FinanceDatabase

This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

JerBouma

1002

135

Python

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!

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 Examples.

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:

Installation
Basic Usage
Examples
Questions & Answers
User Contributions
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

Basic Usage

This section explains in detail how the database can be queried with the related financedatabase package, also see the Jupyter Notebook in which you can run the examples also demonstrated here. You can find this document here.

Quick Start

Same methods apply to all other asset classes as well. Columns may vary.

import financedatabase as fd

# Initialize the Equities database
equities = fd.Equities()

# Obtain all countries from the database
equities_countries = equities.options("country")

# Obtain all sectors from the database
equities_sectors = equities.options("sector")

# Obtain all industry groups from the database
equities_industry_groups = equities.options("industry_group")

# Obtain all industries from a country from the database
equities_germany_industries = equities.options("industry", country="Germany")

# Obtain a selection from the database
equities_united_states = equities.select(country="United States")

# Obtain a detailed selection from the database
equities_usa_machinery = equities.select(
    country="United States", industry="Machinery"
)

# Search specific fields from the database
equities_uk_biotech = equities.search(
    country="United Kingdom", summary="biotech", exchange="LSE"
)

# Search specific fields from the database with lists
equities_media_services = equities.search(
    industry="Interactive Media & Services",
    country="United States",
    market_cap=["Large Cap", "Mega Cap"]
)

# Use the tickers to obtain data via the Finance Toolkit
telecomunication_services = equities.search(
    industry="Diversified Telecommunication Services",
    country="United States",
    market_cap="Mega Cap",
    exclude_exchanges=True)

toolkit = telecomunication_services.to_toolkit(
    api_key="FINANCIAL_MODELING_PREP_KEY",
    start_date="2000-01-01",
    progress_bar=False
)

# For example, obtain the historical data
historical_data = toolkit.get_historical_data()

Scroll down below for a more elaborate explanation and detailed examples.

Collecting information from the database

Please see the Jupyter Notebook for an elaborate explanation of each asset class. This includes Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

Find code examples of all Asset Classes in the Jupyter Notebook here.

As an example for Equities, If you wish to collect data from all equities you can use the following:

import financedatabase as fd

# Initialize the Equities database
equities = fd.Equities()

# Obtain all data available excluding international exchanges
equities.select()

Which returns the following DataFrame:

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
A	Agilent Technologies, Inc.	USD	Health Care	Pharmaceuticals, Biotechnology & Life Sciences	Biotechnology	NYQ	New York Stock Exchange	United States	CA	Santa Clara	95051	http://www.agilent.com	Large Cap	US00846U1016	00846U101	BBG000C2V541	BBG000C2V3D6	BBG001SCTQY4
AA	Alcoa Corporation	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	PA	Pittsburgh	15212-5858	http://www.alcoa.com	Mid Cap	US0138721065	13872106	BBG00B3T3HK5	BBG00B3T3HD3	BBG00B3T3HF1
AAALF	Aareal Bank AG	USD	Financials	Banks	Banks	PNK	OTC Bulletin Board	Germany	nan	Wiesbaden	65189	http://www.aareal-bank.com	Small Cap	US00254K1088	00254K108	nan	nan	nan
AAALY	Aareal Bank AG	USD	Financials	Banks	Banks	PNK	OTC Bulletin Board	Germany	nan	Wiesbaden	65189	http://www.aareal-bank.com	Small Cap	US00254K1088	00254K108	nan	nan	nan
AABB	Asia Broadband, Inc.	USD	Materials	Materials	Metals & Mining	PNK	OTC Bulletin Board	United States	NV	Las Vegas	89135	http://www.asiabroadbandinc.com	Micro Cap	nan	nan	nan	nan	nan

This returns approximately 20.000 different equities. Note that by default, only the American exchanges are selected. These are symbols like TSLA (Tesla) and MSFT (Microsoft) that tend to be recognized by a majority of data providers and therefore is the default. To disable this, you can set the exclude_exchanges argument to False which then results in approximately 155,000 different symbols.

Note that the summary column is taken out on purpose to keep it organized for markdown. The summary is however very handy when it comes to querying specific words as found with the following description given for Apple. All of this information is available when you query the database. Find a more elaborate explanation with help(equities.select).

As an example, we can use equities.options to obtain specific country, sector and industry options. For we can acquire all industries within the sector Basic Materials within the United States. This allows us to look at a specific industry in the United States in detail.

industry_options = equities.options(selection='industry', country="United States", sector="Materials")

Which returns:

array(['Chemicals', 'Construction Materials', 'Metals & Mining',
       'Paper & Forest Products'], dtype=object)

So with this information in hand, I can now query the industry Metals & Mining as follows:

metals_and_mining = equities.search(industry="Metals & Mining", country="United States", market_cap="Large Cap", exclude_exchanges=True)

metals_and_mining

This gives you a DataFrame with the following information:

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
FCX	Freeport-McMoRan Inc.	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	AZ	Phoenix	85004-2189	http://fcx.com	Large Cap	US35671D8570	35671D857	BBG000BJDCQ6	BBG000BJDB15	BBG001S5R3F3
NEM	Newmont Corporation	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	CO	Denver	80237	http://www.newmont.com	Large Cap	US6516391066	651639106	BBG000BPWYG4	BBG000BPWXK1	BBG001S5TKX3
NUE	Nucor Corporation	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	NC	Charlotte	28211	http://www.nucor.com	Large Cap	US6703461052	670346105	BBG000BQ8MY5	BBG000BQ8KV2	BBG001S5TRV0
RS	Reliance Steel & Aluminum Co.	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	CA	Los Angeles	90071	http://www.rsac.com	Large Cap	US7595091023	759509102	BBG000CJ2332	BBG000CJ2181	BBG001S81M27
SCCO	Southern Copper Corporation	USD	Materials	Materials	Metals & Mining	NYQ	New York Stock Exchange	United States	AZ	Phoenix	85014	http://www.southerncoppercorp.com	Large Cap	US84265V1052	84265V105	BBG000BSHKK0	BBG000BSHH72	BBG001S6ZM88
STLD	Steel Dynamics, Inc.	USD	Materials	Materials	Metals & Mining	NMS	NASDAQ Global Select	United States	IN	Fort Wayne	46804	http://www.steeldynamics.com	Large Cap	US8581191009	858119100	BBG000HH03N1	BBG000HGYNZ9	BBG001S98JK5

As you can imagine, looking at such a specific selection only yields a few results but picking the entire sector Materials would have returned 403 different companies (which excludes exchanges other than the United States).

