The FinanceDatabase serves the role of providing anyone with any type of financial product categorisation 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. 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 FinanceToolkit. 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.
To install the FinanceDatabase it simply requires the following:
pip install financedatabase -U
Then within Python use:
import financedatabase as fd
import financedatabase as fd
Same methods apply to all other asset classes as well. Columns may vary.
# 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()
Understanding which countries, sectors, industries and categories exist is important to be able to search the database properly. Not only to understand the focus a specific the country but also to understand which area holds the most data. The output of all functionalities is cut off in this README for illustration purposes.
With obtain_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 follow:
# Obtain all possible options for equities
fd.obtain_options("equities")
{'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', 'ZAR', '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), 'industry': array(['Aerospace & Defense', 'Air Freight & Logistics', 'Airlines', 'Auto Components', 'Automobiles', 'Banks', 'Beverages', 'Biotechnology', 'Building Products', 'Capital Markets', 'Chemicals', 'Commercial Services & Supplies', 'Communications Equipment', 'Construction & Engineering', 'Construction Materials', 'Consumer Finance', 'Distributors', 'Diversified Consumer Services', 'Diversified Financial Services', 'Diversified Telecommunication Services', 'Electric Utilities', 'Electrical Equipment', 'Electronic Equipment, Instruments & Components', 'Energy Equipment & Services', 'Entertainment', 'Equity Real Estate Investment Trusts (REITs)', 'Food & Staples Retailing', 'Food Products', 'Gas Utilities', 'Health Care Equipment & Supplies', 'Health Care Providers & Services', 'Health Care Technology', 'Hotels, Restaurants & Leisure', 'Household Durables', 'Household Products', 'IT Services', 'Independent Power and Renewable Electricity Producers', 'Industrial Conglomerates', 'Insurance', 'Interactive Media & Services', 'Internet & Direct Marketing Retail', 'Machinery', 'Marine', 'Media', 'Metals & Mining', 'Multi-Utilities', 'Oil, Gas & Consumable Fuels', 'Paper & Forest Products', 'Pharmaceuticals', 'Professional Services', 'Real Estate Management & Development', 'Road & Rail', 'Semiconductors & Semiconductor Equipment', 'Software', 'Specialty Retail', 'Technology Hardware, Storage & Peripherals', 'Textiles, Apparel & Luxury Goods', 'Thrifts & Mortgage Finance', 'Tobacco', 'Trading Companies & Distributors', 'Transportation Infrastructure', 'Water Utilities'], dtype=object), 'exchange': array(['AMS', 'AQS', 'ASE', 'ASX', 'ATH', 'BER', 'BRU', 'BSE', 'BTS', 'BUD', 'BUE', 'CAI', 'CCS', 'CNQ', 'CPH', 'CSE', 'DOH', 'DUS', 'EBS', 'ENX', 'FKA', 'FRA', 'GER', 'HAM', 'HAN', 'HEL', 'HKG', 'ICE', 'IOB', 'ISE', 'IST', 'JKT', 'JNB', 'JPX', 'KLS', 'KOE', 'KSC', 'LIS', 'LIT', 'LSE', 'MCE', 'MCX', 'MEX', 'MIL', 'MUN', 'NAE', 'NAS', 'NCM', 'NEO', 'NGM', 'NMS', 'NSE', 'NSI', 'NYQ', 'NYS', 'NZE', 'OBB', 'OSL', 'PAR', 'PCX', 'PNK', 'PRA', 'RIS', 'SAO', 'SAP', 'SAT', 'SAU', 'SES', 'SET', 'SGO', 'SHH', 'SHZ', 'STO', 'STU', 'TAI', 'TAL', 'TLO', 'TLV', 'TOR', 'TWO', 'VAN', 'VIE'], dtype=object), 'market': array(['Aequitas NEO Exchange (Lit Book)', 'Aktie Torget', 'Aquis Exchange', 'Athens Stock Exchange', 'Australian Securities Exchange', 'BATS BZX Exchange', 'BSE India', 'BX Worldcaps', 'Berlin Stock Exchange', 'Bolsa De Valores De Caracas', 'Bolsa de Comercio de Santiago de Chile', 'Borsa Istanbul', 'Borsa Italiana', 'Bovespa Soma', 'Budapest Stock Exchange', 'Buenos Aires Mercato De Valores', 'Bursa Malaysia', 'Canadian Securities Exchange', 'Dusseldorf Stock Exchange', 'Egyptian Exchange', 'EuroTLX', 'Euronext', 'Euronext Amsterdam', 'Euronext Brussels', 'Euronext Lisbon', 'Euronext Paris', 'First North Copenhagen', 'First North Iceland', 'Frankfurt Stock Exchange', 'Fukuoka Stock Exchange', 'Hamburg Stock Exchange', 'Hanover Stock Exchange', 'Hong Kong Stock Exchange', 'Indonesia Stock Exchange', 'Irish Stock Exchange', 'Johannesburg Stock Exchange', 'KONEX', 'KOSPI Stock Market', 'London Stock Exchange (OTC and ITR)', 'London Stock Exchange (international)', 'Metropolitan Stock Exchange', 'Mexico Stock Exchange', 'Moscow Exchange - MICEX', 'Munich Stock Exchange', 'NASDAQ Capital Market', 'NASDAQ Global Select', 'NASDAQ OMX Helsinki', 'NASDAQ OMX Riga', 'NASDAQ OMX Stockholm', 'NASDAQ OMX Tallinn', 'NASDAQ OMX Vilnius', 'NYSE Arca', 'NYSE MKT', 'Nasdaq Copenhagen', 'National Stock Exchange of India', 'New York Stock Exchange', 'New Zealand Exchange', 'Nordic Growth Market', 'OTC Bulletin Board', 'Oslo Bors', 'Prague Stock Exchange', 'Qatar Exchange', 'Sapporo Securities Exchange', 'Saudi Arabian Stock Exchange', 'Shanghai Stock Exchange', 'Shenzhen Stock Exchange', 'Singapore Exchange', 'Sociedad de Bolsas (SIBE)', 'Stuttgart Stock Exchange', 'TSX Toronto Exchange', 'TSX Venture Exchange', 'Taiwan Stock Exchange', 'Tel Aviv Stock Exchange', 'The Stock Exchange of Thailand', 'Tokyo Stock Exchange', 'Vienna Stock Exchange', 'XETRA', 'us24_market', 'us_market'], dtype=object), 'country': array(['Afghanistan', 'Anguilla', 'Argentina', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bangladesh', 'Barbados', 'Belgium', 'Belize', 'Bermuda', 'Botswana', 'Brazil', 'British Virgin Islands', 'Cambodia', 