Call for Contributors to the FinanceDatabase
The FinanceDatabase serves the role of providing anyone with any type of financial product categorization entirely for free. To be able to achieve this, the FinanceDatabase relies on involvement from the community to add, edit and remove tickers over time. This is made easy enough that anyone, even with a lack of coding experience can contribute because of the usage of CSV files that can be manually edited with ease.
I’d like to invite you to go to the Contributing Guidelines to understand how you can help. Thank you!

As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being 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&P500 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 gives the most essential information about each product. With this information, you can analyse specific areas of the financial world and/or find a product that is hard to find. See for examples on how you can combine this database, and the earlier mentioned packages the section Usage.

Some key statistics of the database:

Product	Quantity	Sectors	Industries	Countries	Exchanges
Equities	158.429	12	63	111	83
ETFs	36.786	295	22	111	53
Funds	57.881	1541	52	111	34

Product	Quantity	Category
Currencies	2.556	175 Currencies
Cryptocurrencies	3.367	352 Cryptocurrencies
Indices	91.183	64 Exchanges
Money Markets	1.367	3 Exchanges

The Finance Database is used within or referenced by:

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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

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 taken out 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 you save the class to a variable so that you can query the database much quicker. A simple example is shown below.

equities = fd.Equities()

equities.select()

A sample of the output is shown below:

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
PMTA.DU	PTC Inc.	EUR	Information Technology	Software & Services	Software	DUS	Dusseldorf Stock Exchange	United States	MA	Boston	2210	http://www.ptc.com	Large Cap	US69370C1009	69370C100	BBG000FC6SC5	BBG000FC5PS5	BBG001S6DNK6
VAW.F	VAALCO Energy, Inc.	EUR	Energy	Energy	Oil, Gas & Consumable Fuels	FRA	Frankfurt Stock Exchange	United States	TX	Houston	77042	http://www.vaalco.com	Micro Cap	US91851C2017	91851C201	BBG000CN15Y5	BBG000CN15F6	BBG001S76ZS7
ORC.DE	Oracle Corporation	EUR	Information Technology	Software & Services	Software	GER	XETRA	United States	TX	Austin	78741	http://www.oracle.com	Mega Cap	US68389X1054	68389X105	BBG000C0RY38	BBG000C0RWW0	BBG001S5SJG6
PAYX	Paychex, Inc.	USD	Industrials	Commercial & Professional Services	Professional Services	NMS	NASDAQ Global Select	United States	NY	Rochester	14625-2396	http://www.paychex.com	Large Cap	US7043261079	704326107	BBG000BQT1J5	BBG000BQSQ38	BBG001S5V135
RI2A.F	Rigel Pharmaceuticals, Inc.	EUR	Health Care	Pharmaceuticals, Biotechnology & Life Sciences	Biotechnology	FRA	Frankfurt Stock Exchange	United States	CA	South San Francisco	94080	http://www.rigel.com	Small Cap	US7665596034	766559603	BBG000BKZNR4	BBG000BKZNC0	BBG001SD33Z0
PGEN	Precigen, Inc.	USD	Health Care	Pharmaceuticals, Biotechnology & Life Sciences	Biotechnology	NMS	NASDAQ Global Select	United States	MD	Germantown	20876	http://www.precigen.com	Small Cap	US74017N1054	74017N105	BBG004TDDJ32	BBG000QL8VH9	BBG001SSB3T5
GOGO	Gogo Inc.	USD	Communication Services	Telecommunication Services	Diversified Telecommunication Services	NMS	NASDAQ Global Select	United States	IL	Chicago	60606	http://www.gogoair.com	Small Cap	US38046C1099	38046C109	BBG002CN8Y71	BBG002CN8XN5	BBG002CN8YD4
CRK	Comstock Resources, Inc.	USD	Energy	Energy	Oil, Gas & Consumable Fuels	NYQ	New York Stock Exchange	United States	TX	Frisco	75034	http://www.comstockresources.com	Mid Cap	US2057683029	205768302	BBG000DNBMJ3	BBG000DNBK89	BBG001S8FX55
OIS	Oil States International, Inc.	USD	Energy	Energy	Energy Equipment & Services	NYQ	New York Stock Exchange	United States	TX	Houston	77002	http://www.oilstatesintl.com	Small Cap	US6780261052	678026105	BBG000BDDQ06	BBG000BDDN94	BBG001S7WK56
CVLC.BE	Vale S.A.	EUR	Materials	Materials	Metals & Mining	BER	Berlin Stock Exchange	Brazil	RJ	Rio De Janeiro	22250-145	http://www.vale.com	Large Cap	US91912E1055	9.19E+109	BBG000HCJTN5	BBG000HCJNQ5	BBG001S7RS91

