gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-19 11:08:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

Codacy cleanup

Browse Source
This commit is contained in:
Miha Kralj
2022-11-14 17:38:37 -08:00
parent 6907c3d92d
commit 39bb7b8a91
15 changed files with 563 additions and 476 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.github/workflows/main_automation.yml
+1
View File
@@ -103,6 +103,7 @@ jobs:
--skip-duplicate --skip-duplicate
- name: Push package to nuget.org - name: Push package to nuget.org
if: ${{ github.ref == 'refs/heads/main' }}
run: dotnet nuget push '.\Source\bin\Release\QuanTAlib.*.nupkg' run: dotnet nuget push '.\Source\bin\Release\QuanTAlib.*.nupkg'
--api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }} --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }}
--source https://api.nuget.org/v3/index.json --source https://api.nuget.org/v3/index.json
Source/Basics/MAX_Series.cs
-1
View File
@@ -23,7 +23,6 @@ public class MAX_Series : Single_TSeries_Indicator
double _max = TValue.v; double _max = TValue.v;
for (int i = 0; i < this._buffer.Count; i++) for (int i = 0; i < this._buffer.Count; i++)
{ {
//_max = (this._buffer[i] > _max) ? this._buffer[i] : _max;
_max = Math.Max(this._buffer[i], _max); _max = Math.Max(this._buffer[i], _max);
} }
Source/Basics/MIN_Series.cs
-1
View File
@@ -23,7 +23,6 @@ public class MIN_Series : Single_TSeries_Indicator
double _min = TValue.v; double _min = TValue.v;
for (int i = 0; i < this._buffer.Count; i++) for (int i = 0; i < this._buffer.Count; i++)
{ {
//_min = (this._buffer[i] < _min) ? this._buffer[i] : _min;
_min = Math.Min(this._buffer[i], _min); _min = Math.Min(this._buffer[i], _min);
} }
Source/Feeds/Alphavantage_Feed.cs
+2 -3
View File
@@ -8,8 +8,6 @@ Alphavantage - Free API to collect 100 recent daily quotes. It requires a (free)
Parameters: Parameters:
Symbol: stock ("AAPL"), Symbol: stock ("AAPL"),
APIkey: unique Alphavantage API key APIkey: unique Alphavantage API key
Usage:
Alphavantage_Feed ticker = new("MSFT", APIkey:"xxxxxxx");
</summary> */ </summary> */
@@ -47,9 +45,10 @@ public class Alphavantage_Feed : TBars
case "3b. low (USD)": l = Convert.ToDouble(val.Value.ToString()); break; case "3b. low (USD)": l = Convert.ToDouble(val.Value.ToString()); break;
case "4. close": c = Convert.ToDouble(val.Value.ToString()); break; case "4. close": c = Convert.ToDouble(val.Value.ToString()); break;
case "4b. close (USD)": c = Convert.ToDouble(val.Value.ToString()); break; case "4b. close (USD)": c = Convert.ToDouble(val.Value.ToString()); break;
//case "5. adjusted close": c = Convert.ToDouble(val.Value.ToString()); break; case "5. adjusted close": c = Convert.ToDouble(val.Value.ToString()); break;
case "5. volume": v = Convert.ToDouble(val.Value.ToString()); break; case "5. volume": v = Convert.ToDouble(val.Value.ToString()); break;
case "6. volume": v = Convert.ToDouble(val.Value.ToString()); break; case "6. volume": v = Convert.ToDouble(val.Value.ToString()); break;
default: o = 0; h = 0; l = 0; c = 0; v = 0; break;
} }
} }
return (date, o, h, l, c, v); return (date, o, h, l, c, v);
Source/Feeds/Yahoo_Feed.cs
+2 -4
View File
@@ -8,16 +8,14 @@ Yahoo Finance - Free API feed to collect daily market quotes
Symbol: stock symbol (default: "IBM") Symbol: stock symbol (default: "IBM")
Period: number of days of collected history (default: 252) Period: number of days of collected history (default: 252)
Usage: Usage:
Yahoo_Feed ticker = new("MSFT", 20); Yahoo_Feed ticker = new("MSFT", 20)
</summary> */ </summary> */
public class Yahoo_Feed : TBars public class Yahoo_Feed : TBars
{ {
private static string requestUrl;
