Finance Database

As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what types of companies or ETFs are available is incredibly challenging, with millions of companies and derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found simply because they are known to the public (for example, Microsoft, Tesla, S&P 500 ETF, or an All-World ETF). However, what else is out there is often unknown.

This is why I created the FinanceDatabase, a database featuring 300,000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. It allows you to obtain a broad overview of sectors, industries, types of investments and much more, entirely for free. Everything is stored in CSV files that anyone can read and edit, so the database grows and improves through the community.

The aim of this database is explicitly _not_ to provide up-to-date fundamentals or stock data, as those can be obtained with ease (with the help of this database) by using the Finance Toolkit 🛠️. Instead, it gives insights into the products that exist in each country, industry and sector and provides the most essential information about each product. With this information, you can analyze specific areas of the financial world and/or find a product that is hard to find. By utilising both, it is possible to do a fully-fledged competitive analysis with the tickers found from the FinanceDatabase inputted into the FinanceToolkit.

Some key statistics of the database:

| | Asset class | Symbols | Exchanges | Coverage | | :-: | :-- | --: | --: | :-- | | 🏢 | Equities | 121,626 | 87 | 11 sectors · 75 industries · 118 countries | | 📦 | ETFs | 32,138 | 63 | 636 issuers · 32 categories | | 💼 | Funds | 57,761 | 29 | 1,534 fund families · 70 categories | | 📈 | Indices | 82,396 | 64 | 42 categories | | 💱 | Currencies | 2,516 | – | 177 currencies | | 🪙 | Cryptocurrencies | 5,845 | – | 681 coins · 12 quote currencies | | 🏦 | Money Markets | 1,225 | 4 | 131 fund families | | | Total | 303,507 | | |

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🔌 The Finance Database is also available as an [MCP Server](https://www.jeroenbouma.com/projects/financedatabase/mcp)

Explore all 300,000+ symbols from Claude, Copilot, Cursor, Windsurf or any MCP-compatible client without writing code. No API key needed.

• Hosted: connect to https://financedatabase.jeroenbouma.com/mcp — nothing to install and no API key.
• Local: uvx --from "financedatabase[mcp]" financedatabase-mcp-setup — sets up your client config automatically. See MCP Server Documentation for manual setup.

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Table of Contents

1. Installation
2. Functionality
3. MCP Server
4. Questions & Answers
5. Contributing
6. Contact

Installation

Before installation, consider starring the project on GitHub which helps others find the project as well.

To install the FinanceDatabase it simply requires the following:

pip install financedatabase -U

Then within Python use:

import financedatabase as fd

equities = fd.Equities()

No API key is needed. Each asset class is downloaded once and cached locally, see Questions & Answers for how the cache works.

Functionality

This section is an introduction to the FinanceDatabase. Find with the link below a Jupyter Notebook in which you can see many more examples, including the full output of each query.

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Find the Getting Started Notebook for the FinanceDatabase here .

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A basic example of how to use the FinanceDatabase is shown below. Every code snippet in the sections that follow builds on this same equities instance. Initialization of each asset class is only required once , so save it to a variable to query the database much more quickly.

import financedatabase as fd

Initialize the Equities database

equities = fd.Equities()

Select all equities

equities.select()

A portion of the output is shown below. The tables in this section are cut off to five entries and a selection of the columns due to the sheer size of the database.

And below the actively listed equities are counted per sector.

Each asset class has the same three functions: select to filter on the database's categories, search to look for any text in any column and show_options to see which values a category can take. The asset classes are fd.Equities(), fd.ETFs(), fd.Indices(), fd.Currencies() and fd.Cryptos(), which are updated weekly from official exchange and market sources.

Three capabilities cut across all of them:

• Lists of values. Every filter accepts a single value or a list, e.g. country=['Netherlands', 'Belgium'], returning the entries that match any of them.
• `only_primary_listing` and `exclude_delisted`. A company is often listed on many exchanges. only_primary_listing=True keeps only its primary listing, and delisted symbols are left out by default (exclude_delisted=False to include them).
• pandas or Polars. Queries run lazily with Polars and return a pandas DataFrame by default, or a Polars DataFrame with as_pandas=False.

Exploring the Options

With show_options, all possible options are given per column. This is useful as it doesn't require loading the larger data files.

# Show the options of every column of the equities

options = fd.show_options("equities")

Select the sectors

options["sector"]

This returns the eleven sectors used for equities:

['Communication Services', 'Consumer Discretionary', 'Consumer Staples',

'Energy', 'Financials', 'Health Care', 'Industrials', 'Information Technology', 'Materials', 'Real Estate', 'Utilities']

Once an asset class is loaded, show_options is also available on the class itself, where it shows the options that remain after filtering. For example, the industries of the financial companies in the Netherlands:

# Show the industries of financial companies in the Netherlands

equities.show_options( selection="industry", sector="Financials", country="Netherlands", )

Which returns:

['Banks', 'Capital Markets', 'Consumer Lending', 'Insurance',

'Payments & Specialty Finance']

And below the number of companies in each of these industries is shown. Each company is counted once, however many exchanges it is listed on: listings that share an ISIN, share class FIGI, website or the first word of their name (such as Aegon and its perpetual bonds) are combined.

The options of every column can be shown this way, including currency, exchange, market, country and market_cap (Mega, Large, Mid, Small, Micro and Nano Cap). Find the Notebook [here](https://www.jeroenbouma.com/projects/financedatabase/getting-started) and the full documentation [here](https://www.jeroenbouma.com/projects/financedatabase).

Selecting Equities

Given these options, it becomes possible to filter the database on the categories you are interested in. For example, the 'Insurance' companies in the 'United States', one row per company. The sector can be omitted here since the industry already implies 'Financials'.

# Select the primary listings of insurance companies in the United States

equities.select( country="United States", industry="Insurance", only_primary_listing=True, )

This returns 194 companies, of which a few of the larger ones are shown below.