To conclude, this information can then be send to the Finance Toolkit 🛠️ to obtain 130+ financial metrics, historical and fundamental data with the to_toolkit function. This functionality can be used with any output as obtained from the Finance Database.

companies = metals_and_mining.to_toolkit(
    api_key="FINANCIAL_MODELING_PREP_KEY",
    start_date="2000-01-01",
    quarterly=False)

companies.get_quote()

This returns the following:

	FCX	NEM	NUE	RS	SCCO	STLD
Symbol	FCX	NEM	NUE	RS	SCCO	STLD
Price	38.755	40.3007	157.54	268.06	78.3	99.49
Beta	2.065006	0.394536	1.627593	0.923236	1.294605	1.523167
Average Volume	10431879	7104366	1315646	265598	1031395	1277711
Market Capitalization	55560715720	31998755800	39183663880	15696816226	60534353894	16479921560
Last Dividend	0.6000000000000001	1.6	2.04	4	4	1.7000000000000002
Range	26.03-46.73	37.45-55.41	102.86-182.68	168.25-295.98	42.42-87.59	69.12-136.46
Changes	-1.435	-0.1693	2.87	4.41	-1.3599999999999999	1.38
Company Name	Freeport-McMoRan Inc.	Newmont Corporation	Nucor Corporation	Reliance Steel & Aluminum Co.	Southern Copper Corporation	Steel Dynamics, Inc.
Currency	USD	USD	USD	USD	USD	USD
CIK	831259	1164727	73309	861884	1001838	1022671
ISIN	US35671D8570	US6516391066	US6703461052	US7595091023	US84265V1052	US8581191009
CUSIP	35671D857	651639106	670346105	759509102	84265V105	858119100
Exchange	New York Stock Exchange	New York Stock Exchange	New York Stock Exchange	New York Stock Exchange	New York Stock Exchange	NASDAQ Global Select
Exchange Short Name	NYSE	NYSE	NYSE	NYSE	NYSE	NASDAQ
Industry	Copper	Gold	Steel	Steel	Copper	Steel
Website	https://fcx.com	https://www.newmont.com	https://www.nucor.com	https://www.rsac.com	https://www.southernperu.com	https://stld.steeldynamics.com
CEO	Mr. Richard C. Adkerson	Mr. Thomas Ronald Palmer	Mr. Leon J. Topalian	Ms. Karla R. Lewis	Mr. Oscar Gonzalez Rocha	Mr. Mark D. Millett
Sector	Basic Materials	Basic Materials	Basic Materials	Basic Materials	Basic Materials	Basic Materials
Country	US	US	US	US	US	US
Full Time Employees	25600	14600	31400	14500	15018	12060
Phone	602 366 8100	303 863 7414	704 366 7000	213 687 7700	602 264 1375	260 969 3500
Address	333 North Central Avenue	6900 East Layton Avenue	1915 Rexford Road	350 South Grand Avenue	1440 East Missouri Avenue	7575 West Jefferson Boulevard
City	Phoenix	Denver	Charlotte	Los Angeles	Phoenix	Fort Wayne
State	AZ	CO	NC	CA	AZ	IN
ZIP Code	85004-2189	80237	28211	90071	85014	46804
DCF Difference	3.24601	2.08	9.70759	13.6802	13.4469	9.8176
DCF	41.574	51.24	157.162	213.01	61.1331	109.112
IPO Date	1995-07-10	1980-03-17	1980-03-17	1994-09-16	1996-01-05	1996-11-22

Searching the database extensively

All asset classes have the capability to search each column with search, for example equities.search(). Through how this functionality is developed you can define multiple columns and search throughoutly. For example:

# Collect all Equities Database
equities = fd.Equities()

# Search Multiple Columns
equities.search(summary='automotive', currency='USD', country='Germany')

Which returns a selection of the DataFrame that matches all criteria.

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
AFRMF	Alphaform AG	USD	Industrials	Capital Goods	Machinery	PNK	OTC Bulletin Board	Germany	nan	Feldkirchen	85622	nan	Nano Cap	nan	nan	nan	nan	nan
AUUMF	Aumann AG	USD	Industrials	Capital Goods	Machinery	PNK	OTC Bulletin Board	Germany	nan	Beelen	48361	http://www.aumann.com	Micro Cap	DE000A2DAM03	nan	nan	nan	nan
BAMXF	Bayerische Motoren Werke Aktiengesellschaft	USD	Consumer Discretionary	Automobiles & Components	Automobiles	PNK	OTC Bulletin Board	Germany	nan	Munich	80788	http://www.bmwgroup.com	Large Cap	DE0005190037	nan	nan	nan	nan
BASFY	BASF SE	USD	Materials	Materials	Chemicals	PNK	OTC Bulletin Board	Germany	nan	Ludwigshafen am Rhein	67056	http://www.basf.com	Large Cap	nan	nan	nan	nan	nan
BDRFF	Beiersdorf Aktiengesellschaft	USD	Consumer Staples	Household & Personal Products	Household Products	PNK	OTC Bulletin Board	Germany	nan	Hamburg	20245	http://www.beiersdorf.com	Large Cap	US07724U1034	07724U103	nan	nan	nan

Storing the database at a different location

If you wish to store the database at a different location (for example your own Fork) you can do so with the variable
base_url which you can find in each of the asset classes. An example would be:

fd.Equities(base_url=<YOUR URL>)

You can also store the database locally and point to your local location with the variable base_url and by setting
use_local_location to True. An example would be:

fd.Equities(base_url=<YOUR PATH>, use_local_location=True)

Examples

This section gives a few examples of the possibilities with this package. These are merely a few of the things you can do with the package. As you can obtain a wide range of symbols, pretty much any package that requires symbols should work.

Find a variety of How-To Guides for the FinanceDatabase here.

Companies in the Netherlands

I want to see how many public companies exist in each sector in the Netherlands. Here, I can obtain all stocks that are located in the Netherlands with country='Netherlands'. I also include all exchanges by setting exclude_exchanges=False. This will give me all stocks that are listed on all exchanges. This is relevant because some stocks are listed on exchanges that are not the American exchanges which the parameter defaults to. Find the related Jupyter Notebook with more examples here.

import financedatabase as fd

dutch_companies = equities.select(country='Netherlands', exclude_exchanges=False)

Which returns:

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
09B.F	lastminute.com N.V.	EUR	Consumer Discretionary	Consumer Services	Hotels, Restaurants & Leisure	FRA	Frankfurt Stock Exchange	Netherlands	nan	Amsterdam	1097 JB	http://lmgroup.lastminute.com	Small Cap	nan	nan	nan	nan	nan
0DI7.L	AFC Ajax NV	EUR	nan	nan	nan	LSE	London Stock Exchange (international)	Netherlands	nan	Amsterdam	1101 AX	http://www.ajax.nl	Micro Cap	NL0000018034	nan	nan	nan	nan
0DLI.L	Amsterdam Commodities N.V.	EUR	nan	nan	nan	LSE	London Stock Exchange (international)	Netherlands	nan	Rotterdam	3011 DD	http://www.acomo.nl	Small Cap	nan	nan	nan	nan	nan
0DQK.L	Beter Bed Holding N.V.	EUR	nan	nan	nan	LSE	London Stock Exchange (international)	Netherlands	nan	Uden	5405 AR	http://www.beterbedholding.com	Micro Cap	NL0000339703	nan	nan	nan	nan
0E2F.L	Ctac N.V.	EUR	nan	nan	nan	LSE	London Stock Exchange (international)	Netherlands	nan	's-Hertogenbosch	5216 TZ	http://www.ctac.nl	Micro Cap	NL0000345577	nan	nan	nan	nan

This will return the same company multiple times. That makes sense, since a company can be listed on multiple exchanges. Filtering is applied by grouping by unique names and the sector.

unique_dutch_companies_per_sector = dutch_companies.groupby('sector').agg({'name': 'nunique'})

Now with this result, I can plot a pie chart to showcase the distribution of companies in each sector.

unique_dutch_companies_per_sector['name'].plot.pie(
    title='Number of companies per sector in the Netherlands',
    ylabel='',
)

This results in the following graph which gives an indication which sectors are dominant within The Netherlands. Of course this is a mere example and to truly understand the importance of certain companies for the Netherlands, you would need to know market cap of each sector as well including demographics.

