Rather than specifying only the package "zipline" in setup.py, use
`find_packages` to find all the subpackages as well, so they (or, most
specifically, their `__init__.py` files) are properly packaged in the
egg file.
Modify setup.py to defer the use of Cython and numpy until
`setup_requires` has already been processed, so that Cython and numpy
are available when they are needed.
- Parse our requirements.txt to keep install_requires up to date.
- Create extras builds for talib and dev.
- Use pip install -e .[dev] on Travis to install Zipline before testing.
If lookup_future_chain was provided with an as_of_date or knowledge date that was pandas.NaT, the query we were forming wasn't what we want. Instead, as_of_date, if not NaT, is used for knowledge_date, and if both are NaT, no date filtering is done in the query.
`period_end` can be outside the range of data for which we have dates.
`last_close` properly gets pulled back to the last date for which we
actually have data.
We should consider whether or not we need to be storing period_end at
all.
- Previously it was returning a DataFrame because of how we applied an &
with a DataFrame mask. The error was masked by the fact that
`np.assert_array_equal` coerces inputs to arrays before comparing.
- Added `zp.utils.test_utils.check_arrays`, which checks type equality
before calling `np.assert_array_equal`.
This patch lays the groundwork for a compute engine designed to
facilitate construction of factor-based universe screening and portfolio
allocation. It contains:
A new module, `zipline.modelling`, containing entities that can be used
to express computations as dependency graphs. Each node in such a graph
is an instance of the base `Term` class, defined in
`zipline.modelling.term`. Dependency graphs are executed by instances
of `FFCEngine`, defined in `zipline.modelling.engine`.
A new module, `zipline.data.ffc`, containing loaders and dataset
definitions for inputs to the modelling API.
New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`.
These methods can only be called from `initialize`, and are used to
inform the algorithm that each day it should compute the given terms.
Computed factor results are made available through a new attribute of
the `data` object in `before_trading_start` and `handle_data`. Computed
filter results control which assets are available in the factor matrix
on each day.
As of logbook 0.10.0, logbook no longer installs a default handler,
which means that if the application doesn't install one, log messages
disappear into the ether.
Therefore, all of our scripts with `__main__` endpoints need to push a
`logbook.StderrHandler` if they're not already pushing some other
handler.
Instead of creating a new cursor with each query, use the same cursor
throughout the lifetime of the finder instance to remove any overhead
from creating a new cursor in tight loops.
The minutely calculation of risk metrics had been removed with a
previous patch, remove vestigial references.
Remove a test which tested the behavior of updating the second minute of
a day.
Remove the logic that changed the datetime index of the risk metrics
depending on emission rate, now only trading_days are needed.
Remove `returns_frequency` parameter since both minute and daily
data frequency always use daily returns.
Fix a change in the information given to the multiple symbol error
during the recent sqlite change to the asset finder.
Instead of the string of sids, return full asset information about the
available options, since internal code relied on the full data.
Joe Jevnik