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 default bundle as well as the ability to register new bundles. To see which bundles we may be 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 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, 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.

Note: Quantopian used to provide a re-packaged version of the quandl bundle as quantopian-quandl that is still available in April 2021. While it ingests much faster, it does not have the country code that the library has since come to require and which the current Zipline version inserts for the quandl bundle. If you want to use quantopian-quandl instead, use this workaround to manually update the database.

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 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 ...

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 quandl data bundle which uses Quandl’s WIKI dataset. The Quandl data bundle includes daily pricing data, splits, cash dividends, and asset metadata. To ingest the quandl data bundle, run either of the following commands:

$ zipline ingest -b quandl
$ zipline ingest

Either command should only take a few minutes to download and process the data.


Quandl has discontinued this dataset early 2018 and it no longer updates. Regardless, it is a useful starting point to try 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. 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:



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 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 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.


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 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.


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 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 is an instance of zipline.utils.calendars.TradingCalendar. The calendar is provided to help some bundles generate queries for the days needed.


start_session is a pandas.Timestamp object indicating the first day that the bundle should load data for.


end_session is a pandas.Timestamp object indicating the last day that the bundle should load data for.


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 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 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.


Once you have your data in the correct format, you can edit your file in ~/.zipline/ and import the csvdir bundle, along with pandas.

import pandas as pd

from import register
from 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:

    calendar_name='NYSE', # US equities

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().

Practical Examples

See examples for Algoseek minute data and Japanese equities at daily frequency from the book Machine Learning for Trading.