FinanceDatabase

Perform a Dupont Analysis on Railroad Companies

A great use-case for the data found in the Finance Database is to do competitive analysis in which companies are compared that compete for the same market. For example, in case I want to look into the Railroad companies in the United States that are marked as “Large Cap”, I can directly search for this with the Finance Database and use the Finance Toolkit 🛠️ to do further research. Find the related Jupyter Notebook with more examples here.

import financedatabase as fd

equities = fd.Equities()

railroad = equities.search(industry='Road & Rail',
                           country='United States',
                           market_cap='Large Cap',
                           exclude_exchanges=True)

This gives the following:

symbol	name	currency	sector	industry_group	industry	exchange	market	country	state	city	zipcode	website	market_cap	isin	cusip	figi	composite_figi	shareclass_figi
CSX	CSX Corporation	USD	Industrials	Transportation	Road & Rail	NMS	NASDAQ Global Select	United States	FL	Jacksonville	32202	http://www.csx.com	Large Cap	US1264081035	1.26408e+08	BBG000BGK1N1	BBG000BGJRC8	BBG001S5Q7Q3
KSU	Kansas City Southern	USD	Industrials	Transportation	Road & Rail	NYQ	New York Stock Exchange	United States	MO	Kansas City	64105	http://www.kcsouthern.com	Large Cap	nan	nan	nan	nan	nan
KSU-P	Kansas City Southern	USD	Industrials	Transportation	Road & Rail	NYQ	New York Stock Exchange	United States	MO	Kansas City	64105	http://www.kcsouthern.com	Large Cap	nan	nan	nan	nan	nan
NSC	Norfolk Southern Corporation	USD	Industrials	Transportation	Road & Rail	NYQ	New York Stock Exchange	United States	VA	Norfolk	23510-2191	http://www.norfolksouthern.com	Large Cap	US6558441084	6.55844e+08	BBG000BQ5GM4	BBG000BQ5DS5	BBG001S5TQJ6
UNP	Union Pacific Corporation	USD	Industrials	Transportation	Road & Rail	NYQ	New York Stock Exchange	United States	NE	Omaha	68179	http://www.up.com	Large Cap	US9078181081	9.07818e+08	BBG000BW3413	BBG000BW3299	BBG001S5X2M0
WAB	Westinghouse Air Brake Technologies Corporation	USD	Industrials	Transportation	Road & Rail	NYQ	New York Stock Exchange	United States	PA	Pittsburgh	15212	http://www.wabteccorp.com	Large Cap	US9297401088	9.2974e+08	BBG000BDDBD5	BBG000BDD940	BBG001S5XBT3

With this information in hand, I can now start collecting data with the FinanceToolkit package. This can be anything from balance sheet, cash flow and income statements to 100+ financial ratios, technical indicators and more. Here I initialize the FinanceToolkit with the tickers as found in the Finance Database.

API_KEY = "YOUR_FMP_API_KEY"

companies = railroad.to_toolkit(api_key=API_KEY, start_date='2005-01-01')

Then, as a demonstration, I can obtain all balance sheet statements for all companies that are marked as Large Cap Railroad companies in the United States. To keep this concise, only the first company is shown.

companies.get_balance_sheet_statement().loc['CSX']

Which returns:

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Cash and Cash Equivalents	3.09e+08	4.61e+08	3.68e+08	6.69e+08	1.029e+09	1.292e+09	7.83e+08	7.84e+08	5.92e+08	6.69e+08	6.28e+08	6.03e+08	4.01e+08	8.58e+08	9.58e+08	3.129e+09	2.239e+09	1.958e+09
Short Term Investments	2.93e+08	4.39e+08	3.46e+08	7.6e+07	6.1e+07	5.4e+07	5.23e+08	5.87e+08	4.87e+08	2.92e+08	8.1e+08	4.17e+08	1.8e+07	2.53e+08	9.96e+08	2e+06	7.7e+07	1.29e+08
Cash and Short Term Investments	6.02e+08	9e+08	7.14e+08	7.45e+08	1.09e+09	1.346e+09	1.306e+09	1.371e+09	1.079e+09	9.61e+08	1.438e+09	1.02e+09	4.19e+08	1.111e+09	1.954e+09	3.131e+09	2.316e+09	2.087e+09
Accounts Receivable	1.202e+09	1.174e+09	1.174e+09	1.107e+09	9.95e+08	9.93e+08	1.129e+09	9.62e+08	1.052e+09	1.129e+09	9.82e+08	9.38e+08	9.7e+08	1.01e+09	9.86e+08	9.12e+08	1.148e+09	1.313e+09
Inventory	1.99e+08	2.04e+08	2.4e+08	2.17e+08	2.03e+08	2.18e+08	2.4e+08	2.74e+08	2.52e+08	2.73e+08	3.5e+08	4.07e+08	3.72e+08	2.63e+08	2.61e+08	3.02e+08	3.39e+08	3.41e+08
Other Current Assets	1.44e+08	1.43e+08	1.09e+08	1.19e+08	1.24e+08	1.06e+08	7.8e+07	1.43e+09	1.523e+09	1.611e+09	1.528e+09	1.467e+09	1.496e+09	1.454e+09	1.324e+09	1.31e+09	1.557e+09	1.762e+09
Total Current Assets	2.372e+09	2.672e+09	2.491e+09	2.391e+09	2.57e+09	2.855e+09	2.935e+09	2.801e+09	2.602e+09	2.572e+09	2.966e+09	2.487e+09	1.915e+09	2.565e+09	3.278e+09	4.441e+09	3.873e+09	3.849e+09
Property, Plant and Equipment	2.0163e+10	2.0923e+10	2.178e+10	2.2688e+10	2.3213e+10	2.3799e+10	2.4974e+10	2.605e+10	2.7291e+10	2.8584e+10	3.0174e+10	3.115e+10	3.1764e+10	3.1998e+10	3.2168e+10	3.2444e+10	3.3015e+10	3.4242e+10
Goodwill	0	0	0	6.4e+07	6.4e+07	7e+07	6.4e+07	6.4e+07	6.4e+07	6.3e+07	6.3e+07	6.3e+07	6.3e+07	0	0	0	2.76e+08	3.19e+08
Intangible Assets	0	7.3e+07	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1.75e+08	1.83e+08
Long Term Investments	6.03e+08	6.07e+08	1.004e+09	1.015e+09	1.088e+09	1.134e+09	1.171e+09	1.206e+09	1.298e+09	1.356e+09	1.394e+09	1.459e+09	1.686e+09	1.779e+09	1.879e+09	1.985e+09	2.099e+09	2.292e+09
Tax Assets	0	0	2.54e+08	2.03e+08	-6.4e+07	4.74e+08	1.82e+08	1.19e+08	1.55e+08	1.41e+08	1.26e+08	9.596e+09	6.418e+09	6.69e+09	6.961e+09	7.168e+09	-4.51e+08	7.569e+09
Other Fixed Assets	1.094e+09	8.54e+08	2.59e+08	-7.3e+07	1.65e+08	-1.91e+08	1.47e+08	3.31e+08	3.72e+08	3.37e+08	3.16e+08	-9.341e+09	-6.107e+09	3.87e+08	9.32e+08	9.23e+08	1.544e+09	-6.542e+09
Fixed Assets	2.186e+10	2.2457e+10	2.3043e+10	2.3897e+10	2.4466e+10	2.5286e+10	2.6538e+10	2.777e+10	2.918e+10	3.0481e+10	3.2073e+10	3.2927e+10	3.3824e+10	3.4164e+10	3.4979e+10	3.5352e+10	3.6658e+10	3.8063e+10
Other Assets	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Total Assets	2.4232e+10	2.5129e+10	2.5534e+10	2.6288e+10	2.7036e+10	2.8141e+10	2.9473e+10	3.0571e+10	3.1782e+10	3.3053e+10	3.5039e+10	3.5414e+10	3.5739e+10	3.6729e+10	3.8257e+10	3.9793e+10	4.0531e+10	4.1912e+10
Accounts Payable	9.54e+08	9.74e+08	9.76e+08	9.73e+08	9.67e+08	1.046e+09	1.147e+09	1.014e+09	9.57e+08	8.45e+08	7.64e+08	8.06e+08	8.47e+08	9.49e+08	1.043e+09	8.09e+08	9.63e+08	1.13e+09
Short Term Debt	9.37e+08	6e+08	7.87e+08	3.2e+08	1.13e+08	6.13e+08	5.07e+08	7.8e+08	5.33e+08	2.28e+08	2e+07	3.31e+08	1.9e+07	1.8e+07	2.45e+08	4.01e+08	1.81e+08	1.51e+08
Tax Payables	1.02e+08	1.14e+08	1.13e+08	1.25e+08	1.12e+08	8.5e+07	1.29e+08	8.5e+07	9.1e+07	1.63e+08	1.08e+08	1.29e+08	1.57e+08	1.06e+08	6.9e+07	7.3e+07	1.34e+08	1.11e+08
Deferred Revenue	6.67e+08	6.09e+08	5.74e+08	5.9e+08	4.95e+08	-1.046e+09	-1.147e+09	-1.014e+09	-9.57e+08	-8.45e+08	-7.64e+08	-8.06e+08	-8.47e+08	1.06e+08	-1.043e+09	-8.09e+08	-9.63e+08	7.569e+09
Other Current Liabilities	4.21e+08	3.39e+08	3.34e+08	5.21e+08	2.9e+08	1.924e+09	2.18e+09	1.847e+09	1.891e+09	1.879e+09	1.932e+09	1.709e+09	1.875e+09	8.42e+08	1.906e+09	1.618e+09	2.052e+09	-6.379e+09
Total Current Liabilities	2.979e+09	2.522e+09	2.671e+09	2.404e+09	1.865e+09	2.537e+09	2.687e+09	2.627e+09	2.424e+09	2.107e+09	1.952e+09	2.04e+09	1.894e+09	1.915e+09	2.151e+09	2.019e+09	2.233e+09	2.471e+09
Long Term Debt	5.093e+09	5.362e+09	6.47e+09	7.512e+09	7.895e+09	8.051e+09	8.734e+09	9.052e+09	9.022e+09	9.514e+09	1.0683e+10	1.0962e+10	1.179e+10	1.4739e+10	1.5993e+10	1.6304e+10	1.6185e+10	1.7896e+10
Deferred Revenue Non Current	0	8.74e+08	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Deferred Tax Liabilities	6.082e+09	6.11e+09	6.096e+09	6.235e+09	6.585e+09	7.053e+09	7.601e+09	8.096e+09	8.662e+09	8.858e+09	9.305e+09	9.596e+09	6.418e+09	6.69e+09	6.961e+09	7.168e+09	7.383e+09	7.569e+09
Other Non Current Liabilities	2.124e+09	1.23e+09	1.612e+09	2.089e+09	1.831e+09	1.8e+09	1.983e+09	1.794e+09	1.17e+09	1.398e+09	1.431e+09	1.122e+09	9.16e+08	8.05e+08	1.289e+09	1.192e+09	1.23e+09	1.351e+09
Total Non Current Liabilities	1.3299e+10	1.3576e+10	1.4178e+10	1.5836e+10	1.6311e+10	1.6904e+10	1.8318e+10	1.8942e+10	1.8854e+10	1.977e+10	2.1419e+10	2.168e+10	1.9124e+10	2.2234e+10	2.4243e+10	2.4664e+10	2.4798e+10	2.6816e+10
Other Liabilities	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Capital Lease Obligations	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4.93e+08	4.55e+08	4.78e+08	4.88e+08
Total Liabilities	1.6278e+10	1.6098e+10	1.6849e+10	1.824e+10	1.8176e+10	1.9441e+10	2.1005e+10	2.1569e+10	2.1278e+10	2.1877e+10	2.3371e+10	2.372e+10	2.1018e+10	2.4149e+10	2.6394e+10	2.6683e+10	2.7031e+10	2.9287e+10
Preferred Stock	0	0	0	-1.824e+10	0	-2.801e+09	1.3e+07	1.4e+07	2.1e+07	2.4e+07	1.6e+07	1.5e+07	1.6e+07	1.7e+07	0	0	0	0
Common Stock	2.18e+08	4.38e+08	4.08e+08	3.91e+08	3.93e+08	3.7e+08	1.049e+09	1.02e+09	1.009e+09	9.92e+08	9.66e+08	9.28e+08	8.9e+08	8.18e+08	7.73e+08	7.63e+08	2.202e+09	2.066e+09
Retained Earnings	6.262e+09	7.427e+09	8.565e+09	8.398e+09	9.182e+09	9.087e+09	8.275e+09	8.876e+09	9.936e+09	1.0734e+10	1.1238e+10	1.1253e+10	1.4084e+10	1.2157e+10	1.1404e+10	1.2527e+10	1.163e+10	1.0363e+10
Accumulated Other Comprehensive Income	-2.77e+08	-3.92e+08	-3.25e+08	-7.41e+08	-8.09e+08	-7.71e+08	-8.75e+08	-9.36e+08	-5.23e+08	-6.66e+08	-6.65e+08	-6.4e+08	-4.86e+08	-6.61e+08	-6.75e+08	-5.98e+08	-4.08e+08	-3.88e+08
Other Total Shareholder Equity	1.751e+09	1.469e+09	3.7e+07	1.824e+10	8e+07	2.815e+09	6e+06	2.8e+07	6.1e+07	9.2e+07	1.13e+08	1.38e+08	2.17e+08	2.49e+08	3.61e+08	4.18e+08	7.6e+07	5.84e+08
Total Shareholder Equity	7.954e+09	8.942e+09	8.685e+09	8.048e+09	8.846e+09	8.7e+09	8.468e+09	9.002e+09	1.0504e+10	1.1176e+10	1.1668e+10	1.1694e+10	1.4721e+10	1.258e+10	1.1863e+10	1.311e+10	1.35e+10	1.2625e+10
Total Equity	7.954e+09	8.942e+09	8.685e+09	8.048e+09	8.846e+09	8.7e+09	8.468e+09	9.002e+09	1.0504e+10	1.1176e+10	1.1668e+10	1.1694e+10	1.4721e+10	1.258e+10	1.1863e+10	1.311e+10	1.35e+10	1.2625e+10
Total Liabilities and Shareholder Equity	2.4232e+10	2.5129e+10	2.5534e+10	2.6288e+10	2.7036e+10	2.8155e+10	2.9486e+10	3.0585e+10	3.1803e+10	3.3077e+10	3.5055e+10	3.5429e+10	3.5755e+10	3.6746e+10	3.8272e+10	3.9802e+10	4.0541e+10	4.1922e+10
Minority Interest	0	8.9e+07	0	0	1.4e+07	1.4e+07	1.3e+07	1.4e+07	2.1e+07	2.4e+07	1.6e+07	1.5e+07	1.6e+07	1.7e+07	1.5e+07	9e+06	1e+07	1e+07
Total Liabilities and Equity	2.4232e+10	2.5129e+10	2.5534e+10	2.6288e+10	2.7036e+10	2.8155e+10	2.9486e+10	3.0585e+10	3.1803e+10	3.3077e+10	3.5055e+10	3.5429e+10	3.5755e+10	3.6746e+10	3.8272e+10	3.9802e+10	4.0541e+10	4.1922e+10
Total Investments	8.96e+08	1.046e+09	1.35e+09	1.091e+09	1.149e+09	1.188e+09	1.694e+09	1.793e+09	1.785e+09	1.648e+09	2.204e+09	1.876e+09	1.704e+09	2.032e+09	2.875e+09	1.987e+09	2.176e+09	2.421e+09
Total Debt	6.03e+09	5.962e+09	7.257e+09	7.832e+09	8.008e+09	8.664e+09	9.241e+09	9.832e+09	9.555e+09	9.742e+09	1.0703e+10	1.1293e+10	1.1809e+10	1.4757e+10	1.6238e+10	1.6705e+10	1.6366e+10	1.8047e+10
Net Debt	5.721e+09	5.501e+09	6.889e+09	7.163e+09	6.979e+09	7.372e+09	8.458e+09	9.048e+09	8.963e+09	9.073e+09	1.0075e+10	1.069e+10	1.1408e+10	1.3899e+10	1.528e+10	1.3576e+10	1.4127e+10	1.6089e+10

