zlma(close: df.close, length: period);
+		Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
+	}
+
+	[Fact]
   void DEMA()
   {
     DEMA_Series QL = new(bars.Close, period, false);

Tests/Validations/Skender_Stock.cs

@@ -27,7 +27,7 @@ public class Skender_Stock
             });
     }
 
-    [Fact]
+	[Fact]
     public void SMA()
     {
         SMA_Series QL = new(bars.Close, period, false);
@@ -196,7 +196,7 @@ public class Skender_Stock
         Assert.Equal(Math.Round((double)SK.Last().Rsi!, 6), Math.Round(QL.Last().v, 6));
     }
 
-    [Fact]
+	[Fact]
     public void ALMA()
     {
         ALMA_Series QL = new(bars.Close, period, useNaN: false);

Tests/Validations/TA_LIB.cs

@@ -102,6 +102,16 @@ public class TA_LIB
 	    Assert.Equal(Math.Round(TALIB[TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
     }
 
+
+	[Fact]
+	public void VAR()
+	{
+		VAR_Series QL = new(bars.Close, period, false);
+		Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
+
+		Assert.Equal(Math.Round(TALIB[TALIB.Length - outBegIdx - 1], 5, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 5));
+	}
+
 	[Fact]
     public void MIDPOINT()
     {

docs/readme.md

@@ -35,158 +35,158 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 
 ⛔= Not implemented (yet)
 
-| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-|--|:--:|:--:|:--:|
-| ✔️ OC2 - (Open+Close)/2 |️ `.OC2` || ️GetBaseQuote |
-| ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | ️GetBaseQuote |
-| ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE ||
-| ✔️ OHL3 - (Open+High+Low)/3 | `.OHL3` |||
-| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ GetBaseQuote |
-| ⭐ HLCC4 - Weighted Price |  `.HLCC4` | WCLPRICE ||
-| ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT ||
-| ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE ||
-| ⭐ MAX - Max value | `MAX_Series` | MAX ||
-| ⭐ MIN - Min value | `MIN_Series` | MIN ||
-| ⭐ SUM - Summation | `SUM_Series` | SUM ||
-| ⭐ ADD - Addition | `ADD_Series` | ADD ||
-| ⭐ SUB - Subtraction | `SUB_Series` | SUB ||
-| ⭐ MUL - Multiplication | `MUL_Series` | MUL ||
-| ⭐ DIV - Division | `DIV_Series` | DIV ||
+| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+|--|:--:|:--:|:--:|:--:|
+| ⭐ OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
+| ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 ||
+| ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 ||
+| ⭐ OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
+| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 ||
+| ⭐ HLCC4 - Weighted Price |  `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
+| ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT |||
+| ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE |||
+| ⭐ MAX - Max value | `MAX_Series` | MAX |||
+| ⭐ MIN - Min value | `MIN_Series` | MIN |||
+| ⭐ SUM - Summation | `SUM_Series` | SUM |||
+| ⭐ ADD - Addition | `ADD_Series` | ADD |||
+| ⭐ SUB - Subtraction | `SUB_Series` | SUB |||
+| ⭐ MUL - Multiplication | `MUL_Series` | MUL |||
+| ⭐ DIV - Division | `DIV_Series` | DIV |||
 |||||
-| **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ✔️ BIAS - Bias | `BIAS_Series` |||
-| ⛔ CORREL - Pearson's Correlation Coefficient || CORREL | GetCorrelation |
-| ⛔ COVAR - Covariance ||| GetCorrelation |
-| ✔️ ENTP - Entropy | `ENTP_Series` |||
-| ✔️ KURT - Kurtosis | `KURT_Series` |||
-| ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope |
-| ⭐ MAD - Mean Absolute Deviation | `MAD_Series` || GetSma |
-| ⭐ MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma |
-| ✔️ MED - Median value | `MED_Series` |||
-| ✔️ MSE - Mean Squared Error | `MSE_Series` || GetSma |
-| ⛔ SKEW - Skewness ||||
-| ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV ||
-| ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` |||
-| ✔️ SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` |||
-| ✔️ VAR - Population Variance | `VAR_Series` | VAR ||
-| ✔️ SVAR - Sample Variance | `SVAR_Series` |||
-| ⛔ QUANT - Quantile ||||
-| ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` |||
-| ⛔ ZSCORE - Number of standard deviations from mean ||||
-|||||
-| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average ||||
-| ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma |
-| ⛔ ARIMA - Autoregressive Integrated Moving Average ||||
-| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema |
-| ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma |
-| ⛔ EPMA - Endpoint Moving Average ||| GetEpma |
-| ⛔ FRAMA - Fractal Adaptive Moving Average ||||
-| ⛔ FWMA - Fibonacci's Weighted Moving Average ||||
-| ⛔ HILO - Gann High-Low Activator ||||
-| ✔️ HEMA - Hull/EMA Average | `HEMA_Series` |||
-| ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline |
-| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma |
-| ⛔ HWMA - Holt-Winter Moving Average ||||
-| ✔️ JMA - Jurik Moving Average | `JMA_Series` |||
-| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama |
-| ⛔ KDJ - KDJ Indicator (trend reversal) ||||
-| ⛔ LSMA - Least Squares Moving Average ||||
-| ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd |
-| ⛔ MAMA - MESA Adaptive Moving Average || MAMA | GetMama |
-| ⛔ MCGD - McGinley Dynamic ||||
-| ⛔ MMA - Modified Moving Average ||||
-| ⛔ PPMA - Pivot Point Moving Average ||||
-| ⛔ PWMA - Pascal's Weighted Moving Average ||||
-| ✔️ RMA - WildeR's Moving Average | `RMA_Series` |||
-| ⛔ SINWMA - Sine Weighted Moving Average ||||
-| ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma |
-| ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` |||
-| ⛔ SSF - Ehler's Super Smoother Filter ||||
-| ⛔ SUP - Supertrend ||||
-| ⛔ SWMA - Symmetric Weighted Moving Average ||||
-| ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 |
-| ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema |
-| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA ||
-| ⛔ TSF - Time Series Forecast || TSF ||
-| ⛔ VIDYA - Variable Index Dynamic Average ||||
-| ⛔ VOR - Vortex Indicator ||||
-| ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma |
-| ✔️ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` |||
-|||||
-| **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl |
-| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl |
-| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr |
-| ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr |
-| ⛔ BETA - Beta coefficient || BETA | GetBeta |
-| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands |
-| ⛔ CHAND - Chandelier Exit ||| GetChandelier |
-| ⛔ CRSI - Connor RSI ||| GetConnorsRsi |
-| ⛔ DON - Donchian Channels ||| GetDonchian |
-| ⛔ FCB - Fractal Chaos Bands ||| GetFcb |
-| ⛔ HV - Historical Volatility ||||
-| ⛔ ICH - Ichimoku ||| GetIchimoku |
-| ⛔ KEL - Keltner Channels ||| GetKeltner |
-| ⛔ NATR - Normalized Average True Range || NATR | GetAtr |
-| ⛔ CHN - Price Channel Indicator ||||
-| ⭐ RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi |
-| ⛔ SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar |
-| ⛔ SRSI - Stochastic RSI || STOCHRSI | GetStochRsi |
-| ⛔ STARC - Starc Bands ||||
-| ⭐ TR - True Range | `TR_Series` | TRANGE | GetTr |
-| ⛔ UI - Ulcer Index ||||
-| ⛔ VSTOP - Volatility Stop ||||
-|||||
-| **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ⛔ AC - Acceleration Oscillator ||||
-| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx |
-| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx |
-| ⛔ AO - Awesome Oscillator ||| GetAwesome |
-| ⛔ APO - Absolute Price Oscillator || APO ||
-| ⛔ AROON - Aroon oscillator || AROON | GetAroon |
-| ⛔ BOP - Balance of Power || BOP | GetBop |
-| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci |
-| ⛔ CFO - Chande Forcast Oscillator ||||
-| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo |
-| ⛔ COG - Center of Gravity ||||
-| ⛔ COPPOCK - Coppock Curve ||||
-| ⛔ CTI - Ehler's Correlation Trend Indicator ||||
-| ⛔ DPO - Detrended Price Oscillator ||| GetDpo |
-| ⛔ DMI - Directional Movement Index || DX | GetAdx |
-| ⛔ EFI - Elder Ray's Force Index ||| GetElderRay |
-| ⛔ GAT - Alligator oscillator ||| GetGator |
-| ⛔ HURST - Hurst Exponent ||| GetHurst |
-| ⛔ KRI - Kairi Relative Index ||||
-| ⛔ KVO - Klinger Volume Oscillator ||||
-| ⛔ MFI - Money Flow Index || MFI | GetMfi |
-| ⛔ MOM - Momentum || MOM ||
-| ⛔ NVI - Negative Volume Index ||||
-| ⛔ PO - Price Oscillator ||||
-| ⛔ PPO - Percentage Price Oscillator || PPO ||
-| ⛔ PMO - Price Momentum Oscillator ||||
-| ⛔ PVI - Positive Volume Index ||||
-| ⛔ ROC - Rate of Change || MOM | GetRoc |
-| ⛔ RVGI - Relative Vigor Index ||||
-| ⛔ SMI - Stochastic Momentum Index ||||
-| ⛔ STC - Schaff Trend Cycle ||||
-| ⛔ STOCH - Stochastic Oscillator || STOCH | GetStoch |
-| ⛔ TRIX - 1-day ROC of TEMA || TRIX | GetTrix |
-| ⛔ TSI - True Strength Index ||||
-| ⛔ UO - Ultimate Oscillator || ULTOSC | GetUltimate |
-| ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR |
-| ⛔ WGAT - Williams Alligator ||||
-|||||
-| **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ⛔ AOBV - Archer On-Balance Volume ||||
-| ⛔ CMF - Chaikin Money Flow ||||
-| ⛔ EOM - Ease of Movement ||||
-| ⭐ OBV - On-Balance Volume | ` OBV_Series` | OBV | GetObv |
-| ⛔ PRS - Price Relative Strength |||
-| ⛔ PVOL - Price-Volume ||||
-| ⛔ PVO - Percentage Volume Oscillator ||||
-| ⛔ PVR - Price Volume Rank ||||
-| ⛔ PVT - Price Volume Trend ||||
-| ⛔ VP - Volume Profile ||||
-| ⛔ VWAP - Volume Weighted Average Price ||||
-| ⛔ VWMA - Volume Weighted Moving Average ||||
+| **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| ⭐ BIAS - Bias | `BIAS_Series` ||| bias |
+| ⛔ CORREL - Pearson's Correlation Coefficient || CORREL | GetCorrelation ||
+| ⛔ COVAR - Covariance ||| GetCorrelation ||
+| ⭐ ENTP - Entropy | `ENTP_Series` ||| entropy |
+| ⭐ KURT - Kurtosis | `KURT_Series` ||| kurtosis |
+| ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
+| ⭐ MAD - Mean Absolute Deviation | `MAD_Series` || GetSma | mad |
+| ⭐ MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma ||
+| ⭐ MED - Median value | `MED_Series` ||| median |
+| ✔️ MSE - Mean Squared Error | `MSE_Series` || GetSma ||
+| ⛔ SKEW - Skewness |||||
+| ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV |||
+| ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` ||||
+| ✔️ SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` ||||
+| ⭐ VAR - Population Variance | `VAR_Series` | VAR || variance |
+| ⭐ SVAR - Sample Variance | `SVAR_Series` ||| variance |
+| ⛔ QUANT - Quantile |||||
+| ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
+| ⛔ ZSCORE - Number of standard deviations from mean |||||
+||||||
+| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
+| ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma ||
+| ⛔ ARIMA - Autoregressive Integrated Moving Average |||||
+| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema |
+| ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma | ema |
+| ⛔ EPMA - Endpoint Moving Average ||| GetEpma ||
+| ⛔ FRAMA - Fractal Adaptive Moving Average |||||
+| ⛔ FWMA - Fibonacci's Weighted Moving Average |||||
+| ⛔ HILO - Gann High-Low Activator |||||
+| ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||||
+| ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline ||
+| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma | hma |
+| ⛔ HWMA - Holt-Winter Moving Average |||||
+| ✔️ JMA - Jurik Moving Average | `JMA_Series` ||||
+| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama |
+| ⛔ KDJ - KDJ Indicator (trend reversal) |||||
+| ⛔ LSMA - Least Squares Moving Average |||||
+| ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd ||
+| ⛔ MAMA - MESA Adaptive Moving Average || MAMA | GetMama ||
+| ⛔ MCGD - McGinley Dynamic |||||
+| ⛔ MMA - Modified Moving Average |||||
+| ⛔ PPMA - Pivot Point Moving Average |||||
+| ⛔ PWMA - Pascal's Weighted Moving Average |||||
+| ⭐ RMA - WildeR's Moving Average | `RMA_Series` ||| rma |
+| ⛔ SINWMA - Sine Weighted Moving Average |||||
+| ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma | sma |
+| ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` || GetSmma ||
+| ⛔ SSF - Ehler's Super Smoother Filter |||||
+| ⛔ SUP - Supertrend |||||
+| ⛔ SWMA - Symmetric Weighted Moving Average |||||
+| ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 ||
+| ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | tema |
+| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA |||
+| ⛔ TSF - Time Series Forecast || TSF |||
+| ⛔ VIDYA - Variable Index Dynamic Average |||||
+| ⛔ VOR - Vortex Indicator |||||
+| ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | wma |
+| ⭐ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| zlma |
+||||||
+| **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad |
+| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc |
+| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr |
+| ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
+| ⛔ BETA - Beta coefficient || BETA | GetBeta ||
+| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands ||
+| ⛔ CHAND - Chandelier Exit ||| GetChandelier ||
+| ⛔ CRSI - Connor RSI ||| GetConnorsRsi ||
+| ⛔ DON - Donchian Channels ||| GetDonchian ||
+| ⛔ FCB - Fractal Chaos Bands ||| GetFcb ||
+| ⛔ HV - Historical Volatility |||||
+| ⛔ ICH - Ichimoku ||| GetIchimoku ||
+| ⛔ KEL - Keltner Channels ||| GetKeltner ||
+| ⛔ NATR - Normalized Average True Range || NATR | GetAtr ||
+| ⛔ CHN - Price Channel Indicator |||||
+| ⭐ RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi | rsi |
+| ⛔ SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar ||
+| ⛔ SRSI - Stochastic RSI || STOCHRSI | GetStochRsi ||
+| ⛔ STARC - Starc Bands |||||
+| ⭐ TR - True Range | `TR_Series` | TRANGE | GetTr | true_range |
+| ⛔ UI - Ulcer Index |||||
+| ⛔ VSTOP - Volatility Stop |||||
+||||||
+| **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| ⛔ AC - Acceleration Oscillator |||||
+| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx ||
+| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx ||
+| ⛔ AO - Awesome Oscillator ||| GetAwesome ||
+| ⛔ APO - Absolute Price Oscillator || APO |||
+| ⛔ AROON - Aroon oscillator || AROON | GetAroon ||
+| ⛔ BOP - Balance of Power || BOP | GetBop ||
+| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci ||
+| ⛔ CFO - Chande Forcast Oscillator |||||
+| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo ||
+| ⛔ COG - Center of Gravity |||||
+| ⛔ COPPOCK - Coppock Curve |||||
+| ⛔ CTI - Ehler's Correlation Trend Indicator |||||
+| ⛔ DPO - Detrended Price Oscillator ||| GetDpo ||
+| ⛔ DMI - Directional Movement Index || DX | GetAdx ||
+| ⛔ EFI - Elder Ray's Force Index ||| GetElderRay ||
+| ⛔ GAT - Alligator oscillator ||| GetGator ||
+| ⛔ HURST - Hurst Exponent ||| GetHurst ||
+| ⛔ KRI - Kairi Relative Index |||||
+| ⛔ KVO - Klinger Volume Oscillator |||||
+| ⛔ MFI - Money Flow Index || MFI | GetMfi ||
+| ⛔ MOM - Momentum || MOM |||
+| ⛔ NVI - Negative Volume Index |||||
+| ⛔ PO - Price Oscillator |||||
+| ⛔ PPO - Percentage Price Oscillator || PPO |||
+| ⛔ PMO - Price Momentum Oscillator |||||
+| ⛔ PVI - Positive Volume Index |||||
+| ⛔ ROC - Rate of Change || MOM | GetRoc ||
+| ⛔ RVGI - Relative Vigor Index |||||
+| ⛔ SMI - Stochastic Momentum Index |||||
+| ⛔ STC - Schaff Trend Cycle |||||
+| ⛔ STOCH - Stochastic Oscillator || STOCH | GetStoch ||
+| ⛔ TRIX - 1-day ROC of TEMA || TRIX | GetTrix ||
+| ⛔ TSI - True Strength Index |||||
+| ⛔ UO - Ultimate Oscillator || ULTOSC | GetUltimate ||
+| ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
+| ⛔ WGAT - Williams Alligator |||||
+||||||
+| **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| ⛔ AOBV - Archer On-Balance Volume |||||
+| ⛔ CMF - Chaikin Money Flow |||||
+| ⛔ EOM - Ease of Movement |||||
+| ⭐ OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
+| ⛔ PRS - Price Relative Strength ||||
+| ⛔ PVOL - Price-Volume |||||
+| ⛔ PVO - Percentage Volume Oscillator |||||
+| ⛔ PVR - Price Volume Rank |||||
+| ⛔ PVT - Price Volume Trend |||||
+| ⛔ VP - Volume Profile |||||
+| ⛔ VWAP - Volume Weighted Average Price |||||
+| ⛔ VWMA - Volume Weighted Moving Average |||||