|arch|

arch
====

Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
financial econometrics, written in Python (with Cython and/or Numba used
to improve performance)

Continuous Integration
                      

|Travis Build Status| |Appveyor Build Status|

Documentation
             

|Documentation Status|

Coverage
        

|Coverage Status| |codecov|

Code Inspections
                

|Code Quality: Python| |Total Alerts| |Codacy Badge| |codebeat badge|

Citation
        

|DOI|

Module Contents
---------------

-  `Univariate ARCH Models <#volatility>`__
-  `Unit Root Tests <#unit-root>`__
-  `Bootstrapping <#bootstrap>`__
-  `Multiple Comparison Tests <#multiple-comparison>`__

Python 3
~~~~~~~~

``arch`` is Python 3 only. Version 4.8 is the final version that
supported Python 2.7.

.. _documentation-1:

Documentation
-------------

Released documentation is hosted on `read the
docs <http://arch.readthedocs.org/en/latest/>`__. Current documentation
from the master branch is hosted on `my github
pages <http://bashtage.github.io/arch/doc/index.html>`__.

More about ARCH
---------------

More information about ARCH and related models is available in the notes
and research available at `Kevin Sheppard's
site <http://www.kevinsheppard.com>`__.

Contributing
------------

Contributions are welcome. There are opportunities at many levels to
contribute:

-  Implement new volatility process, e.g., FIGARCH
-  Improve docstrings where unclear or with typos
-  Provide examples, preferably in the form of IPython notebooks

Examples
--------

Volatility Modeling
~~~~~~~~~~~~~~~~~~~

-  Mean models

   -  Constant mean
   -  Heterogeneous Autoregression (HAR)
   -  Autoregression (AR)
   -  Zero mean
   -  Models with and without exogenous regressors

-  Volatility models

   -  ARCH
   -  GARCH
   -  TARCH
   -  EGARCH
   -  EWMA/RiskMetrics

-  Distributions

   -  Normal
   -  Student's T
   -  Generalized Error Distribution

See the `univariate volatility example
notebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/univariate_volatility_modeling.ipynb>`__
for a more complete overview.

.. code:: python

   import datetime as dt
   import pandas.io.data as web
   st = dt.datetime(1990,1,1)
   en = dt.datetime(2014,1,1)
   data = web.get_data_yahoo('^FTSE', start=st, end=en)
   returns = 100 * data['Adj Close'].pct_change().dropna()

   from arch import arch_model
   am = arch_model(returns)
   res = am.fit()

Unit Root Tests
~~~~~~~~~~~~~~~

-  Augmented Dickey-Fuller
-  Dickey-Fuller GLS
-  Phillips-Perron
-  KPSS
-  Zivot-Andrews
-  Variance Ratio tests

See the `unit root testing example
notebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/unitroot_examples.ipynb>`__
for examples of testing series for unit roots.

Bootstrap
~~~~~~~~~

-  Bootstraps

   -  IID Bootstrap
   -  Stationary Bootstrap
   -  Circular Block Bootstrap
   -  Moving Block Bootstrap

-  Methods

   -  Confidence interval construction
   -  Covariance estimation
   -  Apply method to estimate model across bootstraps
   -  Generic Bootstrap iterator

See the `bootstrap example
notebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb>`__
for examples of bootstrapping the Sharpe ratio and a Probit model from
Statsmodels.

.. code:: python

   # Import data
   import datetime as dt
   import pandas as pd
   import pandas.io.data as web
   start = dt.datetime(1951,1,1)
   end = dt.datetime(2014,1,1)
   sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
   start = sp500.index.min()
   end = sp500.index.max()
   monthly_dates = pd.date_range(start, end, freq='M')
   monthly = sp500.reindex(monthly_dates, method='ffill')
   returns = 100 * monthly['Adj Close'].pct_change().dropna()

   # Function to compute parameters
   def sharpe_ratio(x):
       mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
       return np.array([mu, sigma, mu / sigma])

   # Bootstrap confidence intervals
   from arch.bootstrap import IIDBootstrap
   bs = IIDBootstrap(returns)
   ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')

Multiple Comparison Procedures
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  Test of Superior Predictive Ability (SPA), also known as the Reality
   Check or Bootstrap Data Snooper
-  Stepwise (StepM)
-  Model Confidence Set (MCS)

See the `multiple comparison example
notebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/multiple-comparison_examples.ipynb>`__
for examples of the multiple comparison procedures.