With the data from the FinanceToolkit, it is now possible to execute a Dupont analysis on all companies. This shows the power of being able to combine a large database with a toolkit that allows you to do proper financial research. Again, only the first company is selected to keep things compact.

companies.models.get_extended_dupont_analysis().loc['CSX']

Which returns:

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Interest Burden Ratio	1.4961	1.1613	1.1677	1.2898	1.2976	1.2062	1.1835	1.1663	1.1886	1.1869	1.1421	1.2364	1.1671	1.1313	1.1504	1.2026	1.1299	1.1125
Tax Burden Ratio	0.7387	0.6127	0.5922	0.4931	0.5042	0.509	0.5331	0.5377	0.5367	0.5334	0.5491	0.5058	1.492	0.6796	0.6709	0.6339	0.6759	0.6917
Operating Profit Margin	0.1202	0.1925	0.1926	0.1907	0.1948	0.2394	0.2459	0.2521	0.243	0.2403	0.2657	0.2476	0.2754	0.3513	0.3616	0.3427	0.3954	0.3645
Asset Turnover	nan	0.3876	0.3959	0.4344	0.3391	0.3855	0.4076	0.3916	0.3857	0.3908	0.3469	0.3142	0.3207	0.3381	0.3184	0.2712	0.3118	0.3603
Equity Multiplier	nan	2.9215	2.8742	3.097	3.1564	3.1447	3.3559	3.437	3.1966	2.9905	2.9807	3.0157	2.6937	2.6544	3.0678	3.1254	3.0186	3.1557
Return on Equity	nan	0.1551	0.1516	0.1632	0.1364	0.1782	0.2123	0.2128	0.1911	0.1778	0.1723	0.1467	0.4142	0.2424	0.2726	0.2214	0.2842	0.3189

It isn’t too difficult to then plot a metric like Return on Equity (RoE) for all companies if you want to delve deeper. You can locate the rows directly from the DuPont Analysis but it is also possible to call the related function.

companies.ratios.get_return_on_equity().T.plot(
    title='Return on Equity (RoE) for Railroad Companies in the United States',
    figsize=(15, 5))

Which returns:

Return on Equity

Technical Analysis of Biotech ETFs

In this example I will show how you can use the FinanceDatabase to do a technical analysis of Biotech ETFs during the Coronacrisis. Let’s find Health Care ETFs that mention something about ‘Biotech’ in their description. This would indicate they are related to Biotechnology. Find the related Jupyter Notebook with more examples here.

import financedatabase as fd

API_KEY = "YOUR_FMP_API_KEY"

etfs = fd.ETFs()

health_care_etfs_in_biotech = etfs.search(category='Health Care', summary='biotech', exclude_exchanges=True)

Which returns:

symbol	name	currency	category_group	category	family	exchange	market
AGNG	Global X Aging Population ETF	USD	Health Care	Health Care	Global X Funds	NMS	us_market
BBC	Virtus LifeSci Biotech Clinical Trials ETF	USD	Health Care	Health Care	Virtus	PCX	us_market
BBH	VanEck Vectors Biotech ETF	USD	Health Care	Health Care	VanEck Asset Management	NMS	us_market
BBP	Virtus LifeSci Biotech Products ETF	USD	Health Care	Health Care	Virtus	PCX	us_market
CNCR	Loncar Cancer Immunotherapy ETF	USD	Health Care	Health Care	Loncar Investments	NGM	us_market
FBT	First Trust NYSE Arca Biotechnology Index Fund	USD	Health Care	Health Care	First Trust Advisors	PCX	us_market
IBB	iShares Nasdaq Biotechnology ETF	USD	Health Care	Health Care	BlackRock Asset Management	NMS	us_market
IBBJ	Defiance Nasdaq Junior Biotechnology ETF	USD	Health Care	Health Care	Defiance ETFs	NGM	us_market
IBBQ	Invesco Nasdaq Biotechnology ETF	USD	Health Care	Health Care	Invesco Investment Management	NMS	us_market
IEIH	iShares Evolved U.S. Innovative Healthcare ETF	USD	Health Care	Health Care	BlackRock Asset Management	BTS	us_market
PBE	Invesco Dynamic Biotechnology & Genome ETF	USD	Health Care	Health Care	Invesco Investment Management	PCX	us_market
SBIO	ALPS Medical Breakthroughs ETF	USD	Health Care	Health Care	ALPS ETF Trust	PCX	us_market
XBI	SPDR S&P Biotech ETF	USD	Health Care	Health Care	State Street Global Advisors	PCX	us_market
XLV	Health Care Select Sector SPDR Fund	USD	Health Care	Health Care	State Street Global Advisors	PCX	us_market

Next up is initializing the Finance Toolkit and obtaining historical data for the chosen tickers. Here a start and end date are also selected that match the period around the initial wave of the Coronacrisis. Then it’s time to collect the historical data for each ETF found.

etfs_in_biotech = health_care_etfs_in_biotech.to_toolkit(api_key=API_KEY, start_date="2020-01-01", end_date="2020-06-01")

etfs_in_biotech.get_historical_data()

Which returns (note that this is a MultiIndex):

