OBV, TRIMA

Source/Basics/MAX_Series.cs

@@ -22,7 +22,10 @@ public class MAX_Series : Single_TSeries_Indicator
 
         double _max = TValue.v;
         for (int i = 0; i < this._buffer.Count; i++)
-        { _max = (this._buffer[i] > _max) ? this._buffer[i] : _max; }
+        { 
+            //_max = (this._buffer[i] > _max) ? this._buffer[i] : _max; 
+            _max = Math.Max(this._buffer[i], _max);
+        }
 
         var result = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _max);
 

Source/Basics/MIN_Series.cs

@@ -22,7 +22,10 @@ public class MIN_Series : Single_TSeries_Indicator
 
         double _min = TValue.v;
         for (int i = 0; i < this._buffer.Count; i++)
-        { _min = (this._buffer[i] < _min) ? this._buffer[i] : _min; }
+        { 
+            //_min = (this._buffer[i] < _min) ? this._buffer[i] : _min; 
+            _min = Math.Min(this._buffer[i], _min);
+        }
 
         var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _min);
 

Source/Trends/ALMA_Series.cs

@@ -1,65 +1,65 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-ALMA: Arnaud Legoux Moving Average
-    The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
-    can be shifted from 0 to 1. This allows regulating the smoothness and high
-    sensitivity of the indicator. Sigma is another parameter that is responsible for
-    the shape of the curve coefficients. This moving average reduces lag of the data
-    in conjunction with smoothing to reduce noise.
-
-
-Sources:
-    https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
-    https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
-
-</summary> */
-
-public class ALMA_Series : Single_TSeries_Indicator
-{
-    private readonly System.Collections.Generic.List<double> _buffer = new();
-    private readonly double[] _weight;
-    private double _norm;
-    private readonly double _offset, _sigma;
-
-    public ALMA_Series(TSeries source, int period, double offset = 0.85, double sigma = 6.0, bool useNaN = false)
-        : base(source, period, useNaN)
-    {
-        _offset = offset;
-        _sigma = sigma;
-        _weight = new double[period];
-
-        if (this._data.Count > 0) { base.Add(this._data); }
-    }
-
-    public override void Add((System.DateTime t, double v) TValue, bool update)
-    {
-        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
-        else { this._buffer.Add(TValue.v); }
-        if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
-
-        if (this._buffer.Count <= _p) { calc_weights(); }
-
-        double _weightedSum = 0;
-        for (int i = 0; i < this._buffer.Count; i++) { _weightedSum += _weight[i] * _buffer[i]; }
-        double _alma = _weightedSum / _norm;
-
-        var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _alma);
-        base.Add(ret, update);
-    }
-
-    private void calc_weights()
-    {
-        int _len = this._buffer.Count;
-        _norm = 0;
-        double _m = _offset * (_len - 1);
-        double _s = _len / _sigma;
-        for (int i = 0; i < _len; i++)
-        {
-            double _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
-            _weight[i] = _wt;
-            _norm += _wt;
-        }
-    }
-}
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+ALMA: Arnaud Legoux Moving Average
+    The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
+    can be shifted from 0 to 1. This allows regulating the smoothness and high
+    sensitivity of the indicator. Sigma is another parameter that is responsible for
+    the shape of the curve coefficients. This moving average reduces lag of the data
+    in conjunction with smoothing to reduce noise.
+
+
+Sources:
+    https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
+    https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
+
+</summary> */
+
+public class ALMA_Series : Single_TSeries_Indicator
+{
+    private readonly System.Collections.Generic.List<double> _buffer = new();
+    private readonly double[] _weight;
+    private double _norm;
+    private readonly double _offset, _sigma;
+
+    public ALMA_Series(TSeries source, int period, double offset = 0.85, double sigma = 6.0, bool useNaN = false)
+        : base(source, period, useNaN)
+    {
+        _offset = offset;
+        _sigma = sigma;
+        _weight = new double[period];
+
+        if (this._data.Count > 0) { base.Add(this._data); }
+    }
+
+    public override void Add((System.DateTime t, double v) TValue, bool update)
+    {
+        if (update) { this._buffer[this._buffer.Count - 1] = TValue.v; }
+        else { this._buffer.Add(TValue.v); }
+        if (this._buffer.Count > this._p) { this._buffer.RemoveAt(0); }
+
+        if (this._buffer.Count <= _p) { calc_weights(); }
+
+        double _weightedSum = 0;
+        for (int i = 0; i < this._buffer.Count; i++) { _weightedSum += _weight[i] * _buffer[i]; }
+        double _alma = _weightedSum / _norm;
+
+        var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _alma);
+        base.Add(ret, update);
+    }
+
+    private void calc_weights()
+    {
+        int _len = this._buffer.Count;
+        _norm = 0;
+        double _m = _offset * (_len - 1);
+        double _s = _len / _sigma;
+        for (int i = 0; i < _len; i++)
+        {
+            double _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
+            _weight[i] = _wt;
+            _norm += _wt;
+        }
+    }
+}

