[ANN] Alphalens 0.2 released

[Thomas Wiecki]

What is it?

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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.

http://quantopian.github.io/alphalens/

https://github.com/quantopian/alphalens

You can install via:

pip install alphalens

Release notes

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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.

# New since v0.1.0

## 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

Contributors

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Besides Quantopian employees, Luca Scarabello made major contributions to this software.