'Canada', 'Cayman Islands', 'Chile', 'China', 'Colombia', 'Costa Rica', 'Cyprus', 'Czech Republic', 'Denmark', 'Dominican Republic', 'Egypt', 'Estonia', 'Falkland Islands', 'Finland', 'France', 'French Guiana', 'Gabon', 'Georgia', 'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Guernsey', 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Ivory Coast', 'Japan', 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kyrgyzstan', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macau', 'Macedonia', 'Malaysia', 'Malta', 'Mauritius', 'Mexico', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Netherlands', 'Netherlands Antilles', 'New Zealand', 'Nigeria', 'Norway', 'Panama', 'Papua New Guinea', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Reunion', 'Romania', 'Russia', 'Saudi Arabia', 'Senegal', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Suriname', 'Sweden', 'Switzerland', 'Taiwan', 'Tanzania', 'Thailand', 'Turkey', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Vietnam', 'Zambia'], dtype=object), 'state': array(['AB', 'ACT', 'AK', 'AL', 'AM', 'AN', 'AP', 'AR', 'AV', 'AZ', 'BA', 'BC', 'BG', 'BI', 'BJ', 'BL', 'BO', 'BS', 'CA', 'CE', 'CI', 'CO', 'CT', 'CU', 'DC', 'DE', 'DF', 'EM', 'ES', 'FE', 'FI', 'FL', 'FO', 'FR', 'GA', 'GE', 'GJ', 'GO', 'GU', 'HI', 'IA', 'ID', 'IL', 'IN', 'JA', 'KS', 'KY', 'LA', 'LC', 'LT', 'LU', 'MA', 'MB', 'MD', 'ME', 'MG', 'MH', 'MI', 'MN', 'MO', 'MS', 'MT', 'NB', 'NC', 'ND', 'NE', 'NF', 'NH', 'NJ', 'NL', 'NM', 'NS', 'NSW', 'NT', 'NU', 'NV', 'NY', 'OH', 'OK', 'ON', 'OR', 'PA', 'PD', 'PE', 'PG', 'PI', 'PR', 'PS', 'PV', 'QC', 'QLD', 'QR', 'RA', 'RE', 'RI', 'RJ', 'RM', 'RN', 'RS', 'SA', 'SC', 'SD', 'SE', 'SI', 'SK', 'SO', 'SP', 'TAS', 'TN', 'TO', 'TR', 'TS', 'TV', 'TX', 'UD', 'UT', 'VA', 'VC', 'VE', 'VI', 'VIC', 'VR', 'VT', 'WA', 'WI', 'WV', 'WY', 'YT'], dtype=object), 'city': array(["'s-Hertogenbosch", '6th of October', 'Aabenraa', ..., 'a€˜s-Hertogenbosch', 'tacheng', 'Ílhavo'], dtype=object), 'zipcode': array(['00-105', '00-116', '00-124', ..., 'YO8 8PH', 'Z05T1X3', 'v4B 3L1'], dtype=object), 'market_cap': array(['Large Cap', 'Mega Cap', 'Micro Cap', 'Mid Cap', 'Nano Cap', 'Small Cap'], dtype=object), 'isin': array(['AN8068571086', 'ANN4327C1220', 'AT000000STR1', ..., 'ZAE000265971', 'ZAE000296554', 'ZAE000298253'], dtype=object), 'cusip': array(['00089H106', '00090Q103', '00108J109', ..., '99406100', '99501108', '99502106'], dtype=object), 'figi': array(['#REF!', 'BBG000B9XKF0', 'BBG000B9XZV9', ..., 'BBG01FC8CFV3', 'BBG01FPC3G48', 'BBG01FRH5MP7'], dtype=object), 'composite_figi': array(['BBG000B9WX45', 'BBG000B9XG87', 'BBG000B9XRY4', ..., 'BBG01FC8CFN2', 'BBG01FP5R015', 'BBG01FRH5MK2'], dtype=object), 'shareclass_figi': array(['BBG001S112S8', 'BBG001S112X2', 'BBG001S112Y1', ..., 'BBG01CCBKDK1', 'BBG01FP5R033', 'BBG01FRH5ND8'], dtype=object)}
Then, when initalising the equities dataset, the unique countries, sectors and industries of all equities in the database with any combination:
# Initialize the Equities database
equities = fd.Equities()
For countries, you will find the following list if you print equities_countries
# Obtain all countries from the database
equities.options("country")
array(['Afghanistan', 'Anguilla', 'Argentina', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bangladesh', 'Barbados', 'Belgium', 'Belize', 'Bermuda', 'Botswana', 'Brazil', 'British Virgin Islands', 'Cambodia', 'Canada', 'Cayman Islands', 'Chile', 'China', 'Colombia', 'Costa Rica', 'Cyprus', 'Czech Republic', 'Denmark', 'Dominican Republic', 'Egypt', 'Estonia', 'Falkland Islands', 'Finland', 'France', 'French Guiana', 'Gabon', 'Georgia', 'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Guernsey', 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Ivory Coast', 'Japan', 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kyrgyzstan', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macau', 'Macedonia', 'Malaysia', 'Malta', 'Mauritius', 'Mexico', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Netherlands', 'Netherlands Antilles', 'New Zealand', 'Nigeria', 'Norway', 'Panama', 'Papua New Guinea', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Reunion', 'Romania', 'Russia', 'Saudi Arabia', 'Senegal', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Suriname', 'Sweden', 'Switzerland', 'Taiwan', 'Tanzania', 'Thailand', 'Turkey', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Vietnam', 'Zambia'], dtype=object)
For sectors, you will find the following list if you print equities_sectors
:
# Obtain all sectors from the database
equities.options("sector")
array(['Communication Services', 'Consumer Discretionary', 'Consumer Staples', 'Energy', 'Financials', 'Health Care', 'Industrials', 'Information Technology', 'Materials', 'Real Estate', 'Utilities'], dtype=object)
For industry groups, you will find the following list if you print equities_industry_groups
:
equities.options("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)
For industries, you will find the following list if you print equities_industries
:
# Obtain all industries from the database
equities.options("industry")
