{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/6"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"},{"label":"Investment","url":"https://skillfed.io/packages/category/office-business-financial-investment"}],"enrichment":{"capability":"Official Python client for accessing live and historical market data from Databento, supporting multiple asset classes, schemas, and data formats with normalized message structures.","skillfed_tags":["market-data","financial-api","time-series"],"use_cases":["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."],"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\u2014the 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.\n\nThe 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.","worth_installing":"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."},"id":"databento","links":{"html":"https://skillfed.io/packages/databento","md":"https://skillfed.io/packages/databento.md","pypi":"https://pypi.org/project/databento/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"databento","python_support":"supports_current","summary":"Official Python client library for Databento"},"popularity":{"monthly_downloads":1745295,"position":3597,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.83.0"}