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)
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:
- Download ta-lib-0.7.1-windows-x86_64.msi.
- 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:
Linux
Download
ta-lib-0.7.1-src.tar.gz
and:
tar -xzf ta-lib-0.7.1-src.tar.gz
cd ta-lib-0.7.1/
./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:
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
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
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
AVGPRICE Average Price
MEDPRICE Median Price
TYPPRICE Typ
一个封装 TA-Lib C 库的 Python 库，用于对金融市场数据做技术分析：包含 RSI、MACD、布林带等 150 多种指标，还能识别 K 线形态。