Date	(‘Open’, ‘AGNG’)	(‘Open’, ‘BBC’)	(‘Open’, ‘BBH’)	(‘Open’, ‘BBP’)	(‘Open’, ‘CNCR’)	(‘Open’, ‘FBT’)	(‘Open’, ‘IBB’)	(‘Open’, ‘IEIH’)	(‘Open’, ‘PBE’)	(‘Open’, ‘SBIO’)	(‘Open’, ‘XBI’)	(‘Open’, ‘XLV’)	(‘High’, ‘AGNG’)	(‘High’, ‘BBC’)	(‘High’, ‘BBH’)	(‘High’, ‘BBP’)	(‘High’, ‘CNCR’)	(‘High’, ‘FBT’)	(‘High’, ‘IBB’)	(‘High’, ‘IEIH’)	(‘High’, ‘PBE’)	(‘High’, ‘SBIO’)	(‘High’, ‘XBI’)	(‘High’, ‘XLV’)	(‘Low’, ‘AGNG’)	(‘Low’, ‘BBC’)	(‘Low’, ‘BBH’)	(‘Low’, ‘BBP’)	(‘Low’, ‘CNCR’)	(‘Low’, ‘FBT’)	(‘Low’, ‘IBB’)	(‘Low’, ‘IEIH’)	(‘Low’, ‘PBE’)	(‘Low’, ‘SBIO’)	(‘Low’, ‘XBI’)	(‘Low’, ‘XLV’)	(‘Close’, ‘AGNG’)	(‘Close’, ‘BBC’)	(‘Close’, ‘BBH’)	(‘Close’, ‘BBP’)	(‘Close’, ‘CNCR’)	(‘Close’, ‘FBT’)	(‘Close’, ‘IBB’)	(‘Close’, ‘IEIH’)	(‘Close’, ‘PBE’)	(‘Close’, ‘SBIO’)	(‘Close’, ‘XBI’)	(‘Close’, ‘XLV’)	(‘Adj Close’, ‘AGNG’)	(‘Adj Close’, ‘BBC’)	(‘Adj Close’, ‘BBH’)	(‘Adj Close’, ‘BBP’)	(‘Adj Close’, ‘CNCR’)	(‘Adj Close’, ‘FBT’)	(‘Adj Close’, ‘IBB’)	(‘Adj Close’, ‘IEIH’)	(‘Adj Close’, ‘PBE’)	(‘Adj Close’, ‘SBIO’)	(‘Adj Close’, ‘XBI’)	(‘Adj Close’, ‘XLV’)	(‘Volume’, ‘AGNG’)	(‘Volume’, ‘BBC’)	(‘Volume’, ‘BBH’)	(‘Volume’, ‘BBP’)	(‘Volume’, ‘CNCR’)	(‘Volume’, ‘FBT’)	(‘Volume’, ‘IBB’)	(‘Volume’, ‘IEIH’)	(‘Volume’, ‘PBE’)	(‘Volume’, ‘SBIO’)	(‘Volume’, ‘XBI’)	(‘Volume’, ‘XLV’)	(‘Return’, ‘AGNG’)	(‘Return’, ‘BBC’)	(‘Return’, ‘BBH’)	(‘Return’, ‘BBP’)	(‘Return’, ‘CNCR’)	(‘Return’, ‘FBT’)	(‘Return’, ‘IBB’)	(‘Return’, ‘IEIH’)	(‘Return’, ‘PBE’)	(‘Return’, ‘SBIO’)	(‘Return’, ‘XBI’)	(‘Return’, ‘XLV’)	(‘Volatility’, ‘AGNG’)	(‘Volatility’, ‘BBC’)	(‘Volatility’, ‘BBH’)	(‘Volatility’, ‘BBP’)	(‘Volatility’, ‘CNCR’)	(‘Volatility’, ‘FBT’)	(‘Volatility’, ‘IBB’)	(‘Volatility’, ‘IEIH’)	(‘Volatility’, ‘PBE’)	(‘Volatility’, ‘SBIO’)	(‘Volatility’, ‘XBI’)	(‘Volatility’, ‘XLV’)	(‘Excess Return’, ‘AGNG’)	(‘Excess Return’, ‘BBC’)	(‘Excess Return’, ‘BBH’)	(‘Excess Return’, ‘BBP’)	(‘Excess Return’, ‘CNCR’)	(‘Excess Return’, ‘FBT’)	(‘Excess Return’, ‘IBB’)	(‘Excess Return’, ‘IEIH’)	(‘Excess Return’, ‘PBE’)	(‘Excess Return’, ‘SBIO’)	(‘Excess Return’, ‘XBI’)	(‘Excess Return’, ‘XLV’)	(‘Excess Volatility’, ‘AGNG’)	(‘Excess Volatility’, ‘BBC’)	(‘Excess Volatility’, ‘BBH’)	(‘Excess Volatility’, ‘BBP’)	(‘Excess Volatility’, ‘CNCR’)	(‘Excess Volatility’, ‘FBT’)	(‘Excess Volatility’, ‘IBB’)	(‘Excess Volatility’, ‘IEIH’)	(‘Excess Volatility’, ‘PBE’)	(‘Excess Volatility’, ‘SBIO’)	(‘Excess Volatility’, ‘XBI’)	(‘Excess Volatility’, ‘XLV’)	(‘Cumulative Return’, ‘AGNG’)	(‘Cumulative Return’, ‘BBC’)	(‘Cumulative Return’, ‘BBH’)	(‘Cumulative Return’, ‘BBP’)	(‘Cumulative Return’, ‘CNCR’)	(‘Cumulative Return’, ‘FBT’)	(‘Cumulative Return’, ‘IBB’)	(‘Cumulative Return’, ‘IEIH’)	(‘Cumulative Return’, ‘PBE’)	(‘Cumulative Return’, ‘SBIO’)	(‘Cumulative Return’, ‘XBI’)	(‘Cumulative Return’, ‘XLV’)
2020-01-02	23.59	38.47	140.44	44.18	23.62	149.6	121.3	27.7942	56.27	42.79	95.82	102.17	23.74	38.47	140.44	44.18	23.62	149.6	121.75	27.948	56.27	42.79	96.03	102.29	23.56	37.01	138.96	42.85	23.17	147.14	118.94	27.7942	55.33	41.32	93.73	101.4	23.665	37.413	139.62	43.171	23.459	148.22	119.89	27.948	55.84	41.62	94.68	102.13	23.1607	37.413	138.222	43.171	21.6018	146.208	118.894	27.948	55.7882	41.62	94.4506	96.6373	2400	65300	15100	2900	5000	324900	2.8444e+06	2927	5400	94400	3.5886e+06	6.2774e+06	0	0	0	0	0	0	0	0	0	0	0	0	0.0243617	0.0355917	0.0250125	0.0301117	0.0336436	0.025867	0.0272492	0.0228432	0.0279196	0.0361503	0.032811	0.0268018	-0.0143432	-0.0356187	-0.0173654	-0.0270471	-0.0179894	-0.022162	-0.0239449	-0.019122	-0.018979	-0.0344102	-0.0233211	-0.0161495	0.0248774	0.0360896	0.0256092	0.0305088	0.0341735	0.0264006	0.0277755	0.0234134	0.0284401	0.0365299	0.0332703	0.0273135	1	1	1	1	1	1	1	1	1	1	1	1
