refactoring

.gitignore

@@ -354,3 +354,4 @@ MigrationBackup/
 # Ionide (cross platform F# VS Code tools) working folder
 .ionide/
 dotCover.Output.dcvr
+/Tests/GlobalSuppressions.cs

Quantower/Quantower.csproj

@@ -34,9 +34,11 @@
       <Link>QuanTAlib\%(RecursiveDir)%(Filename)%(Extension)</Link>
     </Compile>
   </ItemGroup>
+  <!--
   <Target Name="CopyCustomContent" AfterTargets="AfterBuild">
     <Copy SourceFiles=".\bin\$(Configuration)\net48\Quantower_QTAlib.dll" DestinationFolder="\Quantower\Settings\Scripts\Indicators\QuanTAlib" />
   </Target>
+  -->
   <ItemGroup>
     <AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
   </ItemGroup>

Source/Basics/Single_TSeries_Abstract.cs

@@ -1,6 +1,7 @@
 namespace QuanTAlib;
 using System;
 using System.Collections.Generic;
+using System.Linq;
 
 /* <summary>
 Abstract classes with all scaffolding required to build indicators.
@@ -17,50 +18,54 @@ Abstract classes with all scaffolding required to build indicators.
 </summary> */
 public abstract class Single_TSeries_Indicator : TSeries
 {
-    protected readonly int _period;
+  protected readonly int _period;
-    protected readonly bool _NaN;
+  protected readonly bool _NaN;
-    protected readonly TSeries _data;
+  protected readonly TSeries _data;
-    protected int _p;
+  protected int _p;
 
-    // Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
+  // Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
-    protected Single_TSeries_Indicator(TSeries source, int period, bool useNaN)
+  protected Single_TSeries_Indicator(TSeries source, int period, bool useNaN)
+  {
+    this._data = source;
+    this._period = period;
+    this._p = _period;
+    this._NaN = useNaN;
+    this._data.Pub += this.Sub;
+  }
+
+  // overridable Add() method to add/update a single item at the end of the list
+
+  public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
+  {
+    if (_period == 0) { _p = this.Length; }
+    var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
+    base.Add(res, update);
+  }
+  public new virtual void Add((System.DateTime t, double v) TValue, bool update) => base.Add(TValue, update);
+
+  // potentially overridable Add() method for the whole series (could be replaced with faster bulk algo)
+  public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { this.Add(TValue: data[i], update: false); } }
+
+  public new void Add((System.DateTime t, double v) TValue) => this.Add(TValue: TValue, update: false);
+  public void Add(bool update) => this.Add(TValue: this._data[this._data.Count - 1], update: update);
+  public void Add() => this.Add(TValue: this._data[this._data.Count - 1], update: false);
+  public new void Sub(object source, TSeriesEventArgs e) => this.Add(TValue: this._data[this._data.Count - 1], update: e.update);
+
+  protected static void Add_Replace(List<double> l, double v, bool update)
+  {
+    if (update)
+    { l[l.Count - 1] = v; }
+    else
+    { l.Add(v); }
+  }
+  protected static double Add_Replace_Trim(List<double> l, double v, int p, bool update)
+  {
+    Add_Replace(l, v, update);
+    double ret = (l.Count > 0) ? l.First() : 0;
+    if (l.Count > p && p != 0)
     {
-        this._data = source;
+      l.RemoveAt(0);
-        this._period = period;
-        this._p = _period;
-        this._NaN = useNaN;
-        this._data.Pub += this.Sub;
-    }
-
-    // overridable Add() method to add/update a single item at the end of the list
-    
-    public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
-    {
-        if (_period == 0) { _p = this.Length; }
-        var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
-        base.Add(res, update);
-    }
-    public new virtual void Add((System.DateTime t, double v) TValue, bool update) => base.Add(TValue, update);
-
-    // potentially overridable Add() method for the whole series (could be replaced with faster bulk algo)
-    public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { this.Add(TValue: data[i], update: false); }}
-
-    public new void Add((System.DateTime t, double v) TValue) => this.Add(TValue: TValue, update: false);
-    public void Add(bool update) => this.Add(TValue: this._data[this._data.Count - 1], update: update);
-    public void Add() => this.Add(TValue: this._data[this._data.Count - 1], update: false);
-    public new void Sub(object source, TSeriesEventArgs e) => this.Add(TValue: this._data[this._data.Count - 1], update: e.update);
-
-    protected static void Add_Replace(List<double> l, double v, bool update)
-    {
-        if (update)
-        { l[l.Count - 1] = v; }
-        else
-        { l.Add(v); }
-    }
-    protected static void Add_Replace_Trim(List<double> l, double v, int p, bool update)
-    {
-        Add_Replace(l, v, update);
-        if (l.Count > p && p!=0)
-        { l.RemoveAt(0); }
     }
+    return ret;
+  }
 }

Source/Trends/EMA_Series.cs

@@ -25,12 +25,14 @@ public class EMA_Series : Single_TSeries_Indicator
     private readonly System.Collections.Generic.List<double> _buffer = new();
     private readonly double _k, _k1m;
     private double _lastema, _lastlastema;
+    private bool _useSMA;
 
-    public EMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
+    public EMA_Series(TSeries source, int period, bool useNaN = false, bool useSMA = true) : base(source, period, useNaN)
     {
         this._k = 2.0 / (this._p + 1);
         this._k1m = 1.0 - this._k;
-        this._lastema = this._lastlastema = double.NaN;
+        this._lastema = this._lastlastema = 0;
+        _useSMA = useSMA;
         if (this._data.Count > 0) { base.Add(this._data); }
     }
 
@@ -38,8 +40,9 @@ public class EMA_Series : Single_TSeries_Indicator
     {
         double _ema;
         if (update) { this._lastema = this._lastlastema; }
+        if (this.Count == 0) { _lastema = TValue.v; }
 
-        if (this.Count < this._p)
+        if (this.Count < this._p && _useSMA)
         {
             Add_Replace(_buffer, TValue.v, update);
             _ema = 0;

Source/Trends/SMA_Series.cs

@@ -1,6 +1,5 @@
 namespace QuanTAlib;
 using System;
-using System.Linq;
 
 /* <summary>
 SMA: Simple Moving Average
@@ -19,19 +18,45 @@ Remark:
 
 public class SMA_Series : Single_TSeries_Indicator
 {
-    public SMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
+	private readonly System.Collections.Generic.List<double> _buffer = new();
-    {
+	private double _sma, _oldsma;
-        if (base._data.Count > 0) { base.Add(base._data); }
+	private double _topv, _oldtopv;
-    }
+	public SMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
-    private readonly System.Collections.Generic.List<double> _buffer = new();
+	{
+		if (base._data.Count > 0)
+		{ base.Add(base._data); }
+	}
+	public override void Add((System.DateTime t, double v) TValue, bool update)
+	{
+		_topv = Add_Replace_Trim(_buffer, TValue.v, _p, update);
 
-    public override void Add((System.DateTime t, double v) TValue, bool update)
+		// rolling back if update, storing data for potential future update
-    {
+		if (update)
-        Add_Replace_Trim(_buffer, TValue.v, _p, update);
+		{
-        double _sma = 0;
+			_sma = _oldsma;
-        for (int i=0; i<_buffer.Count; i++) { _sma+= _buffer[i]; }
+			_topv = _oldtopv;
-        _sma /= _buffer.Count;
+		}
+		else
+		{
+			_oldsma = _sma;
+			_oldtopv = _topv;
+		}
 
-        base.Add((TValue.t, _sma), update, _NaN);
+		// main additive calculation of SMA - for data points that are larger than _p period
-    }
+		// this.Count > _p
-}
+		if (this.Count > _p)
+		{
+			_sma += (TValue.v - _topv) / _p;
+		}
+		else
+		{
+			// calculate SMA the traditional way (sum all, divide with _p) for data points within _p period
+			_sma = 0;
+			for (int i = 0; i < _buffer.Count; i++)
+			{ _sma += _buffer[i]; }
+			_sma /= _buffer.Count;
+		}
+
+		base.Add((TValue.t, _sma), update, _NaN);
+	}
+}

Source/Volatility/ADOSC_Series.cs

@@ -20,10 +20,10 @@ public class ADOSC_Series : Single_TBars_Indicator
     private double _lastema1, _lastlastema1, _lastema2, _lastlastema2;
     private double _lastadl, _lastlastadl;
 
-    public ADOSC_Series(TBars source, bool useNaN = false) : base(source, period: 0, useNaN)
+	public ADOSC_Series(TBars source, int shortPeriod = 3, int longPeriod =10, bool useNaN = false) : base(source, period: 0, useNaN)
 	{
-        _k1 = 2.0 / (3 + 1);
+        _k1 = 2.0 / (shortPeriod + 1);
-        _k2 = 2.0 / (10 + 1);
+        _k2 = 2.0 / (longPeriod + 1);
         _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0;
         if (_bars.Count > 0) { base.Add(_bars); }
     }