array(['Aerospace & Defense', 'Air Freight & Logistics', 'Airlines', 'Auto Components', 'Automobiles', 'Banks', 'Beverages', 'Biotechnology', 'Building Products', 'Capital Markets', 'Chemicals', 'Commercial Services & Supplies', 'Communications Equipment', 'Construction & Engineering', 'Construction Materials', 'Consumer Finance', 'Distributors', 'Diversified Consumer Services', 'Diversified Financial Services', 'Diversified Telecommunication Services', 'Electric Utilities', 'Electrical Equipment', 'Electronic Equipment, Instruments & Components', 'Energy Equipment & Services', 'Entertainment', 'Equity Real Estate Investment Trusts (REITs)', 'Food & Staples Retailing', 'Food Products', 'Gas Utilities', 'Health Care Equipment & Supplies', 'Health Care Providers & Services', 'Health Care Technology', 'Hotels, Restaurants & Leisure', 'Household Durables', 'Household Products', 'IT Services', 'Independent Power and Renewable Electricity Producers', 'Industrial Conglomerates', 'Insurance', 'Interactive Media & Services', 'Internet & Direct Marketing Retail', 'Machinery', 'Marine', 'Media', 'Metals & Mining', 'Multi-Utilities', 'Oil, Gas & Consumable Fuels', 'Paper & Forest Products', 'Pharmaceuticals', 'Professional Services', 'Real Estate Management & Development', 'Road & Rail', 'Semiconductors & Semiconductor Equipment', 'Software', 'Specialty Retail', 'Technology Hardware, Storage & Peripherals', 'Textiles, Apparel & Luxury Goods', 'Thrifts & Mortgage Finance', 'Tobacco', 'Trading Companies & Distributors', 'Transportation Infrastructure', 'Water Utilities'], dtype=object)
When you wish to get country, sector or industry specific lists, you can use the related country
, sector
and industry
tags as also found in the help window with help(equities.options)
help(equities.options)
Help on method options in module financedatabase.Equities: options(selection: str, country: str = '', sector: str = '', industry_group: str = '', industry: str = '') -> pandas.core.series.Series method of financedatabase.Equities.Equities instance Retrieve all options for the specified selection. This method returns a series containing all available options for the specified selection, which can be one of the following: "currency", "sector", "industry_group", "industry", "exchange", "market", "country", "state", "zip_code", "market_cap". Args: selection (str): The selection you want to see the options for. Choose from: - "currency" - "sector" - "industry_group" - "industry" - "exchange" - "market" - "country" - "state" - "zip_code" - "market_cap" country (str, optional): Specific country to retrieve data for. If not provided, returns data for all countries. sector (str, optional): Specific sector to retrieve data for. If not provided, returns data for all sectors. industry_group (str, optional): Specific industry group to retrieve data for. If not provided, returns data for all industry groups. industry (str, optional): Specific industry to retrieve data for. If not provided, returns data for all industries. Returns: pd.Series: A series with all options for the specified selection, sorted and without duplicates.
For example, if I wish to know all available industries within the sector "Basic Materials" in the country United States I can use
# Obtain a filtered selection of available industries
equities.options(selection="industry", country="United States", sector="Materials")
array(['Chemicals', 'Construction Materials', 'Metals & Mining', 'Paper & Forest Products'], dtype=object)
This also extends further if you are looking into a different category. For example, find all available currencies by using
# Initialize the Currencies database
currencies = fd.Currencies()
# Obtain all available currencies
currencies.options(selection="base_currency")
array(['AED', 'AFN', 'ALL', 'AMD', 'ANG', 'AOA', 'ARS', 'AUD', 'AUS', 'AUX', 'AWG', 'AZN', 'BAM', 'BBD', 'BDT', 'BGN', 'BHD', 'BIF', 'BMD', 'BND', 'BOB', 'BRL', 'BSD', 'BTN', 'BWP', 'BYN', 'BZD', 'CAD', 'CDF', 'CHF', 'CLF', 'CLP', 'CNH', 'CNY', 'COP', 'CRC', 'CUC', 'CUP', 'CVE', 'CZK', 'DJF', 'DKK', 'DOP', 'DZD', 'EGP', 'ERN', 'ETB', 'EUR', 'EUX', 'FJD', 'FKP', 'GBP', 'GEL', 'GHS', 'GIP', 'GMD', 'GNF', 'GTQ', 'GYD', 'HKD', 'HNL', 'HRK', 'HTG', 'HUF', 'IDR', 'ILS', 'INR', 'IQD', 'IRR', 'ISK', 'JMD', 'JOD', 'JPY', 'KES', 'KGS', 'KHR', 'KMF', 'KRW', 'KWD', 'KYD', 'KZT', 'LAK', 'LBP', 'LKR', 'LRD', 'LSL', 'LYD', 'MAD', 'MDL', 'MGA', 'MKD', 'MMK', 'MNT', 'MOP', 'MRU', 'MUR', 'MVR', 'MWK', 'MXN', 'MXV', 'MYR', 'MZN', 'NAD', 'NGN', 'NIO', 'NOK', 'NPR', 'NZD', 'OMR', 'PAB', 'PEN', 'PGK', 'PHP', 'PKR', 'PLN', 'PYG', 'QAR', 'RON', 'RSD', 'RUB', 'RWF', 'SAR', 'SBD', 'SCR', 'SDG', 'SEK', 'SGD', 'SHP', 'SLL', 'SOS', 'SRD', 'SSP', 'STN', 'SVC', 'SYP', 'SZL', 'THB', 'TJS', 'TMT', 'TND', 'TOP', 'TRY', 'TTD', 'TWD', 'TZS', 'UAH', 'UGX', 'USD', 'USY', 'UYU', 'UZS', 'VES', 'VND', 'VUV', 'WST', 'XAF', 'XCD', 'XCU', 'XDR', 'XOF', 'XPF', 'YER', 'ZAR', 'ZMW'], dtype=object)
But also when it comes to etfs
with
# Initialize the ETFs database
etfs = fd.ETFs()
# Obtain all availables categories
etfs.options(selection="category")
array(['Alternative', 'Bonds', 'Commodities Broad Basket', 'Communications', 'Consumer Discretionary', 'Consumer Staples', 'Currencies', 'Derivatives', 'Developed Markets', 'Emerging Markets', 'Energy', 'Equities', 'Factors', 'Financials', 'Health Care', 'Industrials', 'Materials', 'Real Estate', 'Technology', 'Trading', 'Utilities'], dtype=object)
If you wish to collect data from all equities you can use the following:
equities = fd.Equities()
equities.select()
name | summary | currency | sector | industry_group | industry | exchange | market | country | state | city | zipcode | website | market_cap | isin | cusip | figi | composite_figi | shareclass_figi | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
symbol | |||||||||||||||||||
