. (#22)

.github/workflows/main_automation.yml

@@ -45,6 +45,19 @@ jobs:
         #configFilePath: GitVersion.yml
         updateAssemblyInfo: true
 
+############## Install Python
+
+    - name: Install pandas-ta
+      run: |
+        sudo apt install python3.10
+        sudo apt install python3.10-dev
+        pip3 install numpy
+        pip3 install pandas
+        pip3 install pandas-ta
+        python --version
+
+############## Install more tools
+
     - name: Install JDK11 for Sonar Scanner
       uses: actions/setup-java@v3
       with:
@@ -158,3 +171,4 @@ jobs:
           --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }}
           --source https://api.nuget.org/v3/index.json
           --skip-duplicate
+

.sonarlint/mihakralj_quantalibcsharp.ruleset

@@ -372,6 +372,7 @@
     <Rule Id="S5547" Action="Warning" />
     <Rule Id="S5659" Action="Warning" />
     <Rule Id="S5773" Action="Warning" />
+    <Rule Id="S5856" Action="None" />
     <Rule Id="S6354" Action="None" />
     <Rule Id="S6419" Action="None" />
     <Rule Id="S6420" Action="None" />

Calculations/Calculations.csproj

@@ -2,7 +2,9 @@
 <Project Sdk="Microsoft.NET.Sdk">
 	<PropertyGroup>
 		<Title>QuanTAlib</Title>
-		<Version>0.2.0</Version>
+		<Version>0.2.5</Version>
+		<AssemblyVersion>0.2.5</AssemblyVersion>
+		<FileVersion>0.2.5</FileVersion>
 		<Product>Library of TA Calculations, Charts and Strategies for Quantower</Product>
 		<Description>Quantitative Technical Analysis Library in C# for Quantower</Description>
 		<RepositoryType>git</RepositoryType>
@@ -33,9 +35,6 @@
 		<PackageLicenseExpression>Apache-2.0</PackageLicenseExpression>
 		<PackageLicenseFile>
 		</PackageLicenseFile>
-		<AssemblyVersion>0.2.1.0</AssemblyVersion>
-		<FileVersion>0.2.1.0</FileVersion>
-		<InformationalVersion>0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d</InformationalVersion>
 		<SuppressNETSdkWarningProperty>NETSDK1057</SuppressNETSdkWarningProperty>
     <SuppressNETSdkWarningProperty>IDE1006</SuppressNETSdkWarningProperty>
 		<SuppressNETCoreSdkPreviewMessage>true</SuppressNETCoreSdkPreviewMessage>

Calculations/Logic/CROSS_Series.cs

@@ -29,9 +29,9 @@ public class CROSS_Series : Pair_TSeries_Indicator {
 		val = TValue1.v == TValue2.v ? 0 : val;
 		double over = TValue1.v > TValue2.v ? 1 : val;
 
-		val = (this._previous < over) ? 1 : -1;
-		(System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t,
-						((this._previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val);
+		val = (_previous < over) ? 1 : -1;
+		val = ((_previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val;
+		(System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t,val);
 
 		this._previous = over;
 

Strategies/Strategies.csproj

@@ -42,6 +42,9 @@
       <Link>QuanTAlib\%(RecursiveDir)%(Filename)%(Extension)</Link>
     </Compile>
   </ItemGroup>
+  <ItemGroup>
+    <AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
+  </ItemGroup>
   <ItemGroup>
     <Reference Include="TradingPlatform.BusinessLayer">
       <HintPath>..\.github\TradingPlatform.BusinessLayer.dll</HintPath>

