This is a Python wrapper for TA-LIB based on Cython instead 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 use SWIG which unfortunately are difficult to install and aren't as efficient as they could be. Therefore this project uses Cython and Numpy to efficiently and cleanly bind to TA-Lib - producing results 2-4 times faster than the SWIG interface.

In addition, this project also supports the use of the Polars and Pandas libraries.

The upstream TA-Lib C library released version 0.6.1 and changed the library name to -lta-lib from -lta_lib. After trying to support both via autodetect and having some issues, we have decided to currently support three feature branches:

• ta-lib-python 0.4.x (supports ta-lib 0.4.x and numpy 1)
• ta-lib-python 0.5.x (supports ta-lib 0.4.x and numpy 2)
• ta-lib-python 0.6.x (supports ta-lib 0.6.x and numpy 2)

You can install from PyPI:

python -m pip install TA-Lib

Or checkout the sources and run setup.py yourself:

python setup.py install

It also appears possible to install via Conda Forge:

conda install -c conda-forge ta-lib

conda install -c conda-forge ta-lib

To use TA-Lib for python, you need to have the TA-Lib already installed. You should probably follow their installation directions for your platform, but some suggestions 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

You can simply install using Homebrew:

brew install ta-lib

If you are using Apple Silicon, such as the M1 processors, and building mixed architecture Homebrew projects, you might want to make sure it's being built for your architecture:

arch -arm64 brew install ta-lib

And 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 several different installations without success:

your-arm64-python -m pip install --no-cache-dir ta-lib

For 64-bit Windows, the easiest way is to get the executable installer:

1. Download ta-lib-0.6.4-windows-x86_64.msi.
2. Run the Installer or run msiexec from the command-line.

Alternatively, if you prefer to get the libraries without installing, or would like to use the 32-bit version:

• Intel/AMD 64-bit ta-lib-0.6.4-windows-x86_64.zip
• Intel/AMD 32-bit ta-lib-0.6.4-windows-x86_32.zip

Download ta-lib-0.6.4-src.tar.gz and:

tar -xzf ta-lib-0.6.4-src.tar.gz cd ta-lib-0.6.4/ ./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 probably fail with No such file or directory errors.

For convenience, and starting with version 0.6.5, we now build binary wheels for different operating systems, architectures, and Python versions using GitHub Actions which include the underlying TA-Lib C library and are easy to install.

Supported platforms:

• Linux
  • x86_64
  • arm64
• macOS
  • x86_64
  • arm64
• Windows
  • x86_64
  • x86
  • arm64

Supported Python versions:

• 3.9
• 3.10
• 3.11
• 3.12
• 3.13

In the event that your operating system, architecture, or Python version are not available as a binary wheel, it is fairly easy to install from source using the instructions above.

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-Lib library, 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 install this python wrapper you can specify additional search paths to find the library and include files for the underlying TA-Lib library using the TA_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-lib

Sometimes 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_SetUnstablePeriod common.obj : error LNK2001: unresolved external symbol TA_Shutdown common.obj : error LNK2001: unresolved external symbol TA_Initialize common.obj : error LNK2001: unresolved external symbol TA_GetUnstablePeriod common.obj : error LNK2001: unresolved external symbol TA_GetVersionString

This typically means that it can't find the underlying TA-Lib library, a dependency which needs to be installed. On Windows, this could be caused by installing 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 1

This typically means that you need the Python headers, and should run something like:

sudo apt-get install python3-dev

Sometimes building the underlying TA-Lib library has errors running make that look like this:

../libtool: line 1717: cd: .libs/libta_lib.lax/libta_abstract.a: No such file or directory make[2]: *** [libta_lib.la] Error 1 make[1]: *** [all-recursive] Error 1 make: *** [all-recursive] Error 1

This might mean that the directory path to the underlying TA-Lib library has spaces in the directory names. Try putting it in a path that does not have any 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 <bits/libc-header-start.h> ^~~~~~~~~~~~~~~~~~~~~~~~~~

This is likely an issue with trying to compile for 32-bit platform but without the appropriate headers. You might find some success looking at the first 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 for use in process: Trying to load an unsigned library)

You might look at this question and 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 different than 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 enough memory - try 1 GB or 2 GB.

