v0.3.6
Add option to compute forward returns non-cumulatively
v0.3.5
This is a minor release from 0.3.4 that includes bugfixes, speed enhancement and compatibility with more recent pandas versions. We recommend that all users upgrade to this version.
v0.3.4
This is a minor release from 0.3.3 that includes bugfixes, small enhancements and backward compatibility breakages. We recommend that all users upgrade to this version.
v0.3.3
TEST: added tests for perf.mean_return_by_quantile
New features since 0.2.1:
- Integration with Pyfolio. It is now possible to simulate a portfolio
using the input alpha factor and analyze the performance with
Pyfolio.
- Added new API utils.get_clean_factor to run Alphalens with returns
instead of prices
- Changed color palette to improve the visual experience for
colorblind users
- Standard deviation bars optional in
tears.create_event_returns_tear_sheet
- Alphalens now properly handles intraday factors
New features since 0.1.0:
- Added event study analysis: an event study is a statistical method
to assess the impact of a particular event on the value of equities
and it is now possible to perform this analysis through the API
alphalens.tears.create_event_study_tear_sheet. Check out the
relative NoteBook in the example folder.
- Added support for group neutral factor analysis (group_neutral
argument): this affects the return analysis that is now able to
compute returns statistics for each group independently and
aggregate them together assuming a portfolio where each group has
equal weight.
- utils.get_clean_factor_and_forward_returns has a new parameter
max_loss that controls how much data the function is allowed to drop
due to not having enough price data or due to binning errors
(pandas.qcut). This gives the users more control on what is
happening and also avoid the function to raise an exception if the
binning doesn't go well on some values.
- Greatly improved API documentation
Alphalens is a Python Library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with the Zipline open
source backtesting library, and Pyfolio which provides performance and
risk analysis of financial portfolios.