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)}

As the equities database has already been loaded in, it is also possible to use a similar functionality from within the class as follows. The main difference is that this functionality allows you to see the options based on a 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 ‘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’.

equities.select(
    country='Netherlands',
    industry='Insurance',
)

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

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
A16.F	ASR Nederland N.V.	EUR	Financials	Insurance	Insurance	FRA	Frankfurt Stock Exchange	Netherlands	nan	Utrecht	3584 BA	http://www.asrnl.com	Mid Cap	NL0011872643	nan	BBG00D2VFV96	BBG00D2VFV78	BBG00CWZ0HK0
A1EG34.SA	Aegon N.V.	BRL	Financials	Insurance	Insurance	SAO	Bovespa Soma	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Mid Cap	NL0000303709	nan	nan	nan	nan
AEG	Aegon N.V.	USD	Financials	Insurance	Insurance	NYQ	New York Stock Exchange	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Large Cap	NL0000303709	nan	BBG000CKQTN4	BBG000CKQSN6	BBG001S6Y6M8
AEGOF	Aegon N.V.	USD	Financials	Insurance	Insurance	PNK	OTC Bulletin Board	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Mid Cap	NL0000303709	nan	nan	nan	nan
AEND.DE	Aegon N.V.	EUR	Financials	Insurance	Insurance	GER	XETRA	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Mid Cap	NL0000303709	nan	BBG000DJK260	BBG000DJHZF1	BBG001S5V8R4

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 into the 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 not necessarily looking into US exchanges and are already filtering on a specific country.

For example, filtering on the Netherlands it makes sense to select a Dutch exchange as well. This is for example the exchange “AMS” or the market “Euronext Amsterdam”. This will already give you a much smaller selection.

equities.select(
    country='Netherlands',
    industry='Insurance',
    market='Euronext Amsterdam',
)

This gives the following three companies (not shortenend):

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
AGN.AS	Aegon N.V.	EUR	Financials	Insurance	Insurance	AMS	Euronext Amsterdam	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Mid Cap	NL0000303709	nan	BBG000JN9DM6	BBG000JN9C93	BBG001S5V8R4
ASRNL.AS	ASR Nederland N.V.	EUR	Financials	Insurance	Insurance	AMS	Euronext Amsterdam	Netherlands	nan	Utrecht	3584 BA	http://www.asrnl.com	Mid Cap	NL0011872643	nan	BBG00CWZ0HG5	BBG00CWZ0HF6	BBG00CWZ0HK0
NN.AS	NN Group N.V.	EUR	Financials	Insurance	Insurance	AMS	Euronext Amsterdam	Netherlands	nan	The Hague	2595 AS	http://www.nn-group.com	Large Cap	nan	nan	nan	nan	nan

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

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

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

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
AFL	Aflac Incorporated	USD	Financials	Insurance	Insurance	NYQ	New York Stock Exchange	United States	GA	Columbus	31999	http://www.aflac.com	Large Cap	US0010551028	1055102	BBG000BBBRC7	BBG000BBBNC6	BBG001S5NGJ4
AJG	Arthur J. Gallagher & Co.	USD	Financials	Insurance	Insurance	NYQ	New York Stock Exchange	United States	IL	Rolling Meadows	60008-4050	http://www.ajg.com	Large Cap	US3635761097	363576109	BBG000BBHZK4	BBG000BBHXQ3	BBG001S5NKC2
AMSF	AMERISAFE, Inc.	USD	Financials	Insurance	Insurance	NMS	NASDAQ Global Select	United States	LA	Deridder	70634	http://www.amerisafe.com	Small Cap	US03071H1005	03071H100	BBG000Q0JWB7	BBG000Q0JJQ0	BBG001SDH7B2
BRO	Brown & Brown, Inc.	USD	Financials	Insurance	Insurance	NYQ	New York Stock Exchange	United States	FL	Daytona Beach	32114	http://www.bbinsurance.com	Large Cap	US1152361010	115236101	BBG000BWSJ77	BBG000BWSGF4	BBG001S5XFN0
CINF	Cincinnati Financial Corporation	USD	Financials	Insurance	Insurance	NMS	NASDAQ Global Select	United States	OH	Fairfield	45014-5141	http://www.cinfin.com	Large Cap	US1720621010	172062101	BBG000BFPVV3	BBG000BFPK65	BBG001S5PTM0

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.

