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 database tries to solve that. It features 300,000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies, and Money Markets. It therefore allows you to obtain a broad overview of sectors, industries, types of investments, and much more.

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. For examples of how you can combine this database with the earlier mentioned packages, see the Usage section.

Some key statistics of the database:

Installation

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

To install the FinanceDatabase, simply use the following:

pip install financedatabase -U

Then within Python use:

import financedatabase as fd

Usage

This section explains in detail how the database can be queried with the related financedatabase package. Note that examples here are purposely cut off to a maximum of 10 entries due to the sheer size of the database. Furthermore, the summary column is also omitted for readability. For the full detailed results, see the Notebook here. Let's start by importing the package:

import financedatabase as fd

Initialization of each asset class is only required once. It is therefore important that you save the class to a variable so that you can query the database much more quickly. A simple example is shown below.

equities = fd.Equities()

equities.select()

A sample of the output is shown below:

With show_options, all possible options are given per column. This is useful as it doesn't require loading the larger data files. For example, obtaining all options for equities is done as follows:

fd.show_options("equities")

This returns all available options for each column.

{'currency': array(['ARS', 'AUD', 'BRL', 'CAD', 'CHF', 'CLP', 'CNY', 'COP', 'CZK',
    'DKK', 'EUR', 'GBP', 'HKD', 'HUF', 'IDR', 'ILA', 'ILS', 'INR',
    'ISK', 'JPY', 'KES', 'KRW', 'LKR', 'MXN', 'MYR', 'NOK', 'NZD',
    'PEN', 'PHP', 'PLN', 'QAR', 'RUB', 'SAR', 'SEK', 'SGD', 'THB',
    'TRY', 'TWD', 'USD', 'ZAC', 'ZAR'], dtype=object),
 'sector': array(['Communication Services', 'Consumer Discretionary',
    'Consumer Staples', 'Energy', 'Financials', 'Health Care',
    'Industrials', 'Information Technology', 'Materials',
    'Real Estate', 'Utilities'], dtype=object),
 'industry_group': array(['Automobiles & Components', 'Banks', 'Capital Goods',
    'Commercial & Professional Services',
    'Consumer Durables & Apparel', 'Consumer Services',
    'Diversified Financials', 'Energy', 'Food & Staples Retailing',
    'Food, Beverage & Tobacco', 'Health Care Equipment & Services',
    'Household & Personal Products', 'Insurance', 'Materials',
    'Media & Entertainment',
    'Pharmaceuticals, Biotechnology & Life Sciences', 'Real Estate',
    'Retailing', 'Semiconductors & Semiconductor Equipment',
    'Software & Services', 'Technology Hardware & Equipment',
    'Telecommunication Services', 'Transportation', 'Utilities'],
       dtype=object)}

Since the equities database has already been loaded, it is also possible to use similar functionality from within the class as follows. The main difference is that this functionality allows you to see the options based on specific filtering. For example:

equities.show_options(country='Netherlands')

This shows a more concise list of parameters given the focus on the Netherlands.

{'currency': array(['ARS', 'AUD', 'BRL', 'CHF', 'CZK', 'EUR', 'GBP', 'ILA', 'MXN',
    'NOK', 'RUB', 'USD', 'ZAC'], dtype=object),
 'sector': array(['Communication Services', 'Consumer Discretionary',
    'Consumer Staples', 'Energy', 'Financials', 'Health Care',
    'Industrials', 'Information Technology', 'Materials',
    'Real Estate', 'Utilities'], dtype=object),
 'industry_group': array(['Automobiles & Components', 'Banks', 'Capital Goods',
    'Commercial & Professional Services',
    'Consumer Durables & Apparel', 'Consumer Services',
    'Diversified Financials', 'Energy', 'Food & Staples Retailing',
    'Food, Beverage & Tobacco', 'Health Care Equipment & Services',
    'Household & Personal Products', 'Insurance', 'Materials',
    'Media & Entertainment',
    'Pharmaceuticals, Biotechnology & Life Sciences', 'Real Estate',
    'Retailing', 'Semiconductors & Semiconductor Equipment',
    'Software & Services', 'Technology Hardware & Equipment',
    'Telecommunication Services', 'Transportation', 'Utilities'],
       dtype=object)}

Or only showing one specific parameter:

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

Which returns:

array(['Banks', 'Capital Markets', 'Consumer Finance',
       'Diversified Financial Services', 'Insurance'], dtype=object)

Given this information, it then becomes possible to filter the database based on the parameters you are interested in. For example, if you are interested in 'Insurance' companies in the 'Netherlands', you can use the following. Note that I omit the sector here, given that the selection I make is on a deeper level and therefore it is a given that the sector is 'Financials'.

```python equities.select( country='Netherland