It has also been reported that using a swapfile could help, for example:

sudo fallocate -l 1G /swapfile sudo chmod 600 /swapfile sudo mkswap /swapfile sudo swapon /swapfile

If you get "permission denied" errors such as this, you might need to give your user access to the location where the underlying TA-Lib C library is installed -- 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 terminated error: command 'gcc' failed with exit status 1

If 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 running make and make install, for example:

./configure --build=aarch64-unknown-linux-gnu

This is caused by old config.guess file, so another way to solve this is to copy a newer version of config.guess into the underlying TA-Lib C library sources:

cp /usr/share/automake-1.16/config.guess /path/to/extracted/ta-lib/config.guess

And then re-run configure:

./configure

If you're having trouble using PyInstaller and get an error that looks like this:

...site-packages\PyInstaller\loader\pyimod03_importers.py", line 493, in exec_module  exec(bytecode, module.__dict__)  File "talib\__init__.py", line 72, in <module> ModuleNotFoundError: No module named 'talib.stream'

Then, perhaps you can use the --hidden-import argument to fix this:

pyinstaller --hidden-import talib.stream "replaceToYourFileName.py"

If you want to use numpy<2, then you should use ta-lib<0.5.

If you want to use numpy>=2, then you should use ta-lib>=0.5.

If you have trouble getting the code autocompletions to work in Visual Studio Code, a suggestion was made to look for the Python extension settings, and an option for Language Server, and change it from Default (which means Pylance if it is installed, Jedi otherwise), to manually set Jedi and the completions should work. It is possible that you might need to install it manually for this to work.

Similar to TA-Lib, the Function API provides a lightweight wrapper of the exposed TA-Lib indicators.

Each function returns an output array and have default values for their parameters, unless specified as keyword arguments. Typically, these functions will have an initial "lookback" period (a required number of observations before an output is generated) set to NaN.

For convenience, the Function API supports both numpy.ndarray and pandas.Series and polars.Series inputs.

All of the following examples use the Function API:

import numpy as np import talib close = 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_Type upper, 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)

The underlying TA-Lib C library handles NaN's in a sometimes surprising manner by 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 to output 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 NaN 1 NaN 2 2.0 3 NaN 4 NaN 5 NaN 6 5.0 dtype: float64

If you're already familiar with using the function API, you should feel right at home using the Abstract API.

Every function takes a collection of named inputs, either a dict of numpy.ndarray or pandas.Series or polars.Series, or a pandas.DataFrame or polars.DataFrame. If a pandas.DataFrame or polars.DataFrame is provided, the output is returned as the same type with 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 # directly SMA = abstract.SMA # or by name SMA = 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 prices output = 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 instead slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0, prices=['high', 'low', 'open'])

An experimental Streaming API was added that allows users to compute the latest value of an indicator. This can be faster than using the Function API, for example in an application that receives streaming data, and wants to know just the most recent updated indicator value.

import talib from talib import stream close = np.random.random(100) # the Function API output = talib.SMA(close) # the Streaming API latest = stream.SMA(close) # the latest value is the same as the last output value assert (output[-1] - latest) < 0.00001

We can show all the TA functions supported by TA-Lib, either as a list or as a dict sorted by group (e.g. "Overlap Studies", "Momentum Indicators", etc):

import talib # list of functions for name in talib.get_functions(): print(name) # dict of functions by group for group, names in talib.get_function_groups().items(): print(group) for name in names: print(f" {name}")

• Overlap Studies
• Momentum Indicators
• Volume Indicators
• Volatility Indicators
• Price Transform
• Cycle Indicators
• Pattern Recognition

BBANDS Bollinger Bands DEMA Double Exponential Moving Average EMA Exponential Moving Average HT_TRENDLINE Hilbert Transform - Instantaneous Trendline KAMA Kaufman Adaptive Moving Average MA Moving average MAMA MESA Adaptive Moving Average MAVP Moving average with variable period MIDPOINT MidPoint over period MIDPRICE Midpoint Price over period SAR Parabolic SAR SAREXT Parabolic SAR - Extended SMA Simple Moving Average T3 Triple Exponential Moving Average (T3) TEMA Triple Exponential Moving Average TRIMA Triangular Moving Average WMA Weighted Moving Average 

ADX Average Directional Movement Index ADXR Average Directional Movement Index Rating APO Absolute Price Oscillator AROON Aroon AROONOSC Aroon Oscillator BOP Balance Of Power CCI Commodity Channel Index CMO Chande Momentum Oscillator DX Directional Movement Index MACD Moving Average Convergence/Divergence MACDEXT MACD with controllable MA type MACDFIX Moving Average Convergence/Divergence Fix 12/26 MFI Money Flow Index MINUS_DI Minus Directional Indicator MINUS_DM Minus Directional Movement MOM Momentum PLUS_DI Plus Directional Indicator PLUS_DM Plus Directional Movement PPO Percentage Price Oscillator ROC Rate of change : ((price/prevPrice)-1)*100 ROCP Rate of change Percentage: (price-prevPrice)/prevPrice ROCR Rate of change ratio: (price/prevPrice) ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100 RSI Relative Strength Index STOCH Stochastic STOCHF Stochastic Fast STOCHRSI Stochastic Relative Strength Index TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA ULTOSC Ultimate Oscillator WILLR Williams' %R 

