databento

What it is and what it does

Databento is the official Python client for the Databento market data platform, providing unified access to both live and historical financial data across multiple asset classes and venues. It abstracts away the complexity of different data formats and symbology systems by offering fully normalized schemas—the same message structure works for both live and historical data, whether you're working with market-by-order, market-by-price, top-of-book, OHLCV, or last-sale events. The library efficiently stores and reads data in Databento Binary Encoding format and supports event-driven market replay at any granularity, from daily bars to high-frequency order book snapshots.

The package is built on a foundation of mature dependencies: aiohttp and requests for HTTP communication, pandas and numpy for data manipulation, pyarrow for columnar storage, and zstandard for compression. It requires Python 3.10 or later and is actively maintained. You interact with it by instantiating a Historical or Live client with an API key, then calling methods like timeseries.get_range() to fetch data, which you can then replay event-by-event, convert to a pandas DataFrame, or export to CSV or JSON. The library handles API authentication, data normalization, and symbology mapping transparently.

Use it for:

• Backtest trading strategies using normalized historical order book or OHLCV data with event-driven replay.
• Fetch and analyze multi-asset time series (equities, futures, options) with point-in-time instrument definitions.
• Build real-time market monitoring dashboards by consuming live feeds and converting to pandas DataFrames.
• Perform high-frequency research on order book dynamics using market-by-order schemas across multiple venues.
• Batch download and store market data in efficient binary format for offline analysis or archival.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Official Python client for accessing live and historical market data from Databento, supporting multiple asset classes, schemas, and data formats with normalized message structures.

Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is the official client for a specialized but well-established market data service. Install it if you need programmatic access to Databento's historical or live market data; skip it if you don't have a Databento account or need data from a different provider.

Install

databento on PyPI

pip install databento

uv add databento

poetry add databento

Installing databento

Before you install

Low friction installation with a pure-Python wheel. Active maintenance with a release 9 days old and a recent commit history. Eight runtime dependencies are all widely-used libraries (aiohttp, pandas, numpy, pyarrow, requests, zstandard, databento-dbn, pip-system-certs).

License in practice

Apache-2.0 permissive license allows commercial and private use without restriction, with minimal attribution requirements.

Quickstart

pip install -U databento

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',
)
df = data.to_df()

Requires Python >=3.10 and a valid Databento API key (free signup available); Windows users need pip-system-certs.

Verify before relying

• Whether the library supports real-time streaming in addition to historical batch retrieval.
• Performance characteristics and throughput limits for large datasets or high-frequency replay.
• Whether smart symbology and point-in-time instrument definitions are available in the free tier.

Package facts

Evidence: databento-0.83.0-py3-none-any.whl

Tags

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