diff --git a/Tests/Validations/Trends/Pandas_TA.cs b/Tests/Validations/Trends/Pandas_TA.cs
index 611d9efe..34b56c1b 100644
--- a/Tests/Validations/Trends/Pandas_TA.cs
+++ b/Tests/Validations/Trends/Pandas_TA.cs
@@ -9,7 +9,7 @@ public class PandasTA : IDisposable
 {
 	private readonly GBM_Feed bars;
     private readonly Random rnd = new();
-	private readonly int period, sample;
+	private readonly int period, skip;
 	private int digits;
 	private readonly string dllpath;
 	private readonly dynamic np;
@@ -20,8 +20,8 @@ public class PandasTA : IDisposable
 	public PandasTA() {
     bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
     period = rnd.Next(maxValue: 28) + 3;
-    sample = period+1;
-	  digits = 10;
+    skip = period+10;
+	  digits = 8;
 
 		Installer.InstallPath = Path.GetFullPath(path: ".");
 		Installer.SetupPython().Wait();
@@ -30,10 +30,7 @@ public class PandasTA : IDisposable
 		Installer.PipInstallModule(module_name: "pandas");
 		Installer.PipInstallModule(module_name: "pandas-ta");
 		dllpath = Installer.InstallPath + "\\" + Installer.InstallDirectory + "\\" + Runtime.PythonDLL;
-
 		Runtime.PythonDLL = dllpath;
-    //Runtime.PythonDLL = "python.dll";
-
 		PythonEngine.Initialize();
 