Tests/Tests.csproj

@@ -1,6 +1,6 @@
 <Project Sdk="Microsoft.NET.Sdk">
   <PropertyGroup>
-    <TargetFrameworks>net8.0</TargetFrameworks>
+    <TargetFrameworks>net7.0</TargetFrameworks>
     <LangVersion>preview</LangVersion>
     <ImplicitUsings>enable</ImplicitUsings>
     <Nullable>enable</Nullable>
@@ -18,8 +18,7 @@
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
       <PrivateAssets>all</PrivateAssets>
     </PackageReference>
-    <PackageReference Include="Python.Included" Version="3.11.2" />
-    <PackageReference Include="pythonnet" Version="3.1.0-preview2023-03-04" />
+    <PackageReference Include="pythonnet" Version="3.0.1" />
     <PackageReference Include="xunit" Version="2.4.2" />
     <PackageReference Include="xunit.runner.visualstudio" Version="2.4.5">
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
@@ -29,7 +28,7 @@
     <PackageReference Include="TALib.NETCore" Version="0.4.4" />
     <PackageReference Include="Skender.Stock.Indicators" Version="3.0.0-preview1014-0015" />
     <PackageReference Include="Tulip.NETCore" Version="0.8.0.1" />
-    <PackageReference Include="System.Text.Json" Version="8.0.0-preview.2.23128.3" />
+    <PackageReference Include="System.Text.Json" Version="8.0.0-preview.3.23174.8" />
   </ItemGroup>
   <ItemGroup>
     <ProjectReference Include="..\Calculations\Calculations.csproj" />

Tests/Validations/Trends/Pandas_TA.cs

@@ -1,439 +1,416 @@
 using Xunit;
 using System;
 using QuanTAlib;
+using System.Runtime.InteropServices;
+using System.Runtime.InteropServices.Marshalling;
 using Python.Runtime;
-using Python.Included;
 
 namespace Validations;
 
-/*
-public class PandasTA : IDisposable
-{
+public class PandasTA : IDisposable {
+	private bool disposed = false;
 	private readonly GBM_Feed bars;
-    private readonly Random rnd = new();
+	private readonly Random rnd = new();
 	private readonly int period, skip;
-	private int digits;
-	private readonly string dllpath;
+	private readonly int digits;
 	private readonly dynamic np;
 	private readonly dynamic ta;
 	private readonly dynamic pd;
 	private readonly dynamic df;
 
 	public PandasTA() {
-    bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
-    period = rnd.Next(maxValue: 28) + 3;
-    skip = period+10;
-	  digits = 8;
+		bars = new GBM_Feed(5000, 0.8, 0.0);
+		period = rnd.Next(28) + 3;
+		skip = period + 50;
+		digits = 8;
 
-		Installer.InstallPath = Path.GetFullPath(path: ".");
-		Installer.SetupPython().Wait();
-		Installer.TryInstallPip();
-		Installer.PipInstallModule(module_name: "numpy");
-		Installer.PipInstallModule(module_name: "pandas");
-		Installer.PipInstallModule(module_name: "pandas-ta");
-		dllpath = Installer.InstallPath + "\\" + Installer.InstallDirectory + "\\" + Runtime.PythonDLL;
-		Runtime.PythonDLL = dllpath;
+		var pythonDLL = PythonLibrary.Locate();
+		Runtime.PythonDLL = pythonDLL;
 		PythonEngine.Initialize();
 
-    np = Py.Import(name: "numpy");
-		pd = Py.Import(name: "pandas");
-		ta = Py.Import(name: "pandas_ta");
+		np = Py.Import("numpy");
+		pd = Py.Import("pandas");
+		ta = Py.Import("pandas_ta");
 
-		string[] cols = { "open", "high", "low", "close", "volume" };
-		double[,] ary = new double[bars.Count, 5];
-		for (int i = 0; i < bars.Count; i++) {
+		string[] cols = {"open", "high", "low", "close", "volume"};
+		var ary = new double[bars.Count, 5];
+		for (var 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));
 	}
-		public void Dispose()
-	{
-    PythonEngine.Shutdown();
-	  GC.SuppressFinalize(this);
+
+	public void Dispose() {
+		Dispose(true);
+		PythonEngine.Shutdown();
+		GC.SuppressFinalize(this);
 	}
 
-	[Fact] void ADL() {
-		ADL_Series QL = new(bars);
-		var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
-		for (int i = QL.Length-1; i > skip; i--)
-		{
-            double QL_item = QL[i-1].v;
-			double PanTA_item = (double)pta[i-1];
-			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+	~PandasTA() {
+		Dispose(false);
+	}
 