Source/Trends/KAMA_Series.cs

@@ -1,65 +1,65 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-KAMA: Kaufman's Adaptive Moving Average
-    Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as
-    Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972,
-    it was not until the popular book titled "Trading Systems and Methods" that it was made widely
-    available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive
-    Moving Average, considers market volatility apart from price fluctuations.
-
-    KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
-
-Sources:
-    https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work
-    https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/
-    https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average
-
-Remark:
-    If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards.
-    Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields
-    slightly different results for the first 50 bars - and then converges with the other one.
-
-</summary> */
-
-public class KAMA_Series : Single_TSeries_Indicator
-{
-  private readonly double _scFast, _scSlow;
-  private readonly System.Collections.Generic.List<double> _buffer = new();
-  private double _lastkama = double.NaN;
-  private double _lastlastkama;
-
-  public KAMA_Series(TSeries source, int period, int fast = 2, int slow= 30, bool useNaN = false) : base(source, period, useNaN) {
-    _scFast = 2.0 / (fast+1);
-    _scSlow = 2.0 / (slow+1);
-    if (base._data.Count > 0) { base.Add(base._data); }
-  }
-  public override void Add((System.DateTime t, double v) TValue, bool update)
-  {
-    if (update){
-      _buffer[_buffer.Count - 1] = TValue.v;
-      this._lastkama = this._lastlastkama;
-    } else {
-      _buffer.Add(TValue.v);
-    }
-    if (_buffer.Count > _p + 1) { _buffer.RemoveAt(0); }
-    double _kama = 0;
-    if (this.Count < this._p) {
-        for (int i = 0; i < this._buffer.Count; i++) { _kama += this._buffer[i]; }
-        _kama /= this._buffer.Count;
-    } else {
-      double _change = Math.Abs(_buffer[_buffer.Count - 1] - _buffer[(_buffer.Count > _p + 1) ? 1 : 0]);
-      double _sumpv = 0;
-      for (int i = 1; i < _buffer.Count; i++)
-      { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); }
-      double _er = (_sumpv == 0) ? 0 : _change / _sumpv;
-      double _sc = (_er * (_scFast - _scSlow)) + _scSlow;
-      _kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama)));
-    }
-    _lastlastkama = _lastkama;
-    _lastkama = _kama;
-    var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _kama);
-    base.Add(result, update);
-  }
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+KAMA: Kaufman's Adaptive Moving Average
+    Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as
+    Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972,
+    it was not until the popular book titled "Trading Systems and Methods" that it was made widely
+    available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive
+    Moving Average, considers market volatility apart from price fluctuations.
+
+    KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
+
+Sources:
+    https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work
+    https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/
+    https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average
+
+Remark:
+    If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards.
+    Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields
+    slightly different results for the first 50 bars - and then converges with the other one.
+
+</summary> */
+
+public class KAMA_Series : Single_TSeries_Indicator
+{
+  private readonly double _scFast, _scSlow;
+  private readonly System.Collections.Generic.List<double> _buffer = new();
+  private double _lastkama = double.NaN;
+  private double _lastlastkama;
+
+  public KAMA_Series(TSeries source, int period, int fast = 2, int slow= 30, bool useNaN = false) : base(source, period, useNaN) {
+    _scFast = 2.0 / (fast+1);
+    _scSlow = 2.0 / (slow+1);
+    if (base._data.Count > 0) { base.Add(base._data); }
+  }
+  public override void Add((System.DateTime t, double v) TValue, bool update)
+  {
+    if (update){
+      _buffer[_buffer.Count - 1] = TValue.v;
+      this._lastkama = this._lastlastkama;
+    } else {
+      _buffer.Add(TValue.v);
+    }
+    if (_buffer.Count > _p + 1) { _buffer.RemoveAt(0); }
+    double _kama = 0;
+    if (this.Count < this._p) {
+        for (int i = 0; i < this._buffer.Count; i++) { _kama += this._buffer[i]; }
+        _kama /= this._buffer.Count;
+    } else {
+      double _change = Math.Abs(_buffer[_buffer.Count - 1] - _buffer[(_buffer.Count > _p + 1) ? 1 : 0]);
+      double _sumpv = 0;
+      for (int i = 1; i < _buffer.Count; i++)
+      { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); }
+      double _er = (_sumpv == 0) ? 0 : _change / _sumpv;
+      double _sc = (_er * (_scFast - _scSlow)) + _scSlow;
+      _kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama)));
+    }
+    _lastlastkama = _lastkama;
+    _lastkama = _kama;
+    var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _kama);
+    base.Add(result, update);
+  }
 }

Source/Trends/TRIMA_Series.cs

@@ -0,0 +1,49 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+TRIMA: Triangular Moving Average
+    A weighted moving average where the shape of the weights are triangular and the greatest
+    weight is in the middle of the period,
+
+Sources:
+    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/
+
+Remark:
+    trima = sma(sma(signal, n/2), n/2)
+
+</summary> */
+
+public class TRIMA_Series : Single_TSeries_Indicator
+{
+    private readonly System.Collections.Generic.List<double> _buffer1 = new();
+    private readonly System.Collections.Generic.List<double> _buffer2 = new();
+    private readonly int _p1a, _p1b;
+    
+    public TRIMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
+    {
+        _p1a = (int) Math.Floor((period * 0.5) + 1);
+        _p1b = (int) Math.Ceiling(0.5 * period);
+        if (base._data.Count > 0) { base.Add(base._data); }
+    }
+
+    public override void Add((System.DateTime t, double v) TValue, bool update)
+    {
+        if (update) { _buffer1[_buffer1.Count - 1] = TValue.v; } else { _buffer1.Add(TValue.v); }
+        if (_buffer1.Count > this._p1b && this._p1b != 0) { _buffer1.RemoveAt(0); }
+        
+        double _sma1 = 0;
+        for (int i = 0; i < _buffer1.Count; i++) { _sma1 += _buffer1[i]; }
+        _sma1 /= this._buffer1.Count;
+
+        if (update) { _buffer2[_buffer2.Count - 1] = _sma1; } else { _buffer2.Add(_sma1); }
+        if (_buffer2.Count > this._p1a && this._p1a != 0) { _buffer2.RemoveAt(0); }
+
+        double _trima = 0;        
+        for (int i = 0; i < _buffer2.Count; i++) { _trima += _buffer2[i]; }
+        _trima /= this._buffer2.Count;
+
+        var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _trima);
+        base.Add(result, update);
+    }
+}