AD Chaikin A/D Line ADOSC Chaikin A/D Oscillator OBV On Balance Volume 

HT_DCPERIOD Hilbert Transform - Dominant Cycle Period HT_DCPHASE Hilbert Transform - Dominant Cycle Phase HT_PHASOR Hilbert Transform - Phasor Components HT_SINE Hilbert Transform - SineWave HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode 

AVGPRICE Average Price MEDPRICE Median Price TYPPRICE Typical Price WCLPRICE Weighted Close Price 

ATR Average True Range NATR Normalized Average True Range TRANGE True Range 

CDL2CROWS Two Crows CDL3BLACKCROWS Three Black Crows CDL3INSIDE Three Inside Up/Down CDL3LINESTRIKE Three-Line Strike CDL3OUTSIDE Three Outside Up/Down CDL3STARSINSOUTH Three Stars In The South CDL3WHITESOLDIERS Three Advancing White Soldiers CDLABANDONEDBABY Abandoned Baby CDLADVANCEBLOCK Advance Block CDLBELTHOLD Belt-hold CDLBREAKAWAY Breakaway CDLCLOSINGMARUBOZU Closing Marubozu CDLCONCEALBABYSWALL Concealing Baby Swallow CDLCOUNTERATTACK Counterattack CDLDARKCLOUDCOVER Dark Cloud Cover CDLDOJI Doji CDLDOJISTAR Doji Star CDLDRAGONFLYDOJI Dragonfly Doji CDLENGULFING Engulfing Pattern CDLEVENINGDOJISTAR Evening Doji Star CDLEVENINGSTAR Evening Star CDLGAPSIDESIDEWHITE Up/Down-gap side-by-side white lines CDLGRAVESTONEDOJI Gravestone Doji CDLHAMMER Hammer CDLHANGINGMAN Hanging Man CDLHARAMI Harami Pattern CDLHARAMICROSS Harami Cross Pattern CDLHIGHWAVE High-Wave Candle CDLHIKKAKE Hikkake Pattern CDLHIKKAKEMOD Modified Hikkake Pattern CDLHOMINGPIGEON Homing Pigeon CDLIDENTICAL3CROWS Identical Three Crows CDLINNECK In-Neck Pattern CDLINVERTEDHAMMER Inverted Hammer CDLKICKING Kicking CDLKICKINGBYLENGTH Kicking - bull/bear determined by the longer marubozu CDLLADDERBOTTOM Ladder Bottom CDLLONGLEGGEDDOJI Long Legged Doji CDLLONGLINE Long Line Candle CDLMARUBOZU Marubozu CDLMATCHINGLOW Matching Low CDLMATHOLD Mat Hold CDLMORNINGDOJISTAR Morning Doji Star CDLMORNINGSTAR Morning Star CDLONNECK On-Neck Pattern CDLPIERCING Piercing Pattern CDLRICKSHAWMAN Rickshaw Man CDLRISEFALL3METHODS Rising/Falling Three Methods CDLSEPARATINGLINES Separating Lines CDLSHOOTINGSTAR Shooting Star CDLSHORTLINE Short Line Candle CDLSPINNINGTOP Spinning Top CDLSTALLEDPATTERN Stalled Pattern CDLSTICKSANDWICH Stick Sandwich CDLTAKURI Takuri (Dragonfly Doji with very long lower shadow) CDLTASUKIGAP Tasuki Gap CDLTHRUSTING Thrusting Pattern CDLTRISTAR Tristar Pattern CDLUNIQUE3RIVER Unique 3 River CDLUPSIDEGAP2CROWS Upside Gap Two Crows CDLXSIDEGAP3METHODS Upside/Downside Gap Three Methods 

BETA Beta CORREL Pearson's Correlation Coefficient (r) LINEARREG Linear Regression LINEARREG_ANGLE Linear Regression Angle LINEARREG_INTERCEPT Linear Regression Intercept LINEARREG_SLOPE Linear Regression Slope STDDEV Standard Deviation TSF Time Series Forecast VAR Variance