This commit:
* Adds a crypto_benchmark that can create benchmarks for symbols found on POLO
* Changes default trading calendars to OPEN
* Properly computes daily bar data from five minute POLO bars
* Allows trading of one hundredth of a coin, later we plan to integrate per the
ratio of a full coin to its base denomination.
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.
This VagrantFile will, on "vagrant up"...
- Create a simple, minimal Precise Pangolin (12.04) Ubuntu 64 bit VM
- Customize the VM with 2 virtual CPU's and 2048MB of RAM
- Configure SSH for passwordless access (from the command-line)
- Add the required packages from the Ubuntu repo to support zipline
- Add (and compile) ta-lib
- Add the required Pip packages
When Vagrant is done, you can start hacking on zipline with:
vagrant ssh
cd /vagrant
python {some python script that uses zipline}
In the spirit of making this a disposable dev environment install
everything in site-packages.
"nosetests" and "examples/dual_moving_average.py" both succeed
after the configuration finishes.
Hopefully, this helps ease ramp up time for developing against
market data, without us distributing the data.
We do a check for the data when attempting to read the msgpack
files, if they don't exist the loader makes a web request and
retrieves and serializes the data for the user.
Provides a loader for:
- curves from data.treasury.gov
- benchmarks from Yahoo! Finance
Adds dependency of requests library in dev requirements.
Instead log to test.log in working directory when running tests.
Also, removes config file for logging module, that is no longer
used since we are now using LogBook.
Ben Feeser