From TA-LIB's homepage:
TA-Lib is widely used by trading software developers requiring to perform technical analysis of financial market data.
- Includes 200 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
Unfortunately, the included Python bindings use SWIG, are a little difficult to install (particularly on Mac OS X), and aren't as efficient as they could be. This project uses Cython and Numpy to efficiently and cleanly bind to TA-Lib -- producing results 2-4 times faster than the SWIG interface.
You can install from PyPI:
$ easy_install TA-Lib
Or checkout the sources and run setup.py yourself:
$ python setup.py install
Note: this requires that you have already installed the TA-Lib library
on your computer (you can download it or
use your computers package manager to install it, e.g.,
brew install ta-lib on Mac OS X).
If you get build errors like this, it typically means that it can't find the
underlying TA-Lib library and needs to be installed:
func.c:256:28: fatal error: ta-lib/ta_libc.h: No such file or directory
compilation terminated.
If you get build errors compiling the underlying TA-Lib such as these:
mv -f .deps/gen_code-gen_code.Tpo .deps/gen_code-gen_code.Po
mv: cannot stat `.deps/gen_code-gen_code.Tpo': No such file or directory
make[3]: *** [gen_code-gen_code.o] Error 1/bin/bash ../../../libtool --tag=CC --mode=link gcc -g -O2 -L../../ta_common -L../../ta_abstract -L../../ta_func -o gen_code gen_code-gen_code.o -lta_common -lta_abstract_gc -lta_func -lm -lpthread -ldl
Simply rerunning make and then sudo make install seems to always do the trick.
Similar to TA-Lib, the function interface 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.
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)TA-Lib also provides an abstract interface for calling functions. Our wrapper for the abstract interface is somewhat different from the upstream implementation. The abstract interface is designed to make the TA-Lib easily introspectable and dynamically programmable. Of course Python allows for these things too, but this helps do some of the heavy lifting for you, making it much easier to for example add a TA-Lib indicator control panel to a GUI charting program. It also unifies the interface for using and calling functions making life easier on the developer.
If you're already familiar with using the function API, you should feel right at home using the abstract API. Every function takes the same input, passed as a dictionary of observed values:
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)
}From this input data, let's again calculate a simple moving average (SMA), this time with the abstract interface:
from talib.abstract import Function
output = Function('sma', input_arrays).outputs
# teaser:
output = Function('sma')(input_arrays, timeperiod=20, price='close')
upper, middle, lower = Function('bbands')(input_arrays, 20, 2, 2)
print Function('STOCH').infoYou'll notice a few things are different. The function is now a class,
initialized with any supported function name (case insensitive) and optionally
input_arrays. To run the TA function with our input data, we access the
outputs property. It wraps a method that ensures the results are always
valid so long as the input_arrays dict was already set. Speaking of which,
the SMA function only takes one input, and we gave it five!
Certain TA functions define which price series names they expect for input.
Others, like SMA, don't (we'll explain how to figure out which in a moment).
Function will use the closing prices by default on TA functions that take
one undefined input, or the high and the low prices for functions taking two.
We can override the default like so:
sma = Function('sma', input_arrays)
sma.set_function_args(timeperiod=10, price='open')
output = sma.outputsThis works by using keyword arguments. For functions with one undefined input,
the keyword is price; for two they are price0 and price1. That's a
lot of typing; let's introduce some shortcuts:
output = Function('sma').run(input_arrays)
output = Function('sma')(input_arrays, price='open')The run() method is a shortcut to outputs that also optionally accepts
an input_arrays dict to use for calculating the function values. You can
also call the Function instance directly; this shortcut to outputs
allows setting both input_arrays and/or any function parameters and
keywords. These methods make up all the ways you can call the TA function and
get its values.
Function.outputs returns either a single ndarray or a list of ndarrays,
depending on how many outputs the TA function has. This information can be
found through Function.output_names or Function.info['outputs'].
Function.info is a very useful property. It returns a dict with almost
every detail of the current state of the Function instance:
print Function('stoch').info
{
'name': 'STOCH',
'display_name': 'Stochastic',
'group': 'Momentum Indicators',
'input_names': OrderedDict([
('prices', ['high', 'low', 'close']),
]),
'parameters': OrderedDict([
('fastk_period', 5),
('slowk_period', 3),
('slowk_matype', 0),
('slowd_period', 3),
('slowd_matype', 0),
]),
'output_names': ['slowk', 'slowd'],
'flags': None,
}Take a look at the value of the input_names key. There's only one input
price variable, 'prices', and its value is a list of price series names.