equities.select(
    country=['Netherlands', 'United States'],
    industry='Insurance',
    market=['Euronext Amsterdam', 'Nordic Growth Market', 'OTC Bulletin Board',
            'New York Stock Exchange', 'NASDAQ Global Select', 'NYSE MKT',
            'NASDAQ Capital Market']
)

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

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
AAME	Atlantic American Corporation	USD	Financials	Insurance	Insurance	NGM	Nordic Growth Market	United States	GA	Atlanta	30319-3054	http://www.atlam.com	Nano Cap	nan	nan	nan	nan	nan
ACMT	ACMAT Corporation	USD	Financials	Insurance	Insurance	PNK	OTC Bulletin Board	United States	CT	Farmington	6032	http://www.acmatcorp.com	Nano Cap	nan	nan	nan	nan	nan
ACMTA	ACMAT Corporation	USD	Financials	Insurance	Insurance	PNK	OTC Bulletin Board	United States	CT	Farmington	6032	http://www.acmatcorp.com	Nano Cap	nan	nan	nan	nan	nan
AEG	Aegon N.V.	USD	Financials	Insurance	Insurance	NYQ	New York Stock Exchange	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Large Cap	NL0000303709	nan	BBG000CKQTN4	BBG000CKQSN6	BBG001S6Y6M8
AEGOF	Aegon N.V.	USD	Financials	Insurance	Insurance	PNK	OTC Bulletin Board	Netherlands	nan	The Hague	2591 TV	http://www.aegon.com	Mid Cap	NL0000303709	nan	nan	nan	nan

In case 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.

equities.search(
    summary=["Robotics", "Education"],
    industry_group="Equipment",
    market='Frankfurt',
    index=".F"
)

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.

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
109.F	Castlight Health, Inc.	EUR	Health Care	Health Care Equipment & Services	Health Care Providers & Services	FRA	Frankfurt Stock Exchange	United States	CA	San Francisco	94105	http://www.castlighthealth.com	Small Cap	nan	nan	nan	nan	nan
1KT.F	Keysight Technologies, Inc.	EUR	Information Technology	Technology Hardware & Equipment	Electronic Equipment, Instruments & Components	FRA	Frankfurt Stock Exchange	United States	CA	Santa Rosa	95403-1738	http://www.keysight.com	Large Cap	US49338L1035	49338L103	BBG007DJZFD2	BBG007DJZFC3	BBG0059FN820
1N1.F	Nanalysis Scientific Corp.	EUR	Information Technology	Technology Hardware & Equipment	Electronic Equipment, Instruments & Components	FRA	Frankfurt Stock Exchange	Canada	AB	Calgary	T2E 7C3	http://www.nanalysis.com	Nano Cap	nan	nan	nan	nan	nan
1YO.F	Yangtze Optical Fibre And Cable Joint Stock Limited Company	EUR	Information Technology	Technology Hardware & Equipment	Communications Equipment	FRA	Frankfurt Stock Exchange	China	nan	Wuhan	430073	http://www.yofc.com	Small Cap	nan	nan	nan	nan	nan
1ZU.F	The Pennant Group, Inc.	EUR	Health Care	Health Care Equipment & Services	Health Care Equipment & Supplies	FRA	Frankfurt Stock Exchange	United States	ID	Eagle	83616	http://pennantgroup.com	Small Cap	US70805E1091	7.08E+113	BBG00QJ35K78	BBG00QJ35K69	BBG00P33SZ15

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 statement 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.

---

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

API_KEY = "FINANCIAL_MODELING_PREP_API_KEY"

dutch_insurance_companies = equities.select(
    country='Netherlands',
    industry='Insurance',
    market='Euronext Amsterdam',
)

toolkit = dutch_insurance_companies.to_toolkit(
    api_key=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.

toolkit.get_historical_data()

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

date	Open	High	Low	Close	Adj Close	Volume	Dividends	Return	Volatility	Excess Return	Excess Volatility	Cumulative Return
2025-03-31	52.86	52.98	52.56	52.98	52.98	547650	0	-0.0049	0.0175	-0.0474	0.0206	4.2726
2025-04-01	53.22	53.6	52.98	53.44	53.44	486098	0	0.0087	0.0175	-0.0329	0.0206	4.3097
2025-04-02	53.18	53.58	52.7	53.3	53.3	485768	0	-0.0026	0.0175	-0.0446	0.0206	4.2984
2025-04-03	52.32	53.22	52.18	52.42	52.42	567242	0	-0.0165	0.0175	-0.057	0.0206	4.2274
2025-04-04	52	52.5	49.45	50.4	50.4	485024	0	-0.0385	0.0175	-0.0775	0.0206	4.0645