Source/Trends/ZLEMA_Series.cs

@@ -1,72 +1,72 @@
-namespace QuanTAlib;
-using System;
-
-/* <summary>
-ZLEMA: Zero Lag Exponential Moving Average
-    The Zero lag exponential moving average (ZLEMA) indicator was created by John
-    Ehlers and Ric Way.
-
-The formula for a given N-Day period and for a given Data series is:
-    Lag = (Period-1)/2
-    Ema Data = {Data+(Data-Data(Lag days ago))
-    ZLEMA = EMA (EmaData,Period)
-
-Remark:
-    The idea is do a regular exponential moving average (EMA) calculation but on a
-    de-lagged data instead of doing it on the regular data. Data is de-lagged by
-    removing the data from "lag" days ago thus removing (or attempting to remove)
-    the cumulative lag effect of the moving average.
-
-</summary> */
-
-public class ZLEMA_Series : Single_TSeries_Indicator
-{
-	private readonly System.Collections.Generic.List<double> _buffer = new();
-	private readonly double _k, _k1m;
-	private double _lastema, _lastlastema;
-
-	public ZLEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
-	{
-		this._k = 2.0 / (this._p + 1);
-		this._k1m = 1.0 - this._k;
-		this._lastema = this._lastlastema = double.NaN;
-		if (base._data.Count > 0)
-		{ base.Add(base._data); }
-	}
-
-	public override void Add((System.DateTime t, double v) TValue, bool update)
-	{
-		int _lag = (int)((_p-1) * 0.5);
-        _lag = (this.Count-_lag < 0) ? 0 : this.Count-_lag;
-        double _zl = TValue.v + (TValue.v - _data[_lag].v);
-
-		double _ema = 0;
-		if (update)
-		{ this._lastema = this._lastlastema; }
-		if (this.Count < this._p)
-		{
-			if (update)
-			{ this._buffer[this._buffer.Count - 1] = _zl; }
-			else
-			{
-				this._buffer.Add(_zl);
-			}
-			if (this._buffer.Count > this._p)
-			{ this._buffer.RemoveAt(0); }
-
-			for (int i = 0; i < this._buffer.Count; i++)
-			{ _ema += this._buffer[i]; }
-			_ema /= this._buffer.Count;
-		}
-		else
-		{
-			_ema = (_zl * this._k) + (this._lastema * this._k1m);
-		}
-
-		this._lastlastema = this._lastema;
-		this._lastema = _ema;
-
-		var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema);
-		base.Add(ret, update);
-	}
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+ZLEMA: Zero Lag Exponential Moving Average
+    The Zero lag exponential moving average (ZLEMA) indicator was created by John
+    Ehlers and Ric Way.
+
+The formula for a given N-Day period and for a given Data series is:
+    Lag = (Period-1)/2
+    Ema Data = {Data+(Data-Data(Lag days ago))
+    ZLEMA = EMA (EmaData,Period)
+
+Remark:
+    The idea is do a regular exponential moving average (EMA) calculation but on a
+    de-lagged data instead of doing it on the regular data. Data is de-lagged by
+    removing the data from "lag" days ago thus removing (or attempting to remove)
+    the cumulative lag effect of the moving average.
+
+</summary> */
+
+public class ZLEMA_Series : Single_TSeries_Indicator
+{
+	private readonly System.Collections.Generic.List<double> _buffer = new();
+	private readonly double _k, _k1m;
+	private double _lastema, _lastlastema;
+
+	public ZLEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
+	{
+		this._k = 2.0 / (this._p + 1);
+		this._k1m = 1.0 - this._k;
+		this._lastema = this._lastlastema = double.NaN;
+		if (base._data.Count > 0)
+		{ base.Add(base._data); }
+	}
+
+	public override void Add((System.DateTime t, double v) TValue, bool update)
+	{
+		int _lag = (int)((_p-1) * 0.5);
+        _lag = (this.Count-_lag < 0) ? 0 : this.Count-_lag;
+        double _zl = TValue.v + (TValue.v - _data[_lag].v);
+
+		double _ema = 0;
+		if (update)
+		{ this._lastema = this._lastlastema; }
+		if (this.Count < this._p)
+		{
+			if (update)
+			{ this._buffer[this._buffer.Count - 1] = _zl; }
+			else
+			{
+				this._buffer.Add(_zl);
+			}
+			if (this._buffer.Count > this._p)
+			{ this._buffer.RemoveAt(0); }
+
+			for (int i = 0; i < this._buffer.Count; i++)
+			{ _ema += this._buffer[i]; }
+			_ema /= this._buffer.Count;
+		}
+		else
+		{
+			_ema = (_zl * this._k) + (this._lastema * this._k1m);
+		}
+
+		this._lastlastema = this._lastema;
+		this._lastema = _ema;
+
+		var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema);
+		base.Add(ret, update);
+	}
 }

