Data Bundles¶
A data bundle is a collection of pricing data, adjustment data, and an asset database. Bundles allow us to preload all of the data we will need to run backtests and store the data for future runs.
Discovering Available Bundles¶
Zipline comes with a few bundles by default as well as the ability to register
new bundles. To see which bundles we have available, we may run the
bundles command, for example:
$ zipline bundles
my-custom-bundle 2016-05-05 20:35:19.809398
my-custom-bundle 2016-05-05 20:34:53.654082
my-custom-bundle 2016-05-05 20:34:48.401767
quandl <no ingestions>
quantopian-quandl 2016-05-05 20:06:40.894956
The output here shows that there are 3 bundles available:
my-custom-bundle(added by the user)quandl(provided by zipline, though deprecated)quantopian-quandl(provided by zipline, the default bundle)
The dates and times next to the name show the times when the data for this
bundle was ingested. We have run three different ingestions for
my-custom-bundle. We have never ingested any data for the quandl bundle
so it just shows <no ingestions> instead. Finally, there is only one
ingestion for quantopian-quandl.
Ingesting Data¶
The first step to using a data bundle is to ingest the data.
The ingestion process will invoke some custom bundle command and then write the data to a standard location that zipline can find.
By default the location where ingested data will be written is $ZIPLINE_ROOT/data/<bundle> where by default ZIPLINE_ROOT=~/.zipline.
The ingestion step may take some time as it could involve downloading and processing a lot of data.
To ingest a bundle, run:
$ zipline ingest [-b <bundle>]
where <bundle> is the name of the bundle to ingest, defaulting to quantopian-quandl.
Old Data¶
When the ingest command is used it will write the new data to a subdirectory
of $ZIPLINE_ROOT/data/<bundle> which is named with the current date. This
makes it possible to look at older data or even run backtests with the older
copies. Running a backtest with an old ingestion makes it easier to reproduce
backtest results later.
One drawback of saving all of the data by default is that the data directory
may grow quite large even if you do not want to use the data. As shown earlier,
we can list all of the ingestions with the bundles command. To solve the problem of leaking old data there is another
command: clean, which will clear data bundles based on some time
constraints.
For example:
# clean everything older than <date>
$ zipline clean [-b <bundle>] --before <date>
# clean everything newer than <date>
$ zipline clean [-b <bundle>] --after <date>
# keep everything in the range of [before, after] and delete the rest
$ zipline clean [-b <bundle>] --before <date> --after <after>
# clean all but the last <int> runs
$ zipline clean [-b <bundle>] --keep-last <int>
Running Backtests with Data Bundles¶
Now that the data has been ingested we can use it to run backtests with the
run command. The bundle to use can be specified with the --bundle option
like:
$ zipline run --bundle <bundle> --algofile algo.py ...
We may also specify the date to use to look up the bundle data with the
--bundle-timestamp option. Setting the --bundle-timestamp will cause
run to use the most recent bundle ingestion that is less than or equal to
the bundle-timestamp. This is how we can run backtests with older data.
bundle-timestamp uses a less-than-or-equal-to relationship so that we can
specify the date that we ran an old backtest and get the same data that would
have been available to us on that date. The bundle-timestamp defaults to
the current day to use the most recent data.
Default Data Bundles¶
Quandl WIKI Bundle¶
By default zipline comes with the quantopian-quandl data bundle which uses quandl’s WIKI dataset.
The quandl data bundle includes daily pricing data, splits, cash dividends, and asset metadata.
Quantopian has ingested the data from quandl and rebundled it to make ingestion much faster.
To ingest the quantopian-quandl data bundle, run either of the following commands:
$ zipline ingest -b quantopian-quandl
$ zipline ingest
Either command should only take a few seconds to download the data.
Note
Quandl has discontinued this dataset. The dataset is no longer updating, but is reasonable for trying out Zipline without setting up your own dataset.
Writing a New Bundle¶
Data bundles exist to make it easy to use different data sources with
zipline. To add a new bundle, one must implement an ingest function.
The ingest function is responsible for loading the data into memory and
passing it to a set of writer objects provided by zipline to convert the data to
zipline’s internal format. The ingest function may work by downloading data from
a remote location like the quandl bundle or it may just
load files that are already on the machine. The function is provided with
writers that will write the data to the correct location transactionally. If an
ingestion fails part way through the bundle will not be written in an incomplete
state.
The signature of the ingest function should be:
ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir)
environ¶
environ is a mapping representing the environment variables to use. This is
where any custom arguments needed for the ingestion should be passed, for
example: the quandl bundle uses the environment to pass the API key and the
download retry attempt count.
asset_db_writer¶
asset_db_writer is an instance of AssetDBWriter.
This is the writer for the asset metadata which provides the asset lifetimes and
the symbol to asset id (sid) mapping. This may also contain the asset name,
exchange and a few other columns. To write data, invoke
write() with dataframes for the various
pieces of metadata. More information about the format of the data exists in the
docs for write.
minute_bar_writer¶
minute_bar_writer is an instance of
BcolzMinuteBarWriter. This writer is used to
convert data to zipline’s internal bcolz format to later be read by a
BcolzMinuteBarReader. If minute data is
provided, users should call
write() with an iterable of
(sid, dataframe) tuples. The show_progress argument should also be forwarded
to this method. If the data source does not provide minute level data, then
there is no need to call the write method. It is also acceptable to pass an
empty iterator to write()
to signal that there is no minutely data.
