FinanceDatabase is a free, MIT-licensed Python package and community-maintained database of over 300,000 financial symbols spanning Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets, built for investors, analysts and developers who need to see the full breadth of what exists across countries, sectors and exchanges.

What it is

FinanceDatabase is a database of more than 300,000 financial symbols, distributed as the financedatabase Python package and categorised across Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. Each product carries classification information: Equities are grouped into 11 sectors, 80 industries, 117 countries and 84 exchanges, while ETFs are described by 313 families, 44 categories and 51 exchanges, and Funds by 1,540 families, 74 categories and 33 exchanges. The underlying records are stored as CSV files that can be edited by hand, which is what allows contributors without coding experience to add, edit and remove tickers as the market changes.

The concrete problem it solves is discovery. Millions of companies and derivatives trade worldwide, and only the best-known names are visible to most investors. FinanceDatabase replaces the manual hunt through scattered listings with one categorised index that can be queried from Python, so a user can survey sectors, industries and investment types across a market rather than guess at what might exist. It deliberately does not attempt to be a market data feed or a source of up-to-date fundamentals; it describes which products exist and gives the essential information about each so that other tools, such as the Finance Toolkit, can then fetch live figures.

Key capabilities

• Covers 112,690 Equities across 11 sectors, 80 industries, 117 countries and 84 exchanges.
• Covers 36,481 ETFs across 313 families, 44 categories and 51 exchanges.
• Covers 57,853 Funds across 1,540 families, 74 categories and 33 exchanges.
• Covers 91,181 Indices across 63 exchanges, plus 1,367 Money Markets across 2 exchanges.
• Covers 2,556 Currencies across 175 currencies and 3,367 Cryptocurrencies across 351 cryptocurrencies.
• Stores records as CSV files that can be edited manually, so contributions need no coding experience.
• Ships as an installable Python package queried with import financedatabase as fd.

Who uses it and how

• Private investors who find the sheer amount of information available on the internet daunting and need a broad overview of sectors, industries and investment types.
• Contributors with little or no coding experience, who add, edit and remove tickers directly in CSV files following the Contributing Guidelines.
• Analysts researching a specific area of the financial world, or searching for a product that is hard to find through ordinary lookups.
• Developers pairing the database with the Finance Toolkit, using FinanceDatabase for categorisation and the toolkit to obtain stock data and fundamentals.
• Python users who query the database through the financedatabase package rather than maintaining their own symbol lists.

Getting started

Install with pip install financedatabase -U, then use import financedatabase as fd inside Python. The README's usage examples are deliberately cut to a maximum of 10 entries because of the size of the database.

How it compares

The facts name no paid products that this project replaces, so the closest reference point is the Finance Toolkit, which sits alongside it rather than competing with it: FinanceDatabase supplies categorisation and the toolkit supplies the fundamental and stock data, and the README frames them as complements. The openbb topic also places it in the wider open-source financial data ecosystem rather than in the commercial data-terminal category.

When to use it — and when not to

Use it when the goal is breadth of coverage and classification, and skip it when the goal is current prices or fundamentals, which the README explicitly states the database does not provide. A user must be willing to pair it with another source for market data, and must accept that the ticker universe depends on community involvement to stay current, since the project relies on contributors to add, edit and remove entries over time. It is also less suited to anyone wanting a single self-contained data product: the value here is the catalogue, and the rest of the pipeline has to come from elsewhere.

pip install financedatabase -U

import financedatabase as fd

import financedatabase as fd

equities = fd.Equities()

equities.select()