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    "info": {
        "author": "Kevin Sheppard",
        "author_email": "kevin.sheppard@economics.ox.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Financial and Insurance Industry",
            "License :: OSI Approved",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
        ],
        "description": "|arch|\n\narch\n====\n\nAutoregressive Conditional Heteroskedasticity (ARCH) and other tools for\nfinancial econometrics, written in Python (with Cython and/or Numba used\nto improve performance)\n\nContinuous Integration\n                      \n\n|Travis Build Status| |Appveyor Build Status|\n\nDocumentation\n             \n\n|Documentation Status|\n\nCoverage\n        \n\n|Coverage Status| |codecov|\n\nCode Inspections\n                \n\n|Code Quality: Python| |Total Alerts| |Codacy Badge| |codebeat badge|\n\nCitation\n        \n\n|DOI|\n\nModule Contents\n---------------\n\n-  `Univariate ARCH Models <#volatility>`__\n-  `Unit Root Tests <#unit-root>`__\n-  `Bootstrapping <#bootstrap>`__\n-  `Multiple Comparison Tests <#multiple-comparison>`__\n\nPython 3\n~~~~~~~~\n\n``arch`` is Python 3 only. Version 4.8 is the final version that\nsupported Python 2.7.\n\n.. _documentation-1:\n\nDocumentation\n-------------\n\nReleased documentation is hosted on `read the\ndocs <http://arch.readthedocs.org/en/latest/>`__. Current documentation\nfrom the master branch is hosted on `my github\npages <http://bashtage.github.io/arch/doc/index.html>`__.\n\nMore about ARCH\n---------------\n\nMore information about ARCH and related models is available in the notes\nand research available at `Kevin Sheppard's\nsite <http://www.kevinsheppard.com>`__.\n\nContributing\n------------\n\nContributions are welcome. There are opportunities at many levels to\ncontribute:\n\n-  Implement new volatility process, e.g., FIGARCH\n-  Improve docstrings where unclear or with typos\n-  Provide examples, preferably in the form of IPython notebooks\n\nExamples\n--------\n\nVolatility Modeling\n~~~~~~~~~~~~~~~~~~~\n\n-  Mean models\n\n   -  Constant mean\n   -  Heterogeneous Autoregression (HAR)\n   -  Autoregression (AR)\n   -  Zero mean\n   -  Models with and without exogenous regressors\n\n-  Volatility models\n\n   -  ARCH\n   -  GARCH\n   -  TARCH\n   -  EGARCH\n   -  EWMA/RiskMetrics\n\n-  Distributions\n\n   -  Normal\n   -  Student's T\n   -  Generalized Error Distribution\n\nSee the `univariate volatility example\nnotebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/univariate_volatility_modeling.ipynb>`__\nfor a more complete overview.\n\n.. code:: python\n\n   import datetime as dt\n   import pandas.io.data as web\n   st = dt.datetime(1990,1,1)\n   en = dt.datetime(2014,1,1)\n   data = web.get_data_yahoo('^FTSE', start=st, end=en)\n   returns = 100 * data['Adj Close'].pct_change().dropna()\n\n   from arch import arch_model\n   am = arch_model(returns)\n   res = am.fit()\n\nUnit Root Tests\n~~~~~~~~~~~~~~~\n\n-  Augmented Dickey-Fuller\n-  Dickey-Fuller GLS\n-  Phillips-Perron\n-  KPSS\n-  Zivot-Andrews\n-  Variance Ratio tests\n\nSee the `unit root testing example\nnotebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/unitroot_examples.ipynb>`__\nfor examples of testing series for unit roots.\n\nBootstrap\n~~~~~~~~~\n\n-  Bootstraps\n\n   -  IID Bootstrap\n   -  Stationary Bootstrap\n   -  Circular Block Bootstrap\n   -  Moving Block Bootstrap\n\n-  Methods\n\n   -  Confidence interval construction\n   -  Covariance estimation\n   -  Apply method to estimate model across bootstraps\n   -  Generic Bootstrap iterator\n\nSee the `bootstrap example\nnotebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb>`__\nfor examples of bootstrapping the Sharpe ratio and a Probit model from\nStatsmodels.\n\n.. code:: python\n\n   # Import data\n   import datetime as dt\n   import pandas as pd\n   import pandas.io.data as web\n   start = dt.datetime(1951,1,1)\n   end = dt.datetime(2014,1,1)\n   sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)\n   start = sp500.index.min()\n   end = sp500.index.max()\n   monthly_dates = pd.date_range(start, end, freq='M')\n   monthly = sp500.reindex(monthly_dates, method='ffill')\n   returns = 100 * monthly['Adj Close'].pct_change().dropna()\n\n   # Function to compute parameters\n   def sharpe_ratio(x):\n       mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())\n       return np.array([mu, sigma, mu / sigma])\n\n   # Bootstrap confidence intervals\n   from arch.bootstrap import IIDBootstrap\n   bs = IIDBootstrap(returns)\n   ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')\n\nMultiple Comparison Procedures\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n-  Test of Superior Predictive Ability (SPA), also known as the Reality\n   Check or Bootstrap Data Snooper\n-  Stepwise (StepM)\n-  Model Confidence Set (MCS)\n\nSee the `multiple comparison example\nnotebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/multiple-comparison_examples.ipynb>`__\nfor examples of the multiple comparison procedures.