Requirements
------------

These requirements reflect the testing environment. It is possible that
arch will work with older versions.

-  Python (3.5+)
-  NumPy (1.13+)
-  SciPy (0.19+)
-  Pandas (0.21+)
-  statsmodels (0.8+)
-  matplotlib (2.0+), optional
-  cached-property (1.5.1+), optional

Optional Requirements
~~~~~~~~~~~~~~~~~~~~~

-  Numba (0.35+) will be used if available **and** when installed using
   the --no-binary option
-  jupyter and notebook are required to run the notebooks

Installing
----------

Standard installation with a compiler requires Cython. If you do not
have a compiler installed, the ``arch`` should still install. You will
see a warning but this can be ignored. If you don't have a compiler,
``numba`` is strongly recommended.

pip
~~~

Releases are available PyPI and can be installed with ``pip``.

.. code:: bash

   pip install arch

This command should work whether you have a compiler installed or not.
If you want to install with the ``--no-binary`` options, use

.. code:: bash

   pip install arch --install-option="--no-binary"

You can alternatively install the latest version from GitHub

.. code:: bash

   pip install git+https://github.com/bashtage/arch.git

``--install-option="--no-binary"`` can be used to disable compilation of
the extensions.

Anaconda
~~~~~~~~

``conda`` users can install from my channel,

.. code:: bash

   conda install arch -c bashtage

Windows
~~~~~~~

Building extension using the community edition of Visual Studio is well
supported for Python 3.5+. Building on other combinations of
Python/Windows is more difficult and is not necessary when Numba is
installed since just-in-time compiled code (Numba) runs as fast as
ahead-of-time compiled extensions.

Developing
~~~~~~~~~~

The development requirements are:

-  Cython (0.24+, if not using --no-binary)
-  py.test (For tests)
-  sphinx (to build docs)
-  sphinx_material (to build docs)
-  jupyter, notebook and nbsphinx (to build docs)

Installation Notes:
~~~~~~~~~~~~~~~~~~~

1. If Cython is not installed, the package will be installed as-if
   ``--no-binary`` was used.
2. Setup does not verify these requirements. Please ensure these are
   installed.

.. |arch| image:: https://bashtage.github.io/arch/doc/_static/images/color-logo-256.png
   :target: https://github.com/bashtage/arch
.. |Travis Build Status| image:: https://travis-ci.org/bashtage/arch.svg?branch=master
   :target: https://travis-ci.org/bashtage/arch
.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/nmt02u7jwcgx7i2x?svg=true
   :target: https://ci.appveyor.com/project/bashtage/arch/branch/master
.. |Documentation Status| image:: https://readthedocs.org/projects/arch/badge/?version=latest
   :target: http://arch.readthedocs.org/en/latest/
.. |Coverage Status| image:: https://coveralls.io/repos/github/bashtage/arch/badge.svg?branch=master
   :target: https://coveralls.io/r/bashtage/arch?branch=master
.. |codecov| image:: https://codecov.io/gh/bashtage/arch/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/bashtage/arch
.. |Code Quality: Python| image:: https://img.shields.io/lgtm/grade/python/g/bashtage/arch.svg?logo=lgtm&logoWidth=18
   :target: https://lgtm.com/projects/g/bashtage/arch/context:python
.. |Total Alerts| image:: https://img.shields.io/lgtm/alerts/g/bashtage/arch.svg?logo=lgtm&logoWidth=18
   :target: https://lgtm.com/projects/g/bashtage/arch/alerts
.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/cea43b588e0f4f2a9d8ba37cf63f8210
   :target: https://www.codacy.com/app/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade
.. |codebeat badge| image:: https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532
   :target: https://codebeat.co/projects/github-com-bashtage-arch-master
.. |DOI| image:: https://zenodo.org/badge/23468876.svg
   :target: https://zenodo.org/badge/latestdoi/23468876