Tests/Tests.csproj

@@ -19,6 +19,8 @@
     <PackageReference Include="TALib.NETCore" Version="0.4.4" />
     <PackageReference Include="Skender.Stock.Indicators" Version="2.4.0" />
     <PackageReference Include="pythonnet" Version="3.0.1" />
+    <PackageReference Include="Tulip.NETCore" Version="0.8.0.1" />
+    <PackageReference Include="System.Text.Json" Version="7.0.0" />
   </ItemGroup>
 
   <ItemGroup>
@@ -28,5 +30,7 @@
   <ItemGroup>
     <None Remove="Python.Included" />
     <None Remove="pythonnet" />
+    <None Remove="Tulip.NETCore" />
+    <None Remove="System.Text.Json" />
   </ItemGroup>
 </Project>

Tests/Validations/DEMA_Test.cs

@@ -0,0 +1,166 @@
+using System;
+using QuanTAlib;
+using Skender.Stock.Indicators;
+using Tulip;
+using Python.Runtime;
+using Python.Included;
+using TALib;
+using Validations;
+using Xunit;
+
+namespace One.by.one;
+public class Dema : IDisposable
+{
+	private readonly GBM_Feed bars;
+	private readonly Random rnd = new();
+	private readonly int period, skip;
+	private readonly double precision;
+
+	private readonly IEnumerable<Quote> quotes;
+	private readonly double[] outdata;
+	private readonly double[] inopen;
+	private readonly double[] inhigh;
+	private readonly double[] inlow;
+	private readonly double[] inclose;
+	private readonly double[] involume;
+
+	private readonly string OStype;
+	private readonly dynamic np;
+	private readonly dynamic ta;
+	private readonly dynamic df;
+
+	public void Dispose() {
+		PythonEngine.Shutdown();
+		GC.SuppressFinalize(this);
+	}
+	public Dema()
+    {
+        bars = new(Bars: 1000, Volatility: 0.5, Drift: 0.0);
+        period = rnd.Next(30) + 5;
+		skip = period*8;
+        precision = 1e-6;
+
+        quotes = bars.Select(q => new Quote
+        {
+            Date = q.t,
+            Open = (decimal)q.o,
+            High = (decimal)q.h,
+            Low = (decimal)q.l,
+            Close = (decimal)q.c,
+            Volume = (decimal)q.v
+        });
+		
+		  outdata = new double[bars.Count];
+		  inopen = bars.Open.v.ToArray();
+		  inhigh = bars.High.v.ToArray();
+		  inlow = bars.Low.v.ToArray();
+		  inclose = bars.Close.v.ToArray();
+		  involume = bars.Volume.v.ToArray();
+
+		// 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";
+
+		Installer.InstallPath = Path.GetFullPath(path: ".");
+		Installer.SetupPython().Wait();
+		Installer.TryInstallPip();
+		Installer.PipInstallModule(module_name: "pandas-ta");
+		Runtime.PythonDLL = OStype;
+		PythonEngine.Initialize();
+		np = Py.Import(name: "numpy");
+		ta = Py.Import(name: "pandas_ta");
+
+		string[] cols = { "open", "high", "low", "close", "volume" };
+		double[,] ary = new double[bars.Count, 5];
+		for (int i = 0; i < bars.Count; i++) {
+			ary[i, 0] = bars.Open[i].v;
+			ary[i, 1] = bars.High[i].v;
+			ary[i, 2] = bars.Low[i].v;
+			ary[i, 3] = bars.Close[i].v;
+			ary[i, 4] = bars.Volume[i].v;
+		}
+		df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
+	}
+	[Fact]
+	public void WeirdData() {
+		DEMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		QL.Add((DateTime.Today.AddDays(1), double.NaN), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((DateTime.Today.AddDays(-1), double.NegativeInfinity), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((new DateTime(), double.Epsilon), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+	}
+	[Fact]
+	public void Updating() {
+		DEMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		int lastLen = QL.Count;
+		QL.Add((DateTime.Today, 0), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastLen, QL.Count); // same size
+		Assert.Equal(lastCalc, QL.Last()); // same data
+	}
+	[Fact]
+  public void Skender_Test()
+  {
+     DEMA_Series QL = new(bars.Close, period, false);
+     var SK = quotes.GetDema(period).Select(i => i.Dema.Null2NaN()!);
+     for (int i = QL.Length; i > skip; i--)
+     {
+     double QL_item = QL[i - 1].v;
+     double SK_item = SK.ElementAt(i - 1);
+		Assert.InRange(SK_item! - QL_item, -precision, precision);
+
+	}
+  }
+	[Fact]
+	public void TALIB_Test()
+	{
+		DEMA_Series QL = new(bars.Close, period, false);
+		Core.Dema(inclose, 0, bars.Count - 1, outdata, out int outBegIdx, out _, period);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = QL[i].v;
+			double TA_item = outdata[i - outBegIdx];
+			Assert.InRange(TA_item! - QL_item, -precision, precision);
+		}
+	}
+	[Fact]
+	public void Tulip_Test() {
+		double[][] arrin = { inclose };
+		double[][] arrout = { outdata };
+		DEMA_Series QL = new(bars.Close, period: period, useNaN: false);
+		Tulip.Indicators.dema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip*3; i--) {
+			double QL_item = QL[i].v;
+			double TU_item = arrout[0][i];
+			Assert.InRange(TU_item! - QL_item, -precision, precision);
+		}
+	}
+	[Fact]
+	void PandasTA_Test() {
+		DEMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.dema(close: df.close, length: period);
+		for (int i = QL.Length; i > skip; i--) {
+			double QL_item = QL[i - 1].v;
+			double PanTA_item = (double)pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -1e-5, 1e-5);
+		}
+	}
+}

Tests/Validations/EMA_Test.cs

@@ -0,0 +1,166 @@
+using System;
+using QuanTAlib;
+using Skender.Stock.Indicators;
+using Tulip;
+using Python.Runtime;
+using Python.Included;
+using TALib;
+using Validations;
+using Xunit;
+
+namespace One.by.one;
+public class Ema : IDisposable
+{
+	private readonly GBM_Feed bars;
+	private readonly Random rnd = new();
+	private readonly int period, skip;
+	private readonly double precision;
+
+	private readonly IEnumerable<Quote> quotes;
+	private readonly double[] outdata;
+	private readonly double[] inopen;
+	private readonly double[] inhigh;
+	private readonly double[] inlow;
+	private readonly double[] inclose;
+	private readonly double[] involume;
+
+	private readonly string OStype;
+	private readonly dynamic np;
+	private readonly dynamic ta;
+	private readonly dynamic df;
+
+	public void Dispose() {
+		PythonEngine.Shutdown();
+		GC.SuppressFinalize(this);
+	}
+	public Ema()
+    {
+        bars = new(Bars: 1000, Volatility: 0.5, Drift: 0.0);
+        period = rnd.Next(30) + 5;
+		skip = period-1;
+        precision = 1e-8;
+
+        quotes = bars.Select(q => new Quote
+        {
+            Date = q.t,
+            Open = (decimal)q.o,
+            High = (decimal)q.h,
+            Low = (decimal)q.l,
+            Close = (decimal)q.c,
+            Volume = (decimal)q.v
+        });
+		
+		  outdata = new double[bars.Count];
+		  inopen = bars.Open.v.ToArray();
+		  inhigh = bars.High.v.ToArray();
+		  inlow = bars.Low.v.ToArray();
+		  inclose = bars.Close.v.ToArray();
+		  involume = bars.Volume.v.ToArray();
+
+		// 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";
+
+		Installer.InstallPath = Path.GetFullPath(path: ".");
+		Installer.SetupPython().Wait();
+		Installer.TryInstallPip();
+		Installer.PipInstallModule(module_name: "pandas-ta");
+		Runtime.PythonDLL = OStype;
+		PythonEngine.Initialize();
+		np = Py.Import(name: "numpy");
+		ta = Py.Import(name: "pandas_ta");
+
+		string[] cols = { "open", "high", "low", "close", "volume" };
+		double[,] ary = new double[bars.Count, 5];
+		for (int i = 0; i < bars.Count; i++) {
+			ary[i, 0] = bars.Open[i].v;
+			ary[i, 1] = bars.High[i].v;
+			ary[i, 2] = bars.Low[i].v;
+			ary[i, 3] = bars.Close[i].v;
+			ary[i, 4] = bars.Volume[i].v;
+		}
+		df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
+	}
+	[Fact]
+	public void WeirdData() {
+		EMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		QL.Add((DateTime.Today.AddDays(1), double.NaN), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((DateTime.Today.AddDays(-1), double.NegativeInfinity), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((new DateTime(), double.Epsilon), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+	}
+	[Fact]
+	public void Updating() {
+		EMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		int lastLen = QL.Count;
+		QL.Add((DateTime.Today, 0), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastLen, QL.Count); // same size
+		Assert.Equal(lastCalc, QL.Last()); // same data
+	}
+	[Fact]
+  public void Skender_Test()
+  {
+     EMA_Series QL = new(bars.Close, period, false);
+     var SK = quotes.GetEma(period).Select(i => i.Ema.Null2NaN()!);
+     for (int i = QL.Length; i > skip; i--)
+     {
+     double QL_item = QL[i - 1].v;
+     double SK_item = SK.ElementAt(i - 1);
+		Assert.InRange(SK_item! - QL_item, -precision, precision);
+
+	}
+  }
+	[Fact]
+	public void TALIB_Test()
+	{
+		EMA_Series QL = new(bars.Close, period, false);
+		Core.Ema(inclose, 0, bars.Count - 1, outdata, out int outBegIdx, out _, period);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = QL[i].v;
+			double TA_item = outdata[i - outBegIdx];
+			Assert.InRange(TA_item! - QL_item, -precision, precision);
+		}
+	}
+	[Fact]
+	public void Tulip_Test() {
+		double[][] arrin = { inclose };
+		double[][] arrout = { outdata };
+		EMA_Series QL = new(bars.Close, period: period, useNaN: false, useSMA: false);
+		Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--) {
+			double QL_item = QL[i].v;
+			double TU_item = arrout[0][i];
+			Assert.InRange(TU_item! - QL_item, -precision, precision);
+		}
+	}
+	[Fact]
+	void PandasTA_Test() {
+		EMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.ema(close: df.close, length: period);
+		for (int i = QL.Length; i > skip; i--) {
+			double QL_item = QL[i - 1].v;
+			double PanTA_item = (double)pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -1e-5, 1e-5);
+		}
+	}
+}

