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

Zipline 0.8.0 Release Notes

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 security.

Example:

# The standard deviation of the price in the last 3 days.
data[security].stdev(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

Contributors

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

   349  Eddie Hebert
   213  Thomas Wiecki
   134  fawce
   119  Jeremiah Lowin
    97  Dale Jung
    52  David Edwards
    45  Joe Jevnik
    45  Scott Sanderson
    32  Delaney Granizo-Mackenzie
    30  Richard Frank
    27  Ryan Day
    22  Ben McCann
    22  Jonathan Kamens
    19  twiecki
    14  Colin Alexander
    11  Tony Worm
    10  John Ricklefs
     8  Brian Cappello
     7  Brian Fink
     7  Moises Trovo
     7  Wes McKinney
     6  David Stephens
     6  Mete Atamel
     5  Elektra58
     5  Seong Lee
     4  Jason Kölker
     4  Mark Dunne
     4  Suminda Dharmasena
     4  Tobias Brandt
     4  llllllllll
     3  Chen Huang
     3  Jamie Kirkpatrick
     3  Jean Bredeche
     3  Luke Schiefelbein
     3  Matti Hanninen
     3  Nicholas Pezolano
     2  Aidan
     2  Martin Dengler
     2  Peter Cawthron
     2  Philipp Kosel
     2  jbredeche
     2  stanh
     1  Aaron Marz
     1  Ben
     1  Corey Farwell
     1  Ian Levesque
     1  Jeremi Joslin
     1  Justin Graves
     1  Michael Schatzow
     1  Pankaj Garg
     1  Stan
     1  Sébastien Drouyer
     1  The Gitter Badger
     1  Tony Lambiris
     1  cowmoo