2020-01-03	23.41	36.9	137.24	42.88	23.11	145.9	118.2	27.74	55.23	41.01	93.21	100.95	23.62	37.24	138.81	42.88	23.43	147.33	119.14	27.7626	55.63	41.5	94.18	101.82	23.18	34.6	137.24	42.714	23.11	145.29	117.77	27.6172	55.15	40.68	92.7	100.45	23.555	36.84	138	42.714	23.196	146.3	118.36	27.7255	55.42	40.96	93.36	101.24	23.053	36.84	136.618	42.714	21.3596	144.314	117.376	27.7255	55.3686	40.96	93.1338	95.7952	23700	107800	26900	400	7600	175100	5.4809e+06	1937	12400	91000	5.1106e+06	8.2475e+06	-0.00464814	-0.0153155	-0.0116029	-0.0105858	-0.0112111	-0.0129537	-0.0127615	-0.00796121	-0.00752148	-0.0158578	-0.0139417	-0.00871437	0.0243617	0.0355917	0.0250125	0.0301117	0.0336436	0.025867	0.0272492	0.0228432	0.0279196	0.0361503	0.032811	0.0268018	-0.0225481	-0.0332155	-0.0295029	-0.0284858	-0.0291111	-0.0308537	-0.0306615	-0.0258612	-0.0254215	-0.0337578	-0.0318417	-0.0266144	0.0248774	0.0360896	0.0256092	0.0305088	0.0341735	0.0264006	0.0277755	0.0234134	0.0284401	0.0365299	0.0332703	0.0273135	0.995352	0.984685	0.988397	0.989414	0.988789	0.987046	0.987238	0.992039	0.992479	0.984142	0.986058	0.991286
2020-01-06	23.242	36.57	137.05	42.3	23.1	145.47	117.69	27.76	55.26	40.54	92.76	100.78	23.61	36.81	139.32	43.025	23.45	147.89	119.18	27.8415	55.38	40.992	94.21	101.9	23.242	35.72	137.05	42.07	22.94	144.9	116.95	27.76	54.86	40.04	91.83	100.75	23.565	36.77	139.29	43.025	23.45	147.89	119.12	27.8415	55.38	40.99	94.17	101.87	23.0628	36.77	137.896	43.025	21.5935	145.882	118.13	27.8415	55.3286	40.99	93.9419	96.3913	1800	35300	15700	5100	10600	92600	2.1991e+06	248	8100	89100	3.3645e+06	6.4418e+06	0.000424587	-0.00190011	0.0093477	0.00728101	0.0109503	0.010868	0.00642103	0.00418384	-0.000721745	0.000732495	0.00867616	0.00622281	0.0243617	0.0355917	0.0250125	0.0301117	0.0336436	0.025867	0.0272492	0.0228432	0.0279196	0.0361503	0.032811	0.0268018	-0.0176754	-0.0200001	-0.0087523	-0.010819	-0.00714974	-0.00723197	-0.011679	-0.0139162	-0.0188217	-0.0173675	-0.00942384	-0.0118772	0.0248774	0.0360896	0.0256092	0.0305088	0.0341735	0.0264006	0.0277755	0.0234134	0.0284401	0.0365299	0.0332703	0.0273135	0.995774	0.982814	0.997636	0.996618	0.999616	0.997774	0.993578	0.996189	0.991762	0.984863	0.994613	0.997454
2020-01-07	23.69	36.85	138.87	42.796	23.51	147.76	119.16	27.8	55.2	41.29	94.49	101.59	23.81	37.29	139.69	43.142	23.661	148.6	119.64	27.85	55.2	41.362	94.73	101.76	23.64	36.44	138.86	42.585	23.34	146.72	117.96	27.8	54.95	40.31	92.98	101.1	23.651	37.068	139.42	43.142	23.632	148.37	119.15	27.8193	55.02	41.12	94.32	101.67	23.147	37.068	138.024	43.142	21.7611	146.356	118.16	27.8193	54.969	41.12	94.0915	96.202	10600	27200	7400	2500	26900	93100	1.7184e+06	2070	3400	61700	3.9548e+06	6.3353e+06	0.00364951	0.00810446	0.000933265	0.00271926	0.00776119	0.00324564	0.000251689	-0.000797335	-0.00650052	0.00317143	0.00159277	-0.00196339	0.0243617	0.0355917	0.0250125	0.0301117	0.0336436	0.025867	0.0272492	0.0228432	0.0279196	0.0361503	0.032811	0.0268018	-0.0146505	-0.0101955	-0.0173667	-0.0155807	-0.0105388	-0.0150544	-0.0180483	-0.0190973	-0.0248005	-0.0151286	-0.0167072	-0.0202634	0.0248774	0.0360896	0.0256092	0.0305088	0.0341735	0.0264006	0.0277755	0.0234134	0.0284401	0.0365299	0.0332703	0.0273135	0.999409	0.990779	0.998567	0.999328	1.00737	1.00101	0.993828	0.995395	0.985315	0.987987	0.996198	0.995496
2020-01-08	23.51	36.84	139.01	43.27	23.66	148.24	119.07	28.0052	54.83	41.17	94.23	101.72	23.823	37.431	141.01	43.937	24.19	150.56	120.88	28.0052	55.25	41.85	95.99	102.79	23.51	36.69	139.01	43.27	23.66	148.24	118.86	28.0052	54.83	40.951	93.89	101.68	23.763	37.43	140.12	43.818	24.11	150.07	120.22	28.0052	55.07	41.68	95.61	102.33	23.2566	37.43	138.717	43.818	22.2012	148.033	119.221	28.0052	55.0189	41.68	95.3784	96.8266	2200	87200	23000	2100	23400	66600	3.3882e+06	230	12000	49300	3.0882e+06	7.4947e+06	0.00473552	0.00976581	0.00502081	0.0156693	0.0202269	0.011458	0.00898025	0.00668238	0.000908676	0.0136187	0.0136769	0.00649166	0.0243617	0.0355917	0.0250125	0.0301117	0.0336436	0.025867	0.0272492	0.0228432	0.0279196	0.0361503	0.032811	0.0268018	-0.0139645	-0.00893419	-0.0136792	-0.00303074	0.00152691	-0.00724201	-0.00971975	-0.0120176	-0.0177913	-0.0050813	-0.00502309	-0.0122083	0.0248774	0.0360896	0.0256092	0.0305088	0.0341735	0.0264006	0.0277755	0.0234134	0.0284401	0.0365299	0.0332703	0.0273135	1.00414	1.00045	1.00358	1.01499	1.02775	1.01248	1.00275	1.00205	0.986211	1.00144	1.00982	1.00196

