Market calendars to use with pandas for trading applications.
Documentation
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 provides access to over 50+ unique exchange calendars for global equity and futures markets.
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.
Major Releases
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.
As of v2.0 this package provides a mirror of all the calendars from the exchange_calendars package, which itself is the now maintained fork of the original trading_calendars package. This adds over 50 calendars.
As of v3.0, the function date_range() is more complete and consistent, for more discussion on the topic refer to PR #142 and Issue #138.
As of v4.0, this package provides the framework to add interruptions to calendars. These can also be added to a schedule and viewed using the new interruptions_df property. A full list of changes can be found in PR #210.
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
Quick Start
import pandas_market_calendars as mcal
# Create a calendar
nyse = mcal.get_calendar('NYSE')
# Show available calendars
print(mcal.get_calendar_names())early = nyse.schedule(start_date='2012-07-01', end_date='2012-07-10')
earlymarket_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)
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.
For complete information on contributing see CONTRIBUTING.md
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.
Sponsor
TradingHours.com provides the most accurate and comprehensive coverage of market holidays and trading hours data available. They cover over 900 markets around the world. Their data is continually monitored for changes and updated daily. Learn more
1 maintainer
Metadata
1 maintainer
Release files for pandas_market_calendars 4.3.1
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