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'.

This returns a small selection of companies on all exchanges where the companies are listed.

You'll see that the same company can appear multiple times. This is because by default all exchanges are shown. There are two methods to focus on one entry:

• Use the only_primary_listing parameter. This will only show the primary listing of each company. This is useful mostly if you are looking at US exchanges.
• Use the exchange or market parameter. This will allow you to filter on a specific exchange or market. This is useful when you are not necessarily looking at US exchanges and are already filtering on a specific country.

For example, when filtering on the Netherlands, it makes sense to select a Dutch exchange as well. This could be the exchange "AMS" or the market "Euronext Amsterdam". This will give you a much smaller selection.

This gives the following three companies (not shortened):

Given that the Netherlands is a relatively small country, it is not uncommon for the list to become small quickly. For example, the same selection for the United States is already much larger, also utilizing the only_primary_listing parameter.

While not immediately obvious in this shortened output, it returns about 180 different companies.

For any of the variables, it is also possible to provide a list instead, which means that it will return all entries that match any of the variables. As an example, the queries above can be combined into one:

This returns a larger selection of companies given the increased number of countries and markets.

If the current categorization doesn't lead to the results you are looking for, it is possible to use the search parameter. This allows you to filter on any column in the database via a custom string. This means that if the word or sentence you input is found somewhere in the column you select, it will return the result.

By default, the result will not be case sensitive, but you can adjust this by setting case_sensitive=True. You can also filter the index (symbol column) by using index as shown below. Just like the select function, you can also provide lists here.

This returns instruments that are listed on the Frankfurt Stock Exchange, are in an industry group with the word "Equipment," and have either "Robotics" or "Education" in the summary column. The index parameter is used to filter on the symbol column, which in this case is ".F". The filtering on the index is an alternative way of finding the exchange or market you are looking for.

Lastly, the Finance Database has a direct integration with the Finance Toolkit, making it possible to do financial analysis on the companies you've found in the Finance Database. Returning to the earlier example of the 3 insurance companies in the Netherlands, it becomes possible to load these into the Finance Toolkit with the to_toolkit functionality.

To be able to get started, you need to obtain an API Key from FinancialModelingPrep. This is used to gain access to 30+ years of financial statements, both annually and quarterly. Note that the Free plan is limited to 250 requests each day, 5 years of data, and only features companies listed on US exchanges.

Then you can go ahead and run the following code, changing the API_KEY to your own API Key:

With this integration, I can now access some of the most important financial metrics for these companies. Let's start simple with historical data:

Which returns, selecting only "ASRNL.AS" as an example:

Now let's make it more advanced by automatically calculating 60+ financial ratios for each company. This is just a small snippet of what is available within the Finance Toolkit; see the GitHub page of the Finance Toolkit here or the example Notebook here for more information.

Which returns, selecting only "ASRNL.AS" as an example with a few ratios:

All of these methods are also available for the other asset classes. The only difference is that the class name changes and the available columns. For example, for ETFs you would use fd.ETFs() instead of fd.Equities() and the select option has parameters such as category_group and family instead.

This gives you results like the following:

This also translates to the available options. For example, let's select fd.Indices() instead:

A sample of the output is shown below:

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