Then, let’s calculate the Bollinger Bands for each ETF.

bollinger_bands = etfs_in_biotech.technicals.get_bollinger_bands()

Which returns (note that this is a MultiIndex):

Date	(‘Close’, ‘AGNG’)	(‘Close’, ‘BBC’)	(‘Close’, ‘BBH’)	(‘Close’, ‘BBP’)	(‘Close’, ‘CNCR’)	(‘Close’, ‘FBT’)	(‘Close’, ‘IBB’)	(‘Close’, ‘IEIH’)	(‘Close’, ‘PBE’)	(‘Close’, ‘SBIO’)	(‘Close’, ‘XBI’)	(‘Close’, ‘XLV’)	(‘Lower Band’, ‘AGNG’)	(‘Lower Band’, ‘BBC’)	(‘Lower Band’, ‘BBH’)	(‘Lower Band’, ‘BBP’)	(‘Lower Band’, ‘CNCR’)	(‘Lower Band’, ‘FBT’)	(‘Lower Band’, ‘IBB’)	(‘Lower Band’, ‘IEIH’)	(‘Lower Band’, ‘PBE’)	(‘Lower Band’, ‘SBIO’)	(‘Lower Band’, ‘XBI’)	(‘Lower Band’, ‘XLV’)	(‘Middle Band’, ‘AGNG’)	(‘Middle Band’, ‘BBC’)	(‘Middle Band’, ‘BBH’)	(‘Middle Band’, ‘BBP’)	(‘Middle Band’, ‘CNCR’)	(‘Middle Band’, ‘FBT’)	(‘Middle Band’, ‘IBB’)	(‘Middle Band’, ‘IEIH’)	(‘Middle Band’, ‘PBE’)	(‘Middle Band’, ‘SBIO’)	(‘Middle Band’, ‘XBI’)	(‘Middle Band’, ‘XLV’)	(‘Upper Band’, ‘AGNG’)	(‘Upper Band’, ‘BBC’)	(‘Upper Band’, ‘BBH’)	(‘Upper Band’, ‘BBP’)	(‘Upper Band’, ‘CNCR’)	(‘Upper Band’, ‘FBT’)	(‘Upper Band’, ‘IBB’)	(‘Upper Band’, ‘IEIH’)	(‘Upper Band’, ‘PBE’)	(‘Upper Band’, ‘SBIO’)	(‘Upper Band’, ‘XBI’)	(‘Upper Band’, ‘XLV’)
2020-01-02	23.1607	37.413	138.222	43.171	21.6018	146.208	118.894	27.948	55.7882	41.62	94.4506	96.6373	22.8653	35.44	136.692	42.8394	20.9387	144.953	118.097	27.6492	55.2137	41.6414	93.6136	94.3335	23.1337	37.7297	139.265	43.9656	21.7711	148.507	120.299	28.0172	55.988	42.7784	95.9705	96.0125	23.4021	40.0195	141.837	45.0918	22.6036	152.06	122.502	28.3852	56.7623	43.9154	98.3274	97.6915
2020-01-03	23.053	36.84	136.619	42.714	21.3596	144.314	117.376	27.7255	55.3686	40.96	93.1338	95.7952	22.9084	35.5338	136.455	42.6106	20.9476	144.258	117.511	27.5954	55.1277	41.156	93.1465	94.6536	23.145	37.7597	139.214	43.9066	21.7739	148.348	120.167	27.989	55.9616	42.6531	95.8665	96.1042	23.3815	39.9857	141.972	45.2026	22.6002	152.439	122.823	28.3826	56.7955	44.1502	98.5864	97.5548
2020-01-06	23.0628	36.77	137.895	43.025	21.5935	145.882	118.13	27.8415	55.3286	40.99	93.9419	96.3913	22.9012	35.679	136.329	42.4918	21.0242	143.947	117.204	27.5757	55.1102	40.7901	92.8938	95.1474	23.1413	37.8004	139.162	43.8646	21.7994	148.23	120.064	27.9694	55.9573	42.5292	95.7817	96.2431	23.3815	39.9219	141.995	45.2374	22.5746	152.512	122.923	28.3632	56.8044	44.2684	98.6695	97.3388
2020-01-07	23.147	37.068	138.024	43.142	21.7611	146.356	118.16	27.8193	54.969	41.12	94.0915	96.202	22.924	35.7637	136.179	42.3802	21.0897	143.686	116.878	27.5851	54.9026	40.5252	92.6191	95.2938	23.1513	37.8291	139.007	43.8016	21.8268	147.994	119.889	27.9727	55.8995	42.4004	95.6228	96.2957	23.3787	39.8946	141.834	45.223	22.5639	152.301	122.901	28.3602	56.8965	44.2756	98.6264	97.2975
2020-01-08	23.2566	37.43	138.717	43.818	22.2012	148.032	119.221	28.0052	55.0189	41.68	95.3784	96.8266	22.9437	35.8584	136.136	42.3848	21.1318	143.658	116.803	27.6124	54.7366	40.4473	92.5768	95.5192	23.1671	37.8677	138.958	43.806	21.8742	147.913	119.835	27.9873	55.8396	42.286	95.5601	96.4002	23.3905	39.877	141.78	45.2272	22.6166	152.167	122.867	28.3621	56.9425	44.1247	98.5433	97.2811

Then, it’s time to visually depict the Bollinger Bands for each ETF during the early stages of the Coronacrisis.

from matplotlib import pyplot as plt

figure, axis = plt.subplots(4, 3)
figure.set_size_inches(15, 10)
row = 0
column = 0

for ticker in bollinger_bands.columns.get_level_values(1).unique():
    name = health_care_etfs_in_biotech.loc[health_care_etfs_in_biotech.index == ticker, 'name'].iloc[0]
    
    bollinger_bands.xs(ticker, level=1, axis=1).plot(
        ax=axis[row, column],
        xlabel='',
        title=name,
        legend=False
        )

    column += 1
    if column == 3:
        row += 1
        column = 0
        
figure.suptitle('Technical Analysis of Biotech ETFs during the Coronacrisis', fontweight='bold')

figure.tight_layout()

This leads to the following graph which gives an indication of whether Biotech ETFs were oversold or overbought and how this effect is neutralised (to some degree) in the months after. Read more about the Bollinger Bands here.

Coronacrisis

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 a variety of sources. The rule that I hold with high regard is that all data needs to be entirely publicly available. Any data that requires API key access or requires a paid tier is never included in this database. Data that you are being charged for is often owned and maintained by the company you have a subscription at and therefore publicly sharing this information online is against their Terms of Service (ToS). However, data that is publicly available can freely be shared (read more about this subject here) especially since this database will never cost any money.
What categorization method is used?
- The categorization for Equities is based on a loose approximation of GICS. No actual data is collected from this source and this database merely tries to reflect the sectors and industries as best as possible. This is completely done through manual curation. The actual datasets as curated by MSCI has not been used in the development of any part of this database and remain the most up to date, paid, solution. Other categorizations are entirely developed by the author and can freely be changed.
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 options function from the package attached to this database. Furthermore, it is also possible to use equities = fd.Equities() and then use equities.options(selection='country') or specific further with equities.options(selection='sector', country='United States'). Please see
  this example
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 the American Exchanges, every Sunday the database automatically updates based on this repository. It also automatically checks if there were any market cap changes and converts assets accordingly. On purpose, most tickers are not removed even after becoming delisted. This is because it can be still of value for research to look into companies that no longer exist. When it comes to further automisation, this is what you usually pay a hefty fee for, think of Bloomberg at over $25.000 a year. Instead of requiring you to pay, this database is meant to be a community-driven project in which you help in identifying these companies. As news about migrations, mergers, bankruptcies and similar occur outside of the American exchanges it is up to the community to identify these and/or users to look into writing scripts that help with this. It is important to note that the vast majority of companies do not change as rapidly that this database becomes irrelevant before it is identified, e.g. a company like Facebook changing to META has already been updated. Furthermore, even though a company goes bankrupt, the old ticker is still relevant when it comes to historical data before the bankruptcy.

User 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/
Twitter: https://twitter.com/JerBouma
LinkedIn: https://www.linkedin.com/in/boumajeroen/
Email: [email protected]
Discord: add me on Discord JerBouma

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

FinanceDatabase

Table of Contents

Installation

Basic Usage

Quick Start

Collecting information from the database

Searching the database extensively

Storing the database at a different location

Examples

Companies in the Netherlands

Perform a Dupont Analysis on Railroad Companies

Technical Analysis of Biotech ETFs

Questions & Answers

User Contributions

Contact