Tests/Validations/SMA_Test.cs

@@ -0,0 +1,160 @@
+using System;
+using QuanTAlib;
+using Skender.Stock.Indicators;
+using Tulip;
+using Python.Runtime;
+using Python.Included;
+using TALib;
+using Validations;
+using Xunit;
+
+namespace One.by.one;
+public class SMA : IDisposable {
+	private readonly GBM_Feed bars;
+	private readonly Random rnd = new();
+	private readonly int period, skip;
+	private readonly double precision;
+
+	private readonly IEnumerable<Quote> quotes;
+	private readonly double[] outdata;
+	private readonly double[] inopen;
+	private readonly double[] inhigh;
+	private readonly double[] inlow;
+	private readonly double[] inclose;
+	private readonly double[] involume;
+
+	private readonly string OStype;
+	private readonly dynamic np;
+	private readonly dynamic ta;
+	private readonly dynamic df;
+
+	public void Dispose() {
+		PythonEngine.Shutdown();
+		GC.SuppressFinalize(this);
+	}
+	public SMA() {
+		bars = new(Bars: 1000, Volatility: 0.5, Drift: 0.0);
+		period = rnd.Next(30) + 5;
+		precision = 1e-8;
+		skip = period-1;
+
+		quotes = bars.Select(q => new Quote {
+			Date = q.t,
+			Open = (decimal)q.o,
+			High = (decimal)q.h,
+			Low = (decimal)q.l,
+			Close = (decimal)q.c,
+			Volume = (decimal)q.v
+		});
+
+		outdata = new double[bars.Count];
+		inopen = bars.Open.v.ToArray();
+		inhigh = bars.High.v.ToArray();
+		inlow = bars.Low.v.ToArray();
+		inclose = bars.Close.v.ToArray();
+		involume = bars.Volume.v.ToArray();
+
+		// 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";
+
+		Installer.InstallPath = Path.GetFullPath(path: ".");
+		Installer.SetupPython().Wait();
+		Installer.TryInstallPip();
+		Installer.PipInstallModule(module_name: "pandas-ta");
+		Runtime.PythonDLL = OStype;
+		PythonEngine.Initialize();
+		np = Py.Import(name: "numpy");
+		ta = Py.Import(name: "pandas_ta");
+
+		string[] cols = { "open", "high", "low", "close", "volume" };
+		double[,] ary = new double[bars.Count, 5];
+		for (int i = 0; i < bars.Count; i++) {
+			ary[i, 0] = bars.Open[i].v;
+			ary[i, 1] = bars.High[i].v;
+			ary[i, 2] = bars.Low[i].v;
+			ary[i, 3] = bars.Close[i].v;
+			ary[i, 4] = bars.Volume[i].v;
+		}
+		df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
+	}
+	[Fact]
+	public void WeirdData() {
+		SMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		QL.Add((DateTime.Today.AddDays(1), double.NaN), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((DateTime.Today.AddDays(-1), double.NegativeInfinity), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+
+		QL.Add((new DateTime(), double.Epsilon), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastCalc, QL.Last()); // back to the same data
+	}
+	[Fact]
+	public void Updating() {
+		SMA_Series QL = new(source: bars.Close, period);
+		var lastData = bars.Close.Last();
+		var lastCalc = QL.Last();
+		int lastLen = QL.Count;
+		QL.Add((DateTime.Today, 0), update: true);
+		Assert.NotEqual(lastCalc, QL.Last()); //value changed
+		QL.Add(lastData, update: true);
+		Assert.Equal(lastLen, QL.Count); // same size
+		Assert.Equal(lastCalc, QL.Last()); // same data
+	}
+	[Fact]
+	public void Skender_Test() {
+		SMA_Series QL = new(bars.Close, period, false);
+		var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!);
+		for (int i = QL.Length; i > skip; i--) {
+			double QL_item = QL[i - 1].v;
+			double SK_item = SK.ElementAt(i - 1);
+			Assert.InRange(SK_item! - QL_item, -precision, precision);
+		}
+	}
+	[Fact]
+	public void TALIB_Test() {
+		SMA_Series QL = new(bars.Close, period, false);
+		Core.Sma(inclose, 0, bars.Count - 1, outdata, out int outBegIdx, out _, period);
+		for (int i = QL.Length - 1; i > skip; i--) {
+			double QL_item = QL[i].v;
+			double TA_item = outdata[i - outBegIdx];
+			Assert.InRange(TA_item! - QL_item, -precision, precision);
+
+		}
+	}
+	[Fact]
+	public void Tulip_Test() {
+		double[][] arrin = { inclose };
+		double[][] arrout = { outdata };
+		SMA_Series QL = new(bars.Close, period, false);
+		Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--) {
+			double QL_item = QL[i].v;
+			double TU_item = arrout[0][i - period + 1];
+			Assert.InRange(TU_item! - QL_item, -precision, precision);
+
+		}
+	}
+	[Fact]
+	void PandasTA_Test() {
+		SMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.sma(close: df.close, length: period);
+		for (int i = QL.Length; i > skip; i--) {
+			double QL_item = QL[i - 1].v;
+			double PanTA_item = (double)pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -precision, precision);
+		}
+	}
+}

