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.
contstruction.
BarData can be falsey. in create_buffer_panel, the intention of the
check against bar_data was to see if it was passed at all, not if it was
truthey. In order to make that check more explicit, the check now
asserts that bar_data is not None.
getting filled with the wrong datetimes and causing errors.
Updates the logic for addressing missing datetimes and adds unit tests
for the 2 main cases (no missing datetimes, and some missing datetimes).
makes it an offset from 13:30 UTC.
This is to be more consistent with the market_close, which is an offset
from 20:00 UTC.
This also makes market_open and market_close cache the dt to offset from
for each day.
Previously, all specs had to be pre-allocated by using the 'add_history'
function. This is now no longer required and instead serves as a hint to
the HistoryContainer to pre-allocate the space for the given spec.
History can grow by increasing the length for a frequency, adding a
frequency, or adding a field. It can grow with any combination of
these.
HistoryContainer now is aware of the data_frequency of the algorithm,
and no longer uses the daily_at_midnight flag; instead, this is the
default behavior.
- NotHalfDay only worked at midnight
- week_(start|end) were actually month_(start|end)
- Removes check_args from api.
- Default offset of 30mins for market_(open|close)
schedule_function takes a date rule, a time rule, and a function and
will call the function, passing context and data only when the two rules
fire. This allows for code that is conditional to the datetime of the
algo.
This is implemented internally with `Event` objects which are pairings
of `EventRule`s and callbacks.
handle_data becomes a special event with a rule that always fires. This
makes the logic for handling events more complete and compact.
Jonathan Kamens