\n\nRequirements\n------------\n\nThese requirements reflect the testing environment. It is possible that\narch will work with older versions.\n\n-  Python (3.5+)\n-  NumPy (1.13+)\n-  SciPy (0.19+)\n-  Pandas (0.21+)\n-  statsmodels (0.8+)\n-  matplotlib (2.0+), optional\n-  cached-property (1.5.1+), optional\n\nOptional Requirements\n~~~~~~~~~~~~~~~~~~~~~\n\n-  Numba (0.35+) will be used if available **and** when installed using\n   the --no-binary option\n-  jupyter and notebook are required to run the notebooks\n\nInstalling\n----------\n\nStandard installation with a compiler requires Cython. If you do not\nhave a compiler installed, the ``arch`` should still install. You will\nsee a warning but this can be ignored. If you don't have a compiler,\n``numba`` is strongly recommended.\n\npip\n~~~\n\nReleases are available PyPI and can be installed with ``pip``.\n\n.. code:: bash\n\n   pip install arch\n\nThis command should work whether you have a compiler installed or not.\nIf you want to install with the ``--no-binary`` options, use\n\n.. code:: bash\n\n   pip install arch --install-option=\"--no-binary\"\n\nYou can alternatively install the latest version from GitHub\n\n.. code:: bash\n\n   pip install git+https://github.com/bashtage/arch.git\n\n``--install-option=\"--no-binary\"`` can be used to disable compilation of\nthe extensions.\n\nAnaconda\n~~~~~~~~\n\n``conda`` users can install from my channel,\n\n.. code:: bash\n\n   conda install arch -c bashtage\n\nWindows\n~~~~~~~\n\nBuilding extension using the community edition of Visual Studio is well\nsupported for Python 3.5+. Building on other combinations of\nPython/Windows is more difficult and is not necessary when Numba is\ninstalled since just-in-time compiled code (Numba) runs as fast as\nahead-of-time compiled extensions.\n\nDeveloping\n~~~~~~~~~~\n\nThe development requirements are:\n\n-  Cython (0.24+, if not using --no-binary)\n-  py.test (For tests)\n-  sphinx (to build docs)\n-  sphinx_material (to build docs)\n-  jupyter, notebook and nbsphinx (to build docs)\n\nInstallation Notes:\n~~~~~~~~~~~~~~~~~~~\n\n1. If Cython is not installed, the package will be installed as-if\n   ``--no-binary`` was used.\n2. Setup does not verify these requirements. Please ensure these are\n   installed.\n\n.. |arch| image:: https://bashtage.github.io/arch/doc/_static/images/color-logo-256.png\n   :target: https://github.com/bashtage/arch\n.. |Travis Build Status| image:: https://travis-ci.org/bashtage/arch.svg?branch=master\n   :target: https://travis-ci.org/bashtage/arch\n.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/nmt02u7jwcgx7i2x?svg=true\n   :target: https://ci.appveyor.com/project/bashtage/arch/branch/master\n.. |Documentation Status| image:: https://readthedocs.org/projects/arch/badge/?version=latest\n   :target: http://arch.readthedocs.org/en/latest/\n.. |Coverage Status| image:: https://coveralls.io/repos/github/bashtage/arch/badge.svg?branch=master\n   :target: https://coveralls.io/r/bashtage/arch?branch=master\n.. |codecov| image:: https://codecov.io/gh/bashtage/arch/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/bashtage/arch\n.. |Code Quality: Python| image:: https://img.shields.io/lgtm/grade/python/g/bashtage/arch.svg?logo=lgtm&logoWidth=18\n   :target: https://lgtm.com/projects/g/bashtage/arch/context:python\n.. |Total Alerts| image:: https://img.shields.io/lgtm/alerts/g/bashtage/arch.svg?logo=lgtm&logoWidth=18\n   :target: https://lgtm.com/projects/g/bashtage/arch/alerts\n.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/cea43b588e0f4f2a9d8ba37cf63f8210\n   :target: https://www.codacy.com/app/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade\n.. |codebeat badge| image:: https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532\n   :target: https://codebeat.co/projects/github-com-bashtage-arch-master\n.. |DOI| image:: https://zenodo.org/badge/23468876.svg\n   :target: https://zenodo.org/badge/latestdoi/23468876",
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