A | Agilent Technologies, Inc. | Agilent Technologies, Inc. provides applicatio... | 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 | Alcoa Corporation, together with its subsidiar... | 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 | Aareal Bank AG, together with its subsidiaries... | 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 | Aareal Bank AG, together with its subsidiaries... | 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. | Asia Broadband Inc., through its subsidiary, A... | 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 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
NaN | Nano Labs Ltd American Depositary Shares | NaN | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | NMS | NASDAQ Global Select | China | NaN | NaN | NaN | NaN | Nano Cap | NaN | NaN | NaN | NaN | NaN |
NaN | Nano Labs Ltd American Depositary Shares | NaN | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | NMS | NASDAQ Global Select | China | NaN | NaN | NaN | NaN | Micro Cap | NaN | NaN | NaN | NaN | NaN |
NaN | Nano Labs Ltd American Depositary Shares | NaN | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | NMS | NASDAQ Global Select | China | NaN | NaN | NaN | NaN | Nano Cap | NaN | NaN | NaN | NaN | NaN |
NaN | Nano Labs Ltd American Depositary Shares | NaN | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | NMS | NASDAQ Global Select | China | NaN | NaN | NaN | NaN | Nano Cap | NaN | NaN | NaN | NaN | NaN |
NaN | Nano Labs Ltd American Depositary Shares | NaN | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | NMS | NASDAQ Global Select | China | NaN | NaN | NaN | NaN | Nano Cap | NaN | NaN | NaN | NaN | NaN |
23198 rows × 19 columns
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. Find a more elaborate explanation with help(equities.select)
:
help(equities.select)
Help on method select in module financedatabase.Equities: select(country: str = '', sector: str = '', industry_group: str = '', industry: str = '', exclude_exchanges: bool = True, capitalize: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Equities.Equities instance Retrieve equity data based on specified criteria. This method allows you to retrieve data for specific equities based on a combination of country, sector, industry group, and industry filters. You can also exclude exchanges from the search. If no input criteria are provided, it returns data for all equities. Args: country (str, optional): Specific country to retrieve data for. If not provided, returns data for all countries. sector (str, optional): Specific sector to retrieve data for. If not provided, returns data for all sectors. industry_group (str, optional): Specific industry group to retrieve data for. If not provided, returns data for all industry groups. industry (str, optional): Specific industry to retrieve data for. If not provided, returns data for all industries. exclude_exchanges (bool, optional): Whether to exclude exchanges from the search. If False, you will receive data for equities from different exchanges. Default is True. capitalize (bool, optional): Indicates whether country, sector, and industry names should be capitalized for matching. Default is True. Returns: pd.DataFrame: A DataFrame containing equity data matching the specified input criteria.
As an example, in Understanding the available options, we've used equities.options(selection='industry', country="United States", sector="Materials")
which allowed us to look at a specific industry in the United States. 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
name | summary | currency | sector | industry_group | industry | exchange | market | country | state | city | zipcode | website | market_cap | isin | cusip | figi | composite_figi | shareclass_figi | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
symbol | |||||||||||||||||||
CLF | Cleveland-Cliffs Inc. | Cleveland-Cliffs Inc. operates as an independe... | USD | Materials | Materials | Metals & Mining | NYQ | New York Stock Exchange | United States | OH | Cleveland | 44114-2315 | http://www.clevelandcliffs.com | Large Cap | US1858991011 | 185899101 | BBG000BFRH97 | BBG000BFRF55 | BBG001S5PW43 |
FCX | Freeport-McMoRan Inc. | Freeport-McMoRan Inc. engages in the mining of... | 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 | Newmont Corporation engages in the production ... | 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 | Nucor Corporation manufactures and sells steel... | 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. | Reliance Steel & Aluminum Co. operates as a me... | 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 | Southern Copper Corporation engages in mining,... | 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. | Steel Dynamics, Inc., together with its subsid... | 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 |
X | United States Steel Corporation | United States Steel Corporation produces and s... | USD | Materials | Materials | Metals & Mining | NYQ | New York Stock Exchange | United States | PA | Pittsburgh | 15219-2800 | http://www.ussteel.com | Large Cap | US9129091081 | 912909108 | BBG000BX3W91 | BBG000BX3TD3 | BBG001S5XL75 |
The companies found from the Finance Database can be used to feed into the Finance Toolkit. For example the companies as found above can be used to obtain the income statements of all companies in the Metals & Mining industry in the United States by using the to_toolkit
functionality.
Get an API key from Financial Modeling Prep here. Note that the Free version only gets you 5 years of data and no quarterly statements but this link offers a 15% discount while also supporting the project.
