This is a Python wrapper for TA-LIB based on Cythoninstead of SWIG. From the homepage:
> TA-Lib is widely used by trading software developers requiring to perform> technical analysis of financial market data.>> * Includes 150+ indicators such as ADX, MACD, RSI, Stochastic, Bollinger> Bands, etc.> * Candlestick pattern recognition> * Open-source API for C/C++, Java, Perl, Python and 100% Managed .NET
The original Python bindings included with TA-Lib useSWIG which unfortunately are difficult to install andaren't as efficient as they could be. Therefore this project usesCython and Numpy to efficientlyand cleanly bind to TA-Lib -- producing results 2-4 times faster than theSWIG interface.
In addition, this project also supports the use of thePolars and Pandaslibraries.
Installation
You can install from PyPI:
$ python -m pip install TA-LibOr checkout the sources and run setup.py yourself:
$ python setup.py installIt also appears possible to install viaConda Forge:
$ conda install -c conda-forge ta-libDependencies
To use TA-Lib for python, you need to have theTA-Lib already installed. You shouldprobably follow their installation directions for your platform, but somesuggestions are included below for reference.
> Some Conda Forge users have reported success installing the underlying TA-Lib C> library using the libta-lib package:>> $ conda install -c conda-forge libta-lib
Mac OS X
You can simply install using Homebrew:
$ brew install ta-libIf you are using Apple Silicon, such as the M1 processors, and building mixedarchitecture Homebrew projects, you might want to make sure it's being builtfor your architecture:
$ arch -arm64 brew install ta-libAnd perhaps you can set these before installing with pip:
$ export TA_INCLUDE_PATH="$(brew --prefix ta-lib)/include"$ export TA_LIBRARY_PATH="$(brew --prefix ta-lib)/lib"You might also find this helpful, particularly if you have tried severaldifferent installations without success:
$ your-arm64-python -m pip install --no-cache-dir ta-libWindows
Download ta-lib-0.4.0-msvc.zipand unzip to C:\ta-lib.
> This is a 32-bit binary release. If you want to use 64-bit Python, you will> need to build a 64-bit version of the library. Some unofficial instructions> for building on 64-bit Windows 10 or Windows 11, here for reference:>> 1. Download and Unzip ta-lib-0.4.0-msvc.zip> 2. Move the Unzipped Folder ta-lib to C:\> 3. Download and Install Visual Studio Community (2015 or later)> * Remember to Select [Visual C++] Feature> 4. Build TA-Lib Library> * From Windows Start Menu, Start [VS2015 x64 Native Tools Command > Prompt]> * Move to C:\ta-lib\c\make\cdr\win32\msvc> * Build the Library nmake
You might also try these unofficial windows binary wheels for both 32-bitand 64-bit:
https://github.com/cgohlke/talib-build/
Linux
Downloadta-lib-0.4.0-src.tar.gzand:
$ tar -xzf ta-lib-0.4.0-src.tar.gz$ cd ta-lib/$ ./configure --prefix=/usr$ make$ sudo make install> If you build TA-Lib using make -jX it will fail but that's OK!> Simply rerun make -jX followed by [sudo] make install.
Note: if your directory path includes spaces, the installation will probablyfail with No such file or directory errors.
Troubleshooting
If you get a warning that looks like this:
setup.py:79: UserWarning: Cannot find ta-lib library, installation may fail.warnings.warn('Cannot find ta-lib library, installation may fail.')This typically means setup.py can't find the underlying TA-Liblibrary, a dependency which needs to be installed.
If you installed the underlying TA-Lib library with a custom prefix(e.g., with ./configure --prefix=$PREFIX), then when you go to installthis python wrapper you can specify additional search paths to find thelibrary and include files for the underlying TA-Lib library using theTA_LIBRARY_PATH and TA_INCLUDE_PATH environment variables:
$ export TA_LIBRARY_PATH=$PREFIX/lib$ export TA_INCLUDE_PATH=$PREFIX/include$ python setup.py install # or pip install ta-libSometimes installation will produce build errors like this:
talib/_ta_lib.c:601:10: fatal error: ta-lib/ta_defs.h: No such file or directory 601 | #include "ta-lib/ta_defs.h" | ^~~~~~~~~~~~~~~~~~compilation terminated.or:
common.obj : error LNK2001: unresolved external symbol TA_SetUnstablePeriodcommon.obj : error LNK2001: unresolved external symbol TA_Shutdowncommon.obj : error LNK2001: unresolved external symbol TA_Initializecommon.obj : error LNK2001: unresolved external symbol TA_GetUnstablePeriodcommon.obj : error LNK2001: unresolved external symbol TA_GetVersionStringThis typically means that it can't find the underlying TA-Lib library, adependency which needs to be installed. On Windows, this could be caused byinstalling the 32-bit binary distribution of the underlying TA-Lib library,but trying to use it with 64-bit Python.
