Changes BcolzDailyBarWriter to not be an abc, data is passed as an
iterator of (sid, dataframe) pairs to the write method.
Changes the AssetsDBWriter to be a single class which accepts an engine
at construction time and has a `write` method for writing dataframes for
the various tables. We no longer support writing the various other data
types, callers should coerce their data into a dataframe themselves. See
zipline.assets.synthetic for some helpers to do this.
Adds many new fixtures and updates some existing fixtures to use the new
ones:
WithDefaultDateBounds
A fixture that provides the suite a START_DATE and END_DATE. This is
meant to make it easy for other fixtures to synchronize their date
ranges without depending on eachother in strange ways. For example,
WithBcolzMinuteBarReader and WithBcolzDailyBarReader by default should
both have data for the same dates, so they may use depend on
WithDefaultDates without forcing a dependency between them.
WithTmpDir, WithInstanceTmpDir
Provides the suite or individual test case a temporary directory.
WithBcolzDailyBarReader
Provides the suite a BcolzDailyBarReader which reads from bcolz data
written to a temporary directory. The data will be read from
dataframes and then converted to bcolz files with
BcolzDailyBarWriter.write
WithBcolzDailyBarReaderFromCSVs
Provides the suite a BcolzDailyBarReader which reads from bcolz data
written to a temporary directory. The data will be read from a
collection of CSV files and then converted into the bcolz data through
BcolzDailyBarWriter.write_csvs
WithBcolzMinuteBarReader
Provides the suite a BcolzMinuteBarReader which reads from bcolz data
written to a temporary directory. The data will be read from
dataframes and then converted to bcolz files with
BcolzMinuteBarWriter.write
WithAdjustmentReader
Provides the suite a SQLiteAdjustmentReader which reads from an in
memory sqlite database. The data will be read from dataframes and then
converted into sqlite with SQLiteAdjustmentWriter.write
WithDataPortal
Provides each test case a DataPortal object with data from temporary
resources.
Add a cap of 5 sliding windows (one per OHCLV column) to the history
loader's cache of sliding windos.
This prevents unbounded growth on algorithms that call history with a
highly varied list of equities.
To follow is splitting the cache up by column and by sid, so that the
loader does not re-prefetch sids which have already been read with
sufficient data; however this patch is enough to fix the issue where an
algo with high rotation can add up a megabyte per day of memory on
algorithms which rotate on a 5% dollar volume pipeline. With this cap
those algorithms have more plateaus with regard to memory consumption.
This patch requires new dependency of `cachetools` library.
Builds docker image with TA-Lib, Jupyter, and checked out version of zipline and
starts Jupyter server on port https://localhost:8888 when container runs. The
default password for the Jupyter server is 'jupyter'.
Instructions on usage found in the Dockerfile comments. Example image at
docker.io/jxstanford/zipline.
Upgrade six 1.9.0 -> 1.10.0 in requirements.txt, and futures 3.0.3 ->
3.0.5, requests-futures 0.9.5 -> 0.9.7, and piprot 0.9.1 -> 0.9.6 in
requirements_dev.txt.
There was a comment in requirements.txt warning not to use
python-dateutil 2.4.0, which was necessary when we were using 2.3.x
and 2.4.0 was broken for us, but now we're using 2.4.2 which is fixed,
so the comment is no longer needed.
Upgrade Logbook to 0.12.5. This required changing a usage of
`logbook.NullHandler()` which passed `bubble=True`, since
`NullHandler` no longer supports the `bubble` argument.
EarningsCalendar loader.
- Moves most of AdjustedArray back into Python. The window iterator is
the only part that's performance-intensive.
- Adds a bootleg templating system for creating specialized versions of
AdjustedArrayWindow for each concrete type we care about.
- Adds support for differently dtyped terms in pipeline. This allows us
to use datetime64s which are needed in the EarningsCalendar.
- Adds EarningsCalendar dataset for the next and previous earnings
announcements in pipeline.
- Adds in memory loader for EarningsCalendar.
- Adds blaze loader for EarningsCalendar.
- 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.
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.
Joe Jevnik