The latest flake8 release in now 1.5, which pulls in pep8: 1.3.4a0
The upgrade pep8 has changes to what it picks up as lint.
Making code base compatible, so that new devs can install pep8
from PyPI and not have friction over the version difference.
Currently using these ignores in the config file:
```
[pep8]
ignore = E124,E125,E126
```
Ignoring these since they are difficult to squash while maintaining
an 80 char line length, and appear spurious.
Should address later.
Updates Travis config, README, and pip requirements to reflect change.
Also drops ipython (which is used for desktop development) and
nose (which is installed by default by Travis CI) from list of
dev packages to be installed during a Travis build.
This answer key needs to be verified with original calculations.
Also, we may want to hardcode values in plain text to use for these
tests, instead of relying on a full history.
Hopefully, this helps ease ramp up time for developing against
market data, without us distributing the data.
We do a check for the data when attempting to read the msgpack
files, if they don't exist the loader makes a web request and
retrieves and serializes the data for the user.
Provides a loader for:
- curves from data.treasury.gov
- benchmarks from Yahoo! Finance
Adds dependency of requests library in dev requirements.
I wrote this a little while ago as I noticed that a lot of time is spent
computing risk statistics. This is done over the complete history over
and over again while this could be done just by using the previously
computed value (iteratively).
We didn't go forward back then because for minute trade data the
difference was not significant enough. However, now with zipline
standalone I think most people will use daily (because that's
what's available) and it makes a huge difference
(speed-up of a couple of 100%).
Unfortunately, we can't just replace the existing one with an
iterative as for the final cumulative stats the batch is still
better. So that's not as nice, but the performance increase is
big enough for me to issue this PR (zipline is actually painfully
slow with daily data).
There is a unittest that compares that both produce exactly
the same outputs.
Speed measurements (for 500 trading days, daily source):
with iterative:
real 26.617 user 12.909 sys 6.112 pcpu 71.46
prior:
real 44.176 user 31.030 sys 11.381 pcpu 96.00
Eddie Hebert