catalyst/docs/release-notes/zipline-0.8.0.md

Zipline 0.8.0 Release Notes

Highlights

• New documentation system with a new website at zipline.io
• Major performance enhancements.
• Dynamic history.

Bug Fixes (BUG)

Fix a bug where the reported returns could sharply dip for random periods of time. PR378

Enhancements (ENH)

Account object: Adds an account object to conext to track information about the trading account. PR396

Example:

context.account.settled_cash

Returns the settled cash value that is stored on the account object. This value is updated accordingly as the algorithm is run.

HistoryContainer can now grow dynamically. PR412

Calls to history will now be able to increase the size or change the shape of the history container to be able to service the call. add_history now acts as a preformance hint to pre-allocate sufficient space in the container. This change is backwards compatible with history, all existing algorithms should continue to work as intended.

Simple transforms ported from quantopian and use history. PR429

SIDData now has methods for:

• stddev
• mavg
• vwap
• returns

These methods, except for returns, accept a number of days. If you are running with minute data, then this will calculate the number of minutes in those days, accounting for early closes and the current time and apply the transform over the set of minutes. returns takes no parameters and will return the daily returns of the given asset.

Example:

data[security].stddev(3)

New fields in Performance Period PR464

Performance Period has new fields accessible in return value of to_dict:

• gross leverage
• net leverage
• short exposure
• long exposure
• shorts count
• longs count

Allow order_percent to work with various market values (by Jeremiah Lowin) PR477

Currently, order_percent() and order_target_percent() both operate as a percentage of self.portfolio.portfolio_value. This PR lets them operate as percentages of other important MVs.

Also adds context.get_market_value(), which enables this functionality.

For example:

this is how it works today (and this still works)

put 50% of my portfolio in AAPL

order_percent('AAPL', 0.5)

note that if this were a fully invested portfolio, it would become 150% levered.

take half of my available cash and buy AAPL

order_percent('AAPL', 0.5, percent_of='cash')

rebalance my short position, as a percentage of my current short book

order_target_percent('MSFT', 0.1, percent_of='shorts')

rebalance within a custom group of stocks

tech_stocks = ('AAPL', 'MSFT', 'GOOGL') tech_filter = lambda p: p.sid in tech_stocks for stock in tech_stocks: order_target_percent(stock, 1/3, percent_of_fn=tech_filter)

### Major performance enhancements to history (by Dale Jung) [PR488](https://github.com/quantopian/zipline/commit/38e8d5214d46f089020703712dc6b3f4f6ee084d)

### Command line option to for printing algo to stdout (by Andrea D'Amore) [PR545](https://github.com/quantopian/zipline/pull/545)

## Contributors

The following people have contributed to this release, ordered by numbers of commit:

39  Thomas Wiecki
36  Joe Jevnik
26  John Fawcett
24  Scott Sanderson
11  Delaney Granizo-Mackenzie
 8  John Ricklefs
 5  Brian Fink
 5  Eddie Hebert
 2  Dale Jung
 2  Jeremiah Lowin
 2  Jonathan Kamens
 2  Richard Frank
 1  David Edwards
 1  Luke Schiefelbein
 1  Mete Atamel
 1  Nicholas Pezolano
 1  Philipp Kosel
 1  Andrea D'Amore