Note
The data passed to
write() may be a lazy
iterator or generator to avoid loading all of the minute data into memory at
a single time. A given sid may also appear multiple times in the data as long
as the dates are strictly increasing.
daily_bar_writer¶
daily_bar_writer is an instance of
BcolzDailyBarWriter. This writer is
used to convert data into zipline’s internal bcolz format to later be read by a
BcolzDailyBarReader. If daily data is
provided, users should call
write() with an iterable of
(sid dataframe) tuples. The show_progress argument should also be forwarded
to this method. If the data source does not provide daily data, then there is
no need to call the write method. It is also acceptable to pass an empty
iterable to write() to
signal that there is no daily data. If no daily data is provided but minute data
is provided, a daily rollup will happen to service daily history requests.
Note
Like the minute_bar_writer, the data passed to
write() may be a lazy
iterable or generator to avoid loading all of the data into memory at once.
Unlike the minute_bar_writer, a sid may only appear once in the data
iterable.
adjustment_writer¶
adjustment_writer is an instance of
SQLiteAdjustmentWriter. This writer is
used to store splits, mergers, dividends, and stock dividends. The data should
be provided as dataframes and passed to
write(). Each of
these fields are optional, but the writer can accept as much of the data as you
have.
calendar¶
calendar is an instance of
zipline.utils.calendars.TradingCalendar. The calendar is provided to
help some bundles generate queries for the days needed.
start_session¶
start_session is a pandas.Timestamp object indicating the first
day that the bundle should load data for.
end_session¶
end_session is a pandas.Timestamp object indicating the last day
that the bundle should load data for.
cache¶
cache is an instance of dataframe_cache. This
object is a mapping from strings to dataframes. This object is provided in case
an ingestion crashes part way through. The idea is that the ingest function
should check the cache for raw data, if it doesn’t exist in the cache, it should
acquire it and then store it in the cache. Then it can parse and write the
data. The cache will be cleared only after a successful load, this prevents the
ingest function from needing to re-download all the data if there is some bug in
the parsing. If it is very fast to get the data, for example if it is coming
from another local file, then there is no need to use this cache.
show_progress¶
show_progress is a boolean indicating that the user would like to receive
feedback about the ingest function’s progress fetching and writing the
data. Some examples for where to show how many files you have downloaded out of
the total needed, or how far into some data conversion the ingest function
is. One tool that may help with implementing show_progress for a loop is
maybe_show_progress. This argument should always be
forwarded to minute_bar_writer.write and daily_bar_writer.write.
output_dir¶
output_dir is a string representing the file path where all the data will be
written. output_dir will be some subdirectory of $ZIPLINE_ROOT and will
contain the time of the start of the current ingestion. This can be used to
directly move resources here if for some reason your ingest function can produce
it’s own outputs without the writers. For example, the quantopian:quandl
bundle uses this to directly untar the bundle into the output_dir.
Ingesting Data from .csv Files¶
Zipline provides a bundle called csvdir, which allows users to ingest data
from .csv files. The format of the files should be in OHLCV format, with dates,
dividends, and splits. A sample is provided below. There are other samples for testing
purposes in zipline/tests/resources/csvdir_samples.
date,open,high,low,close,volume,dividend,split
2012-01-03,58.485714,58.92857,58.42857,58.747143,75555200,0.0,1.0
2012-01-04,58.57143,59.240002,58.468571,59.062859,65005500,0.0,1.0
2012-01-05,59.278572,59.792858,58.952858,59.718571,67817400,0.0,1.0
2012-01-06,59.967144,60.392857,59.888573,60.342857,79573200,0.0,1.0
2012-01-09,60.785713,61.107143,60.192856,60.247143,98506100,0.0,1.0
2012-01-10,60.844284,60.857143,60.214287,60.462856,64549100,0.0,1.0
2012-01-11,60.382858,60.407143,59.901428,60.364285,53771200,0.0,1.0
Once you have your data in the correct format, you can edit your extension.py file in
~/.zipline/extension.py and import the csvdir bundle, along with pandas.
import pandas as pd
from zipline.data.bundles import register
from zipline.data.bundles.csvdir import csvdir_equities
We’ll then want to specify the start and end sessions of our bundle data:
start_session = pd.Timestamp('2016-1-1', tz='utc')
end_session = pd.Timestamp('2018-1-1', tz='utc')
And then we can register() our bundle, and pass the location of the directory in which
our .csv files exist:
register(
'custom-csvdir-bundle',
csvdir_equities(
['daily'],
'/path/to/your/csvs',
),
calendar_name='NYSE', # US equities
start_session=start_session,
end_session=end_session
)
To finally ingest our data, we can run:
$ zipline ingest -b custom-csvdir-bundle
Loading custom pricing data: [############------------------------] 33% | FAKE: sid 0
Loading custom pricing data: [########################------------] 66% | FAKE1: sid 1
Loading custom pricing data: [####################################] 100% | FAKE2: sid 2
Loading custom pricing data: [####################################] 100%
Merging daily equity files: [####################################]
# optionally, we can pass the location of our csvs via the command line
$ CSVDIR=/path/to/your/csvs zipline ingest -b custom-csvdir-bundle
If you would like to use equities that are not in the NYSE calendar, or the existing zipline calendars,
you can look at the Trading Calendar Tutorial to build a custom trading calendar that you can then pass
the name of to register().