Source/Volume/OBV_Series.cs

@@ -0,0 +1,62 @@
+namespace QuanTAlib;
+using System;
+
+/* <summary>
+OBV: On-Balance Volume
+    On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict
+    changes in stock price. Joseph Granville first developed the OBV metric in the 1963 book 
+    Granville's New Key to Stock Market Profits.
+
+                          | +volume; if close > close[previous]
+    OBV = OBV[previous] + | 0;       if close = close[previous]
+                          | -volume; if close < close[previous]
+
+Sources:
+    https://www.investopedia.com/terms/o/onbalancevolume.asp
+    https://www.tradingview.com/wiki/On_Balance_Volume_(OBV)  
+    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/
+    https://www.motivewave.com/studies/on_balance_volume.htm
+
+Note:
+    There is no consensus on what is the first OBV value in the series:
+    - TA-LIB uses the first volume: OBV[0] = volume[0]
+    - Skender stock library uses 0: OBV[0] = 0
+
+</summary> */
+
+public class OBV_Series : Single_TBars_Indicator
+{
+	private double _lastobv, _lastlastobv;
+	private double _lastclose, _lastlastclose;
+	public OBV_Series(TBars source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN)
+	{
+		this._lastobv = this._lastlastobv = 0;
+		this._lastclose = this._lastlastclose = 0;
+		if (_bars.Count > 0) { base.Add(_bars); }
+  }
+
+  public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update) 
+  {
+    if (update)
+    {
+	    this._lastobv = this._lastlastobv;
+	    this._lastclose = this._lastlastclose;
+    }
+
+    double _obv = this._lastobv;
+    if (TBar.c > this._lastclose) { _obv += TBar.v; }
+    if (TBar.c < this._lastclose) { _obv -= TBar.v; }
+
+    // Unclear what the first value in OBV series is - currently set to volume[0]
+    // if (this.Count == 0) { _obv = 0; }
+
+    this._lastlastobv = this._lastobv;
+    this._lastobv = _obv;
+
+    this._lastlastclose = this._lastclose;
+    this._lastclose = TBar.c;
+
+		var result = (TBar.t, (this.Count < this._p  && this._NaN) ? double.NaN : _obv);
+    base.Add(result, update);
+  }
+}

Tests/Validations/Skender_Stock.cs

@@ -13,7 +13,7 @@ public class Skender_Stock
 
 	public Skender_Stock()
 	{
-		this.bars = new(1000);
+		this.bars = new(Bars: 1, Volatility:0.7, Drift:0.0);
 		this.period = this.rnd.Next(28) + 3;
 		this.quotes = this.bars.Select(
 			q => new Quote
@@ -33,7 +33,7 @@ public class Skender_Stock
 		SMA_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetSma(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Sma!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Sma!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 		[Fact]
@@ -42,7 +42,7 @@ public class Skender_Stock
 		EMA_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetEma(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Ema!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Ema!, 6), Math.Round(QL.Last().v, 6));
 	}
 	[Fact]
 	public void WMA()
@@ -50,7 +50,7 @@ public class Skender_Stock
 		WMA_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetWma(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Wma!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Wma!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -59,7 +59,7 @@ public class Skender_Stock
 		DEMA_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetDema(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Dema!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Dema!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -68,7 +68,7 @@ public class Skender_Stock
 		TEMA_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetTema(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Tema!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Tema!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -77,7 +77,7 @@ public class Skender_Stock
 		MAD_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetSmaAnalysis(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Mad!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Mad!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -86,7 +86,7 @@ public class Skender_Stock
 		MAPE_Series QL = new(this.bars.Close, this.period, false);
 		var SK = this.quotes.GetSmaAnalysis(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Mape!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Mape!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -95,7 +95,18 @@ public class Skender_Stock
 		ATR_Series QL = new(this.bars, this.period, false);
 		var SK = this.quotes.GetAtr(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Atr!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Atr!, 6), Math.Round(QL.Last().v, 6));
+	}
+
+	[Fact]
+	public void OBV()
+	{
+		OBV_Series QL = new(this.bars, this.period, false);
+		var SK = this.quotes.GetObv(this.period);
+
+		// adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB
+		Assert.Equal(Math.Round((double)SK.Last().Obv!, 6) + Math.Round((double)this.quotes.First().Volume!, 6),
+			Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -104,7 +115,7 @@ public class Skender_Stock
 		ADL_Series QL = new(this.bars, false);
 		var SK = this.quotes.GetAdl();
 
-		Assert.Equal(Math.Round((double)SK.Last().Adl!, 6), Math.Round(QL.Last().v, 6));
+		Assert.Equal(Math.Round((double)SK.Last().Adl!, 5), Math.Round(QL.Last().v, 5));
 	}
 
 	[Fact]
@@ -113,7 +124,7 @@ public class Skender_Stock
 		CCI_Series QL = new(this.bars, this.period, false);
 		var SK = this.quotes.GetCci(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Cci!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Cci!, 6), Math.Round(QL.Last().v, 6));
 	}
 
 	[Fact]
@@ -122,7 +133,7 @@ public class Skender_Stock
 		ATRP_Series QL = new(this.bars, this.period, false);
 		var SK = this.quotes.GetAtr(this.period);
 
-		Assert.Equal(Math.Round((double)SK.Last().Atrp!, 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round((double)SK.Last().Atrp!, 6), Math.Round(QL.Last().v, 6));
 	}
 
     [Fact]
@@ -131,7 +142,7 @@ public class Skender_Stock
         KAMA_Series QL = new(this.bars.Close, this.period, useNaN: false);
         var SK = this.quotes.GetKama(this.period);
 
