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py-alphalens: fix build with Python 3.12

py-alphalens: updated to 0.4.0

v0.4.0
This is a minor release from 0.3.6 that includes bugfixes, performance improvements, and build changes.

finance: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes

finance: Remove SHA1 hashes for distfiles

py-alphalens: updated to 0.3.6

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

finance/py-alphalens: Update to 0.3.2

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

finance/py-alphalens: Update to 0.2.1

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

finance/py-alphalens: version 0.1.1

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.