TA-Lib 📈

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.

Versions 🗂️

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)
• ta-lib-python 0.7.x (supports ta-lib 0.7.x and numpy 2)
• ta-lib-python 0.8.x (supports ta-lib 0.8.x and numpy 2)

Installation 💾

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

Dependencies 🧩

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

Mac OS X

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

Windows

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

1. Download ta-lib-0.8.1-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-0.8.1-windows-x86_64.zip
• Intel/AMD 32-bit ta-lib-0.8.1-windows-x86_32.zip

Linux

Download ta-lib-0.8.1-src.tar.gz and:

tar -xzf ta-lib-0.8.1-src.tar.gz
cd ta-lib-0.8.1/
./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.

Wheels ⚙️

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
• 3.14

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.

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-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.

Function API

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.

The lookback is function-specific and does not always match the value passed to timeperiod. For example, RSI(timeperiod=14) needs 15 price observations because the first RSI value depends on 14 price changes between consecutive bars, not just 14 raw prices.

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)

Functions marked in the docs as having an unstable period start with that extra unstable-period setting at 0. In other words, get_unstable_period() returns 0 until you explicitly change it with set_unstable_period().

NaN's

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

Abstract API

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'])

Streaming API

The Streaming API keeps a handle per indicator instead of recomputing from the whole array. Opening one costs a pass over the history; every bar after that is O(1), and each value it produces is identical to the one the Function API reports for that bar.

import talib
from talib import stream

# the Function API: the whole series, from the whole array
output = talib.SMA(close)

# the Streaming API: a handle, positioned at the end of the history
s = stream.SMA(close)
assert s.value == output[-1]

for price in feed:
    latest = s.update(price)      # one closed bar in, its value out

s.peek(forming)                   # what update would return, committing nothing
fork = s.copy()                   # an independent handle at the same bar

stream.SMA takes exactly the arguments talib.SMA takes. A single-output function answers with a float (an int where the Function API returns an integer array); a multi-output one with the same tuple the Function API returns:

m = stream.MACD(close)
macd, macdsignal, macdhist = m.update(price)
m.value[2]                                    # the histogram, last bar

abstract.Function('MACD').output_names        # ['macd', 'macdsignal', 'macdhist']

Opening needs at least lookback + 1 bars, which abstract knows, and a little more where a function's seeding does -- so rather than computing the number, treat a short history as "not yet":

from talib import abstract

need = abstract.Function('RSI', timeperiod=14).lookback + 1   # 15, usually enough

try:
    s = stream.RSI(history, timeperiod=14)
except talib.InsufficientHistory:
    ...                                                       # collect more bars

Leading bars that are NaN in any input are not history. They are skipped, as the Function API skips them, and do not count toward the warm-up. A NaN or an infinity anywhere else in the history is undefined behaviour in TA-Lib C, and for a few window functions a handle and the Function API do then disagree.

A bar that is not finite is likewise rejected: update raises and the handle is left exactly as it was, neither its value nor its range moved. For a bar you mean to skip rather than re-feed, say so with advance(), or two handles on one feed drift a bar apart.

If you want the series over the history as well, one pass gives both:

s, rsi = stream.RSI.open_and_fill(history, timeperiod=14)     # rsi == talib.RSI(history)

A handle also reports the range it has an output for, in the input series' coordinates, and can be told about a bar it was not fed:

s.out_range                       # (begidx, nbelement), as the Function API's output
s.advance()                       # count a skipped bar: the range moves, the value holds

A handle points into the TA-Lib C library, so it cannot be pickled or shared with another process. Keep the history and re-open instead.

Threads

TA-Lib supports both regular and free-threaded CPython. Native calculations, including stream opening, updates, reads and cloning, release the GIL on regular CPython. Python argument conversion, array allocation and result construction still require Python thread state. Independent calculations run concurrently; operations on the same stream handle use a per-handle mutex.

Inputs must remain unchanged during a call: do not write to or resize an input array from another thread. Calls allocate their own output arrays.

Settings are process-wide. Set unstable periods, compatibility and candle settings before starting calculations or opening streams. Changing settings while a calculation is active or any stream is alive raises RuntimeError. After calls finish and all streams (including copies) are released, settings can be changed again. The settings gate protects lookback calculation, output allocation and computation together, while allowing simultaneous readers.

The synchronization covers calls through this Python extension; other code calling the same C library directly must observe TA-Lib's settings and lifecycle contract too. Separate subinterpreters are not supported.

Free-threaded Python requires a matching free-threaded extension build and compatible dependencies. Importing TA-Lib does not enable the GIL in that build.

Supported Indicators and Functions 📋

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}")

Indicator Groups 🏷️

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

Overlap Studies

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

Momentum Indicators

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

Volume Indicators

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

Cycle Indicators

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

Price Transform

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

Volatility Indicators

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

Pattern Recognition

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

Statistic Functions

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