public Yahoo_Feed(string Symbol = "IBM", int Period = 252) { public Yahoo_Feed(string Symbol = "IBM", int Period = 252) {
requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/"+ string requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/"+
Symbol+"?interval=1d&period1="+ Symbol+"?interval=1d&period1="+
(int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period+1)).ToUnixTimeSeconds()+"&period2="+ (int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period+1)).ToUnixTimeSeconds()+"&period2="+
(int)new DateTimeOffset(DateTime.UtcNow).ToUnixTimeSeconds(); (int)new DateTimeOffset(DateTime.UtcNow).ToUnixTimeSeconds();
Source/Statistics/LINREG_Series.cs
+3 -3
View File
@@ -71,11 +71,11 @@ public class LINREG_Series : Single_TSeries_Indicator
double _intercept = avgY - (_slope * avgX); double _intercept = avgY - (_slope * avgX);
// calculate Standard Deviation and R-Squared // calculate Standard Deviation and R-Squared
double stdDevX = Math.Sqrt((double)sumSqX / _len); double stdDevX = Math.Sqrt(sumSqX / _len);
double stdDevY = Math.Sqrt((double)sumSqY / _len); double stdDevY = Math.Sqrt(sumSqY / _len);
double _StdDev = stdDevY; double _StdDev = stdDevY;
double arrr = (stdDevX * stdDevY != 0) ? (double)sumSqXY / (stdDevX * stdDevY) / _len : 0; double arrr = (stdDevX * stdDevY != 0) ? sumSqXY / (stdDevX * stdDevY) / _len : 0;
double _RSquared = arrr * arrr; double _RSquared = arrr * arrr;
var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _slope); var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _slope);
Source/Volume/OBV_Series.cs
-3
View File
@@ -47,9 +47,6 @@ public class OBV_Series : Single_TBars_Indicator
if (TBar.c > this._lastclose) { _obv += TBar.v; } if (TBar.c > this._lastclose) { _obv += TBar.v; }
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._lastlastobv = this._lastobv;
this._lastobv = _obv; this._lastobv = _obv;
Tests/Validations/Pandas_TA.cs
+105 -21
View File
@@ -7,10 +7,10 @@ using Python.Included;
namespace Validations; namespace Validations;
public class PandasTA : IDisposable public class PandasTA : IDisposable
{ {
private GBM_Feed bars; private readonly GBM_Feed bars;
private Random rnd = new(); private readonly Random rnd = new();
private int period; private readonly int period;
private string OStype; private readonly string OStype;
private dynamic np; private dynamic np;
private dynamic ta; private dynamic ta;
private dynamic df; private dynamic df;
@@ -23,14 +23,19 @@ public class PandasTA : IDisposable
// Checking the host OS and setting PythonDLL accordingly // Checking the host OS and setting PythonDLL accordingly
OStype = Environment.OSVersion.ToString(); OStype = Environment.OSVersion.ToString();
if (OStype == "Unix 13.1.0") if (OStype == "Unix 13.1.0")
OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib"; {
else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll"; OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
}
else
{
OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
}
Installer.InstallPath = Path.GetFullPath("."); Installer.InstallPath = Path.GetFullPath(".");
Installer.SetupPython().Wait(); Installer.SetupPython().Wait();
Installer.TryInstallPip(); Installer.TryInstallPip();
Installer.PipInstallModule("pandas-ta"); Installer.PipInstallModule("pandas-ta");
//Installer.PipInstallModule("git+https://github.com/twopirllc/pandas-ta@development"); //alternative: git+https://github.com/twopirllc/pandas-ta
Runtime.PythonDLL = OStype; Runtime.PythonDLL = OStype;
PythonEngine.Initialize(); PythonEngine.Initialize();
@@ -76,7 +81,80 @@ public class PandasTA : IDisposable
