I'm not sure what the raw csv pulled from the federal reserve looked like before, but when trying to download fresh treasure data (data not stored in `./zipline`), there is an error that says "Time Period not in list". After checking the raw csv now, it looks like there are 5 header rows rather than just 1, so skipping those rows removes that error.
We need to call gc.collect before tearing down our class because we have
a cycle between TradingAlgorithm and AlgorithmSimulator which ultimately
holds a reference to the pipeline engine passed to the tests here.
This means that we're not guaranteed to have deleted our disk-backed
resource readers (e.g. SQLiteAdjustmentReader) before trying to delete
the tempdir, which causes failures on Windows because Windows doesn't
allow you to delete a file if someone still has an open handle to that
file.
The real fix for this is to break the cycle between TradingAlgorithm and
AlgorithmSimulator, but that requires significant breaking API changes.
This data bundle will use the quantopian mirror of the quandl WIKI data
instead of downloading from quandl directly. This dramatically improves
the speed because we do not pay the rate limiting for quandl and we can
send the data in the format zipline expects.
Adds the data bundle concept which makes it easy for users to register
loading functions to build out minute and daily data along with an
assets db and adjustments db. By default we have provided a `quandl`
bundle which pulls from the public domain WIKI dataset. Users may
register new bundles by decorating an ingest function with
`zipline.data.bundles.register(<name>)`. This also provides a
`yahoo_equities` function for creating an ingestion function that will
load a static set of assets from yahoo.
The cli is now structured as a couple of subcommands and has been
changed to `python -m zipline`. The old behavior of `run_algo.py` has
been moved to the `run` subcommand. This is almost entirely the same
except that it now takes the name of the data bundle to use, defaulting
to `quandl`.
The next subcommand is `ingest` which takes the name of
a data bundle to ingest. This will run the loading machinery and write
the data to a specified location that `run` can find.
There is also a `clean` subcommand which deletes the data that was
written with `ingest`.
Extensions have also been added to zipline. This is an experimental
feature where users can provide an extra set of python files to run at
the start of the process. These can be used to configure aspects of
zipline. Right now the only thing that is supported in an extension file
is the registration of a new data bundle.
This commit removes the ability to reference a shared TradingEnvironment through the zipline.finance.trading module. In place, the classes that require a TradingEnvironment, or its child AssetFinder, contain their own references to those objects.
This commit also adds serialization utilities that allow for the pickling/unpickling of objects without unintentionally their TradingEnvironments or AssetFinders.
The rename of walk is not provided by six, so check the import error
via an exception.
Also, callback behavior slightly changes between the two versions,
so instead iterate over the walked files and call what was formerly
a callback, directly as a function.
Conner Fromknecht