Tests/Validations/Trends/Pandas_TA.cs

@@ -1,354 +1,360 @@
-using Xunit;
+using Xunit;
-using System;
+using System;
-using QuanTAlib;
+using QuanTAlib;
-using Python.Runtime;
+using Python.Runtime;
-using Python.Included;
+using Python.Included;
-
+
-namespace Validations;
+namespace Validations;
-public class PandasTA : IDisposable
+public class PandasTA : IDisposable
-{
+{
-	private readonly GBM_Feed bars;
+	private readonly GBM_Feed bars;
-    private readonly Random rnd = new();
+    private readonly Random rnd = new();
-	private readonly int period;
+	private readonly int period, sample;
-	private int digits;
+	private int digits;
-	private readonly string OStype;
+	private readonly string OStype;
-	private readonly dynamic np;
+	private readonly dynamic np;
-	private readonly dynamic ta;
+	private readonly dynamic ta;
-	private readonly dynamic df;
+	private readonly dynamic df;
-	
+
-	public PandasTA() {
+	public PandasTA() {
-        bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
+        bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
-        period = rnd.Next(maxValue: 28) + 3;
+        period = rnd.Next(maxValue: 28) + 3;
-		digits = 4; //minimizing rounding errors in type conversions
+        sample = 200;
-
+		    digits = 3; //minimizing rounding errors in type conversions
-		// Checking the host OS and setting PythonDLL accordingly
+
-		OStype = Path.GetFullPath(path: ".") + @"\python-3.10.0-embed-amd64\python310.dll";
+		// Checking the host OS and setting PythonDLL accordingly
-
+        OStype = Environment.OSVersion.ToString();
-		Installer.InstallPath = Path.GetFullPath(path: ".");
+        if (OStype == "Unix 13.1.0")
-		Installer.SetupPython().Wait();
+            OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
-		Installer.TryInstallPip();
+        else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
-		Installer.PipInstallModule(module_name: "pandas-ta");
+
-		Runtime.PythonDLL = OStype;
+		Installer.InstallPath = Path.GetFullPath(path: ".");
-	    PythonEngine.Initialize();
+		Installer.SetupPython().Wait();
-		np = Py.Import(name: "numpy");
+		Installer.TryInstallPip();
-		ta = Py.Import(name: "pandas_ta");
+		Installer.PipInstallModule(module_name: "pandas-ta");
-
+		Runtime.PythonDLL = OStype;
-		string[] cols = { "open", "high", "low", "close", "volume" };
+	    PythonEngine.Initialize();
-		double[,] ary = new double[bars.Count, 5];
+		np = Py.Import(name: "numpy");
-		for (int i = 0; i < bars.Count; i++) {
+		ta = Py.Import(name: "pandas_ta");
-			ary[i, 0] = bars.Open[i].v;
+
-			ary[i, 1] = bars.High[i].v;
+		string[] cols = { "open", "high", "low", "close", "volume" };
-			ary[i, 2] = bars.Low[i].v;
+		double[,] ary = new double[bars.Count, 5];
-			ary[i, 3] = bars.Close[i].v;
+		for (int i = 0; i < bars.Count; i++) {
-			ary[i, 4] = bars.Volume[i].v;
+			ary[i, 0] = bars.Open[i].v;
-		}
+			ary[i, 1] = bars.High[i].v;
-		df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
+			ary[i, 2] = bars.Low[i].v;
-	}
+			ary[i, 3] = bars.Close[i].v;
-		public void Dispose()
+			ary[i, 4] = bars.Volume[i].v;
-	{
+		}
-    PythonEngine.Shutdown();
+		df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
-	GC.SuppressFinalize(this);
+	}
-	}
+		public void Dispose()
-
+	{
-	[Fact] void ADL() {
+    PythonEngine.Shutdown();
-		ADL_Series QL = new(bars);
+	GC.SuppressFinalize(this);
-		var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
+	}
-		for (int i = QL.Length; i > 0; i--)
+
-		{
+	[Fact] void ADL() {
-            double QL_item = Math.Round(QL[i-1].v, digits: digits);
+		ADL_Series QL = new(bars);
-			double PanTA_item = Math.Round((double)pta[i-1], digits: digits);
+		var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
-            Assert.Equal(PanTA_item, QL_item);
+		for (int i = QL.Length; i > QL.Length-sample; i--)
-		}
+		{
-	}
+            double QL_item = Math.Round(QL[i-1].v, digits: digits);
-	[Fact] void ADOSC() {
+			double PanTA_item = Math.Round((double)pta[i-1], digits: digits);
-		ADOSC_Series QL = new(bars);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
+		}
-        for (int i = QL.Length; i > 0; i--)
+	}
-        {
+	[Fact] void ADOSC() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		ADOSC_Series QL = new(bars);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-    [Fact] void ATR() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		ATR_Series QL = new(bars, period);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > 0; i--)
+    }
-        {
+    [Fact] void ATR() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		ATR_Series QL = new(bars, period);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void BIAS() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		BIAS_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.bias(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void BIAS() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		BIAS_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.bias(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void DEMA() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		DEMA_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.dema(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void DEMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		DEMA_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.dema(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void EMA() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		EMA_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.ema(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void EMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		EMA_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.ema(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void ENTROPY() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.entropy(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period+1; i--)
+    }
-        {
+	[Fact] void ENTROPY() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.entropy(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void HL2() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		var pta = df.ta.hl2(high: df.high, low: df.low);
+            Assert.Equal(PanTA_item, QL_item);
-        for (int i = bars.HL2.Length; i > 0; i--)
+        }
-        {
+    }
-            double QL_item = Math.Round(bars.HL2[i - 1].v, digits: digits);
+	[Fact] void HL2() {
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.hl2(high: df.high, low: df.low);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = bars.HL2.Length; i > bars.HL2.Length-sample; i--)
-        }
+        {
-	}
+            double QL_item = Math.Round(bars.HL2[i - 1].v, digits: digits);
-	[Fact] void HLC3() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
+            Assert.Equal(PanTA_item, QL_item);
-        for (int i = bars.HLC3.Length; i > 0; i--)
+        }
-        {
+	}
-            double QL_item = Math.Round(bars.HLC3[i - 1].v, digits: digits);
+	[Fact] void HLC3() {
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = bars.HLC3.Length; i > bars.HLC3.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(bars.HLC3[i - 1].v, digits: digits);
-    [Fact] void HMA() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		HMA_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.hma(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period+1; i--)
+    }
-        {
+    [Fact] void HMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		HMA_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.hma(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-        
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	}
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-    [Fact] void KAMA() {
+            Assert.Equal(PanTA_item, QL_item);
-        KAMA_Series QL = new(bars.Close, period);
+        }
-        var pta = df.ta.kama(close: df.close, length: period);
+
-        for (int i = QL.Length; i > 0; i--)
+	}
-        {
+    [Fact] void KAMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+        KAMA_Series QL = new(bars.Close, period);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+        var pta = df.ta.kama(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void KURTOSIS() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.kurtosis(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period+1; i--)
+    }
-        {
+	[Fact] void KURTOSIS() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.kurtosis(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void MAD()
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-	{
+            Assert.Equal(PanTA_item, QL_item);
-		MAD_Series QL = new(bars.Close, period, useNaN: false);
+        }
-		var pta = df.ta.mad(close: df.close, length: period);
+    }
-        for (int i = QL.Length; i > period-1; i--)
+	[Fact] void MAD()
-        {
+	{
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		MAD_Series QL = new(bars.Close, period, useNaN: false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.mad(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void MEDIAN() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		MEDIAN_Series QL = new(bars.Close, period);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.median(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void MEDIAN() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		MEDIAN_Series QL = new(bars.Close, period);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.median(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-    [Fact] void OBV() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        OBV_Series QL = new(bars);
+            Assert.Equal(PanTA_item, QL_item);
-        var pta = df.ta.obv(close: df.close, volume: df.volume);
+        }
-        for (int i = QL.Length; i > 0; i--)
+    }
-        {
+    [Fact] void OBV() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+        OBV_Series QL = new(bars);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+        var pta = df.ta.obv(close: df.close, volume: df.volume);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void OHLC4() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
+            Assert.Equal(PanTA_item, QL_item);
-        for (int i = bars.OHLC4.Length; i > 0; i--)
+        }
-        {
+    }
-            double QL_item = Math.Round(bars.OHLC4[i - 1].v, digits: digits);
+	[Fact] void OHLC4() {
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = bars.OHLC4.Length; i > bars.OHLC4.Length-sample; i--)
-        }
+        {
-	}
+            double QL_item = Math.Round(bars.OHLC4[i - 1].v, digits: digits);
-	[Fact] void RMA() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		RMA_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.rma(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > 0; i--)
+	}
-        {
+	[Fact] void RMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		RMA_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.rma(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void RSI() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		RSI_Series QL = new(bars.Close, period);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.rsi(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > 0; i--)
+    }
-        {
+	[Fact] void RSI() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		RSI_Series QL = new(bars.Close, period);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.rsi(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void SDEV()	{
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		SDEV_Series QL = new(bars.Close, period, useNaN: false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void SDEV()	{
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		SDEV_Series QL = new(bars.Close, period, useNaN: false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void SMA() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		SMA_Series QL = new(bars.Close, period, false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.sma(close: df.close, length: period);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void SMA() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		SMA_Series QL = new(bars.Close, period, false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.sma(close: df.close, length: period);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void SSDEV() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		SSDEV_Series QL = new(bars.Close, period, useNaN: false);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
+        }
-        for (int i = QL.Length; i > period-1; i--)
+    }
-        {
+	[Fact] void SSDEV() {
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+		SSDEV_Series QL = new(bars.Close, period, useNaN: false);
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
-            Assert.Equal(PanTA_item, QL_item);
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-        }
+        {
-    }
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-	[Fact] void SVARIANCE() {
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		SVAR_Series QL = new(bars.Close, period);
+            Assert.Equal(PanTA_item, QL_item);
-		var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
+        }
-        for (int i = QL.Length; i > 0; i--)
+    }
-        {
+	/*
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+	[Fact] void SVARIANCE() {
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+		SVAR_Series QL = new(bars.Close, period);
-            Assert.Equal(PanTA_item, QL_item);
+		var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
-        }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-    }
+        {
-    [Fact] void T3() {
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-        T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        var pta = df.ta.t3(close: df.close, length: period, a: 0.7);
+            Assert.Equal(PanTA_item, QL_item);
-        for (int i = QL.Length; i > 0; i--)
+        }
-        {
+    }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+*/
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+    [Fact] void T3() {
-            Assert.Equal(PanTA_item, QL_item);
+        T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
-        }
+        var pta = df.ta.t3(close: df.close, length: period, a: 0.7);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-    [Fact] void TEMA() {
+        {
-		TEMA_Series QL = new(bars.Close, period, false);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.tema(close: df.close, length: period);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > period; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+    [Fact] void TEMA() {
-            Assert.Equal(PanTA_item, QL_item);
+		TEMA_Series QL = new(bars.Close, period, false);
-        }
+		var pta = df.ta.tema(close: df.close, length: period);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void TR() {
+        {
-		TR_Series QL = new(bars);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > 1; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+	[Fact] void TR() {
-            Assert.Equal(PanTA_item, QL_item);
+		TR_Series QL = new(bars);
-        }
+		var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void TRIMA() {
+        {
-    // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		TRIMA_Series QL = new(bars.Close, 11);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-		var pta = df.ta.trima(close: df.close, length: 11);
+            Assert.Equal(PanTA_item, QL_item);
-        for (int i = QL.Length; i > period-1; i--)
+        }
-        {
+    }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+	[Fact] void TRIMA() {
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+    // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
-            Assert.Equal(PanTA_item, QL_item);
+		TRIMA_Series QL = new(bars.Close, 11);
-        }
+		var pta = df.ta.trima(close: df.close, length: 11);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void VARIANCE()	{
+        {
-		VAR_Series QL = new(bars.Close, period);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.variance(close: df.close, length: period, ddof:0);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > 0; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+	[Fact] void VARIANCE()	{
-            Assert.Equal(PanTA_item, QL_item);
+		VAR_Series QL = new(bars.Close, period);
-        }
+		var pta = df.ta.variance(close: df.close, length: period, ddof:0);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void WMA() {
+        {
-		WMA_Series QL = new(bars.Close, period, false);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.wma(close: df.close, length: period);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > period-1; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+	[Fact] void WMA() {
-            Assert.Equal(PanTA_item, QL_item);
+		WMA_Series QL = new(bars.Close, period, false);
-        }
+		var pta = df.ta.wma(close: df.close, length: period);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void ZLEMA() {
+        {
-		ZLEMA_Series QL = new(bars.Close, period, false);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.zlma(close: df.close, length: period);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > 0; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+	[Fact] void ZLEMA() {
-            Assert.Equal(PanTA_item, QL_item);
+		ZLEMA_Series QL = new(bars.Close, period, false);
-        }
+		var pta = df.ta.zlma(close: df.close, length: period);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-	[Fact] void ZSCORE() {
+        {
-		ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
-		var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
-        for (int i = QL.Length; i > period-1; i--)
+            Assert.Equal(PanTA_item, QL_item);
-        {
+        }
-            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+    }
-            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+	[Fact] void ZSCORE() {
-            Assert.Equal(PanTA_item, QL_item);
+		ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
-        }
+		var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
-    }
+        for (int i = QL.Length; i > QL.Length-sample; i--)
-
+        {
+            double QL_item = Math.Round(QL[i - 1].v, digits: digits);
+            double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
+            Assert.Equal(PanTA_item, QL_item);
+        }
+    }
+
 }