companies = metals_and_mining.to_toolkit(api_key="FINANCIAL_MODEL_PREP_KEY", start_date="2000-01-01", quarterly=False)
companies.get_profile()
Obtaining company profiles: 100%|██████████| 8/8 [00:00<00:00, 9.30it/s]
CLF | FCX | NEM | NUE | RS | SCCO | STLD | X | |
---|---|---|---|---|---|---|---|---|
Symbol | CLF | FCX | NEM | NUE | RS | SCCO | STLD | X |
Price | 19.53 | 37.2 | 32.12 | 181.06 | 294.65 | 80.12 | 119.74 | 45.64 |
Beta | 2.027 | 2.059 | 0.526 | 1.624 | 0.862 | 1.298 | 1.409 | 2.043 |
Average Volume | 9399009 | 12314373 | 11644590 | 1547147 | 235091 | 1023808 | 1313057 | 6936880 |
Market Capitalization | 9731584170 | 53344056000 | 37017978800 | 44511609340 | 16933889080 | 61941653320 | 19375847840 | 10211037200 |
Last Dividend | 0 | 0.6000000000000001 | 1.6 | 2.16 | 4 | 3.2 | 1.7000000000000002 | 0.2 |
Range | 13.61-22.83 | 32.83-44.7 | 31.615-52.76 | 129.79-190.96 | 227.87-302.89 | 64.66-88.4 | 90.55-136.46 | 20.4-50.2 |
Changes | 0.36 | 0.54 | 0.08 | 1.58 | 5.58 | 2.09 | 1.37 | 0.04 |
Company Name | Cleveland-Cliffs Inc. | Freeport-McMoRan Inc. | Newmont Corporation | Nucor Corporation | Reliance Steel & Aluminum Co. | Southern Copper Corporation | Steel Dynamics, Inc. | United States Steel Corporation |
Currency | USD | USD | USD | USD | USD | USD | USD | USD |
CIK | 764065 | 831259 | 1164727 | 73309 | 861884 | 1001838 | 1022671 | 1163302 |
ISIN | US1858991011 | US35671D8570 | US6516391066 | US6703461052 | US7595091023 | US84265V1052 | US8581191009 | US9129091081 |
CUSIP | 185899101 | 35671D857 | 651639106 | 670346105 | 759509102 | 84265V105 | 858119100 | 912909108 |
Exchange | New York Stock Exchange | New York Stock Exchange | New York Stock Exchange | New York Stock Exchange | New York Stock Exchange | New York Stock Exchange | NASDAQ Global Select | New York Stock Exchange |
Exchange Short Name | NYSE | NYSE | NYSE | NYSE | NYSE | NYSE | NASDAQ | NYSE |
Industry | Steel | Copper | Gold | Steel | Steel | Copper | Steel | Steel |
Website | https://www.clevelandcliffs.com | https://fcx.com | https://www.newmont.com | https://www.nucor.com | https://www.rsac.com | https://www.southernperu.com | https://stld.steeldynamics.com | https://www.ussteel.com |
Description | Cleveland-Cliffs Inc. operates as a flat-rolle... | Freeport-McMoRan Inc. engages in the mining of... | Newmont Corporation engages in the production ... | Nucor Corporation manufactures and sells steel... | Reliance Steel & Aluminum Co. operates as a di... | Southern Copper Corporation engages in mining,... | Steel Dynamics, Inc., together with its subsid... | United States Steel Corporation produces and s... |
CEO | Mr. C. Lourenco Goncalves | Mr. Richard C. Adkerson | Mr. Thomas Ronald Palmer | Mr. Leon J. Topalian | Ms. Karla R. Lewis | Mr. Oscar Gonzalez Rocha | Mr. Mark D. Millett | Mr. David Boyd Burritt |
Sector | Basic Materials | Basic Materials | Basic Materials | Basic Materials | Basic Materials | Basic Materials | Basic Materials | Basic Materials |
Country | US | US | US | US | US | US | US | US |
Full Time Employees | 28000 | 25600 | 14600 | 31400 | 14500 | 15018 | 12060 | 21803 |
Phone | 216 694 5700 | 602 366 8100 | 303 863 7414 | 704 366 7000 | 213 687 7700 | 602 264 1375 | 260 969 3500 | 412 433 1121 |
Address | 200 Public Square | 333 North Central Avenue | 6900 East Layton Avenue | 1915 Rexford Road | 350 South Grand Avenue | 1440 East Missouri Avenue | 7575 West Jefferson Boulevard | 600 Grant Street |
City | Cleveland | Phoenix | Denver | Charlotte | Los Angeles | Phoenix | Fort Wayne | Pittsburgh |
State | OH | AZ | CO | NC | CA | AZ | IN | PA |
ZIP Code | 44114-2315 | 85004-2189 | 80237 | 28211 | 90071 | 85014 | 46804 | 15219-2800 |
DCF Difference | 16.51996 | 21.57819 | -231.65902 | -128.8227 | 13.6802 | 22.55334 | -122.2899 | 29.47643 |
DCF | 3.010037895253636 | 15.621806675801688 | 268.0290222054616 | 304.1727004463441 | 213.01 | 57.566656979207266 | 238.42990084781277 | 16.16356712951636 |
IPO Date | 1987-11-05 | 1995-07-10 | 1980-03-17 | 1980-03-17 | 1994-09-16 | 1996-01-05 | 1996-11-22 | 1991-04-12 |
If you wish to collect data from all etfs you can use the following:
etfs = fd.ETFs()
etfs.select()
name | currency | summary | category_group | category | family | exchange | market | |
---|---|---|---|---|---|---|---|---|
symbol | ||||||||
^ACWI | ISHARES TRUST | USD | NaN | NaN | NaN | NaN | NIM | us_market |
^ADFI-IV | NFIELD DYNAMIC FIXED INCOME ETF | USD | NaN | NaN | NaN | NaN | ASE | us_market |
^ADRE | INVESCO ACTIVELY M | USD | NaN | NaN | NaN | NaN | NIM | us_market |
^ARB-EU | ALTSHARES MERGER ARBITRAGE ETF | USD | NaN | NaN | NaN | NaN | ASE | us_market |
^ARB-IV | ALTSHARES MERGER ARBITRAGE ETF | USD | NaN | NaN | NaN | NaN | ASE | us_market |
... | ... | ... | ... | ... | ... | ... | ... | ... |
VGFPF | Vanguard Funds Public Limited Company - Vangua... | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
VFDEF | Vanguard Funds Public Limited Company - Vangua... | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
WSDMF | WisdomTree Issuer ICAV - WisdomTree Europe Equ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
WDSSF | WisdomTree Issuer ICAV - WisdomTree US Quality... | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
ZKBHF | ZKB Gold ETF | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2860 rows × 8 columns
This returns approximately 2.500 different ETFs. Note that by default, only the American exchanges are selected. These are symbols like SPY
(SPDR S&P 500 ETF Trust) and VTI
(Vanguard Total Stock Market Index Fund ETF) 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 35.000 different symbols. Find a more elaborate explanation with help(etfs.select)
:
help(etfs.select)
Help on method select in module financedatabase.ETFs: select(category_group: str = '', category: str = '', family: str = '', exclude_exchanges: bool = True, capitalize: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.ETFs.ETFs instance Retrieve ETF data based on specified criteria. This method allows you to retrieve data for specific ETFs based on a combination of category group, category, and family filters. You can also exclude exchanges from the search. If no input criteria are provided, it returns data for all ETFs. Args: category_group (str, optional): Specific category group to retrieve data for. If not provided, returns data for all category groups. category (str, optional): Specific category to retrieve data for. If not provided, returns data for all categories. family (str, optional): Specific family to retrieve data for. If not provided, returns data for all families. exclude_exchanges (bool, optional): Whether to exclude exchanges from the search. If False, you will receive data for ETFs from different exchanges. Default is True. capitalize (bool, optional): Indicates whether category group, category, and family names should be capitalized for matching. Default is True. Returns: pd.DataFrame: A DataFrame containing ETF data matching the specified input criteria.