     np = Py.Import(name: "numpy");
@@ -60,7 +57,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+		for (int i = QL.Length-1; i > skip; i--)
 		{
             double QL_item = QL[i-1].v;
 			double PanTA_item = (double)pta[i-1];
@@ -72,7 +69,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -82,7 +79,7 @@ public class PandasTA : IDisposable
     [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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -93,7 +90,7 @@ public class PandasTA : IDisposable
 	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 > QL.Length - sample; i--) {
+		for (int i = QL.Length-1; i > skip; i--) {
 			double QL_item = QL.Lower[i].v;
 			double PanTA_item = (double)pta[i][0]; //lower
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -108,7 +105,7 @@ public class PandasTA : IDisposable
 	[Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -119,7 +116,7 @@ public class PandasTA : IDisposable
 	void CCI() {
 		CCI_Series QL = new(bars, period, false);
 		var pta = df.ta.cci(close: df.close, length: period);
-		for (int i = QL.Length; i > QL.Length - sample; i--) {
+		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));
@@ -130,7 +127,7 @@ public class PandasTA : IDisposable
 	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; i > QL.Length - sample; i--) {
+		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));
@@ -140,7 +137,7 @@ public class PandasTA : IDisposable
 	[Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -150,7 +147,7 @@ public class PandasTA : IDisposable
 	[Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -160,7 +157,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -169,7 +166,7 @@ public class PandasTA : IDisposable
     }
 	[Fact] void HL2() {
 		var pta = df.ta.hl2(high: df.high, low: df.low);
-        for (int i = bars.HL2.Length; i > bars.HL2.Length-sample; i--)
+        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];
@@ -178,7 +175,7 @@ public class PandasTA : IDisposable
 	}
 	[Fact] void HLC3() {
 		var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
-        for (int i = bars.HLC3.Length; i > bars.HLC3.Length-sample; i--)
+        for (int i = bars.HLC3.Length; i > skip; i--)
         {
             double QL_item = bars.HLC3[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -188,7 +185,7 @@ public class PandasTA : IDisposable
     [Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -199,7 +196,7 @@ public class PandasTA : IDisposable
     [Fact] void HWMA() {
 		HWMA_Series QL = new(bars.Close, useNaN: false);
 		var pta = df.ta.hwma(close: df.close);
-        for (int i = QL.Length; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -210,7 +207,7 @@ public class PandasTA : IDisposable
     [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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -220,7 +217,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -231,7 +228,7 @@ public class PandasTA : IDisposable
 	void MACD() {
 		MACD_Series QL = new(bars.Close, 26,fast: 12,signal:9);
 		var pta = df.ta.macd(close: df.close).to_numpy();
-		for (int i = QL.Length; i > QL.Length - sample; i--) {
+		for (int i = QL.Length-1; i > skip; i--) {
 			double QL_item = QL[i - 1].v;
 			double PanTA_item = (double)pta[i - 1][0];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -244,7 +241,7 @@ public class PandasTA : IDisposable
 	{
 		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 > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -254,7 +251,7 @@ public class PandasTA : IDisposable
 	[Fact] void MEDIAN() {
 		MEDIAN_Series QL = new(bars.Close, period);
 		var pta = df.ta.median(close: df.close, length: period);
-        for (int i = QL.Length; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -264,7 +261,7 @@ public class PandasTA : IDisposable
     [Fact] void OBV() {
         OBV_Series QL = new(bars);
         var pta = df.ta.obv(close: df.close, volume: df.volume);
-        for (int i = QL.Length; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -273,7 +270,7 @@ public class PandasTA : IDisposable
     }
 	[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 > bars.OHLC4.Length-sample; i--)
+        for (int i = bars.OHLC4.Length; i > skip; i--)
         {
             double QL_item = bars.OHLC4[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -283,7 +280,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -293,7 +290,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -303,7 +300,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -313,7 +310,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -323,7 +320,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -333,7 +330,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -344,7 +341,7 @@ public class PandasTA : IDisposable
     [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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -354,7 +351,7 @@ public class PandasTA : IDisposable
     [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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -364,7 +361,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -375,7 +372,7 @@ public class PandasTA : IDisposable
     // 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -385,7 +382,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length - sample; i--) {
+		for (int i = QL.Length-1; i > skip; i--) {
 			double QL_item = QL[i - 1].v;
 			double PanTA_item = (double)pta[i - 1][0];
 			Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -394,7 +391,7 @@ public class PandasTA : IDisposable
 	[Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -404,7 +401,7 @@ public class PandasTA : IDisposable
 	[Fact] 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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -414,7 +411,7 @@ public class PandasTA : IDisposable
 	[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; i > QL.Length-sample; i--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
@@ -424,7 +421,7 @@ public class PandasTA : IDisposable
 	[Fact] void ZSCORE() {
 		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--)
+        for (int i = QL.Length-1; i > skip; i--)
         {
             double QL_item = QL[i - 1].v;
             double PanTA_item = (double)pta[i - 1];
diff --git a/docs/SMA.md b/docs/SMA.md
index 8a1d0707..94b1da1d 100644
--- a/docs/SMA.md
+++ b/docs/SMA.md
@@ -1,9 +1,5 @@
 # SMA: Simple Moving Average
 
-period = 10
-
-![Alt text](./img/SMA_chart.svg)
-
 SMA is is an arithmetic moving average where the weights in SMA are **equally** distributed across the given period, resulting in a mean() of the data within the period.
 
 ## Calculation
@@ -12,38 +8,50 @@ SMA is a rolling calculation that is looking backwards from the position ${n}$ a
 $$
 SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i
 $$
-When calculating the value of the next $SMA_{p,next}$ while knowing all previous SMA values, SMA calculation can be reduced to:
+When calculating the value of the next $SMA_{p,next}$ while knowing previous SMA values, SMA calculation can be reduced to:
 $$
 SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right)
 $$
 
-## Reference Calculation
+## Implementation
+`TSeries SMA_Series (TSeries source, int period = 0, bool useNaN = false)`
+
+- SMA_Series returns TSeries list
+- `source`: input of type TSeries; SMA_Series automatically subscribes to events of new data added to the source
+- `period`: optional size of a lookback window; if set to 0, SMA calculates cumulative average across the whole source
+- `useNaN`: if set to _true_, SMA_Series will hide values within the initial period with NaN (for compatibility with other libraries)
+
+[Link to source](..\Source\Trends\SMA_Series.cs)
+
+## Behavior
+![Alt text](./img/SMA_chart.svg)
+## Reference Calculation & Validation
 period = 5
 ```
 TSeries data = new() {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29};
 SMA_Series sma = new(data, 5, useNaN: false);
-SMA_Series sma_nan = new(data, 5, useNaN: true);
-for (int i=0; i< data.Count; i++)
-    Console.WriteLine($"{i}\t{data[i].v,7:f2}\t{sma_nan[i].v,7:f3}\t{sma[i].v,7:f3}");
 ```
 