Tests/Validations/Trends/Skender.cs

@@ -1,21 +1,23 @@
-using System;
+using System;
-using QuanTAlib;
+using QuanTAlib;
-using Skender.Stock.Indicators;
+using Skender.Stock.Indicators;
-using Xunit;
+using Xunit;
-
+
-namespace Validations;
+namespace Validations;
-public class Skender_Stock
+public class Skender
 {
   private readonly GBM_Feed bars;
   private readonly Random rnd = new();
-  private readonly int period, digits;
+  private readonly int period, digits, skip;
   private readonly IEnumerable<Quote> quotes;
+	
 
-  public Skender_Stock()
+  public Skender()
   {
     bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2);
     period = rnd.Next(30) + 5;
-    digits = 4; //minimizing rounding errors in type conversions
+    digits = 2; //minimizing rounding errors in type conversions
+    skip = 300;
 
     quotes = bars.Select(q => new Quote
     {
@@ -27,27 +29,26 @@ public class Skender_Stock
       Volume = (decimal)q.v
     });
   }
-  /*
+
   [Fact]
   public void ADL()
   {
     // TODO: check precision of ADL()
     ADL_Series QL = new(bars, false);
     var SK = quotes.GetAdl().Select(i => i.Adl);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1)!, digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
   [Fact]
   public void ALMA()
   {
     ALMA_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetAlma(period).Select(i => i.Alma.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
@@ -59,7 +60,7 @@ public class Skender_Stock
   {
     ATR_Series QL = new(bars, period, false);
     var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
@@ -71,7 +72,7 @@ public class Skender_Stock
   {
     ATRP_Series QL = new(bars, period, false);
     var SK = quotes.GetAtr(period).Select(i => i.Atrp.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
@@ -83,7 +84,7 @@ public class Skender_Stock
   {
     BBANDS_Series QL = new(bars.Close, period, 2.0, useNaN: false);
     var SK = quotes.GetBollingerBands(period, 2.0);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL.Mid[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1).Sma!.Value, digits: digits);
@@ -110,7 +111,7 @@ public class Skender_Stock
   {
     CCI_Series QL = new(bars, period, false);
     var SK = quotes.GetCci(period).Select(i => i.Cci.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
@@ -122,20 +123,19 @@ public class Skender_Stock
   {
     CORR_Series QL = new(bars.High, bars.Low, period, false);
     var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Correlation.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  /*
   [Fact]
   public void COVAR()
   {
     COVAR_Series QL = new(bars.High, bars.Low, period, false);
     var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Covariance.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
@@ -147,33 +147,32 @@ public class Skender_Stock
   {
     DEMA_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetDema(period).Select(i => i.Dema.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
   [Fact]
   public void EMA()
   {
     EMA_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetEma(period).Select(i => i.Ema.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  /*
+	/*
   [Fact]
   public void HL2()
   {
     TSeries QL = bars.HL2;
     var SK = quotes.GetBaseQuote(CandlePart.HL2).Select(i => i.Value);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
@@ -185,33 +184,33 @@ public class Skender_Stock
   {
     TSeries QL = bars.HLC3;
     var SK = quotes.GetBaseQuote(CandlePart.HLC3).Select(i => i.Value);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
+	*/
   [Fact]
   public void HMA()
   {
     HMA_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetHma(period).Select(i => i.Hma.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
   [Fact]
   public void KAMA()
   {
     // TODO: check precision of KAMA()
     KAMA_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetKama(period).Select(i => i.Kama.Null2NaN()!);
-    for (int i = QL.Length; i > 600; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -223,7 +222,7 @@ public class Skender_Stock
   {
     LINREG_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetSlope(period);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1).Slope!, digits: digits);
@@ -244,7 +243,7 @@ public class Skender_Stock
   {
     MACD_Series QL = new(bars.Close, 26, 12, 9, useNaN: false);
     var SK = quotes.GetMacd(12, 26, 9);
-    for (int i = QL.Length; i > 500; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1).Macd.Null2NaN()!, digits: digits);
@@ -259,7 +258,7 @@ public class Skender_Stock
   {
     MAD_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mad.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -271,7 +270,7 @@ public class Skender_Stock
   {
     MAMA_Series QL = new(bars.HL2, fastlimit: 0.5, slowlimit: 0.05);
     var SK = quotes.GetMama(fastLimit: 0.5, slowLimit: 0.05);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1).Mama.Null2NaN()!, digits: digits);
@@ -286,7 +285,7 @@ public class Skender_Stock
   {
     MAPE_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mape.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -298,7 +297,7 @@ public class Skender_Stock
   {
     MSE_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mse.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -311,27 +310,32 @@ public class Skender_Stock
     OBV_Series QL = new(bars, period, false);
     var SK = quotes.GetObv(period).Select(i => i.Obv!);
     // adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB
-    Assert.Equal(Math.Round(SK.Last()! + (double)quotes.First().Volume!, digits: digits), Math.Round(QL.Last().v, digits: digits));
+        for (int i = QL.Length; i > skip; i--)
+    {
+      double QL_item = Math.Round(QL.Last().v, digits: digits);
+      double SK_item = Math.Round(SK.Last()! + (double)quotes.First().Volume!, digits: digits);
+      Assert.Equal(SK_item!, QL_item);
+    }
   }
-  /*
+	/*
   [Fact]
   public void OC2()
   {
     TSeries QL = bars.OC2;
     var SK = quotes.GetBaseQuote(CandlePart.OC2).Select(i => i.Value);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  [Fact]
+	[Fact]
   public void OHL3()
   {
     TSeries QL = bars.OHL3;
     var SK = quotes.GetBaseQuote(CandlePart.OHL3).Select(i => i.Value);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
@@ -343,20 +347,20 @@ public class Skender_Stock
   {
     TSeries QL = bars.OHLC4;
     var SK = quotes.GetBaseQuote(CandlePart.OHLC4).Select(i => i.Value);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
+	*/
-  [Fact]
+	[Fact]
   public void RSI()
   {
     RSI_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetRsi(period).Select(i => i.Rsi.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -368,7 +372,7 @@ public class Skender_Stock
   {
     SDEV_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetStdDev(period).Select(i => i.StdDev.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -380,7 +384,7 @@ public class Skender_Stock
   {
     SMA_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -392,33 +396,31 @@ public class Skender_Stock
   {
     SMMA_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetSmma(period).Select(i => i.Smma.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  /*
   [Fact]
   public void T3()
   {
     T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, false);
     var SK = quotes.GetT3(lookbackPeriods: period, volumeFactor: 0.7).Select(i => i.T3.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
   [Fact]
   public void TEMA()
   {
     TEMA_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetTema(period).Select(i => i.Tema.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
@@ -430,38 +432,36 @@ public class Skender_Stock
   {
     TR_Series QL = new(bars, useNaN: false);
     var SK = quotes.GetTr().Select(i => i.Tr.Null2NaN()!);
-    for (int i = QL.Length; i > 1; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  /*
   [Fact]
   public void WMA()
   {
     WMA_Series QL = new(bars.Close, period, false);
     var SK = quotes.GetWma(period).Select(i => i.Wma.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
   }
-  */
   [Fact]
   public void ZSCORE()
   {
     ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
     var SK = quotes.GetStdDev(period).Select(i => i.ZScore.Null2NaN()!);
-    for (int i = QL.Length; i > period; i--)
+    for (int i = QL.Length; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i - 1].v, digits: digits);
       double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
       Assert.Equal(SK_item!, QL_item);
     }
-  }
+  }
-
+
-}
+}