-        Assert.Equal(Math.Round((double)SK.Last().Kama!, 8), Math.Round(QL.Last().v, 8));
+        Assert.Equal(Math.Round((double)SK.Last().Kama!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -140,7 +151,7 @@ public class Skender_Stock
 	    HMA_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetHma(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Hma!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Hma!, 6), Math.Round(QL.Last().v, 6));
     }
 
 	[Fact]
@@ -149,7 +160,7 @@ public class Skender_Stock
 	    SMMA_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetSmma(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Smma!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Smma!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -158,8 +169,8 @@ public class Skender_Stock
 	    MACD_Series QL = new(this.bars.Close, 26,12,9, useNaN: false);
 	    var SK = this.quotes.GetMacd(12,26,9);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Macd!, 8), Math.Round(QL.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().Signal!, 8), Math.Round(QL.Signal.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Macd!, 6), Math.Round(QL.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().Signal!, 6), Math.Round(QL.Signal.Last().v, 6));
     }
 
     [Fact]
@@ -168,12 +179,12 @@ public class Skender_Stock
 	    BBANDS_Series QL = new(this.bars.Close, this.period, 2.0, useNaN: false);
 	    var SK = this.quotes.GetBollingerBands(this.period, 2.0);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Sma!, 8), Math.Round(QL.Mid.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().UpperBand!, 8), Math.Round(QL.Upper.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().LowerBand!, 8), Math.Round(QL.Lower.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().Width!, 8), Math.Round(QL.Bandwidth.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().PercentB!, 8), Math.Round(QL.PercentB.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().ZScore!, 8), Math.Round(QL.Zscore.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Sma!, 6), Math.Round(QL.Mid.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().UpperBand!, 6), Math.Round(QL.Upper.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().LowerBand!, 6), Math.Round(QL.Lower.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().Width!, 6), Math.Round(QL.Bandwidth.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().PercentB!, 6), Math.Round(QL.PercentB.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().ZScore!, 6), Math.Round(QL.Zscore.Last().v, 6));
     }
 
 	[Fact]
@@ -182,7 +193,7 @@ public class Skender_Stock
 	    RSI_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetRsi(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Rsi!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Rsi!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -191,7 +202,7 @@ public class Skender_Stock
 	    ALMA_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetAlma(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Alma!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Alma!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -200,7 +211,7 @@ public class Skender_Stock
 	    SDEV_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetStdDev(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().StdDev!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().StdDev!, 6), Math.Round(QL.Last().v, 6));
     }
 
 	[Fact]
@@ -209,10 +220,10 @@ public class Skender_Stock
 	    LINREG_Series QL = new(this.bars.Close, this.period, useNaN: false);
 	    var SK = this.quotes.GetSlope(this.period);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Slope!, 8), Math.Round(QL.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().Intercept!, 8), Math.Round(QL.Intercept.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().RSquared!, 8), Math.Round(QL.RSquared.Last().v, 8));
-	    Assert.Equal(Math.Round((double)SK.Last().StdDev!, 8), Math.Round(QL.StdDev.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Slope!, 6), Math.Round(QL.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().Intercept!, 6), Math.Round(QL.Intercept.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().RSquared!, 6), Math.Round(QL.RSquared.Last().v, 6));
+	    Assert.Equal(Math.Round((double)SK.Last().StdDev!, 6), Math.Round(QL.StdDev.Last().v, 6));
 	}
 
     [Fact]
@@ -221,7 +232,7 @@ public class Skender_Stock
 	    TR_Series QL = new(this.bars, useNaN: false);
 	    var SK = this.quotes.GetTr();
 
-	    Assert.Equal(Math.Round((double)SK.Last().Tr!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Tr!, 6), Math.Round(QL.Last().v, 6));
     }
 
 	[Fact]
@@ -230,7 +241,7 @@ public class Skender_Stock
 	    TSeries QL = this.bars.HL2;
 	    var SK = this.quotes.GetBaseQuote(CandlePart.HL2);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Value!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -239,7 +250,7 @@ public class Skender_Stock
 	    TSeries QL = this.bars.OC2;
 	    var SK = this.quotes.GetBaseQuote(CandlePart.OC2);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Value!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
     }
 
 	[Fact]
@@ -248,7 +259,7 @@ public class Skender_Stock
 	    TSeries QL = this.bars.HLC3;
 	    var SK = this.quotes.GetBaseQuote(CandlePart.HLC3);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Value!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -257,7 +268,7 @@ public class Skender_Stock
 	    TSeries QL = this.bars.OHL3;
 	    var SK = this.quotes.GetBaseQuote(CandlePart.OHL3);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Value!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
     }
 
     [Fact]
@@ -266,6 +277,6 @@ public class Skender_Stock
 	    TSeries QL = this.bars.OHLC4;
 	    var SK = this.quotes.GetBaseQuote(CandlePart.OHLC4);
 
-	    Assert.Equal(Math.Round((double)SK.Last().Value!, 8), Math.Round(QL.Last().v, 8));
+	    Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
     }
 }