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(bars.OHLC4.Last().v, 7)); Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(bars.OHLC4.Last().v, 7));
} }
[Fact] [Fact]
void MEDIAN()
{
MED_Series QL = new(bars.Close, period);
var pta = df.ta.median(close: df.close, length: period);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void VARIANCE()
{
VAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof:0);
Assert.Equal(Math.Round((double)pta.tail(1), 5), Math.Round(QL.Last().v, 5));
}
[Fact]
void SVARIANCE()
{
SVAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
Assert.Equal(Math.Round((double)pta.tail(1), 5), Math.Round(QL.Last().v, 5));
}
[Fact]
void ADL()
{
ADL_Series QL = new(bars);
var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void ADOSC()
{
ADOSC_Series QL = new(bars);
var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void TR()
{
TR_Series QL = new(bars);
var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void ATR()
{
ATR_Series QL = new(bars, period);
var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void RSI()
{
RSI_Series QL = new(bars.Close, period);
var pta = df.ta.rsi(close: df.close, length: period);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void TRIMA()
{
//TODO: return length to variable length (period) when Pandas-TA fixes trima
TRIMA_Series QL = new(bars.Close, 11);
var pta = df.ta.trima(close: df.close, length: 11);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void KAMA() void KAMA()
{ {
KAMA_Series QL = new(bars.Close, period); KAMA_Series QL = new(bars.Close, period);
@@ -84,17 +162,7 @@ public class PandasTA : IDisposable
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
/* [Fact]
[Fact]
void ALMA()
{
ALMA_Series QL = new(bars.Close, period: period, offset: 0.85, sigma: 6.0, false);
var pta = df.ta.alma(close: df.close, length: period, distribution_offset: 0.85, sigma: 6.0);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
*/
[Fact]
void HMA() void HMA()
{ {
HMA_Series QL = new(bars.Close, period, false); HMA_Series QL = new(bars.Close, period, false);
@@ -102,7 +170,7 @@ public class PandasTA : IDisposable
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void SMA() void SMA()
{ {
SMA_Series QL = new(bars.Close, period, false); SMA_Series QL = new(bars.Close, period, false);
@@ -142,7 +210,23 @@ public class PandasTA : IDisposable
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void RMA()
{
RMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.rma(close: df.close, length: period);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void ZLEMA()
{
ZLEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.zlma(close: df.close, length: period);
Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
}
[Fact]
void DEMA() void DEMA()
{ {
DEMA_Series QL = new(bars.Close, period, false); DEMA_Series QL = new(bars.Close, period, false);
Tests/Validations/Skender_Stock.cs
+2 -2
View File
@@ -27,7 +27,7 @@ public class Skender_Stock
}); });
} }
[Fact] [Fact]
public void SMA() public void SMA()
{ {
SMA_Series QL = new(bars.Close, period, false); SMA_Series QL = new(bars.Close, period, false);
@@ -196,7 +196,7 @@ public class Skender_Stock
Assert.Equal(Math.Round((double)SK.Last().Rsi!, 6), Math.Round(QL.Last().v, 6)); Assert.Equal(Math.Round((double)SK.Last().Rsi!, 6), Math.Round(QL.Last().v, 6));
} }
[Fact] [Fact]
public void ALMA() public void ALMA()
{ {
ALMA_Series QL = new(bars.Close, period, useNaN: false); ALMA_Series QL = new(bars.Close, period, useNaN: false);
Tests/Validations/TA_LIB.cs
+10
View File
@@ -102,6 +102,16 @@ public class TA_LIB
Assert.Equal(Math.Round(TALIB[TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero)); Assert.Equal(Math.Round(TALIB[TALIB.Length - outBegIdx - 1], 6, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 6, MidpointRounding.AwayFromZero));