With this information in hand, and having seen the available options within Understanding the available options, we can specify the selection as follows:
etfs.select(category="Developed Markets")
name | currency | summary | category_group | category | family | exchange | market | |
---|---|---|---|---|---|---|---|---|
symbol | ||||||||
AFK | VanEck Vectors Africa Index ETF | USD | The investment seeks to replicate as closely a... | Equities | Developed Markets | VanEck Asset Management | PCX | us_market |
AGT | iShares MSCI Argentina and Global Exposure ETF | USD | The investment seeks to track the investment r... | Equities | Developed Markets | BlackRock Asset Management | BTS | us_market |
ARGT | Global X MSCI Argentina ETF | USD | The investment seeks to provide investment res... | Equities | Developed Markets | Global X Funds | PCX | us_market |
AVMU | Avantis Core Municipal Fixed Income ETF | USD | The investment seeks current income that is ex... | Fixed Income | Developed Markets | Avantis Investors | PCX | us_market |
BBEU | JPMorgan BetaBuilders Europe ETF | USD | The investment seeks investment results that c... | Equities | Developed Markets | JPMorgan Asset Management | BTS | us_market |
... | ... | ... | ... | ... | ... | ... | ... | ... |
XMPT | VanEck Vectors CEF Municipal Income ETF | USD | The investment seeks to replicate as closely a... | Fixed Income | Developed Markets | VanEck Asset Management | BTS | us_market |
ZCAN | SPDR Solactive Canada ETF | USD | The investment seeks to track the performance ... | Equities | Developed Markets | State Street Global Advisors | PCX | us_market |
ZDEU | SPDR Solactive Germany ETF | USD | The investment seeks to track the performance ... | Equities | Developed Markets | State Street Global Advisors | PCX | us_market |
ZGBR | SPDR Solactive United Kingdom ETF | USD | The investment seeks to track the performance ... | Equities | Developed Markets | State Street Global Advisors | PCX | us_market |
ZJPN | SPDR Solactive Japan ETF | USD | The investment seeks to track the performance ... | Equities | Developed Markets | State Street Global Advisors | PCX | us_market |
169 rows × 8 columns
If you wish to collect data from all funds you can use the following:
funds = fd.Funds()
funds.select()
name | currency | summary | manager_name | manager_bio | category_group | category | family | exchange | market | |
---|---|---|---|---|---|---|---|---|---|---|
symbol | ||||||||||
AAAAX | DWS RREEF Real Assets Fund - Class A | USD | The investment seeks total return in excess of... | John Vojticek | Co-Head of Liquid Real Assets / Chief Investme... | Equities | World | DWS | NAS | us_market |
AAACX | A3 Alternative Credit Fund | USD | NaN | NaN | NaN | NaN | NaN | NaN | NAS | us_market |
AAAEX | Virtus AllianzGI Health Sciences Fund Class P | USD | The investment seeks long-term capital appreci... | Peter Pirsch | Mr. Pirsch is a senior portfolio manager, a se... | Equities | Health Care | Virtus | NAS | us_market |
AAAFX | American Century One Choice Blend+ 2015 Portfo... | USD | The investment seeks the highest total return ... | Scott A. Wilson | Mr. Wilson, Vice President and Portfolio Manag... | Equities | Target Date | American Investments | NAS | us_market |
AAAGX | Thrivent Large Cap Growth Fund Class A | USD | The investment seeks long-term capital appreci... | Lauri Brunner | Ms. Brunner has been with Thrivent Financial s... | Equities | Factors | Thrivent Funds | NAS | us_market |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
ZVNBX | Zevenbergen Growth Fund Investor Class | USD | The investment seeks long-term capital appreci... | Joseph Dennison | Mr. Dennison joined ZCI in 2011.   In hi... | Equities | Factors | Zevenbergen Capital Investments | NAS | us_market |
ZVNIX | Zevenbergen Growth Fund Institutional Class | USD | The investment seeks long-term capital appreci... | Joseph Dennison | Mr. Dennison joined ZCI in 2011.   In hi... | Equities | Factors | Zevenbergen Capital Investments | NAS | us_market |
ZVZZCNX | Nasdaq NextShares Test Instrume | USD | NaN | NaN | NaN | NaN | NaN | NaN | NAS | us_market |
ZZZAX | Test Demand Deposit Account - T | USD | NaN | NaN | NaN | NaN | NaN | NaN | NAS | us_market |
ZZZZIX | Nasdaq Test - AIP Managed Futur | USD | NaN | NaN | NaN | NaN | NaN | NaN | NAS | us_market |
31440 rows × 10 columns
This returns approximately 30.000 different Funds. Note that by default, only the American exchanges are selected. These are symbols 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 55.000 different symbols. Find a more elaborate explanation with help(funds.select)
:
help(funds.select)
Help on method select in module financedatabase.Funds: select(category_group: str = '', category: str = '', family: str = '', exclude_exchanges: bool = True, capitalize: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Funds.Funds instance Retrieve fund data based on specified criteria. This method allows you to retrieve data for specific funds based on a combination of category group, category, and family filters. You can also exclude exchanges from the search. If no input criteria are provided, it returns data for all funds. Args: category_group (str, optional): Specific category group to retrieve data for. If not provided, returns data for all category groups. category (str, optional): Specific category to retrieve data for. If not provided, returns data for all categories. family (str, optional): Specific family to retrieve data for. If not provided, returns data for all families. exclude_exchanges (bool, optional): Whether to exclude exchanges from the search. If False, you will receive data for funds from different exchanges. Default is True. capitalize (bool, optional): Indicates whether category group, category, and family names should be capitalized for matching. Default is True. Returns: pd.DataFrame: A DataFrame containing fund data matching the specified input criteria.
