pandas_market_calendars

Market calendars to use with pandas for trading applications.

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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. Additionally the package contains product specific calendars for future exchanges which have different market open, closes, breaks and holidays based on product type.

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.

As of v1.4 this package now has the concept of a break during the trading day. For example this can accommodate Asian markets that have a lunch break, or futures markets that are open 24 hours with a break in the day for trade processing.

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/

Calendars
---------
The list of `available calendars <https://pandas-market-calendars.readthedocs.io/en/latest/calendars.html>`_

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.

Merger with Quantopian Trading-Calendars
========================================
Work has begun to merge this project with the Quantopian `trading-calendars <https://github.com/quantopian/trading_calendars>`_ project that it was originally forked from.
The end-state is to have one unified project that brings together the superset of all funcationality in this project
and trading-calendars, and to have one source for all market calendars.

The process of this merger will be updated in `#120 <https://github.com/rsheftel/pandas_market_calendars/issues/120>`_, everyone is welcome to comment or provide their input