Tests/Validations/Trends/TA_LIB.cs

@@ -8,7 +8,7 @@ public class Ta_Lib
 {
   private readonly GBM_Feed bars;
   private readonly Random rnd = new();
-  private readonly int period, digits;
+  private readonly int period, digits, skip;
   private readonly double[] TALIB;
   private readonly double[] TALIB2;
   private readonly double[] inopen;
@@ -21,6 +21,7 @@ public class Ta_Lib
   {
     bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
     period = rnd.Next(28) + 3;
+    skip = 200;
     digits = 6;
 
     TALIB = new double[bars.Count];
@@ -37,7 +38,7 @@ public class Ta_Lib
   {
     ADD_Series QL = new(bars.Open, bars.Close);
     Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -59,9 +60,9 @@ public class Ta_Lib
   [Fact]
   public void ADOSC()
   {
-    ADOSC_Series QL = new(bars, false);
+    ADOSC_Series QL = new(bars, 3, 10, false);
     Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -73,7 +74,7 @@ public class Ta_Lib
   {
     ATR_Series QL = new(bars, period, false);
     Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx * 15; i--)
+    for (int i = QL.Length - 1; i > skip * 15; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -88,7 +89,7 @@ public class Ta_Lib
     double[] outLower = new double[bars.Count];
     BBANDS_Series QL = new(bars.Close, period: 26, multiplier: 2.0, false);
     Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: 26, optInNbDevUp: 2.0, optInNbDevDn: 2.0);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL.Upper[i].v, digits: digits);
       double TA_item = Math.Round(outUpper[i - outBegIdx], digits: digits);
@@ -109,7 +110,7 @@ public class Ta_Lib
   {
     CCI_Series QL = new(bars, period, false);
     Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -121,7 +122,7 @@ public class Ta_Lib
   {
     CORR_Series QL = new(bars.Open, bars.Close, period);
     Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -133,7 +134,7 @@ public class Ta_Lib
   {
     DEMA_Series QL = new(bars.Close, period, false);
     Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -145,7 +146,7 @@ public class Ta_Lib
   {
     DIV_Series QL = new(bars.Open, bars.Close);
     Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -157,7 +158,7 @@ public class Ta_Lib
   {
     EMA_Series QL = new(bars.Close, period, false);
     Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -169,7 +170,7 @@ public class Ta_Lib
   {
     TSeries QL = bars.HL2;
     Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -181,7 +182,7 @@ public class Ta_Lib
   {
     TSeries QL = bars.HLC3;
     Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -193,7 +194,7 @@ public class Ta_Lib
   {
     TSeries QL = bars.HLCC4;
     Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -207,7 +208,7 @@ public class Ta_Lib
     double[] macdHist = new double[bars.Count];
     MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false);
     Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx * 10; i--)
+    for (int i = QL.Length - 1; i > skip * 10; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -222,7 +223,7 @@ public class Ta_Lib
   {
     MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05);
     Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05);
-    for (int i = QL.Length - 1; i > outBegIdx * 15; i--)
+    for (int i = QL.Length - 1; i > skip * 15; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -234,7 +235,7 @@ public class Ta_Lib
   {
     MAX_Series QL = new(bars.Close, period, false);
     Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -246,7 +247,7 @@ public class Ta_Lib
   {
     MIDPOINT_Series QL = new(bars.Close, period, false);
     Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -258,7 +259,7 @@ public class Ta_Lib
   {
     MIDPRICE_Series QL = new(bars, period, false);
     Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -270,7 +271,7 @@ public class Ta_Lib
   {
     MIN_Series QL = new(bars.Close, period, false);
     Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -282,7 +283,7 @@ public class Ta_Lib
   {
     MUL_Series QL = new(bars.Open, bars.Close);
     Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -294,7 +295,7 @@ public class Ta_Lib
   {
     OBV_Series QL = new(bars, period, false);
     Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -306,7 +307,7 @@ public class Ta_Lib
   {
     TSeries QL = bars.OHLC4;
     Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -318,7 +319,7 @@ public class Ta_Lib
   {
     RSI_Series QL = new(bars.Close, period, false);
     Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -330,7 +331,7 @@ public class Ta_Lib
   {
     SDEV_Series QL = new(bars.Close, period, false);
     Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -342,7 +343,7 @@ public class Ta_Lib
   {
     SMA_Series QL = new(bars.Close, period, false);
     Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -354,7 +355,7 @@ public class Ta_Lib
   {
     SUB_Series QL = new(bars.Open, bars.Close);
     Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -366,7 +367,7 @@ public class Ta_Lib
   {
     SUM_Series QL = new(bars.Close, period, false);
     Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -378,7 +379,7 @@ public class Ta_Lib
   {
     T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
     Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7);
-    for (int i = QL.Length - 1; i > outBegIdx * 15; i--)
+    for (int i = QL.Length - 1; i > skip * 15; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -390,7 +391,7 @@ public class Ta_Lib
   {
     TEMA_Series QL = new(bars.Close, period, false);
     Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx * 15; i--)
+    for (int i = QL.Length - 1; i > skip * 15; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -402,7 +403,7 @@ public class Ta_Lib
   {
     TR_Series QL = new(bars, false);
     Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -414,7 +415,7 @@ public class Ta_Lib
   {
     TRIMA_Series QL = new(bars.Close, period, false);
     Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -426,7 +427,7 @@ public class Ta_Lib
   {
     VAR_Series QL = new(bars.Close, period, false);
     Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx * 15; i--)
+    for (int i = QL.Length - 1; i > skip * 15; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -438,7 +439,7 @@ public class Ta_Lib
   {
     WMA_Series QL = new(bars.Close, period, false);
     Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
-    for (int i = QL.Length - 1; i > outBegIdx; i--)
+    for (int i = QL.Length - 1; i > skip; i--)
     {
       double QL_item = Math.Round(QL[i].v, digits: digits);
       double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);