+	protected virtual void Dispose(bool disposing) {
+		if (!disposed) {
+			disposed = true;
 		}
 	}
-  /*
-	[Fact] void ADOSC() {
-		ADOSC_Series QL = new(bars);
-		var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-    [Fact] void ATR() {
-		ATR_Series QL = new(bars, period);
-		var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
+
 	[Fact]
-	void BBANDS() {
+	private void ADL() {
+		ADL_Series QL = new(bars);
+		var pta = df.ta.ad(high: df.high, low: df.low, close: df.close, volume: df.volume);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void BBANDS() {
 		BBANDS_Series QL = new(bars.Close, period);
 		var pta = df.ta.bbands(close: df.close, length: period).to_numpy();
-		for (int i = QL.Length-1; i > skip; i--) {
-			double QL_item = QL.Lower[i].v;
-			double PanTA_item = (double)pta[i][0]; //lower
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL.Lower[i].v;
+			var PanTA_item = (double) pta[i][0]; //lower
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 			QL_item = QL.Mid[i].v;
-			PanTA_item = (double)pta[i][1]; //mid
+			PanTA_item = (double) pta[i][1]; //mid
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 			QL_item = QL.Upper[i].v;
-			PanTA_item = (double)pta[i][2]; //upper
+			PanTA_item = (double) pta[i][2]; //upper
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 		}
 	}
-	[Fact] void BIAS() {
+
+	[Fact]
+	private void BIAS() {
 		BIAS_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.bias(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
 	[Fact]
-	void CCI() {
+	private void CCI() {
 		CCI_Series QL = new(bars, period, false);
 		var pta = df.ta.cci(close: df.close, length: period);
-		for (int i = QL.Length-1; i > skip; i--) {
-			double QL_item = QL[i - 1].v;
-			double PanTA_item = (double)pta[i - 1];
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 		}
 	}
-  
+
 	[Fact]
-	void CMO() {
-		CMO_Series QL = new(bars.Close, period, false);
-		var pta = df.ta.cmo(close: df.close, length: period);
-		for (int i = QL.Length-1; i > skip; i--) {
-			double QL_item = QL[i - 1].v;
-			double PanTA_item = (double)pta[i - 1];
-			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-		}
-	}
-  
-	[Fact] void DEMA() {
+	private void DEMA() {
 		DEMA_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.dema(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-  
-	[Fact] void EMA() {
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void EMA() {
 		EMA_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.ema(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-  
-	[Fact] void ENTROPY() {
-		ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
-		var pta = df.ta.entropy(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void HL2() {
-		var pta = df.ta.hl2(high: df.high, low: df.low);
-        for (int i = bars.HL2.Length-1; i > skip; i--)
-        {
-            double QL_item = bars.HL2[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
 	}
-	[Fact] void HLC3() {
+
+	[Fact]
+	private void ENTROPY() {
+		ENTROPY_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.entropy(close: df.close, length: period);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void HL2() {
+		var pta = df.ta.hl2(high: df.high, low: df.low);
+		for (var i = bars.HL2.Length - 1; i > skip; i--) {
+			var QL_item = bars.HL2[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void HLC3() {
 		var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
-        for (int i = bars.HLC3.Length; i > skip; i--)
-        {
-            double QL_item = bars.HLC3[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-    [Fact] void HMA() {
+		for (var i = bars.HLC3.Length; i > skip; i--) {
+			var QL_item = bars.HLC3[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void HMA() {
 		HMA_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.hma(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
 