With this information in hand, and having seen the available options within Understanding the available options, we can specify the selection as follows:
funds.options(selection="category_group")
array(['Alternative', 'Commodities', 'Derivatives', 'Equities', 'Fixed Income', 'Infrastructure', 'Miscellaneous', 'Real Estate'], dtype=object)
funds.options(selection="category", category_group="Equities")
array(['Africa', 'Allocation', 'Asia', 'Australia', 'Austria', 'Canada', 'China', 'Communications', 'Consumer Discretionary', 'Consumer Staples', 'Emerging Markets', 'Energy', 'Equities', 'Europe', 'Factors', 'Financials', 'France', 'Germany', 'Health Care', 'Hong Kong', 'India', 'Industrials', 'Islamic', 'Italy', 'Japan', 'Latin America', 'Mexico', 'Netherlands', 'New Zealand', 'North America', 'Scandinavia', 'Sector', 'Spain', 'Target Date', 'Technology', 'Thailand', 'Trading', 'United Kingdom', 'United States', 'Utilities', 'Vietnam', 'World'], dtype=object)
funds.select(category_group="Equities", category="China")
name | currency | summary | manager_name | manager_bio | category_group | category | family | exchange | market | |
---|---|---|---|---|---|---|---|---|---|---|
symbol | ||||||||||
AACFX | Invesco Greater China Fund Class A | USD | The investment seeks long-term growth of capit... | Mike Shiao | Mike Shiao has 25 years of investment experien... | Equities | China | Invesco Asset Management | NAS | us_market |
ACEAX | AB All China Equity Portfolio Class A | USD | The investment seeks long-term growth of capit... | Stuart Rae | Stuart Rae has been Chief Investment Officer o... | Equities | China | AllianceBernstein | NAS | us_market |
ACEYX | AB All China Equity Portfolio Advisor Class | USD | The investment seeks long-term growth of capit... | Stuart Rae | Stuart Rae has been Chief Investment Officer o... | Equities | China | AllianceBernstein | NAS | us_market |
AMCYX | Invesco Greater China Fund Class Y | USD | The investment seeks long-term growth of capit... | Mike Shiao | Mike Shiao has 25 years of investment experien... | Equities | China | Invesco Asset Management | NAS | us_market |
BCAKX | Baillie Gifford China A Shares Fund Class K | USD | The investment seeks capital appreciation. Th... | Sophie Earnshaw | Sophie Earnshaw, CFA, Portfolio Manager, joine... | Equities | China | Baillie Gifford Funds | NAS | us_market |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
USCOX | U.S. Global Investors China Region Fund Invest... | USD | The investment seeks long-term growth of capit... | Frank Holmes | NaN | Equities | China | U.S. Global Investors | NAS | us_market |
WAGCX | Wasatch Greater China Fund Investor Class Shares | USD | The investment seeks long-term growth of capit... | Kevin I. Huerta Unger | Kevin Unger, CFA has been an associate portfol... | Equities | China | Wasatch | NAS | us_market |
WCMCX | WCM China Quality Growth Fund Institutional Cl... | USD | The investment seeks long-term capital appreci... | Michael Z. Tian | As Business Analyst, Michael’s primary re... | Equities | China | WCM Investment Management | NAS | us_market |
WCQGX | WCM China Quality Growth Fund Investor Class S... | USD | The investment seeks long-term capital appreci... | Michael Z. Tian | As Business Analyst, Michael’s primary re... | Equities | China | WCM Investment Management | NAS | us_market |
WGGCX | Wasatch Greater China Fund Institutional Class... | USD | The investment seeks long-term growth of capit... | Kevin I. Huerta Unger | Kevin Unger, CFA has been an associate portfol... | Equities | China | Wasatch | NAS | us_market |
79 rows × 10 columns
If you wish to collect data from all indices you can use the following:
indices = fd.Indices()
indices.select()
name | currency | market | exchange | exchange timezone | |
---|---|---|---|---|---|
symbol | |||||
GBKX | KBW Nasdaq Global Bank Index | NaN | us_market | NIM | EDT |
GBKXN | KBW Nasdaq Global Bank Net Total Return Index | NaN | us_market | NIM | EDT |
GBKXT | KBW Nasdaq Global Bank Total Return Index | NaN | us_market | NIM | EDT |
^A1BSC | Dow Jones Americas Basic Materi | USD | us_market | DJI | EDT |
^A1CYC | Dow Jones Americas Consumer Ser | USD | us_market | DJI | EDT |
... | ... | ... | ... | ... | ... |
^ZSL-EU | ProShares UltraShort Silver (Es | USD | us_market | ASE | EDT |
^ZSL-IV | ProShares UltraShort Silver (In | USD | us_market | ASE | EDT |
^ZSL-NV | ProShares UltraShort Silver (Ne | USD | us_market | ASE | EDT |
^ZSL-TC | ProShares UltraShort Silver (To | USD | us_market | ASE | EDT |
^ZVZZT-624 | ZVZZT.624 Test Symbol | USD | us_market | NYS | EDT |
62140 rows × 5 columns
This returns approximately 60.000 different indices. Note that by default, only the American exchanges are selected. These are symbols like ^GSPC
(S&P 500) 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 90.000 different symbols. Find a more elaborate explanation with help(indices.select)
:
help(indices.select)
Help on method select in module financedatabase.Indices: select(currency: str = '', capitalize: bool = True, exclude_exchanges: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Indices.Indices instance Returns all indices when no input is given and has the option to give a specific combination of indices based on the currency defined. Args: currency (str, optional): If filled, gives all data for a specific currency. capitalize (bool, optional): Whether the currency needs to be capitalized. By default, the values are always capitalized as that is also how it is represented in the CSV files. exclude_exchanges (bool, optional): Whether you want to exclude exchanges from the search. If False, you will receive multiple instances of the same product from different exchanges. Returns: indices_df (pd.DataFrame): Returns a DataFrame with a selection or all data based on the input.
