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.
You can install from PyPI:
$ python3 -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
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.
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
Download ta-lib-0.4.0-msvc.zip
and 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 (and unsupported) instructions for building on 64-bit Windows 10, here for reference:
- Download and Unzip
ta-lib-0.4.0-msvc.zip- Move the Unzipped Folder
ta-libtoC:\- Download and Install Visual Studio Community 2015
- Remember to Select
[Visual C++]Feature- 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 binaries for both 32-bit and 64-bit:
https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
Download ta-lib-0.4.0-src.tar.gz and:
$ tar -xzf ta-lib-0.4.0-src.tar.gz
$ cd ta-lib/
$ ./configure --prefix=/usr
$ make
$ sudo make install
If you build
TA-Libusingmake -jXit will fail but that's OK! Simply rerunmake -jXfollowed by[sudo] make install.
Note: if your directory path includes spaces, the installation will probably
fail with No such file or directory errors.
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-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_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 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
>>> c = numpy.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.
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
import talib
close = numpy.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 = numpy.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: float64If 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, 2)
# 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) < 0.00001We 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
print talib.get_functions()
# dict of functions by group
print talib.get_function_groups()- 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