Market calendars to use with pandas for trading applications.
http://pandas_market_calendars.readthedocs.io/en/latest/
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.
Hosted on GitHub: https://github.com/rsheftel/pandas_market_calendars
pip install pandas_market_calendars
Arch Linux package available here: https://aur.archlinux.org/packages/python-pandas_market_calendars/
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)
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.
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.