https://github.com/databento/databento-python
The official Python client library for Databento
https://github.com/databento/databento-python
api client databento historical http market-data python python3 real-time tcp
Last synced: 8 months ago
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The official Python client library for Databento
- Host: GitHub
- URL: https://github.com/databento/databento-python
- Owner: databento
- License: apache-2.0
- Created: 2021-12-22T06:31:05.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-04-30T15:25:12.000Z (9 months ago)
- Last Synced: 2025-05-08T05:44:45.501Z (9 months ago)
- Topics: api, client, databento, historical, http, market-data, python, python3, real-time, tcp
- Language: Python
- Homepage: https://docs.databento.com
- Size: 1.11 MB
- Stars: 172
- Watchers: 10
- Forks: 25
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# databento-python
[](https://github.com/databento/databento-python/actions/workflows/test.yml)
[](https://pypi.org/project/databento)
[](./LICENSE)
[](https://github.com/psf/black)
[](https://to.dbn.to/slack)
The official Python client library for [Databento](https://databento.com).
Key features include:
- Fast, lightweight access to both live and historical data from [multiple markets](https://databento.com/docs/faqs/venues-and-publishers).
- [Multiple schemas](https://databento.com/docs/schemas-and-data-formats/whats-a-schema?historical=python&live=python) such as MBO, MBP, top of book, OHLCV, last sale, and more.
- [Fully normalized](https://databento.com/docs/standards-and-conventions/normalization?historical=python&live=python), i.e. identical message schemas for both live and historical data, across multiple asset classes.
- Provides mappings between different symbology systems, including [smart symbology](https://databento.com/docs/api-reference-historical/basics/symbology?historical=python&live=python) for futures rollovers.
- [Point-in-time]() instrument definitions, free of look-ahead bias and retroactive adjustments.
- Reads and stores market data in an extremely efficient file format using [Databento Binary Encoding](https://databento.com/docs/standards-and-conventions/databento-binary-encoding?historical=python&live=python).
- Event-driven [market replay](https://databento.com/docs/api-reference-historical/helpers/bento-replay?historical=python&live=python), including at high-frequency order book granularity.
- Support for [batch download](https://databento.com/docs/faqs/streaming-vs-batch-download?historical=python&live=python) of flat files.
- Support for [pandas](https://pandas.pydata.org/docs/), CSV, and JSON.
## Documentation
The best place to begin is with our [Getting started](https://databento.com/docs/quickstart?historical=python&live=python) guide.
You can find our full client API reference on the [Historical Reference](https://databento.com/docs/api-reference-historical?historical=python&live=python) and
[Live Reference](https://databento.com/docs/reference-live?historical=python&live=python) sections of our documentation. See also the
[Examples](https://databento.com/docs/examples?historical=python&live=python) section for various tutorials and code samples.
## Requirements
The library is fully compatible with the latest distribution of Anaconda 3.9 and above.
The minimum dependencies as found in the `pyproject.toml` are also listed below:
- python = "^3.9"
- aiohttp = "^3.8.3"
- databento-dbn = "0.34.0"
- numpy= ">=1.23.5"
- pandas = ">=1.5.3"
- pip-system-certs = ">=4.0" (Windows only)
- pyarrow = ">=13.0.0"
- requests = ">=2.25.1"
- zstandard = ">=0.21.0"
## Installation
To install the latest stable version of the package from PyPI:
pip install -U databento
## Usage
The library needs to be configured with an API key from your account.
[Sign up](https://databento.com/signup) for free and you will automatically
receive a set of API keys to start with. Each API key is a 32-character
string starting with `db-`, that can be found on the API Keys page of your [Databento user portal](https://databento.com/platform/keys).
A simple Databento application looks like this:
```python
import databento as db
client = db.Historical('YOUR_API_KEY')
data = client.timeseries.get_range(
dataset='GLBX.MDP3',
symbols='ES.FUT',
stype_in='parent',
start='2022-06-10T14:30',
end='2022-06-10T14:40',
)
data.replay(callback=print) # market replay, with `print` as event handler
```
Replace `YOUR_API_KEY` with an actual API key, then run this program.
This uses `.replay()` to access the entire block of data
and dispatch each data event to an event handler. You can also use
`.to_df()` or `.to_ndarray()` to cast the data into a Pandas `DataFrame` or numpy `ndarray`:
```python
df = data.to_df() # to DataFrame
array = data.to_ndarray() # to ndarray
```
Note that the API key was also passed as a parameter, which is
[not recommended for production applications](https://databento.com/docs/portal/api-keys?historical=python&live=python).
Instead, you can leave out this parameter to pass your API key via the `DATABENTO_API_KEY` environment variable:
```python
import databento as db
# Pass as parameter
client = db.Historical('YOUR_API_KEY')
# Or, pass as `DATABENTO_API_KEY` environment variable
client = db.Historical()
```
## License
Distributed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html).