pandas_market_calendars

Market calendars to use with pandas for trading applications.

https://coveralls.io/repos/github/rsheftel/pandas_market_calendars/badge.svg?branch=master

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.

Installation

pip install pandas_market_calendars

Arch Linux package available here: https://aur.archlinux.org/packages/python-pandas_market_calendars/

Quick Start

import pandas_market_calendars as mcal
nyse = mcal.get_calendar('NYSE')

early = nyse.schedule(start_date='2012-07-01', end_date='2012-07-10')
early

                  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

mcal.date_range(early, frequency='1D')

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)

mcal.date_range(early, frequency='1H')

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)

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.

Metadata

Release files for pandas_market_calendars 0.16

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for pandas_market_calendars 0.16
File	Size	Uploaded
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Built distribution (wheel)

Table of built distributions (wheels) for pandas_market_calendars 0.16
File	Interpreter	ABI	Platform	Reset
pandas_market_calendars-0.16-py2.py3-none-any.whl 31.3 kB May 12, 2018	Python 3, Python 2	none	any	Details

Total release size: 51.3 kB

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