This is one of those TA functions where TA-Lib defines which price series it
expects for input. Any time input_names is an OrderedDict with one key,
prices, and a list for a value, it means TA-Lib defined the expected price
series names. You can override these just the same as undefined inputs, just
make sure to use a list with the correct number of price series names! (it
varies across functions)
You can also use Function.input_names to get/set the price series names,
and Function.parameters to get/set the function parameters. Let's expand
on the other ways to set TA function arguments:
from talib import MA_Type
output = Function('sma')(input_arrays, timeperiod=10, price='high')
upper, middle, lower = Function('bbands')(input_arrays, timeperiod=20, matype=MA_Type.EMA)
stoch = Function('stoch', input_arrays)
stoch.set_function_args(slowd_period=5)
slowk, slowd = stoch(15, fastd_period=5) # 15 == fastk_period specified positionallyinput_arrays must be passed as a positional argument (or left out
entirely). TA function parameters can be passed as positional or keyword
arguments. Input price series names must be passed as keyword arguments (or
left out entirely). In fact, the __call__ method of Function simply
calls set_function_args().
For your convenience, we create Function wrappers for all of the available
TA-Lib functions:
from talib import SMA, BBANDS, STOCH
output = SMA(input_arrays)
upper, middle, lower = BBANDS(input_arrays, timeperiod=20)
slowk, slowd = STOCH(input_arrays, fastk_period=15, fastd_period=5)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
print talib.get_functions()
print talib.get_function_groups()Here are some of the included indicators:
AD Chaikin A/D Line
ADOSC Chaikin A/D Oscillator
ADX Average Directional Movement Index
ADXR Average Directional Movement Index Rating
APO Absolute Price Oscillator
AROON Aroon
AROONOSC Aroon Oscillator
ATR Average True Range
AVGPRICE Average Price
BBANDS Bollinger Bands
BETA Beta
BOP Balance Of Power
CCI Commodity Channel Index
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
CMO Chande Momentum Oscillator
CORREL Pearson's Correlation Coefficient (r)
DEMA Double Exponential Moving Average
DX Directional Movement Index
EMA Exponential Moving Average
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_TRENDLINE Hilbert Transform - Instantaneous Trendline
HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode
KAMA Kaufman Adaptive Moving Average
LINEARREG Linear Regression
LINEARREG_ANGLE Linear Regression Angle
LINEARREG_INTERCEPT Linear Regression Intercept
LINEARREG_SLOPE Linear Regression Slope
MA All Moving Average
MACD Moving Average Convergence/Divergence
MACDEXT MACD with controllable MA type
MACDFIX Moving Average Convergence/Divergence Fix 12/26
MAMA MESA Adaptive Moving Average
MAX Highest value over a specified period
MAXINDEX Index of highest value over a specified period
MEDPRICE Median Price
MFI Money Flow Index
MIDPOINT MidPoint over period
MIDPRICE Midpoint Price over period
MIN Lowest value over a specified period
MININDEX Index of lowest value over a specified period
MINMAX Lowest and highest values over a specified period
MINMAXINDEX Indexes of lowest and highest values over a specified period
MINUS_DI Minus Directional Indicator
MINUS_DM Minus Directional Movement
MOM Momentum
NATR Normalized Average True Range
OBV On Balance Volume
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
SAR Parabolic SAR
SAREXT Parabolic SAR - Extended
SMA Simple Moving Average
STDDEV Standard Deviation
STOCH Stochastic
STOCHF Stochastic Fast
STOCHRSI Stochastic Relative Strength Index
SUM Summation
T3 Triple Exponential Moving Average (T3)
TEMA Triple Exponential Moving Average
TRANGE True Range
TRIMA Triangular Moving Average
TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
TSF Time Series Forecast
TYPPRICE Typical Price
ULTOSC Ultimate Oscillator
VAR Variance
WCLPRICE Weighted Close Price
WILLR Williams' %R
WMA Weighted Moving Average