} }
[Fact]
public void VAR()
{
VAR_Series QL = new(bars.Close, period, false);
Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
Assert.Equal(Math.Round(TALIB[TALIB.Length - outBegIdx - 1], 5, MidpointRounding.AwayFromZero), Math.Round(QL.Last().v, 5));
}
[Fact] [Fact]
public void MIDPOINT() public void MIDPOINT()
{ {
docs/readme.md
+154 -154
View File
@@ -35,158 +35,158 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
⛔= Not implemented (yet) ⛔= Not implemented (yet)
| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | | **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
|--|:--:|:--:|:--:| |--|:--:|:--:|:--:|:--:|
| ✔️ OC2 - (Open+Close)/2 |️ `.OC2` || ️GetBaseQuote | | ⭐ OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
| ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | ️GetBaseQuote | | ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 ||
| ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE || | ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 ||
| ✔️ OHL3 - (Open+High+Low)/3 | `.OHL3` ||| | ⭐ OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ GetBaseQuote | | ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 ||
| ⭐ HLCC4 - Weighted Price | `.HLCC4` | WCLPRICE || | ⭐ HLCC4 - Weighted Price | `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
| ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || | ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT |||
| ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || | ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE |||
| ⭐ MAX - Max value | `MAX_Series` | MAX || | ⭐ MAX - Max value | `MAX_Series` | MAX |||
| ⭐ MIN - Min value | `MIN_Series` | MIN || | ⭐ MIN - Min value | `MIN_Series` | MIN |||
| ⭐ SUM - Summation | `SUM_Series` | SUM || | ⭐ SUM - Summation | `SUM_Series` | SUM |||
| ⭐ ADD - Addition | `ADD_Series` | ADD || | ⭐ ADD - Addition | `ADD_Series` | ADD |||
| ⭐ SUB - Subtraction | `SUB_Series` | SUB || | ⭐ SUB - Subtraction | `SUB_Series` | SUB |||
| ⭐ MUL - Multiplication | `MUL_Series` | MUL || | ⭐ MUL - Multiplication | `MUL_Series` | MUL |||
| ⭐ DIV - Division | `DIV_Series` | DIV || | ⭐ DIV - Division | `DIV_Series` | DIV |||
||||| |||||
| **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** | | **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
| ✔️ BIAS - Bias | `BIAS_Series` ||| | ⭐ BIAS - Bias | `BIAS_Series` ||| bias |
| ⛔ CORREL - Pearson's Correlation Coefficient || CORREL | GetCorrelation | | ⛔ CORREL - Pearson's Correlation Coefficient || CORREL | GetCorrelation ||
| ⛔ COVAR - Covariance ||| GetCorrelation | | ⛔ COVAR - Covariance ||| GetCorrelation ||
| ✔️ ENTP - Entropy | `ENTP_Series` ||| | ⭐ ENTP - Entropy | `ENTP_Series` ||| entropy |
| ✔️ KURT - Kurtosis | `KURT_Series` ||| | ⭐ KURT - Kurtosis | `KURT_Series` ||| kurtosis |
| ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope | | ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
| ⭐ MAD - Mean Absolute Deviation | `MAD_Series` || GetSma | | ⭐ MAD - Mean Absolute Deviation | `MAD_Series` || GetSma | mad |
| ⭐ MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma | | ⭐ MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma ||
| ✔️ MED - Median value | `MED_Series` ||| | ⭐ MED - Median value | `MED_Series` ||| median |
| ✔️ MSE - Mean Squared Error | `MSE_Series` || GetSma | | ✔️ MSE - Mean Squared Error | `MSE_Series` || GetSma ||
| ⛔ SKEW - Skewness |||| | ⛔ SKEW - Skewness |||||
| ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV || | ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV |||
| ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` ||| | ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` ||||
| ✔️ SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` ||| | ✔️ SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` ||||
| ✔️ VAR - Population Variance | `VAR_Series` | VAR || | ⭐ VAR - Population Variance | `VAR_Series` | VAR || variance |
| ✔️ SVAR - Sample Variance | `SVAR_Series` ||| | ⭐ SVAR - Sample Variance | `SVAR_Series` ||| variance |
| ⛔ QUANT - Quantile |||| | ⛔ 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 |||| | ⛔ ZSCORE - Number of standard deviations from mean |||||
||||| ||||||
| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | | **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
| ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average |||| | ⛔ 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 |||| | ⛔ ARIMA - Autoregressive Integrated Moving Average |||||
| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | | ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema |
| ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma | | ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma | ema |
| ⛔ EPMA - Endpoint Moving Average ||| GetEpma | | ⛔ EPMA - Endpoint Moving Average ||| GetEpma ||
| ⛔ FRAMA - Fractal Adaptive Moving Average |||| | ⛔ FRAMA - Fractal Adaptive Moving Average |||||
| ⛔ FWMA - Fibonacci's Weighted Moving Average |||| | ⛔ FWMA - Fibonacci's Weighted Moving Average |||||
| ⛔ HILO - Gann High-Low Activator |||| | ⛔ HILO - Gann High-Low Activator |||||
| ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||| | ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||||
| ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline | | ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline ||
| ⭐ HMA - Hull Moving Average | `HMA_Series` || GetHma | | ⭐ HMA - Hull Moving Average | `HMA_Series` || GetHma | hma |
| ⛔ HWMA - Holt-Winter Moving Average |||| | ⛔ HWMA - Holt-Winter Moving Average |||||
| ✔️ JMA - Jurik Moving Average | `JMA_Series` ||| | ✔️ JMA - Jurik Moving Average | `JMA_Series` ||||
| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | | ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama |
| ⛔ KDJ - KDJ Indicator (trend reversal) |||| | ⛔ KDJ - KDJ Indicator (trend reversal) |||||
| ⛔ LSMA - Least Squares Moving Average |||| | ⛔ 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 | | ⛔ MAMA - MESA Adaptive Moving Average || MAMA | GetMama ||
| ⛔ MCGD - McGinley Dynamic |||| | ⛔ MCGD - McGinley Dynamic |||||
| ⛔ MMA - Modified Moving Average |||| | ⛔ MMA - Modified Moving Average |||||
| ⛔ PPMA - Pivot Point Moving Average |||| | ⛔ PPMA - Pivot Point Moving Average |||||
| ⛔ PWMA - Pascal's Weighted Moving Average |||| | ⛔ PWMA - Pascal's Weighted Moving Average |||||
| ✔️ RMA - WildeR's Moving Average | `RMA_Series` ||| | ⭐ RMA - WildeR's Moving Average | `RMA_Series` ||| rma |
| ⛔ SINWMA - Sine Weighted Moving Average |||| | ⛔ SINWMA - Sine Weighted Moving Average |||||
| ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma | | ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma | sma |
| ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` ||| | ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` || GetSmma ||
| ⛔ SSF - Ehler's Super Smoother Filter |||| | ⛔ SSF - Ehler's Super Smoother Filter |||||
| ⛔ SUP - Supertrend |||| | ⛔ SUP - Supertrend |||||
| ⛔ SWMA - Symmetric Weighted Moving Average |||| | ⛔ SWMA - Symmetric Weighted Moving Average |||||
| ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 | | ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 ||
| ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | | ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | tema |
| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA || | ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA |||
| ⛔ TSF - Time Series Forecast || TSF || | ⛔ TSF - Time Series Forecast || TSF |||
| ⛔ VIDYA - Variable Index Dynamic Average |||| | ⛔ VIDYA - Variable Index Dynamic Average |||||
| ⛔ VOR - Vortex Indicator |||| | ⛔ VOR - Vortex Indicator |||||
| ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | | ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | wma |
| ✔️ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| | ⭐ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| zlma |
||||| ||||||
| **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | | **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | | ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad |
| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | | ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc |
| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | | ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr |
| ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr | | ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
| ⛔ BETA - Beta coefficient || BETA | GetBeta | | ⛔ BETA - Beta coefficient || BETA | GetBeta ||
| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands | | ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands ||
| ⛔ CHAND - Chandelier Exit ||| GetChandelier | | ⛔ CHAND - Chandelier Exit ||| GetChandelier ||
| ⛔ CRSI - Connor RSI ||| GetConnorsRsi | | ⛔ CRSI - Connor RSI ||| GetConnorsRsi ||
| ⛔ DON - Donchian Channels ||| GetDonchian | | ⛔ DON - Donchian Channels ||| GetDonchian ||
| ⛔ FCB - Fractal Chaos Bands ||| GetFcb | | ⛔ FCB - Fractal Chaos Bands ||| GetFcb ||
| ⛔ HV - Historical Volatility |||| | ⛔ HV - Historical Volatility |||||
| ⛔ ICH - Ichimoku ||| GetIchimoku | | ⛔ ICH - Ichimoku ||| GetIchimoku ||
| ⛔ KEL - Keltner Channels ||| GetKeltner | | ⛔ KEL - Keltner Channels ||| GetKeltner ||
| ⛔ NATR - Normalized Average True Range || NATR | GetAtr | | ⛔ NATR - Normalized Average True Range || NATR | GetAtr ||
| ⛔ CHN - Price Channel Indicator |||| | ⛔ CHN - Price Channel Indicator |||||
| ⭐ RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi | | ⭐ RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi | rsi |
| ⛔ SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar | | ⛔ SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar ||
| ⛔ SRSI - Stochastic RSI || STOCHRSI | GetStochRsi | | ⛔ SRSI - Stochastic RSI || STOCHRSI | GetStochRsi ||
| ⛔ STARC - Starc Bands |||| | ⛔ STARC - Starc Bands |||||
| ⭐ TR - True Range | `TR_Series` | TRANGE | GetTr | | ⭐ TR - True Range | `TR_Series` | TRANGE | GetTr | true_range |
| ⛔ UI - Ulcer Index |||| | ⛔ UI - Ulcer Index |||||
| ⛔ VSTOP - Volatility Stop |||| | ⛔ VSTOP - Volatility Stop |||||
||||| ||||||
| **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | | **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
| ⛔ AC - Acceleration Oscillator |||| | ⛔ AC - Acceleration Oscillator |||||
| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx | | ⛔ ADX - Average Directional Movement Index || ADX | GetAdx ||
| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx | | ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx ||
| ⛔ AO - Awesome Oscillator ||| GetAwesome | | ⛔ AO - Awesome Oscillator ||| GetAwesome ||
| ⛔ APO - Absolute Price Oscillator || APO || | ⛔ APO - Absolute Price Oscillator || APO |||
| ⛔ AROON - Aroon oscillator || AROON | GetAroon | | ⛔ AROON - Aroon oscillator || AROON | GetAroon ||