-	  }
-    [Fact] void HWMA() {
-		HWMA_Series QL = new(bars.Close, useNaN: false);
-		var pta = df.ta.hwma(close: df.close);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-
-	  }
-    [Fact] void KAMA() {
-        KAMA_Series QL = new(bars.Close, period);
-        var pta = df.ta.kama(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void KURTOSIS() {
-		KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
-		var pta = df.ta.kurtosis(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-  }
 	[Fact]
-	void MACD() {
-		MACD_Series QL = new(bars.Close, 26,fast: 12,signal:9);
+	private void KURTOSIS() {
+		KURTOSIS_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.kurtosis(close: df.close, length: period);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void MACD() {
+		MACD_Series QL = new(bars.Close, 26, 12, 9, false);
 		var pta = df.ta.macd(close: df.close).to_numpy();
-		for (int i = QL.Length-1; i > skip; i--) {
-			double QL_item = QL[i - 1].v;
-			double PanTA_item = (double)pta[i - 1][0];
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1][0];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 			QL_item = QL.Signal[i - 1].v;
-			PanTA_item = (double)pta[i - 1][2];
+			PanTA_item = (double) pta[i - 1][2];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 		}
 	}
-	[Fact] void MAD()
-	{
-		MAD_Series QL = new(bars.Close, period, useNaN: false);
+
+	[Fact]
+	private void MAD() {
+		MAD_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.mad(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void MEDIAN() {
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void MEDIAN() {
 		MEDIAN_Series QL = new(bars.Close, period);
 		var pta = df.ta.median(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-    [Fact] void OBV() {
-        OBV_Series QL = new(bars);
-        var pta = df.ta.obv(close: df.close, volume: df.volume);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void OHLC4() {
-		var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
-        for (int i = bars.OHLC4.Length; i > skip; i--)
-        {
-            double QL_item = bars.OHLC4[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-	}
-	[Fact] void RMA() {
-		RMA_Series QL = new(bars.Close, period, false);
-		var pta = df.ta.rma(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void RSI() {
-		RSI_Series QL = new(bars.Close, period);
-		var pta = df.ta.rsi(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void SDEV()	{
-		SDEV_Series QL = new(bars.Close, period, useNaN: false);
-		var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-  
-	[Fact] void SMA() {
-		SMA_Series QL = new(bars.Close, period, false);
-		var pta = df.ta.sma(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-  
-	[Fact] void SSDEV() {
-		SSDEV_Series QL = new(bars.Close, period, useNaN: false);
-		var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void SVARIANCE() {
-		SVAR_Series QL = new(bars.Close, period);
-		var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-
-    [Fact] void T3() {
-        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-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-    [Fact] void TEMA() {
-		TEMA_Series QL = new(bars.Close, period, false);
-		var pta = df.ta.tema(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void TR() {
-		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-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void TRIMA() {
-    // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
-		TRIMA_Series QL = new(bars.Close, 11);
-		var pta = df.ta.trima(close: df.close, length: 11);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact]	void TRIX() {
-		TRIX_Series QL = new(bars.Close, period);
-		var pta = df.ta.trix(close: df.close, length: period).to_numpy();
-		for (int i = QL.Length-1; i > skip; i--) {
-			double QL_item = QL[i - 1].v;
-			double PanTA_item = (double)pta[i - 1][0];
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
 		}
 	}
-	[Fact] void VARIANCE()	{
+
+	[Fact]
+	private void OBV() {
+		OBV_Series QL = new(bars);
+		var pta = df.ta.obv(close: df.close, volume: df.volume);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void OHLC4() {
+		var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
+		for (var i = bars.OHLC4.Length; i > skip; i--) {
+			var QL_item = bars.OHLC4[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void SDEV() {
+		SDEV_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void SMA() {
+		SMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.sma(close: df.close, length: period);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void SSDEV() {
+		SSDEV_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void SVARIANCE() {
+		SVAR_Series QL = new(bars.Close, period);
+		var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void TEMA() {
+		TEMA_Series QL = new(bars.Close, period, false);
+		var pta = df.ta.tema(close: df.close, length: period);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void TR() {
+		TR_Series QL = new(bars);
+		var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void TRIMA() {
+		// TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
+		TRIMA_Series QL = new(bars.Close, 11);
+		var pta = df.ta.trima(close: df.close, length: 11);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void VARIANCE() {
 		VAR_Series QL = new(bars.Close, period);
-		var pta = df.ta.variance(close: df.close, length: period, ddof:0);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void WMA() {
+		var pta = df.ta.variance(close: df.close, length: period, ddof: 0);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void WMA() {
 		WMA_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.wma(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void ZLEMA() {
-		ZLEMA_Series QL = new(bars.Close, period, false);
-		var pta = df.ta.zlma(close: df.close, length: period);
-        for (int i = QL.Length-1; i > skip; i--)
-        {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
-        }
-    }
-	[Fact] void ZSCORE() {
-		ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+
+	[Fact]
+	private void ZSCORE() {
+		ZSCORE_Series QL = new(bars.Close, period, false);
 		var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
-        for (int i = QL.Length-1; i > skip; i--)
+		for (var i = QL.Length - 1; i > skip; i--) {
+			var QL_item = QL[i - 1].v;
+			var PanTA_item = (double) pta[i - 1];
+			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+		}
+	}
+}
+
+public static class PythonLibrary {
+	public static string Locate() {
+		if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) {
+			string[] paths = Environment.GetEnvironmentVariable("PATH")?.Split(';') ?? Array.Empty<string>();
+			foreach (string path in paths) {
+				string[] pythonDLLs = Directory.GetFiles(path, "python3*.dll");
+				if (pythonDLLs.Length > 0) {
+					foreach (string item in pythonDLLs) {
+						if (!item.EndsWith("python3.dll", StringComparison.OrdinalIgnoreCase)) {
+							return item;
+						}
+					}
+
+				}
+			}
+			throw new FileNotFoundException("Python library not found in PATH");
+		}
+		else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) {
+            return "/usr/lib/x86_64-linux-gnu/libpython3.10.so";
+/*
+            List<string> pythonLibraries = new List<string>();
+			List<string> directoriesToSearch = new List<string> { "/home/runner/.local/lib" }; // Add more directories as needed
+        	string filePattern = "libpython3.*.so";
+        	SearchFiles(directoriesToSearch, filePattern, pythonLibraries);
+
+			if (pythonLibraries.Count > 0) {
+				return pythonLibraries[0];
+			}
+			else {
+				throw new FileNotFoundException("Python library not found");
+			}
+*/
+		}
+
+		else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) {
+			throw new NotSupportedException("Not supported yet");
+		}
+
+		else { throw new NotSupportedException("Unsupported operating system"); }
+	}
+	static void SearchFiles(List<string> directoriesToSearch, string filePattern, List<string> foundFiles)
+    {
+        foreach (string directory in directoriesToSearch)
         {
-            double QL_item = QL[i - 1].v;
-            double PanTA_item = (double)pta[i - 1];
-            Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
+            if (Directory.Exists(directory))
+            {
+                try
+                {
+                    string[] files = Directory.GetFiles(directory, filePattern, SearchOption.AllDirectories);
+                    foundFiles.AddRange(files);
+                }
+                catch (Exception e)
+                {
+                    Console.WriteLine("Error searching in directory: " + directory + " - " + e.Message);
+                }
+            }
         }
     }
-  
-}
-*/
+}

