73.2k stars · AGPL-3.0 · v1.0.2 (2026-04-25), read from /releases/latest, where the tag is labelled ODP for Open Data Platform; the GitHub date carried no year and falls in the past, so it is this year · Track this in Scout
One Python interface in front of many financial data providers, so the answer comes back in the same shape whichever source it came from.
▶Repo detailsthe review · specs · pros & cons · install
What it is
An open data platform for market and economic information. You install a Python package and call it: one command for a share's price history, another for a company's income statement, another for an economic series. Each source is an extension, so adding a paid provider already in use is installing one more package and setting a key.
What it is good for. Anyone doing research on markets who is tired of writing a new reader for every source. The problem it removes is the glue code. Anyone who already gets prices from one broker will find that what this adds is the surrounding picture — interest-rate decisions, economic releases, and the calendar of the days when a strategy tends to behave differently.
- One shape of answer across many providers, so changing source does not mean rewriting the analysis.
- Several providers work with no key and no account at all, so the first hour costs nothing.
- Very large and very active, with 73,200 stars and a working Python interface rather than only a terminal.
- AGPL-3.0 licence. Running it for research is fine. Building a product that other people use on top of it brings real conditions.
- It is a big install with many dependencies. Use a virtual environment and expect it to take a while.
- Free data sources have limits and gaps, and none of them are meant for live trading decisions.
ranaroussi/yfinanceReads one source, Yahoo Finance, and it is a much smaller install when that one source is all you need.
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microsoft/qlibA platform for building and testing machine-learning trading models rather than for fetching data.
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TA-Lib/ta-lib-pythonCalculates the 150-odd standard indicators on price data you already have, which is the step after this one.
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python3 -m venv venv
source venv/bin/activate
pip install openbb
# In Python:
# from openbb import obb
# df = obb.equity.price.historical("AAPL", start_date="2026-01-01").to_df()
# print(df.tail())
# Add a provider you have a key for, for example:
pip install openbb-fmp
