Metadata-Version: 2.1
Name: pandas_market_calendars
Version: 1.3.5
Summary: Market and exchange trading calendars for pandas
Home-page: https://github.com/rsheftel/pandas_market_calendars
Author: Ryan Sheftel
Author-email: rsheftel@alumni.upenn.edu
License: MIT
Project-URL: Documentation, https://pandas-market-calendars.readthedocs.io/en/latest/
Project-URL: Source, https://github.com/rsheftel/pandas_market_calendars
Description: pandas_market_calendars
        =======================
        Market calendars to use with pandas for trading applications.
        
        .. image:: https://badge.fury.io/py/pandas-market-calendars.svg
            :target: https://badge.fury.io/py/pandas-market-calendars
        
        .. image:: https://travis-ci.org/rsheftel/pandas_market_calendars.svg?branch=master
            :target: https://travis-ci.org/rsheftel/pandas_market_calendars
        
        .. image:: https://coveralls.io/repos/github/rsheftel/pandas_market_calendars/badge.svg?branch=master
            :target: https://coveralls.io/github/rsheftel/pandas_market_calendars?branch=master
        
        .. image:: https://readthedocs.org/projects/pandas-market-calendars/badge/?version=latest
           :target: http://pandas-market-calendars.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
        Documentation
        -------------
        http://pandas_market_calendars.readthedocs.io/en/latest/
        
        Overview
        --------
        The Pandas package is widely used in finance and specifically for time series analysis. It includes excellent
        functionality for generating sequences of dates and capabilities for custom holiday calendars, but as an explicit
        design choice it does not include the actual holiday calendars for specific exchanges or OTC markets.
        
        The pandas_market_calendars package looks to fill that role with the holiday, late open and early close calendars
        for specific exchanges and OTC conventions. pandas_market_calendars also adds several functions to manipulate the
        market calendars and includes a date_range function to create a pandas DatetimeIndex including only the datetimes
        when the markets are open.
        
        This package is a fork of the Zipline package from Quantopian and extracts just the relevant parts. All credit for
        their excellent work to Quantopian.
        
        As of v1.0 this package only works with Python3. This is consistent with Pandas dropping support for Python2.
        
        Source location
        ~~~~~~~~~~~~~~~
        Hosted on GitHub: https://github.com/rsheftel/pandas_market_calendars
        
        Installation
        ~~~~~~~~~~~~
        ``pip install pandas_market_calendars``
        
        Arch Linux package available here: https://aur.archlinux.org/packages/python-pandas_market_calendars/
        
        Quick Start
        -----------
        .. code:: python
        
            import pandas_market_calendars as mcal
            
            # Create a calendar
            nyse = mcal.get_calendar('NYSE')
        
            # Show available calendars
            print(mcal.get_calendar_names())
        
        .. code:: python
        
            early = nyse.schedule(start_date='2012-07-01', end_date='2012-07-10')
            early
        
            
        .. parsed-literal::
        
                              market_open             market_close
            =========== ========================= =========================
             2012-07-02 2012-07-02 13:30:00+00:00 2012-07-02 20:00:00+00:00
             2012-07-03 2012-07-03 13:30:00+00:00 2012-07-03 17:00:00+00:00
             2012-07-05 2012-07-05 13:30:00+00:00 2012-07-05 20:00:00+00:00
             2012-07-06 2012-07-06 13:30:00+00:00 2012-07-06 20:00:00+00:00
             2012-07-09 2012-07-09 13:30:00+00:00 2012-07-09 20:00:00+00:00
             2012-07-10 2012-07-10 13:30:00+00:00 2012-07-10 20:00:00+00:00
        
            
        .. code:: python
        
            mcal.date_range(early, frequency='1D')
        
        
        
        
        .. parsed-literal::
        
            DatetimeIndex(['2012-07-02 20:00:00+00:00', '2012-07-03 17:00:00+00:00',
                           '2012-07-05 20:00:00+00:00', '2012-07-06 20:00:00+00:00',
                           '2012-07-09 20:00:00+00:00', '2012-07-10 20:00:00+00:00'],
                          dtype='datetime64[ns, UTC]', freq=None)
        
        
        
        .. code:: python
        
            mcal.date_range(early, frequency='1H')
        
        
        
        
        .. parsed-literal::
        
            DatetimeIndex(['2012-07-02 14:30:00+00:00', '2012-07-02 15:30:00+00:00',
                           '2012-07-02 16:30:00+00:00', '2012-07-02 17:30:00+00:00',
                           '2012-07-02 18:30:00+00:00', '2012-07-02 19:30:00+00:00',
                           '2012-07-02 20:00:00+00:00', '2012-07-03 14:30:00+00:00',
                           '2012-07-03 15:30:00+00:00', '2012-07-03 16:30:00+00:00',
                           '2012-07-03 17:00:00+00:00', '2012-07-05 14:30:00+00:00',
                           '2012-07-05 15:30:00+00:00', '2012-07-05 16:30:00+00:00',
                           '2012-07-05 17:30:00+00:00', '2012-07-05 18:30:00+00:00',
                           '2012-07-05 19:30:00+00:00', '2012-07-05 20:00:00+00:00',
                           '2012-07-06 14:30:00+00:00', '2012-07-06 15:30:00+00:00',
                           '2012-07-06 16:30:00+00:00', '2012-07-06 17:30:00+00:00',
                           '2012-07-06 18:30:00+00:00', '2012-07-06 19:30:00+00:00',
                           '2012-07-06 20:00:00+00:00', '2012-07-09 14:30:00+00:00',
                           '2012-07-09 15:30:00+00:00', '2012-07-09 16:30:00+00:00',
                           '2012-07-09 17:30:00+00:00', '2012-07-09 18:30:00+00:00',
                           '2012-07-09 19:30:00+00:00', '2012-07-09 20:00:00+00:00',
                           '2012-07-10 14:30:00+00:00', '2012-07-10 15:30:00+00:00',
                           '2012-07-10 16:30:00+00:00', '2012-07-10 17:30:00+00:00',
                           '2012-07-10 18:30:00+00:00', '2012-07-10 19:30:00+00:00',
                           '2012-07-10 20:00:00+00:00'],
                          dtype='datetime64[ns, UTC]', freq=None)
        
        Contributing
        ------------
        All improvements and additional (and corrections) in the form of pull requests are welcome. This package will grow in
        value and correctness the more eyes are on it.
        
        To add new functionality please include tests which are in standard pytest format. 
        
        Use pytest to run the test suite.
        
        Future
        ------
        This package is open sourced under the MIT license. Everyone is welcome to add more exchanges or OTC markets, confirm
        or correct the existing calendars, and generally do whatever they desire with this code.
        
Keywords: trading exchanges markets OTC datetime holiday business days
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5.0
Description-Content-Type: text/x-rst