Tests/Validations/Trends/Skender.cs

@@ -30,6 +30,7 @@ public class Skender
     });
   }
 
+/*
   [Fact]
   public void ADL()
   {
@@ -42,6 +43,7 @@ public class Skender
       Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
 		}
   }
+  */
   [Fact]
   public void ALMA()
   {
@@ -57,7 +59,7 @@ public class Skender
   [Fact]
   public void ATR()
   {
-    ATR_Series QL = new(bars, period, false);
+    ATR_Series QL = new(bars, period:period,useNaN: false);
     var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!);
     for (int i = QL.Length; i > skip; i--)
     {

Tests/Validations/Trends/TA_LIB.cs

@@ -72,7 +72,7 @@ public class Ta_Lib
   [Fact]
   public void ATR()
   {
-    ATR_Series QL = new(bars, period, false);
+    ATR_Series QL = new(bars, period:period, useNaN: false);
     Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
     for (int i = QL.Length - 1; i > skip; i--)
     {

Tests/Validations/Trends/Tulip.cs

@@ -80,7 +80,7 @@ public class Tulip_Test
 		double[][] arrin = { inhigh, inlow, inclose };
 		double[][] arrout = { outdata };
 
-		ATR_Series QL = new(bars, period, false);
+		ATR_Series QL = new(bars, period:period, useNaN:false);
 		Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
 		//Tulip ATR doesn't use warm-up SMA, compensating with 200 warming bars
 		for (int i = QL.Length - 1; i > 200+skip; i--)