Sometimes installation will fail with errors like this:
talib/common.c:8:22: fatal error: pyconfig.h: No such file or directory #include "pyconfig.h" ^compilation terminated.error: command 'x86_64-linux-gnu-gcc' failed with exit status 1This typically means that you need the Python headers, and should runsomething like:
$ sudo apt-get install python3-devSometimes building the underlying TA-Lib library has errors runningmake that look like this:
../libtool: line 1717: cd: .libs/libta_lib.lax/libta_abstract.a: No such file or directorymake[2]: *** [libta_lib.la] Error 1make[1]: *** [all-recursive] Error 1make: *** [all-recursive] Error 1This might mean that the directory path to the underlying TA-Lib libraryhas spaces in the directory names. Try putting it in a path that does not haveany spaces and trying again.
Sometimes you might get this error running setup.py:
/usr/include/limits.h:26:10: fatal error: bits/libc-header-start.h: No such file or directory#include ^~~~~~~~~~~~~~~~~~~~~~~~~~This is likely an issue with trying to compile for 32-bit platform butwithout the appropriate headers. You might find some success looking at thefirst answer to this question.
If you get an error on macOS like this:
code signature in <141BC883-189B-322C-AE90-CBF6B5206F67>'python3.9/site-packages/talib/_ta_lib.cpython-39-darwin.so' not valid foruse in process: Trying to load an unsigned library)You might look at this questionand use xcrun codesign to fix it.
If you wonder why STOCHRSI gives you different results than you expect,probably you want STOCH applied to RSI, which is a little differentthan the STOCHRSI which is STOCHF applied to RSI:
>>> import talib>>> import numpy as np>>> c = np.random.randn(100)# this is the library function>>> k, d = talib.STOCHRSI(c)# this produces the same result, calling STOCHF>>> rsi = talib.RSI(c)>>> k, d = talib.STOCHF(rsi, rsi, rsi)# you might want this instead, calling STOCH>>> rsi = talib.RSI(c)>>> k, d = talib.STOCH(rsi, rsi, rsi)If the build appears to hang, you might be running on a VM with not enoughmemory -- try 1 GB or 2 GB.
If you get "permission denied" errors such as this, you might need to giveyour user access to the location where the underlying TA-Lib C library isinstalled -- or install it to a user-accessible location.
talib/_ta_lib.c:747:28: fatal error: /usr/include/ta-lib/ta_defs.h: Permission denied #include "ta-lib/ta-defs.h" ^compilation terminatederror: command 'gcc' failed with exit status 1If you're having trouble compiling the underlying TA-Lib C library on ARM64,you might need to configure it with an explicit build type before runningmake and make install, for example:
$ ./configure --build=aarch64-unknown-linux-gnuThis is caused by old config.guess file, so another way to solve this isto copy a newer version of config.guess into the underyling TA-Lib C librarysources:
$ cp /usr/share/automake-1.16/config.guess /path/to/extracted/ta-lib/config.guessAnd then re-run configure:
$ ./configureFunction API
Similar to TA-Lib, the Function API provides a lightweight wrapper of theexposed TA-Lib indicators.
Each function returns an output array and have default values for theirparameters, unless specified as keyword arguments. Typically, these functionswill have an initial "lookback" period (a required number of observationsbefore an output is generated) set to NaN.
For convenience, the Function API supports both numpy.ndarray andpandas.Series and polars.Series inputs.
