TA-Lib-0.4.9.zip

# TA-Lib This is a Python wrapper for [TA-LIB](http://ta-lib.org) 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 use [SWIG](http://swig.org) 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. ## Installation You can install from PyPI: ``` $ pip install TA-Lib ``` Or checkout the sources and run ``setup.py`` yourself: ``` $ python setup.py install ``` ### Troubleshooting Sometimes installation will produce build errors like this: ``` func.c:256:28: fatal error: ta-lib/ta_libc.h: No such file or directory compilation terminated. ``` This typically means that it can't find the underlying ``TA-Lib`` library, a dependency which needs to be installed. ### Dependencies To use TA-Lib for python, you need to have the [TA-Lib](http://ta-lib.org/hdr_dw.html) already installed: ##### Mac OS X ``` $ brew install ta-lib ``` ##### Windows Download [ta-lib-0.4.0-msvc.zip](http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-msvc.zip) and unzip to ``C:\ta-lib`` ##### Linux Download [ta-lib-0.4.0-src.tar.gz](http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz) and: ``` $ untar and cd $ ./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``. ## 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``. For convenience, the Function API supports both ``numpy.ndarray`` and ``pandas.Series`` types. All of the following examples use the Function API: ```python import numpy import talib close = numpy.random.random(100) ``` Calculate a simple moving average of the close prices: ```python output = talib.SMA(close) ``` Calculating bollinger bands, with triple exponential moving average: ```python 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: ```python output = talib.MOM(close, timeperiod=5) ``` ## 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 a ``pandas.DataFrame``. If a ``pandas.DataFrame`` is provided, the output is returned as a ``pandas.DataFrame`` with named output columns. For example, inputs could be provided for the typical "OHLCV" data: ```python 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: ```python 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: ```python 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']) ``` ## 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): ```python import talib # list of functions print talib.get_functions() # dict of functions by group print talib.get_function_groups() ``` ### 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