Tests/Validations/Trends/Tulip.cs

@@ -0,0 +1,158 @@
+using Xunit;
+using System;
+using Tulip;
+using QuanTAlib;
+
+namespace Validations;
+public class Tulip_Test
+{
+  private readonly GBM_Feed bars;
+  private readonly Random rnd = new();
+  private readonly int period, digits, skip;
+  private readonly double[] outdata;
+  private readonly double[] inopen;
+  private readonly double[] inhigh;
+  private readonly double[] inlow;
+  private readonly double[] inclose;
+  private readonly double[] involume;
+
+  public Tulip_Test()
+  {
+    bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
+    period = rnd.Next(28) + 3;
+    skip = 600;
+    digits = 5;
+
+    outdata = new double[bars.Count];
+    inopen = bars.Open.v.ToArray();
+    inhigh = bars.High.v.ToArray();
+    inlow = bars.Low.v.ToArray();
+    inclose = bars.Close.v.ToArray()!;
+    involume = bars.Volume.v.ToArray()!;
+
+  }
+	[Fact]
+	public void AD()
+	{
+		double[][] arrin = {inhigh, inlow, inclose, involume };
+		double[][] arrout = { outdata };
+		ADL_Series QL = new(bars, false);
+		Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+	public void ADD()
+	{
+		double[][] arrin = { inhigh, inlow };
+		double[][] arrout = { outdata };
+		ADD_Series QL = new(bars.High, bars.Low);
+		Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+	public void ADOSC()
+	{
+		double[][] arrin = { inhigh, inlow, inclose, involume };
+		double[][] arrout = { outdata };
+		int s = 3;
+		ADOSC_Series QL = new(bars, s, period, false);
+		Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--) 
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i-period+1], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+	public void ATR()
+	{
+		double[][] arrin = { inhigh, inlow, inclose };
+		double[][] arrout = { outdata };
+
+		ATR_Series QL = new(bars, period, false);
+		Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i - period + 1], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+	public void BBANDS()
+	{
+		double[][] arrin = { inclose };
+		double[] outmid = new double[bars.Count];
+		double[] outlower = new double[bars.Count];
+		double[] outupper = new double[bars.Count];
+		double[][] arrout = { outlower, outmid, outupper};
+		BBANDS_Series QL = new(bars.Close, period, 2, false);
+		Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL.Lower[i].v, digits: digits);
+			double TU_item = Math.Round(outlower[i - period + 1], digits);
+			Assert.Equal(TU_item!, QL_item);
+			QL_item = Math.Round(QL.Mid[i].v, digits: digits);
+			TU_item = Math.Round(outmid[i - period + 1], digits);
+			Assert.Equal(TU_item!, QL_item);
+			QL_item = Math.Round(QL.Upper[i].v, digits: digits);
+			TU_item = Math.Round(outupper[i - period + 1], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+  public void EMA()
+  {
+    double[][] arrin = { inclose };
+		double[][] arrout = { outdata };
+		EMA_Series QL = new(bars.Close, period, false);
+    Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+    for (int i = QL.Length - 1; i > skip; i--)
+    {
+      double QL_item = Math.Round(QL[i].v, digits: digits);
+      double TU_item = Math.Round(arrout[0][i], digits);
+      Assert.Equal(TU_item!, QL_item);
+    }
+  }
+	[Fact]
+	public void AVGPRICE()
+	{
+		double[][] arrin = { inopen, inhigh, inlow, inclose };
+		double[][] arrout = { outdata };
+
+		TSeries QL = bars.OHLC4;
+		Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+	[Fact]
+	public void SMA()
+	{
+		double[][] arrin = { inclose };
+		double[][] arrout = { outdata };
+		SMA_Series QL = new(bars.Close, period, false);
+		Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
+		for (int i = QL.Length - 1; i > skip; i--)
+		{
+			double QL_item = Math.Round(QL[i].v, digits: digits);
+			double TU_item = Math.Round(arrout[0][i-period+1], digits);
+			Assert.Equal(TU_item!, QL_item);
+		}
+	}
+}

docs/SMA.md

@@ -0,0 +1,40 @@
+# SMA: Simple Moving Average
+SMA is one of the most basic trend-following indicators used in Technical Analysis. It is calculated as the *unweighted mean* of the previous $p$ (period) data-points.
+
+
+## Calculation
+
+SMA is a rolling calculation looking backwards from the position ${n}$ and is denoted as ${SMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points:
+$$
+SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i
+$$
+When calculating the value of next $SMA_{p,next}$ while knowing all previous SMA values, SMA calculation can be reduced to:
+$$
+SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right)
+$$
+
+## Implementation
+
+``` csharp
+SMA_Series mean = new(source: data, period: p, useNaN: false);
+
+QuanTA fluent = data.SMA(period: p);
+```
+
+## Parameters
+
+- `TSeries source` -  List of value tuples (DateTime, double)
+- `int period` - Integer representing the period of SMA
+- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
+
+## Sample chart
+
+picture of SMA
+
+## Comparison & Validation
+
+Validation tests
+Performance tests
+
+## References
+   - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/

docs/getting_started.ipynb

@@ -2,7 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "# Quick Start\n",
     "\n",
@@ -17,35 +24,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<div><div></div><div></div><div></div></div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "Error",
-     "evalue": "(3,1): error CS0246: The type or namespace name 'Yahoo_Feed' could not be found (are you missing a using directive or an assembly reference?)\r\n(10,15): error CS0019: Operator '<' cannot be applied to operands of type 'int' and 'method group'",
-     "output_type": "error",
-     "traceback": [
-      "(3,1): error CS0246: The type or namespace name 'Yahoo_Feed' could not be found (are you missing a using directive or an assembly reference?)\r\n",
-      "(10,15): error CS0019: Operator '<' cannot be applied to operands of type 'int' and 'method group'"
-     ]
-    }
-   ],
    "source": [
     "#r \"nuget:QuanTAlib;\"\n",
     "using QuanTAlib;\n",
@@ -63,7 +51,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "## Understanding QuanTAlib data model\n",
     "\n",
@@ -72,26 +67,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>Item1</th><th>Item2</th></tr></thead><tbody><tr><td>0</td><td><span>2022-11-10 00:00:00Z</span></td><td><div class=\"dni-plaintext\">105.3</div></td></tr><tr><td>1</td><td><span>2022-11-10 15:47:46Z</span></td><td><div class=\"dni-plaintext\">293.1</div></td></tr><tr><td>2</td><td><span>2022-11-10 15:47:46Z</span></td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><span>2022-11-07 15:47:46Z</span></td><td><div class=\"dni-plaintext\">10</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "var item1 = (DateTime.Today, 105.3);        // (DateTime, Value) tuple\n",
     "double item2 = 293.1;                       // a simple double\n",
@@ -107,66 +92,60 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">105.3</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">293.1</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">10</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "data.v"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "The last element on the list can be accessed by .Last() or by [^1] - and using `.t` (time) and `.v` (value) properties. Also, casting a TSeries into (double) will return the value of the last element"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"dni-plaintext\">10</div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "bool IsTheSame = data.Last().v == data[^1].v;\n",
     "double lastvalue = data;\n",
@@ -176,33 +155,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "All indicators are just modified TSeries classes; they get all required input during class construction (source of the datafeed, period...) and they automatically subscribe to events of the datafeed. Whenever datafeed gets a new value, indicator will calculate its own value. Indicators are also event publishers, so other indicators can subscribe to their results, chaining indicators together:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">Infinity</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">0.6666666666666666</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0.3333333333333333</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">0.2</div></td></tr><tr><td>4</td><td><div class=\"dni-plaintext\">0.14285714285714285</div></td></tr><tr><td>5</td><td><div class=\"dni-plaintext\">0.1111111111111111</div></td></tr><tr><td>6</td><td><div class=\"dni-plaintext\">0.09090909090909091</div></td></tr><tr><td>7</td><td><div class=\"dni-plaintext\">0.07692307692307693</div></td></tr><tr><td>8</td><td><div class=\"dni-plaintext\">0.06666666666666667</div></td></tr><tr><td>9</td><td><div class=\"dni-plaintext\">0.058823529411764705</div></td></tr><tr><td>10</td><td><div class=\"dni-plaintext\">0.25</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n",
     "EMA_Series t2 = new(t1, 3);     // t2 will auto-load all history of t1 and wait for events from t1\n",
@@ -218,7 +194,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
    "source": [
     "# MACD compounded indicator\n",
     "\n",
@@ -227,26 +210,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
     },
     "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>4</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>5</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>6</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>7</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>8</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>9</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>10</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>11</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>12</td><td><div class=\"dni-plaintext\">0.13543589743590018</div></td></tr><tr><td>13</td><td><div class=\"dni-plaintext\">-0.03897954353340993</div></td></tr><tr><td>14</td><td><div class=\"dni-plaintext\">-0.17731008431411102</div></td></tr><tr><td>15</td><td><div class=\"dni-plaintext\">-0.24030671152304095</div></td></tr><tr><td>16</td><td><div class=\"dni-plaintext\">-0.08247055673614988</div></td></tr><tr><td>17</td><td><div class=\"dni-plaintext\">-0.47898448490240814</div></td></tr><tr><td>18</td><td><div class=\"dni-plaintext\">-0.9020715041856615</div></td></tr><tr><td>19</td><td><div class=\"dni-plaintext\">-1.3489730137363423</div></td></tr><tr><td colspan=\"2\"><i>(51 more)</i></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "Yahoo_Feed aapl = new(\"AAPL\", 100);\n",
     "TSeries close = aapl.Close;                 // close will get data from history\n",
@@ -266,14 +239,33 @@
    "language": "C#",
    "name": ".net-csharp"
   },
-  "language_info": {
+  "polyglot_notebook": {
-   "file_extension": ".cs",
+   "kernelInfo": {
-   "mimetype": "text/x-csharp",
+    "defaultKernelName": "csharp",
-   "name": "C#",
+    "items": [
-   "pygments_lexer": "csharp",
+     {
-   "version": "9.0"
+      "aliases": [
-  },
+       "c#",
-  "orig_nbformat": 4
+       "C#"
+      ],
+      "languageName": "C#",
+      "name": "csharp"
+     },
+     {
+      "aliases": [
+       "frontend"
+      ],
+      "languageName": null,
+      "name": "vscode"
+     },
+     {
+      "aliases": [],
+      "languageName": "KQL",
+      "name": "kql"
+     }
+    ]
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/index.html