| ⛔ BOP - Balance of Power || BOP | GetBop | | ⛔ 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 |||| | ⛔ CFO - Chande Forcast Oscillator |||||
| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo | | ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo ||
| ⛔ COG - Center of Gravity |||| | ⛔ COG - Center of Gravity |||||
| ⛔ COPPOCK - Coppock Curve |||| | ⛔ COPPOCK - Coppock Curve |||||
| ⛔ CTI - Ehler's Correlation Trend Indicator |||| | ⛔ CTI - Ehler's Correlation Trend Indicator |||||
| ⛔ DPO - Detrended Price Oscillator ||| GetDpo | | ⛔ DPO - Detrended Price Oscillator ||| GetDpo ||
| ⛔ DMI - Directional Movement Index || DX | GetAdx | | ⛔ DMI - Directional Movement Index || DX | GetAdx ||
| ⛔ EFI - Elder Ray's Force Index ||| GetElderRay | | ⛔ EFI - Elder Ray's Force Index ||| GetElderRay ||
| ⛔ GAT - Alligator oscillator ||| GetGator | | ⛔ GAT - Alligator oscillator ||| GetGator ||
| ⛔ HURST - Hurst Exponent ||| GetHurst | | ⛔ HURST - Hurst Exponent ||| GetHurst ||
| ⛔ KRI - Kairi Relative Index |||| | ⛔ KRI - Kairi Relative Index |||||
| ⛔ KVO - Klinger Volume Oscillator |||| | ⛔ KVO - Klinger Volume Oscillator |||||
| ⛔ MFI - Money Flow Index || MFI | GetMfi | | ⛔ MFI - Money Flow Index || MFI | GetMfi ||
| ⛔ MOM - Momentum || MOM || | ⛔ MOM - Momentum || MOM |||
| ⛔ NVI - Negative Volume Index |||| | ⛔ NVI - Negative Volume Index |||||
| ⛔ PO - Price Oscillator |||| | ⛔ PO - Price Oscillator |||||
| ⛔ PPO - Percentage Price Oscillator || PPO || | ⛔ PPO - Percentage Price Oscillator || PPO |||
| ⛔ PMO - Price Momentum Oscillator |||| | ⛔ PMO - Price Momentum Oscillator |||||
| ⛔ PVI - Positive Volume Index |||| | ⛔ PVI - Positive Volume Index |||||
| ⛔ ROC - Rate of Change || MOM | GetRoc | | ⛔ ROC - Rate of Change || MOM | GetRoc ||
| ⛔ RVGI - Relative Vigor Index |||| | ⛔ RVGI - Relative Vigor Index |||||
| ⛔ SMI - Stochastic Momentum Index |||| | ⛔ SMI - Stochastic Momentum Index |||||
| ⛔ STC - Schaff Trend Cycle |||| | ⛔ STC - Schaff Trend Cycle |||||
| ⛔ STOCH - Stochastic Oscillator || STOCH | GetStoch | | ⛔ STOCH - Stochastic Oscillator || STOCH | GetStoch ||
| ⛔ TRIX - 1-day ROC of TEMA || TRIX | GetTrix | | ⛔ TRIX - 1-day ROC of TEMA || TRIX | GetTrix ||
| ⛔ TSI - True Strength Index |||| | ⛔ TSI - True Strength Index |||||
| ⛔ UO - Ultimate Oscillator || ULTOSC | GetUltimate | | ⛔ UO - Ultimate Oscillator || ULTOSC | GetUltimate ||
| ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR | | ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
| ⛔ WGAT - Williams Alligator |||| | ⛔ WGAT - Williams Alligator |||||
||||| ||||||
| **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | | **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
| ⛔ AOBV - Archer On-Balance Volume |||| | ⛔ AOBV - Archer On-Balance Volume |||||
| ⛔ CMF - Chaikin Money Flow |||| | ⛔ CMF - Chaikin Money Flow |||||
| ⛔ EOM - Ease of Movement |||| | ⛔ EOM - Ease of Movement |||||
| ⭐ OBV - On-Balance Volume | ` OBV_Series` | OBV | GetObv | | ⭐ OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
| ⛔ PRS - Price Relative Strength ||| | ⛔ PRS - Price Relative Strength ||||
| ⛔ PVOL - Price-Volume |||| | ⛔ PVOL - Price-Volume |||||
| ⛔ PVO - Percentage Volume Oscillator |||| | ⛔ PVO - Percentage Volume Oscillator |||||
| ⛔ PVR - Price Volume Rank |||| | ⛔ PVR - Price Volume Rank |||||
| ⛔ PVT - Price Volume Trend |||| | ⛔ PVT - Price Volume Trend |||||
| ⛔ VP - Volume Profile |||| | ⛔ VP - Volume Profile |||||
| ⛔ VWAP - Volume Weighted Average Price |||| | ⛔ VWAP - Volume Weighted Average Price |||||
| ⛔ VWMA - Volume Weighted Moving Average |||| | ⛔ VWMA - Volume Weighted Moving Average |||||