The trading day index is all business days in range minus the
non trading days we are already calculating.
Also, uses trading calendar indexes for batch transform, since the
batch transform was the only use of non_trading_days.
Instead of constantly adding and removing holidays to do market
day delta math, uses pandas DatetimeIndex to get the index of the dates
and uses the index difference to calculate market days.
The test factory was creating non-market days.
i.e. the date range spanned the weekend.
Using pandas' BDay frequency so that only business days are created.
This specific date range doesn't have holidays, so not accounting
for holidays in the factory.
Also, widens the range of the trading calendar to cover the test dates
generated by the factory which include 1990.
Previously the trading calendar began with 2002, meaning that holiday
and weekend adjustments with the data exercised by the factory did
not trigger when run with data in 1990.
This does increase the memory footprint of the tradingcalendar module.
However, only by a couple MB, so taking the hit there to enable
correct behavior.
Updated the search for treasury data when there is none for the
test end date.
It could be that the end date is not a trading day, or we could
just be missing treasury data. In either case, we try to recover
more gracefully now, by searching as far as possible and maybe
logging a warning.
Similarly, if there is no benchmark data for the test end date,
look for the next trading day. If we really have no data,
blow up with our own explicit exception, instead of overflowing
in our search for dates in the future.
By having run() use a capital_base member of the algorithm to
create the trading environment, the capital base should now be
configurable in the instantiation of the algorithm.
e.g.:
```
algo = LowCapitalBaseAlgorithm(capital_base=1000.0):
```
So that the zipline library can be used when installed to a
write-protected location, e.g. the global site-packages,
moving the download files to a directory in the user's path,
which should be writeable.
For now, choosing a ~/.zipline/data location.
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.
Instead log to test.log in working directory when running tests.
Also, removes config file for logging module, that is no longer
used since we are now using LogBook.
Mostly whitespace, line width and other spacing changes.
Also, removes use of deprecated has_key in favor of `in`
Going forward new patches should pass running `flake8` before
submission.
Eddie Hebert