@@ -6,20 +6,26 @@
   <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
   <meta name="description" content="Description">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, minimum-scale=1.0">
-  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsify-themeable@0/dist/css/theme-simple.css">
+  <link rel="stylesheet" href="//cdn.jsdelivr.net/npm/docsify@4/lib/themes/vue.css">
+
 </head>
 <body>
   <div id="app"></div>
   <script>
     window.$docsify = {
-      name: 'QuanTAlib',
+      name: '',
-      repo: 'mihakralj/quantalib'
+      repo: '',
-    }
+      latex: {
+        inlineMath   : [['$', '$'], ['\\(', '\\)']], // default
+        displayMath  : [['$$', '$$']],               // default
+        }
+    };
   </script>
-  <script src="//cdn.jsdelivr.net/npm/prismjs@1/components/prism-csharp.min.js"></script>
   <!-- Docsify v4 -->
   <script src="//cdn.jsdelivr.net/npm/docsify@4"></script>
-  <script src="//cdn.jsdelivr.net/npm/docsify-themeable@0/dist/js/docsify-themeable.min.js"></script>
+  <!-- LaTeX display engine -->
-  <script src="//unpkg.com/@rakutentech/docsify-code-inline/dist/index.min.js"></script>
+  <script src="//cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--  docsify-latex plugin -->
+  <script src="//cdn.jsdelivr.net/npm/docsify-latex@0"></script>
 </body>
 </html>

docs/readme.md

@@ -34,28 +34,31 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 
 ⛔= Not implemented (yet)
 
-| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** | **Tulip** |
-|--|:--:|:--:|:--:|:--:|
+|--|:--:|:--:|:--:|:--:|:--:|
 | ⭐ OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
 | ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 | hl2 |
 | ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 | hlc3 |
 | ⭐ OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
-| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 |
+| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 | avgprice |
 | ⭐ HLCC4 - Weighted Price |  `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
 | ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || midpoint |
 | ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || midprice |
-| ⭐ MAX - Max value | `MAX_Series` | MAX |||
+| ⭐ MAX - Max value | `MAX_Series` | MAX ||| max |
-| ⭐ MIN - Min value | `MIN_Series` | MIN |||
+| ⭐ MIN - Min value | `MIN_Series` | MIN ||| min |
-| ⭐ SUM - Summation | `SUM_Series` | SUM |||
+| ⭐ SUM - Summation | `SUM_Series` | SUM ||| sum |
-| ⭐ ADD - Addition | `ADD_Series` | ADD |||
+| ⭐ ADD - Addition | `ADD_Series` | ADD ||| add |
-| ⭐ SUB - Subtraction | `SUB_Series` | SUB |||
+| ⭐ SUB - Subtraction | `SUB_Series` | SUB ||| sub |
-| ⭐ MUL - Multiplication | `MUL_Series` | MUL |||
+| ⭐ MUL - Multiplication | `MUL_Series` | MUL ||| mul |
-| ⭐ DIV - Division | `DIV_Series` | DIV |||
+| ⭐ DIV - Division | `DIV_Series` | DIV ||| div |
 |||||
-| **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **STATISTICS & NUMERICAL ANALYSIS** |
+||||||
 | ⭐ BIAS - Bias | `BIAS_Series` ||| bias |
 | ⭐ CORR - Pearson's Correlation Coefficient | `CORR_Series` | CORREL | GetCorrelation ||
 | ⭐ COVAR - Covariance | `COVAR_Series` || GetCorrelation ||
+| ⛔ DECAY - Linear Decay ||||| decay |
+| ⛔ EDECAY - Exponential Decay ||||| edecay |
 | ⭐ ENTROPY - Entropy | `ENTROPY_Series` ||| entropy |
 | ⭐ KURTOSIS - Kurtosis | `KURT_Series` ||| kurtosis |
 | ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
@@ -73,22 +76,23 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
 | ⭐ ZSCORE - Number of standard deviations from mean | `ZSCORE_Series` || GetStdDev | zscore |
 ||||||
-| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **TREND INDICATORS & AVERAGES** |
+||||||
 | ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
 | ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma | alma |
 | ⛔ ARIMA - Autoregressive Integrated Moving Average |||||
-| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema |
+| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema | dema |
-| ⭐ EMA - Exponential Moving Average | `EMA_Series` | EMA | GetEma | ema |
+| ⭐ EMA - Exponential Moving Average | `EMA_Series` | EMA | GetEma | ema | ema |
 | ⛔ EPMA - Endpoint Moving Average ||| GetEpma ||
 | ⛔ FRAMA - Fractal Adaptive Moving Average |||||
 | ⛔ FWMA - Fibonacci's Weighted Moving Average |||| fwma |
 | ⛔ HILO - Gann High-Low Activator |||| hilo |
 | ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||||
 | ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline ||
-| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma | hma |
+| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma | hma | hma |
 | ⛔ HWMA - Holt-Winter Moving Average |||| hwma |
 | ✔️ JMA - Jurik Moving Average | `JMA_Series` ||| jma |
-| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama |
+| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama | kama |
 | ⛔ KDJ - KDJ Indicator (trend reversal) |||| kdj |
 | ⛔ LSMA - Least Squares Moving Average |||||
 | ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd | macd |
@@ -113,17 +117,20 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⭐ 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** |
+| **VOLATILITY INDICATORS** |
-| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad |
+||||||
-| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc |
+| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad | ad |
-| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr |
+| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc | adosc |
+| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr | atr |
 | ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
 | ⛔ BETA - Beta coefficient || BETA | GetBeta ||
-| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands ||
+| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands || bbands |
 | ⛔ CHAND - Chandelier Exit ||| GetChandelier ||
 | ⛔ CRSI - Connor RSI ||| GetConnorsRsi ||
+| ⛔ CVI - Chaikins Volatility ||||| cvi |
 | ⛔ DON - Donchian Channels ||| GetDonchian ||
 | ⛔ FCB - Fractal Chaos Bands ||| GetFcb ||
+| ⛔ FISHER - Fisher Transform ||| GetFcb || fisher |
 | ⛔ HV - Historical Volatility |||||
 | ⛔ ICH - Ichimoku ||| GetIchimoku ||
 | ⛔ KEL - Keltner Channels ||| GetKeltner ||
@@ -137,23 +144,25 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ UI - Ulcer Index |||||
 | ⛔ VSTOP - Volatility Stop |||||
 ||||||
-| **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **MOMENTUM INDICATORS & OSCILLATORS** |
+||||||
 | ⛔ AC - Acceleration Oscillator |||||
-| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx ||
+| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx || adx |
-| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx ||
+| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx || adxr |
-| ⛔ AO - Awesome Oscillator ||| GetAwesome ||
+| ⛔ AO - Awesome Oscillator ||| GetAwesome || ao |
-| ⛔ APO - Absolute Price Oscillator || APO |||
+| ⛔ APO - Absolute Price Oscillator || APO ||| apo |
-| ⛔ AROON - Aroon oscillator || AROON | GetAroon ||
+| ⛔ AROON - Aroon oscillator || AROON | GetAroon || aroon |
-| ⛔ BOP - Balance of Power || BOP | GetBop ||
+| ⛔ BOP - Balance of Power || BOP | GetBop || bop |
-| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci ||
+| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci || cci |
 | ⛔ CFO - Chande Forcast Oscillator |||||
-| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo ||
+| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo || cmo |
 | ⛔ 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 ||
+| ⛔ FOSC - Forecast oscillator ||||| fosc |
 | ⛔ GAT - Alligator oscillator ||| GetGator ||
 | ⛔ HURST - Hurst Exponent ||| GetHurst ||
 | ⛔ KRI - Kairi Relative Index |||||
@@ -176,10 +185,12 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
 | ⛔ WGAT - Williams Alligator |||||
 ||||||
-| **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **VOLUME INDICATORS** |
+||||||
 | ⛔ AOBV - Archer On-Balance Volume |||||
 | ⛔ CMF - Chaikin Money Flow |||||
-| ⛔ EOM - Ease of Movement |||||
+| ⛔ EOM - Ease of Movement ||||| emv |
+| ⛔ KVO - Klinger Volume Oscilaltor ||||| kvo |
 | ⭐ OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
 | ⛔ PRS - Price Relative Strength ||||
 | ⛔ PVOL - Price-Volume |||||