All of the following examples use the Function API:
import numpy as npimport talibclose = np.random.random(100)Calculate a simple moving average of the close prices:
output = talib.SMA(close)Calculating bollinger bands, with triple exponential moving average:
from talib import MA_Typeupper, middle, lower = talib.BBANDS(close, matype=MA_Type.T3)Calculating momentum of the close prices, with a time period of 5:
output = talib.MOM(close, timeperiod=5)NaN's
The underlying TA-Lib C library handles NaN's in a sometimes surprising mannerby typically propagating NaN's to the end of the output, for example:
>>> c = np.array([1.0, 2.0, 3.0, np.nan, 4.0, 5.0, 6.0])>>> talib.SMA(c, 3)array([nan, nan, 2., nan, nan, nan, nan])You can compare that to a Pandas rolling mean, where their approach is tooutput NaN until enough "lookback" values are observed to generate new outputs:
>>> c = pandas.Series([1.0, 2.0, 3.0, np.nan, 4.0, 5.0, 6.0])>>> c.rolling(3).mean()0 NaN1 NaN2 2.03 NaN4 NaN5 NaN6 5.0dtype: float64Abstract API
If you're already familiar with using the function API, you should feel rightat home using the Abstract API.
Every function takes a collection of named inputs, either a dict ofnumpy.ndarray or pandas.Series or polars.Series, or apandas.DataFrame or polars.DataFrame. If a pandas.DataFrame orpolars.DataFrame is provided, the output is returned as the same typewith named output columns.
For example, inputs could be provided for the typical "OHLCV" data:
import numpy as np# note that all ndarrays must be the same length!inputs = { 'open': np.random.random(100), 'high': np.random.random(100), 'low': np.random.random(100), 'close': np.random.random(100), 'volume': np.random.random(100)}Functions can either be imported directly or instantiated by name:
from talib import abstract# directlySMA = abstract.SMA# or by nameSMA = abstract.Function('sma')From there, calling functions is basically the same as the function API:
from talib.abstract import *# uses close prices (default)output = SMA(inputs, timeperiod=25)# uses open pricesoutput = SMA(inputs, timeperiod=25, price='open')# uses close prices (default)upper, middle, lower = BBANDS(inputs, 20, 2.0, 2.0)# uses high, low, close (default)slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0) # uses high, low, close by default# uses high, low, open insteadslowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0, prices=['high', 'low', 'open'])Streaming API
An experimental Streaming API was added that allows users to compute the latestvalue of an indicator. This can be faster than using the Function API, forexample in an application that receives streaming data, and wants to know justthe most recent updated indicator value.
import talibfrom talib import streamclose = np.random.random(100)# the Function APIoutput = talib.SMA(close)# the Streaming APIlatest = stream.SMA(close)# the latest value is the same as the last output valueassert (output[-1] - latest) < 0.00001Supported Indicators and Functions
We can show all the TA functions supported by TA-Lib, either as a list oras a dict sorted by group (e.g. "Overlap Studies", "Momentum Indicators",etc):
import talib# list of functionsfor name in talib.get_functions(): print(name)# dict of functions by groupfor group, names in talib.get_function_groups().items(): print(group) for name in names: print(f" {name}")Indicator Groups
- Overlap Studies
- Momentum Indicators
- Volume Indicators
- Volatility Indicators
- Price Transform
- Cycle Indicators
- Pattern Recognition
Overlap Studies
BBANDS Bollinger BandsDEMA Double Exponential Moving AverageEMA Exponential Moving AverageHT_TRENDLINE Hilbert Transform - Instantaneous TrendlineKAMA Kaufman Adaptive Moving AverageMA Moving averageMAMA MESA Adaptive Moving AverageMAVP Moving average with variable periodMIDPOINT MidPoint over periodMIDPRICE Midpoint Price over periodSAR Parabolic SARSAREXT Parabolic SAR - ExtendedSMA Simple Moving AverageT3 Triple Exponential Moving Average (T3)TEMA Triple Exponential Moving AverageTRIMA Triangular Moving AverageWMA Weighted Moving AverageMomentum Indicators
