Alphalens

This is a major release from 0.2.1, we recommend that all users upgrade to this version.

New features

- Integration with [Pyfolio](https://github.com/quantopian/pyfolio). It is now possible to simulate a portfolio using the input alpha factor and analyze the performance with Pyfolio. Please see the relevant [example notebook](https://github.com/quantopian/alphalens/blob/master/alphalens/examples/pyfolio_integration.ipynb). [[PR 227](https://github.com/quantopian/alphalens/pull/227)] and [[PR 250](https://github.com/quantopian/alphalens/pull/250)]
- Added new API [utils.get_clean_factor](https://github.com/quantopian/alphalens/blob/master/alphalens/utils.pyL360) to run Alphalens with returns instead of prices [[PR 270](https://github.com/quantopian/alphalens/pull/270)]
- Changed color palette to improve the visual experience for colorblind users [[PR 248](https://github.com/quantopian/alphalens/pull/248)]
- Standard deviation bars optional in [tears.create_event_returns_tear_sheet](https://github.com/quantopian/alphalens/blob/master/alphalens/tears.pyL605)
- Alphalens now properly handles intraday factors

Bugfixes

- Alphalens now works with both tz-aware and tz-naive data (but not mixed)
- "Cumulative Returns by Quantile" plot used a different color scheme for quantiles than "Average Cumulative Returns by Quantile" plot
- Many small but useful bug fixes that avoid sporadic crashes and memory leaks. Please see the git history for more details

Documentation

- Added several new example [Notebooks](https://github.com/quantopian/alphalens/tree/master/alphalens/examples)

Maintenance

- Removed deprecated pandas.TimeGrouper
- Migrated tests from deprecated nose-parameterized (251)
- Fixed compatibility with matplotlib 2.2.0
- Alphalens is now available via conda-forge. Install via `conda install -c conda-forge alphalens`

Credits

The following people contributed to this release:

luca-s - Luca Scarabello
twiecki - Thomas Wiecki
mmargenot - Max Margenot
MichaelJMath
HereticSK
TimShawver - Tim Shawver
alen12345 - Alessio Nava

This is a bugfix release from v0.2.0. All users are recommended to upgrade.

Bugfixes

- `tears.create_information_tear_sheet`: argument `group_adjust` was erroneously removed without a replacement. From this release argument`group_adjust` is still deprecated but `group_neutral` can be used instead

This is a major new release since v0.1.0. It contains small API breakage, several new features and many bug fixes. All users are recommended to upgrade.

* Removed deprecated API 'alphalens.tears.create_factor_tear_sheet'

* Added event study API 'alphalens.tears.create_event_study_tear_sheet' and relative example NB

* Added Long only option to 'alphalens.performance.factor_alpha_beta'

* Improved docstrings all around

* Small bug fixes

New features

- 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](https://github.com/quantopian/alphalens/blob/master/alphalens/examples/event_study.ipynb) 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

Bugfixes

- [Fix alpha and beta calculation on a Long only factor](https://github.com/quantopian/alphalens/issues/167)

- [Inconsistent "Returns Analysis" Table](https://github.com/quantopian/alphalens/commit/a9a47aae928a5d75a211538402300a6453027eec)

- [Standard Error Conversion from n-Periods to 1-Period](https://github.com/quantopian/alphalens/commit/72dcedaa1d03079788a8e7044d8dec8d0776591f)

- [Improved help message for 'Bin edges must be unique' error and explain possible solutions](https://github.com/quantopian/alphalens/pull/197)

- [ValueError: Invalid RGBA argument: 0.0`](https://github.com/quantopian/alphalens/issues/157)

- [Cumulative returns plots not properly computed](https://github.com/quantopian/alphalens/commit/5629cc2a13de6b43e02a1f6fb9f0f7d4683d7f5e)

- [Update IC Risk Adjusted Ratio Calculation](https://github.com/quantopian/alphalens/pull/215)


API change

- Removed deprecated `alphalens.tears.create_factor_tear_sheet`
- `tears.create_summary_tear_sheet`: added argument `group_neutral`.
- `tears.create_returns_tear_sheet`: added argument `group_neutral`. Please consider using keyword arguments to avoid API breakage
- `tears.create_information_tear_sheet`: `group_adjust` is now deprecated and `group_neutral` should be used instead
- `tears.create_full_tear_sheet`: `group_adjust` is now deprecated and `group_neutral` should be used instead
- `tears.create_event_returns_tear_sheet`: added argument `group_neutral`. Please consider using keyword arguments to avoid API breakage
- Several small changes to lower level API (`alphalens.performance`)

Maintenance

- Depends on pandas>=0.18.0
- Changed deprecated `pd.rolling_mean()` to use the new `*.rolling().mean()` API
- Changed deprecated `pd.rolling_apply()` to use the new `*.rolling().apply()` API
- Use versioneer to pull version from git tag