Tests/Validations/TA_LIB.cs

@@ -18,7 +18,7 @@ public class TA_LIB
 
 	public TA_LIB()
 	{
-		this.bars = new(1000);
+		this.bars = new(5000);
 		this.period = this.rnd.Next(28) + 3;
 		this.TALIB = new double[this.bars.Count];
 		this.inopen = this.bars.Open.v.ToArray();
@@ -36,7 +36,7 @@ public class TA_LIB
 		ADD_Series QL = new(this.bars.Open, this.bars.Close);
 		Core.Add(this.inopen, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -45,7 +45,7 @@ public class TA_LIB
 		SUB_Series QL = new(this.bars.Open, this.bars.Close);
 		Core.Sub(this.inopen, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -54,7 +54,7 @@ public class TA_LIB
 		MUL_Series QL = new(this.bars.Open, this.bars.Close);
 		Core.Mult(this.inopen, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -63,7 +63,7 @@ public class TA_LIB
 		DIV_Series QL = new(this.bars.Open, this.bars.Close);
 		Core.Div(this.inopen, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -72,7 +72,7 @@ public class TA_LIB
 		SDEV_Series QL = new(this.bars.Close, this.period, false);
 		Core.StdDev(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -81,7 +81,16 @@ public class TA_LIB
 		SMA_Series QL = new(this.bars.Close, this.period, false);
 		Core.Sma(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
+	}
+
+	[Fact]
+	public void TRIMA()
+	{
+		TRIMA_Series QL = new(this.bars.Close, this.period, false);
+		Core.Trima(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
+
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -90,7 +99,7 @@ public class TA_LIB
 		EMA_Series QL = new(this.bars.Close, this.period, false);
 		Core.Ema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -98,8 +107,8 @@ public class TA_LIB
 	{
 		WMA_Series QL = new(this.bars.Close, this.period, false);
 		Core.Wma(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
-
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -108,7 +117,7 @@ public class TA_LIB
 		DEMA_Series QL = new(this.bars.Close, this.period, false);
 		Core.Dema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -117,7 +126,7 @@ public class TA_LIB
 		TEMA_Series QL = new(this.bars.Close, this.period, false);
 		Core.Tema(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -126,7 +135,7 @@ public class TA_LIB
 		MAX_Series QL = new(this.bars.Close, this.period, false);
 		Core.Max(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -135,7 +144,7 @@ public class TA_LIB
 		MIN_Series QL = new(this.bars.Close, this.period, false);
 		Core.Min(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -144,7 +153,16 @@ public class TA_LIB
 		ADL_Series QL = new(this.bars, false);
 		Core.Ad(this.inhigh, this.inlow, this.inclose, this.involume, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
+	}
+
+	[Fact]
+	public void OBV()
+	{
+		OBV_Series QL = new(this.bars, this.period, false);
+		Core.Obv(this.inclose, this.involume,  0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
+
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -153,7 +171,7 @@ public class TA_LIB
 		ADOSC_Series QL = new(this.bars, false);
 		Core.AdOsc(this.inhigh, this.inlow, this.inclose, this.involume, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -162,7 +180,7 @@ public class TA_LIB
 		ATR_Series QL = new(this.bars, this.period, false);
 		Core.Atr(this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -171,7 +189,7 @@ public class TA_LIB
 		CCI_Series QL = new(this.bars, this.period, false);
 		Core.Cci(this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -180,7 +198,7 @@ public class TA_LIB
 		RSI_Series QL = new(this.bars.Close, this.period, false);
 		Core.Rsi(this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _, this.period);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -189,7 +207,7 @@ public class TA_LIB
 		TR_Series QL = new(this.bars, false);
 		Core.TRange(this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -199,8 +217,8 @@ public class TA_LIB
 		double[] macdHist = new double[this.bars.Count];
 		MACD_Series QL = new(this.bars.Close, slow: 26, fast: 12, signal: 9, false);
 		Core.Macd(this.inclose, 0, this.bars.Count - 1, outMacd: this.TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _);
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
-		Assert.Equal(Math.Round(macdSignal[macdSignal.Length - outBegIdx - 1], 8), Math.Round(QL.Signal.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
+		Assert.Equal(Math.Round(macdSignal[macdSignal.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Signal.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -211,9 +229,9 @@ public class TA_LIB
 		double[] outLower = new double[this.bars.Count];
 		BBANDS_Series QL = new(this.bars.Close, period:26, multiplier:2.0, false);
 		Core.Bbands(this.inclose, 0, this.bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod:26, optInNbDevUp:2.0, optInNbDevDn:2.0);
-		Assert.Equal(Math.Round(outUpper[outUpper.Length - outBegIdx - 1], 7), Math.Round(QL.Upper.Last().v, 7));
-		Assert.Equal(Math.Round(outMiddle[outMiddle.Length - outBegIdx - 1], 7), Math.Round(QL.Mid.Last().v, 7));
-	  Assert.Equal(Math.Round(outLower[outLower.Length - outBegIdx - 1], 7), Math.Round(QL.Lower.Last().v, 7));
+		Assert.Equal(Math.Round(outUpper[outUpper.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Upper.Last().v, 6, MidpointRounding.AwayFromZero));
+		Assert.Equal(Math.Round(outMiddle[outMiddle.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Mid.Last().v, 6, MidpointRounding.AwayFromZero));
+	  Assert.Equal(Math.Round(outLower[outLower.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Lower.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -222,7 +240,7 @@ public class TA_LIB
 		TSeries QL = this.bars.HL2;
 		Core.MedPrice(this.inhigh, this.inlow, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -231,7 +249,7 @@ public class TA_LIB
 		TSeries QL = this.bars.HLC3;
 		Core.TypPrice(this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -240,7 +258,7 @@ public class TA_LIB
 		TSeries QL = this.bars.OHLC4;
 		Core.AvgPrice(this.inopen, this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 
 	[Fact]
@@ -249,6 +267,6 @@ public class TA_LIB
 		TSeries QL = this.bars.HLCC4;
 		Core.WclPrice( this.inhigh, this.inlow, this.inclose, 0, this.bars.Count - 1, this.TALIB, out int outBegIdx, out _);
 