ADX Average Directional Movement IndexADXR Average Directional Movement Index RatingAPO Absolute Price OscillatorAROON AroonAROONOSC Aroon OscillatorBOP Balance Of PowerCCI Commodity Channel IndexCMO Chande Momentum OscillatorDX Directional Movement IndexMACD Moving Average Convergence/DivergenceMACDEXT MACD with controllable MA typeMACDFIX Moving Average Convergence/Divergence Fix 12/26MFI Money Flow IndexMINUS_DI Minus Directional IndicatorMINUS_DM Minus Directional MovementMOM MomentumPLUS_DI Plus Directional IndicatorPLUS_DM Plus Directional MovementPPO Percentage Price OscillatorROC Rate of change : ((price/prevPrice)-1)*100ROCP Rate of change Percentage: (price-prevPrice)/prevPriceROCR Rate of change ratio: (price/prevPrice)ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100RSI Relative Strength IndexSTOCH StochasticSTOCHF Stochastic FastSTOCHRSI Stochastic Relative Strength IndexTRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMAULTOSC Ultimate OscillatorWILLR Williams' %RVolume Indicators
AD Chaikin A/D LineADOSC Chaikin A/D OscillatorOBV On Balance VolumeCycle Indicators
HT_DCPERIOD Hilbert Transform - Dominant Cycle PeriodHT_DCPHASE Hilbert Transform - Dominant Cycle PhaseHT_PHASOR Hilbert Transform - Phasor ComponentsHT_SINE Hilbert Transform - SineWaveHT_TRENDMODE Hilbert Transform - Trend vs Cycle ModePrice Transform
AVGPRICE Average PriceMEDPRICE Median PriceTYPPRICE Typical PriceWCLPRICE Weighted Close PriceVolatility Indicators
ATR Average True RangeNATR Normalized Average True RangeTRANGE True RangePattern Recognition
CDL2CROWS Two CrowsCDL3BLACKCROWS Three Black CrowsCDL3INSIDE Three Inside Up/DownCDL3LINESTRIKE Three-Line StrikeCDL3OUTSIDE Three Outside Up/DownCDL3STARSINSOUTH Three Stars In The SouthCDL3WHITESOLDIERS Three Advancing White SoldiersCDLABANDONEDBABY Abandoned BabyCDLADVANCEBLOCK Advance BlockCDLBELTHOLD Belt-holdCDLBREAKAWAY BreakawayCDLCLOSINGMARUBOZU Closing MarubozuCDLCONCEALBABYSWALL Concealing Baby SwallowCDLCOUNTERATTACK CounterattackCDLDARKCLOUDCOVER Dark Cloud CoverCDLDOJI DojiCDLDOJISTAR Doji StarCDLDRAGONFLYDOJI Dragonfly DojiCDLENGULFING Engulfing PatternCDLEVENINGDOJISTAR Evening Doji StarCDLEVENINGSTAR Evening StarCDLGAPSIDESIDEWHITE Up/Down-gap side-by-side white linesCDLGRAVESTONEDOJI Gravestone DojiCDLHAMMER HammerCDLHANGINGMAN Hanging ManCDLHARAMI Harami PatternCDLHARAMICROSS Harami Cross PatternCDLHIGHWAVE High-Wave CandleCDLHIKKAKE Hikkake PatternCDLHIKKAKEMOD Modified Hikkake PatternCDLHOMINGPIGEON Homing PigeonCDLIDENTICAL3CROWS Identical Three CrowsCDLINNECK In-Neck PatternCDLINVERTEDHAMMER Inverted HammerCDLKICKING KickingCDLKICKINGBYLENGTH Kicking - bull/bear determined by the longer marubozuCDLLADDERBOTTOM Ladder BottomCDLLONGLEGGEDDOJI Long Legged DojiCDLLONGLINE Long Line CandleCDLMARUBOZU MarubozuCDLMATCHINGLOW Matching LowCDLMATHOLD Mat HoldCDLMORNINGDOJISTAR Morning Doji StarCDLMORNINGSTAR Morning StarCDLONNECK On-Neck PatternCDLPIERCING Piercing PatternCDLRICKSHAWMAN Rickshaw ManCDLRISEFALL3METHODS Rising/Falling Three MethodsCDLSEPARATINGLINES Separating LinesCDLSHOOTINGSTAR Shooting StarCDLSHORTLINE Short Line CandleCDLSPINNINGTOP Spinning TopCDLSTALLEDPATTERN Stalled PatternCDLSTICKSANDWICH Stick SandwichCDLTAKURI Takuri (Dragonfly Doji with very long lower shadow)CDLTASUKIGAP Tasuki GapCDLTHRUSTING Thrusting PatternCDLTRISTAR Tristar PatternCDLUNIQUE3RIVER Unique 3 RiverCDLUPSIDEGAP2CROWS Upside Gap Two CrowsCDLXSIDEGAP3METHODS Upside/Downside Gap Three MethodsStatistic Functions
BETA BetaCORREL Pearson's Correlation Coefficient (r)LINEARREG Linear RegressionLINEARREG_ANGLE Linear Regression AngleLINEARREG_INTERCEPT Linear Regression InterceptLINEARREG_SLOPE Linear Regression SlopeSTDDEV Standard DeviationTSF Time Series ForecastVAR Variance