Rather than drop files temporarily into the master security lists
directory during unit tests, create temporary directories for the
tests. This avoids issues when the tests are being run at the same
time as other code that uses the real security lists data.
Only fill limit order if impacted fill price is better than the limit price.
If a limit order is partially filled, only fill the remaining shares if the
impacted fill price is better than the limit price.
If a SID hasn't started trading yet, pandas' convention is to use nans.
Before this change, zipline would raise an exception if there were nans in the
input data.
We now skip events where the prices contains a nan and has not been traded
before (in which case forward fill).
Fixes#446.
Remove pieces that are no longer used now that the simple transforms are
wrappers around history via the SIDData object.
Move window length related pieces into batch_transform, since the rest
of the utils module is no longer used.
This commit refactors the Security cython class to Asset, and refactors some fields of the class accordingly. This change is so the terminology is consistent and correct when Asset is extended to asset types that are not securities, such as futures.
on the number of per-tick update that occur since they were duplicated
per each PerformancePeriod. Also opens up the path to cythonizing the
entire object
A cython __richcmp__ function isn't allowed to assume that its first
argument is the same as the type of the class to which it belongs, so
our code needs to account for either of its two arguments being of the
wrong type.
Furthermore, the correct way for __richcmp__ to handle when it doesn't
know how to do a comparison is to return NotImplemented.
The >= comparison for the Cythonized Security object was actually
doing <=. Fix this and add unit tests for all the Security object rich
comparison operators.
Now version testing will only be run if the pandas version is
current. This is to allow the test matrix to pass on travis.
The version must be updated in file whenever pandas is upgraded.
The class is not yet used. Adding this class is part of the effort to allow Zipline
simulation of more types of assets than stocks.
DEV: Adds build_ext to .travis.yml
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:
```python
order_percent('AAPL', 0.5)
order_percent('AAPL', 0.5, percent_of='cash')
order_target_percent('MSFT', 0.1, percent_of='shorts')
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)
```
Limited use of `pandas` data structures in both `HistoryContainer` and
`RollingPanel`. Where possible, methods were amended to return raw
`ndarrays` with the indexing logic done separately. This allows us to
cut down the number of times pandas objects are created both as returns
and intermediate values. The separation of indexing from data access
allowed us to minimize the times we’d make use of pandas indexes.
This required that that certain methods like `NDFrame.ffill` be replaced
with versions that work with `ndarrays`. Some of this was done via
straight numpy methods and others by access pandas internal
machinery. Outside of allowing us to use faster ndarrays, many of these
function provided speedups over their pandas counterparts as we didn’t
require the extra features like handling multiple dtypes. i.e. np.isnan
is faster than pd.isnull, but only works with certain dtypes.
and added a new test case
was not iterating over lookup date directory names, and
therefore mising all by one list of stocks.
discovered because of differing sort orders between
my local machine, other devs, and travis ci.
Jonathan Kamens