Trading Calendars and Session Alignment in Backtests
Summary
A trading calendar defines an exchange’s sessions, timezone, opening and closing times, and holiday schedule. Session labels represent trading days rather than precise instants. These details matter when a strategy places orders or evaluates prices: a backtest should not schedule trades on days the market is closed, and its calendar must align with the dates present in the data bundle. A mismatch can cause errors in either daily or minute-based simulations.
The document outlines the properties needed to define an exchange calendar, using the London Stock Exchange’s hours and holiday rules as an example. It also describes a custom UTC calendar that runs every day of the week, with near-full-day sessions, for assets traded continuously. The example depends on the data bundle containing dates for all seven days. The material explains calendar setup rather than testing trading performance, and users must supply accurate exchange schedules and matching data for their own markets.
Key ideas
- A trading calendar describes exchange sessions, time zones, opening and closing times, and holidays.
- Session labels identify trading days and should not be interpreted as exact timestamps.
- Backtest calendars and data bundle dates must agree to represent tradable dates correctly.
- A custom calendar can model seven-day markets, provided the underlying data covers every day.
Tags
Full text
# trading calendars
.. _calendars:
Calendars
---------
What is a Trading Calendar?
~~~~~~~~~~~~~~~~~~~~~~~~~~~
A trading calendar represents the timing information of a single market exchange. The timing information is made up of two parts: sessions, and opens/closes.
This is represented by the Zipline :class:`~zipline.utils.calendars.trading_calendar.TradingCalendar` class,
and is used as the parent class for all new ``TradingCalendar`` classes.
A session represents a contiguous set of minutes, and has a label that is midnight UTC.
It is important to note that a session label should not be considered a specific point in time,
and that midnight UTC is just being used for convenience.
For an average day of the `New York Stock Exchange <https://www.nyse.com/index>`__,
the market opens at 9:30AM and closes at 4PM. Trading sessions can change depending on the exchange, day of the year, etc.
Why Should You Care About Trading Calendars?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Let's say you want to buy a share of some equity on Tuesday, and then sell it on Saturday.
If the exchange in which you're trading that equity is not open on Saturday, then in reality
it would not be possible to trade that equity at that time, and you would have to wait
until some other number of days past Saturday. Since you wouldn't be able to place
the trade in reality, it would also be unreasonable for your backtest to place a trade on Saturday.
In order for you to backtest your strategy, the dates in that are accounted for in your
`data bundle <https://zipline.ml4trading.io/bundles.html>`__ and the dates in your ``TradingCalendar``
should match up; if the dates don't match up, then you you're going to see some errors along the way.
This holds for both minutely and daily data.
The TradingCalendar Class
~~~~~~~~~~~~~~~~~~~~~~~~~
The ``TradingCalendar`` class has many properties we should be thinking about
if we were to build our own ``TradingCalendar`` for an exchange. These include properties such as:
- Name of the Exchange
- Timezone
- Open Time
- Close Time
- Regular & Ad hoc Holidays
- Special Opens & Closes
And several others. If you'd like to see all of the properties and methods available to you through the ``TradingCalendar`` API, please take a look at the `API Reference <https://ml4t.zipline.io/api_reference.html#trading-calendar-api>`__
Now we'll take a look at the London Stock Exchange Calendar :class:`~zipline.utils.calendars.exchange_calendar_lse.LSEExchangeCalendar` as an example below:
.. code-block:: python
class LSEExchangeCalendar(TradingCalendar):
"""
Exchange calendar for the London Stock Exchange
Open Time: 8:00 AM, GMT
Close Time: 4:30 PM, GMT
Regularly-Observed Holidays:
- New Years Day (observed on first business day on/after)
- Good Friday
- Easter Monday
- Early May Bank Holiday (first Monday in May)
- Spring Bank Holiday (last Monday in May)
- Summer Bank Holiday (last Monday in May)
- Christmas Day
- Dec. 27th (if Christmas is on a weekend)
- Boxing Day
- Dec. 28th (if Boxing Day is on a weekend)
"""
@property
def name(self):
return "LSE"
@property
def tz(self):
return timezone('Europe/London')
@property
def open_time(self):
return time(8, 1)
@property
def close_time(self):
return time(16, 30)
@property
def regular_holidays(self):
return HolidayCalendar([
LSENewYearsDay,
GoodFriday,
EasterMonday,
MayBank,
SpringBank,
SummerBank,
Christmas,
WeekendChristmas,
BoxingDay,
WeekendBoxingDay
])
You can create the ``Holiday`` objects mentioned in ``def regular_holidays(self)`` using
the `pandas <https://pandas.pydata.org/pandas-docs/stable/>`__
module ``pandas.tseries.holiday.Holiday``.
Take a look at the `LSEExchangeCalendar <https://github.com/quantopian/zipline/blob/master/zipline/utils/calendars/exchange_calendar_lse.py>`__ code above as an example, as well as at the code snippet below.
.. code-block:: python
from pandas.tseries.holiday import (
Holiday,
DateOffset,
MO
)
SomeSpecialDay = Holiday(
"Some Special Day",
month=1,
day=9,
offset=DateOffSet(weekday=MO(-1))
)
Building a Custom Trading Calendar
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Now we'll build our own custom trading calendar. This calendar will be used for trading assets that can be traded on a 24/7 exchange calendar. This means that it will be open on Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, and the exchange will open at 12AM and close at 11:59PM. The timezone which we'll use is UTC.
First we'll start off by importing some modules that will be useful to us.
.. code-block:: python
# for setting our open and close times
from datetime import time
# for setting our start and end sessions
import pandas as pd
# for setting which days of the week we trade on
from pandas.tseries.offsets import CustomBusinessDay
# for setting our timezone
from pytz import timezone
# for creating and registering our calendar
from zipline.utils.calendar_utils import register_calendar, TradingCalendar
from zipline.utils.memoize import lazyval
And now we'll actually build this calendar, which we'll call ``TFSExchangeCalendar``:
.. code-block:: python
class TFSExchangeCalendar(TradingCalendar):
"""
An exchange calendar for trading assets 24/7.
Open Time: 12AM, UTC
Close Time: 11:59PM, UTC
"""
@property
def name(self):
"""
The name of the exchange, which Zipline will look for
when we run our algorithm and pass TFS to
the --trading-calendar CLI flag.
"""
return "TFS"
@property
def tz(self):
"""
The timezone in which we'll be running our algorithm.
"""
return timezone("UTC")
@property
def open_time(self):
"""
The time in which our exchange will open each day.
"""
return time(0, 0)
@property
def close_time(self):
"""
The time in which our exchange will close each day.
"""
return time(23, 59)
@lazyval
def day(self):
"""
The days on which our exchange will be open.
"""
weekmask = "Mon Tue Wed Thu Fri Sat Sun"
return CustomBusinessDay(
weekmask=weekmask
)
Conclusions
~~~~~~~~~~~
In order for you to run your algorithm with this calendar, you'll need have a data bundle in which
your assets have dates that run through all days of the week. You can read about how to make your
own data bundle in the section :ref:`new_bundle` of this documentation, or use
the code in `csvdir bundle <https://github.com/stefan-jansen/zipline-reloaded/blob/master/zipline/data/bundles/csvdir.py>`__
for creating a bundle from CSV files.Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.