-|#|input|sma_NaN|sma|
-|--|:--:|:--:|:--:|
-|0|  81.59|    NaN| 81.590|
-|1|  81.06|    NaN| 81.325|
-|2|  82.87|    NaN| 81.840|
-|3|  83.00|    NaN| 82.130|
-|4|  83.61| 82.426| 82.426|
-|5|  83.15| 82.738| 82.738|
-|6|  82.84| 83.094| 83.094|
-|7|  83.99| 83.318| 83.318|
-|8|  84.55| 83.628| 83.628|
-|9|  84.36| 83.778| 83.778|
-|10|  85.53| 84.254| 84.254|
-|11|  86.54| 84.994| 84.994|
-|12|  86.89| 85.574| 85.574|
-|13|  87.77| 86.218| 86.218|
-|14|  87.29| 86.804| 86.804|
+| #| Input | **QuanTAlib** | _TA-LIB_ | _Skender_ | _Pandas-TA_ | _Tulip_ |
+|--|:--:|:--:|:--:|:--:|:--:|:--:|
+| 0| 212.80|**212.80**| _NaN_| _NaN_| _NaN_| _NaN_|
+| 1| 214.06|**213.43**| _NaN_| _NaN_| _NaN_| _NaN_|
+| 2| 213.89|**213.58**| _NaN_| _NaN_| _NaN_| _NaN_|
+| 3| 214.66|**213.85**| _NaN_| _NaN_| _NaN_| _NaN_|
+| 4| 213.95|**213.87**| _213.87_| _213.87_| _213.87_| _213.87_|
+| 5| 213.95|**214.10**| _214.10_| _214.10_| _214.10_| _214.10_|
+| 6| 214.55|**214.20**| _214.20_| _214.20_| _214.20_| _214.20_|
+| 7| 214.02|**214.23**| _214.23_| _214.23_| _214.23_| _214.23_|
+| 8| 214.51|**214.20**| _214.20_| _214.20_| _214.20_| _214.20_|
+| 9| 213.75|**214.16**| _214.16_| _214.16_| _214.16_| _214.16_|
+|10| 214.22|**214.21**| _214.21_| _214.21_| _214.21_| _214.21_|
+|11| 213.43|**213.99**| _213.99_| _213.99_| _213.99_| _213.99_|
+|12| 214.21|**214.02**| _214.02_| _214.02_| _214.02_| _214.02_|
+|13| 213.66|**213.85**| _213.85_| _213.85_| _213.85_| _213.85_|
+|14| 215.03|**214.11**| _214.11_| _214.11_| _214.11_| _214.11_|
+|15| 216.89|**214.64**| _214.64_| _214.64_| _214.64_| _214.64_|
+|16| 216.66|**215.29**| _215.29_| _215.29_| _215.29_| _215.29_|
+
 
 ## References
    - https://en.wikipedia.org/wiki/Moving_average#Simple_moving_average
diff --git a/docs/indicators.md b/docs/indicators.md
index 36a3e7d6..df76b040 100644
--- a/docs/indicators.md
+++ b/docs/indicators.md
@@ -75,7 +75,7 @@
 |PWMA - Pascal's Weighted Moving Average||||pwma|
 |⭐RMA - WildeR's Moving Average|`RMA_Series`|||✔️rma|✔️rma|
 |SINWMA - Sine Weighted Moving Average||||sinwma|
-|⭐SMA - Simple Moving Average|`SMA_Series`|✔️SMA|✔️GetSma|✔️sma|✔️sma|
+|⭐[SMA - Simple Moving Average](SMA.md)|`SMA_Series`|✔️SMA|✔️GetSma|✔️sma|✔️sma|
 |SMMA - Smoothed Moving Average|`SMMA_Series`||✔️GetSmma||
 |SSF - Ehler's Super Smoother Filter||||ssf|
 |SUPERTREND - Supertrend||||supertrend|