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

toolkit.ratios.collect_all_ratios()

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

	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Interest Coverage Ratio	4.0535	4.2287	6.6142	5.152	3.2238	2.2508	3.1188	3.0962	4.1177	0
Income Before Tax Profit Margin	0.1078	0.1265	0.1843	0.159	0.1515	0.1104	0.1231	0.1783	0.1089	0.0701
Effective Tax Rate	0.1923	0.2351	0.195	0.2334	0.1983	0.2075	0.2233	0.2196	0.2181	0.2647
Return on Capital Employed	0.0183	0.0192	0.0235	0.0176	0.0218	0.0145	0.0205	0.0205	0.0283	0.0382
Net Income per EBT	0.7908	0.7603	0.7985	0.743	0.7917	0.7798	0.768	0.7705	0.774	0.6972
EBT to EBIT Ratio	0.7469	0.7611	0.8472	0.801	0.7654	0.7023	0.7628	0.6654	0.3289	0.2389
EBIT to Revenue	0.1327	0.163	0.2107	0.1803	0.1885	0.1481	0.1553	0.2564	0.3196	0.2567
Debt-to-Assets Ratio	0.0442	0.0605	0.0504	0.0546	0.094	0.1172	0.0923	0.068	0.0856	0.0771

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.

etfs = fd.ETFs()

etfs.select(
    category_group='Fixed Income'
)

This gives you results like the following:

symbol	name	currency	category_group	category	family	exchange
^BND	VANGUARD BD IDX FD	USD	Fixed Income	Investment Grade Bonds	Vanguard Asset Management	NIM
^BNDX	VANGUARD CHARLOTTE	USD	Fixed Income	Investment Grade Bonds	Vanguard Asset Management	NIM
^VCIT	VANGUARD SCOTTSDAL	USD	Fixed Income	Corporate Bonds	Vanguard Asset Management	NIM
^VCLT	VANGUARD SCOTTSDAL	USD	Fixed Income	Corporate Bonds	Vanguard Asset Management	NIM
^VCSH	VANGUARD SCOTTSDAL	USD	Fixed Income	Corporate Bonds	Vanguard Asset Management	NIM

This also translates to the available options, for example let’s select fd.Indices() instead.

indices = fd.Indices()

indices.show_options()

A sample of the output is shown below:

{'category_group': array(['Alternatives', 'Cash', 'Commodities', 'Communication Services',
        'Consumer Discretionary', 'Consumer Staples', 'Currencies',
        'Derivatives', 'Energy', 'Equities', 'Financials', 'Fixed Income',
        'Health Care', 'Industrials', 'Information Technology',
        'Materials', 'Real Estate', 'Utilities'], dtype=object),
 'category': array(['Alternative', 'Blend', 'Bonds', 'Cash', 'Commercial Real Estate',
        'Commodities Broad Basket', 'Communications',
        'Consumer Discretionary', 'Consumer Staples', 'Corporate Bonds',
        'Currencies', 'Derivatives', 'Developed Markets',
        'Emerging Markets', 'Energy', 'Equities', 'Factors', 'Financials',
        'Frontier Markets', 'Government Bonds', 'Growth', 'Health Care',
        'High Yield Bonds', 'Industrials',
        'Inflation-Protected Securities', 'Investment Grade Bonds',
        'Large Cap', 'Materials', 'Micro Cap', 'Mid Cap',
        'Money Market Instruments', 'Municipal Bonds', 'REITs',
        'Real Estate Development', 'Real Estate Services',
        'Residential Real Estate', 'Small Cap', 'Technology', 'Trading',
        'Treasury Bonds', 'Utilities', 'Value'], dtype=object)}

And lastly, both the search and to_toolkit metrics also apply to each of the asset classes, using fd.Funds() and fd.Cryptos() respectively. For example, let’s find the funds that focus around pension plans.

funds = fd.Funds()

funds.search(summary='Pension')

A sample of the output is shown below:

symbol	name	currency	category_group	category	family	exchange
0P000017AH.F	OpenBank Renta Variable Europa PP	EUR	Equities	Equities	Santander Asset Management SGIIC	FRA
0P000017AJ.F	Alcal Futuro Uno PP	EUR	Financials	Allocation	Caser Pensiones EGFP	FRA
0P0000189U.F	Caser Julio 2021 PP Acc	EUR	Financials	Bonds	Caser Pensiones EGFP	FRA
0P000018ML.F	Cajamar Renta Variable PP	EUR	Equities	Equities	Cajamar Vida Se. y Re.	FRA
0P000019H0.F	Bestinver Global PP	EUR	Financials	Blend	Bestinver Pensiones	FRA

