3.1

• Various updates to ensure NumPy 2.0 compatibility for Python >= 3.10

3.0.4

• update to Python 3.12

Version 3.0.3

What's Changed

• Bump pypa/cibuildwheel from 2.14.0 to 2.14.1 by @dependabot in #207
• DOC: updating docs by @stefan-jansen in #208
• Fix connection pool by @stefan-jansen in #210
• CI: Fix windows wheels by @stefan-jansen in #212

Full Changelog: 3.0...3.0.3

Version 3.0

What's Changed

• MAINT: Feature/zipline3dev (#18) by @MBounouar in #202
• REL 3.0-beta by @stefan-jansen in #206

New Contributors

• @ramashisx made their first contribution in #168
• @SilverSteven made their first contribution in #175

Full Changelog: 2.2.1...3.0

2.4

• finalizing conda-forge package
• moving out of beta after a couple weeks without issues

What's Changed

• RELEASE: v2.3 by @stefan-jansen in #146
• MAINT: Don't track _version.py by @MBounouar in #147
• devcontainer by @MBounouar in #148
• Fix codecov and CI badges by @MBounouar in #149
• MAINT: Move to exchange-calendars 4.x and more by @MBounouar in #158
• MAINT: Update to Python 3.11 by @stefan-jansen in #159
• Bump pypa/cibuildwheel from 2.11.4 to 2.12.0 by @dependabot in #160
• MAINT: remove unused argcheck by @MBounouar in #161

Full Changelog: 2.2.1...2.4

2.4-beta.1

What's Changed

The most important change is compatibility with exchange_calendars >= 4.0, which in turn changes the datatype of time zones for trading sessions from timezone-naive to UTC:

Sessions are now timezone-naive (previously UTC). Schedule columns now have the timezone set as UTC. The times have always been defined in terms of UTC, but the dtype used to be timezone-naive.

• MAINT: Move to exchange-calendars 4.x and more by @MBounouar in #158
• MAINT: Update to Python 3.11 by @stefan-jansen in #159

Full Changelog: 2.3.0...2.4.0-beta.1

2.3

What's Changed

• RELEASE: v2.3 by @stefan-jansen in #146

Full Changelog: 2.2.1...2.3.0

2.2

With this release, we're moving to the actively maintained ecxhange_calendars from the no longer maintained trading_calendars - thanks to @MBounouar for making this happen! As a result, Zipline is now again compatible with the latest pandas versions.

2.1.1

The release simplifies the code by removing elements that are no longer necessary, addresses a few issues and improves the automatic build and test GHA workflows.

Pandas 1.3+ causes issues with trading-calendars; while we work out migration to the more actively maintained exchange_calendars, pandas is temporarily limited to version <=1.2.5.

Big shout-out to @MBounouar who made quite a few very useful contributions, namely:

• Migrate from nosetest to pytest (#9, #15 )
• Apply patch for futures backtesting (#3)
• Various revisions of the Cython extensions to facilitate maintenance and enhancements going forward (#46, #49)
• Address warnings and other maintenance issues.

In addition, there are several improvements to the automatic test and build process (#47, #48, #50, #51, #52).

Release 2.0.0rc4

Highlights

This release updates Zipline to be compatible with Python >= 3.7 as well as the current versions of relevant NumFOCUS libraries like Pandas, scikit-learn, and others.

• Conda packages for Zipline and key dependencies Bcolz and TA-Lib are now available for Python 3.7-3.9 on the ml4t Anaconda channel.
• Binary wheels are available on PyPI for Linux ( Python 3.7-3.9) and MacOSx (3.7 and 3.8).

As part of the update, the BlazeLoader functionality was removed. It was built on the Blaze Ecosystem. Unfortunately, the three relevant projects (Blaze, Odo, and datashape have received limited support over the last several years.

Other updates include:

• A new release for Bcolz which has been marked unmaintained since September 2020 by the author. The new release updates the underlying
  c-blosc library from version 1.14 to the latest 1.21.0. There are also conda packages for Bcolz (see links above).
• Networkx - used by Zipline to process the Pipeline Graph - now uses the better performing version 2.0.
• Conda packages for TA-Lib version 0.4.19.

This new release also makes it easier to load custom data sources into a Pipeline (such as the predictions of an ML model) when backtesting.
See the relevant examples in the Github repo of the book Machine Learning for Trading,, such as these ones.

Enhancements

• custom_loader() for custom Pipeline data
• compatibility with the latest versions of Pandas, scikit-learn, and other relevant libraries.

Bug Fixes

• Numerous tests updates to accommodate recent Python and dependency versions.

Performance

• The latest blosc library may improve compression and I/O performance.

Maintenance and Refactorings

• Removed Python 2 support

Build

• All builds consolidated on GitHub Actions CI

Documentation

• Expanded with additional information on Pipeline and related DataLoaders