-		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 8), Math.Round(QL.Last().v, 8));
+		Assert.Equal(Math.Round(this.TALIB[this.TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
 	}
 }

docs/readme.md

@@ -37,90 +37,90 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 
 | **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** |
 |--|:--:|:--:|:--:|
-| ✔️ OC2 - (Open+Close)/2 |️ `.OC2` || ️`GetBaseQuote` |
-| ⭐ HL2 - Median Price | `.HL2` | `MEDPRICE` | ️`GetBaseQuote` |
-| ⭐ HLC3 - Typical Price | `.HLC3` | `TYPPRICE` ||
+| ✔️ OC2 - (Open+Close)/2 |️ `.OC2` || ️GetBaseQuote |
+| ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | ️GetBaseQuote |
+| ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE ||
 | ✔️ OHL3 - (Open+High+Low)/3 | `.OHL3` |||
-| ⭐ OHLC4 - Average Price | `.OHLC4` | `AVGPRICE` |️ `GetBaseQuote` |
-| ⭐ HLCC4 - Weighted Price |  `.HLCC4` | `WCLPRICE` ||
-| ⭐ MAX - Max value | `MAX_Series` | `MAX` ||
-| ⭐ MIN - Min value | `MIN_Series` | `MIN` ||
-| ⛔ MID - Midpoint value || `MIDPOINT` ||
-| ⛔ MIDP - Midpoint price || `MIDPRICE` ||
-| ⛔ SUM - Summation || `SUM` ||
-| ⭐ ADD - Addition | `ADD_Series` | `ADD` ||
-| ⭐ SUB - Subtraction | `SUB_Series` | `SUB` ||
-| ⭐ MUL - Multiplication | `MUL_Series` | `MUL` ||
-| ⭐ DIV - Division | `DIV_Series` | `DIV` ||
+| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ GetBaseQuote |
+| ⭐ HLCC4 - Weighted Price |  `.HLCC4` | WCLPRICE ||
+| ⭐ MAX - Max value | `MAX_Series` | MAX ||
+| ⭐ MIN - Min value | `MIN_Series` | MIN ||
+| ⛔ MID - Midpoint value || MIDPOINT ||
+| ⛔ MIDP - Midpoint price || MIDPRICE ||
+| ⛔ SUM - Summation || SUM ||
+| ⭐ ADD - Addition | `ADD_Series` | ADD ||
+| ⭐ SUB - Subtraction | `SUB_Series` | SUB ||
+| ⭐ MUL - Multiplication | `MUL_Series` | MUL ||
+| ⭐ DIV - Division | `DIV_Series` | DIV ||
 |||||
 | **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ✔️ BIAS - Bias | BIAS_Series |||
+| ✔️ BIAS - Bias | `BIAS_Series` |||
 | ⛔ CORREL - Pearson's Correlation Coefficient || CORREL | GetCorrelation |
 | ⛔ COVAR - Covariance ||| GetCorrelation |
-| ✔️ ENTP - Entropy | ENTP_Series |||
-| ✔️ KURT - Kurtosis | KURT_Series |||
-| ⭐ LINREG - Linear Regression | LINREG_Series || GetSlope |
-| ⭐ MAD - Mean Absolute Deviation | MAD_Series || GetSma |
-| ⭐ MAPE - Mean Absolute Percent Error | MAPE_Series || GetSma |
-| ✔️ MED - Median value | MED_Series |||
-| ✔️ MSE - Mean Squared Error | MSE_Series || GetSma |
+| ✔️ ENTP - Entropy | `ENTP_Series` |||
+| ✔️ KURT - Kurtosis | `KURT_Series` |||
+| ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope |
+| ⭐ MAD - Mean Absolute Deviation | `MAD_Series` || GetSma |
+| ⭐ MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma |
+| ✔️ MED - Median value | `MED_Series` |||
+| ✔️ MSE - Mean Squared Error | `MSE_Series` || GetSma |
 | ⛔ SKEW - Skewness ||||
-| ⭐ SDEV - Standard Deviation (Volatility) | SDEV_Series | STDDEV ||
-| ✔️ SSDEV - Sample Standard Deviation | SSDEV_Series |||
-| ✔️ SMAPE - Symmetric Mean Absolute Percent Error | SMAPE_Series |||
-| ✔️ VAR - Population Variance | VAR_Series | VAR ||
-| ✔️ SVAR - Sample Variance | SVAR_Series |||
+| ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV ||
+| ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` |||
+| ✔️ SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` |||
+| ✔️ VAR - Population Variance | `VAR_Series` | VAR ||
+| ✔️ SVAR - Sample Variance | `SVAR_Series` |||
 | ⛔ QUANT - Quantile ||||
-| ✔️ WMAPE - Weighted Mean Absolute Percent Error | WMAPE_Series |||
+| ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` |||
 | ⛔ ZSCORE - Number of standard deviations from mean ||||
 |||||
 | **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** |
 | ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average ||||
-| ⭐ ALMA - Arnaud Legoux Moving Average | ALMA_Series || GetAlma |
+| ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma |
 | ⛔ ARIMA - Autoregressive Integrated Moving Average ||||
-| ⭐ DEMA - Double EMA Average | DEMA_Series | DEMA | GetDema |
-| ⭐ EMA - Exponential Moving Average | EMA_Series || GetEma |
+| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema |
+| ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma |
 | ⛔ EPMA - Endpoint Moving Average ||| GetEpma |
 | ⛔ FRAMA - Fractal Adaptive Moving Average ||||
 | ⛔ FWMA - Fibonacci's Weighted Moving Average ||||
 | ⛔ HILO - Gann High-Low Activator ||||
-| ✔️ HEMA - Hull/EMA Average | HEMA_Series |||
+| ✔️ HEMA - Hull/EMA Average | `HEMA_Series` |||
 | ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline |
-| ⭐ HMA - Hull  Moving Average | HMA_Series || GetHma |
+| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma |
 | ⛔ HWMA - Holt-Winter Moving Average ||||
-| ✔️ JMA - Jurik Moving Average | JMA_Series |||
-| ⭐ KAMA - Kaufman's Adaptive Moving Average | KAMA_Series | KAMA | GetKama |
+| ✔️ JMA - Jurik Moving Average | `JMA_Series` |||
+| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama |
 | ⛔ KDJ - KDJ Indicator (trend reversal) ||||
 | ⛔ LSMA - Least Squares Moving Average ||||
-| ⭐ MACD - Moving Average Convergence/Divergence | MACD_Series | MACD | GetMacd |
+| ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd |
 | ⛔ MAMA - MESA Adaptive Moving Average || MAMA | GetMama |
 | ⛔ MCGD - McGinley Dynamic ||||
 | ⛔ MMA - Modified Moving Average ||||
 | ⛔ PPMA - Pivot Point Moving Average ||||
 | ⛔ PWMA - Pascal's Weighted Moving Average ||||
-| ✔️ RMA - WildeR's Moving Average | RMA__Series |||
+| ✔️ RMA - WildeR's Moving Average | `RMA_Series` |||
 | ⛔ SINWMA - Sine Weighted Moving Average ||||
-| ⭐ SMA - Simple Moving Average | SMA_Series |||
-| ⭐ SMMA - Smoothed Moving Average | SMMA_Series |||
+| ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma |
+| ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` |||
 | ⛔ SSF - Ehler's Super Smoother Filter ||||
 | ⛔ SUP - Supertrend ||||
 | ⛔ SWMA - Symmetric Weighted Moving Average ||||
 | ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 |
-| ⭐ TEMA - Triple EMA Average | TEMA_Series | TEMA | GetTema |
-| ⛔ TRIMA - Triangular Moving Average || TRIMA ||
+| ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema |
+| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA ||
 | ⛔ TSF - Time Series Forecast || TSF ||
 | ⛔ VIDYA - Variable Index Dynamic Average ||||
 | ⛔ VOR - Vortex Indicator ||||
-| ⭐ WMA - Weighted Moving Average | WMA_Series | WMA | GetWma |
-| ✔️ ZLEMA - Zero Lag EMA Average | ZLEMA_Series |||
+| ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma |
+| ✔️ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` |||
 |||||
 | **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** |
-| ⭐ ADL - Chaikin Accumulation Distribution Line | ADL_Series | AD | GetAdl |
-| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | ADOSC_Series | ADOSC| GetAdl |
-| ⭐ ATR - Average True Range | ATR_Series | ATR | GetAtr |
-| ⭐ ATRP - Average True Range Percent | ATRP_Series || GetAtr |
+| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl |
+| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl |
+| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr |
+| ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr |
 | ⛔ BETA - Beta coefficient || BETA | GetBeta |
-| ⭐ BBANDS - Bollinger Bands® | BBANDS_Series | BBANDS | GetBollingerBands |
+| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands |
 | ⛔ CHAND - Chandelier Exit ||| GetChandelier |
 | ⛔ CRSI - Connor RSI ||| GetConnorsRsi |
 | ⛔ DON - Donchian Channels ||| GetDonchian |
@@ -130,11 +130,11 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ KEL - Keltner Channels ||| GetKeltner |
 | ⛔ NATR - Normalized Average True Range || NATR | GetAtr |
 | ⛔ CHN - Price Channel Indicator ||||
-| ⭐ RSI - Relative Strength Index | RSI_Series | RSI | GetRsi |
+| ⭐ RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi |
 | ⛔ SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar |
 | ⛔ SRSI - Stochastic RSI || STOCHRSI | GetStochRsi |
 | ⛔ STARC - Starc Bands ||||
-| ⭐ TR - True Range | TR_Series | TRANGE | GetTr |
+| ⭐ TR - True Range | `TR_Series` | TRANGE | GetTr |
 | ⛔ UI - Ulcer Index ||||
 | ⛔ VSTOP - Volatility Stop ||||
 |||||
@@ -146,7 +146,7 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ APO - Absolute Price Oscillator || APO ||
 | ⛔ AROON - Aroon oscillator || AROON | GetAroon |
 | ⛔ BOP - Balance of Power || BOP | GetBop |
-| ⭐ CCI - Commodity Channel Index | CCI_Series | CCI | GetCci |
+| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci |
 | ⛔ CFO - Chande Forcast Oscillator ||||
 | ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo |
 | ⛔ COG - Center of Gravity ||||
@@ -181,7 +181,7 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ AOBV - Archer On-Balance Volume ||||
 | ⛔ CMF - Chaikin Money Flow ||||
 | ⛔ EOM - Ease of Movement ||||
-| ⛔ OBV - On-Balance Volume || OBV | GetObv |
+| ⭐ OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv |
 | ⛔ PRS - Price Relative Strength |||
 | ⛔ PVOL - Price-Volume ||||
 | ⛔ PVO - Percentage Volume Oscillator ||||