And for Cryptos, let’s collect the historical data of Ethereum in multiple currencies.

cryptos = fd.Cryptos()

eth_cryptos = cryptos.select(
    cryptocurrency='ETH'
)

cryptos_toolkit = eth_cryptos.to_toolkit(
    api_key=API_KEY,
    start_date='2020-01-01'
)

cryptos_toolkit.get_historical_data(period='quarterly')

A sample of the output is shown below, focussig on ETH-BTC:

Date	Open	High	Low	Close	Adj Close	Volume	Dividends	Return	Volatility	Excess Return	Excess Volatility	Cumulative Return
2024Q2	0.0559	0.0558	0.0554	0.0554	0.0554	108145	0	0.0992	0.159	0.0558	0.144	2.6763
2024Q3	0.0406	0.0407	0.0403	0.0405	0.0405	169579	0	-0.269	0.1433	-0.307	0.1445	1.9565
2024Q4	0.0358	0.0365	0.036	0.0362	0.0362	291317	0	-0.1062	0.1857	-0.1519	0.1679	1.7488
2025Q1	0.0221	0.0222	0.0217	0.0219	0.0219	119665	0	-0.395	0.1813	-0.4375	0.1639	1.058
2025Q2	0.0218	0.0217	0.0216	0.0216	0.0216	195229	0	-0.0137	0.1415	-0.053	0.1361	1.0435

Questions & Answers

In this section you can find answers to commonly asked questions. In case the answer to your question is not here,
consider creating an Issue.

How is the data obtained?

The data is an aggregation of various publicly available sources. I strictly maintain the rule that all data in this database must be freely accessible to everyone. Data requiring API keys or paid subscriptions is never included. Information that companies charge for is typically owned and maintained by those companies, making public sharing of such data a violation of their Terms of Service (ToS). However, publicly available data can be freely shared (read more about the legality of web scraping here). This database will always remain completely free.

What categorization method is used?

The categorization for Equities is based on a loose approximation of GICS (Global Industry Classification Standard). This database attempts to reflect sectors and industries as accurately as possible through manual curation, without collecting any actual data from MSCI’s proprietary sources. The official GICS datasets curated by MSCI remain the most up-to-date, paid solution and were not used in developing any part of this database. All other categorizations in the database are independently developed and can be freely modified.

How can I contribute?

Please see the Contributing Guidelines. Thank you!

How can I find out which countries, sectors and/or industries exist within the database without needing to check the database manually?

For this you can use the show_options function from the package attached to this database within a specific asset class or on a higher level without requiring any data to be loaded beforehand. See Usage for more information.

When I try collect data I notice that not all tickers return output, why is that?

Some tickers are merely holdings of companies and therefore do not really have any data attached to them. Therefore, it makes sense that not all tickers return data. If you are still in doubt, search the ticker on Google to see if there is really no data available. If you can’t find anything about the ticker, consider updating the database by visiting the Contributing Guidelines.

How does the database handle changes to companies over time - like symbol/exchange migration, mergers, bankruptcies, or symbols getting reused?

For American exchanges, the database automatically updates every Sunday using data from this repository. This process includes checks for market cap changes and updates asset classifications accordingly. Delisted tickers are intentionally retained for historical research purposes.

While professional financial data services like Bloomberg charge over $25,000 annually for comprehensive market data maintenance, this database relies on community contributions. When companies outside American exchanges undergo changes (migrations, mergers, bankruptcies), we depend on community members to identify and update these entries.

Most companies don’t change so rapidly that the database becomes obsolete - major changes like Facebook’s rebrand to META are quickly incorporated. Even when companies go bankrupt, their ticker information remains valuable for historical analysis.

If you notice outdated information, please consider contributing through the Contributing Guidelines.

Contributions

This section is meant to thank those that contributed to the project. Looking to contribute as well? Have a look here.

User	Contribution
desaijimmy	Made changes to Equities dataset including the Split of Daimler to Mercedes-Benz and Daimler Trucks
nindogo	Introduced a variety of new equities from the Nairobi Securities Exchange and introduced the country Kenya into the dataset.
colin99d	Helped in the conversion of the Finance Database package to Object-Orientated, making the code much more efficient.

Contact

If you have any questions about the FinanceDatabase or would like to share with me what you have been working on, feel free to reach out to me via:

• Website: https://jeroenbouma.com/
• LinkedIn: https://www.linkedin.com/in/boumajeroen/
• Email: [email protected]

f you’d like to support my efforts, either help me out via the Contributing Guidelines or Buy me a Coffee.