If you wish to collect data from all currencies you can use the following:
currencies = fd.Currencies()
currencies.select()
name | base_currency | quote_currency | exchange | market | |
---|---|---|---|---|---|
symbol | |||||
AED=X | USD/AED | USD | AED | CCY | ccy_market |
AEDAUD=X | AED/AUD | AED | AUD | CCY | ccy_market |
AEDBRX=X | AED/BRX | AED | BRX | CCY | ccy_market |
AEDCAD=X | AED/CAD | AED | CAD | CCY | ccy_market |
AEDCHF=X | AED/CHF | AED | CHF | CCY | ccy_market |
... | ... | ... | ... | ... | ... |
ZMWEUR=X | ZMW/EUR | ZMW | EUR | CCY | ccy_market |
ZMWGBP=X | ZMW/GBP | ZMW | GBP | CCY | ccy_market |
ZMWJPY=X | ZMW/JPY | ZMW | JPY | CCY | ccy_market |
ZMWUSD=X | ZMW/USD | ZMW | USD | CCY | ccy_market |
ZMWZAR=X | ZMW/ZAR | ZMW | ZAR | CCY | ccy_market |
2556 rows × 5 columns
This returns approximately 2.500 different currencies. Find a more elaborate explanation with help(currencies.select)
:
# Help Window
help(currencies.select)
Help on method select in module financedatabase.Currencies: select(base_currency: str = '', quote_currency: str = '', capitalize: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Currencies.Currencies instance Retrieve currency data based on specified criteria. This method allows you to retrieve data for specific base or quote currencies, with the option to customize the capitalization of currency names. If no input criteria are provided, it returns data for all currencies. Args: base_currency (str, optional): Specific base currency to retrieve data for. If not provided, returns data for all base currencies. quote_currency (str, optional): Specific quote currency to retrieve data for. If not provided, returns data for all quote currencies. capitalize (bool, optional): Indicates whether the currency names should be capitalized for matching. Default is True. Returns: pd.DataFrame: A DataFrame containing currency data matching the specified input criteria.
With this information in hand, and having seen the available options within Understanding the available options, we can specify the selection as follows:
currencies.select(base_currency="AED")
name | base_currency | quote_currency | exchange | market | |
---|---|---|---|---|---|
symbol | |||||
AEDAUD=X | AED/AUD | AED | AUD | CCY | ccy_market |
AEDBRX=X | AED/BRX | AED | BRX | CCY | ccy_market |
AEDCAD=X | AED/CAD | AED | CAD | CCY | ccy_market |
AEDCHF=X | AED/CHF | AED | CHF | CCY | ccy_market |
AEDEUR=X | AED/EUR | AED | EUR | CCY | ccy_market |
AEDGBP=X | AED/GBP | AED | GBP | CCY | ccy_market |
AEDHKD=X | AED/HKD | AED | HKD | CCY | ccy_market |
AEDINR=X | AED/INR | AED | INR | CCY | ccy_market |
AEDJPY=X | AED/JPY | AED | JPY | CCY | ccy_market |
AEDKRW=X | AED/KRW | AED | KRW | CCY | ccy_market |
AEDKWD=X | AED/KWD | AED | KWD | CCY | ccy_market |
AEDLKR=X | AED/LKR | AED | LKR | CCY | ccy_market |
AEDMAD=X | AED/MAD | AED | MAD | CCY | ccy_market |
AEDMUR=X | AED/MUR | AED | MUR | CCY | ccy_market |
AEDMYX=X | AED/MYX | AED | MYX | CCY | ccy_market |
AEDNOK=X | AED/NOK | AED | NOK | CCY | ccy_market |
AEDNZD=X | AED/NZD | AED | NZD | CCY | ccy_market |
AEDOMR=X | AED/OMR | AED | OMR | CCY | ccy_market |
AEDPKR=X | AED/PKR | AED | PKR | CCY | ccy_market |
AEDQAR=X | AED/QAR | AED | QAR | CCY | ccy_market |
AEDSAR=X | AED/SAR | AED | SAR | CCY | ccy_market |
AEDSEK=X | AED/SEK | AED | SEK | CCY | ccy_market |
AEDSGD=X | AED/SGD | AED | SGD | CCY | ccy_market |
AEDTHX=X | AED/THX | AED | THX | CCY | ccy_market |
AEDTRY=X | AED/TRY | AED | TRY | CCY | ccy_market |
AEDUSD=X | AED/USD | AED | USD | CCY | ccy_market |
AEDXDR=X | AED/XDR | AED | XDR | CCY | ccy_market |
AEDZAR=X | AED/ZAR | AED | ZAR | CCY | ccy_market |
If you wish to collect data from all cryptocurrencies you can use the following:
cryptos = fd.Cryptos()
cryptos.select()
name | cryptocurrency | currency | summary | exchange | market | |
---|---|---|---|---|---|---|
symbol | ||||||
AAVE-CAD | Aave CAD | AAVE | CAD | Aave (AAVE) is a cryptocurrency and operates o... | CCC | ccc_market |
AAVE-CNY | Aave CNY | AAVE | CNY | Aave (AAVE) is a cryptocurrency and operates o... | CCC | ccc_market |
AAVE-ETH | Aave ETH | AAVE | ETH | Aave (AAVE) is a cryptocurrency and operates o... | CCC | ccc_market |
AAVE-EUR | Aave EUR | AAVE | EUR | Aave (AAVE) is a cryptocurrency and operates o... | CCC | ccc_market |
AAVE-GBP | Aave GBP | AAVE | GBP | Aave (AAVE) is a cryptocurrency and operates o... | CCC | ccc_market |
... | ... | ... | ... | ... | ... | ... |
ZYN-INR | Zynecoin INR | ZYN | INR | Zynecoin (ZYN) is a cryptocurrency . Users are... | CCC | ccc_market |
ZYN-JPY | Zynecoin JPY | ZYN | JPY | Zynecoin (ZYN) is a cryptocurrency . Users are... | CCC | ccc_market |
ZYN-KRW | Zynecoin KRW | ZYN | KRW | Zynecoin (ZYN) is a cryptocurrency . Users are... | CCC | ccc_market |
ZYN-RUB | Zynecoin RUB | ZYN | RUB | Zynecoin (ZYN) is a cryptocurrency . Users are... | CCC | ccc_market |
ZYN-USD | Zynecoin USD | ZYN | USD | Zynecoin (ZYN) is a cryptocurrency . Users are... | CCC | ccc_market |
3367 rows × 6 columns
This returns approximately 3.000 different cryptocurrencies. Find a more elaborate explanation with help(cryptos.select)
:
help(cryptos.select)
Help on method select in module financedatabase.Cryptos: select(crypto: str = '', currency: str = '', capitalize: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Cryptos.Cryptos instance Obtain cryptocurrency data based on specified criteria. This method allows you to retrieve data for specific cryptocurrencies and currencies, with the option to customize the capitalization of cryptocurrency names. If no input criteria are provided, it returns data for all cryptocurrencies. Args: crypto (str, optional): Specific cryptocurrency to retrieve data for. If not provided, returns data for all cryptocurrencies. currency (str, optional): Specific currency to filter by. If not provided, no currency filtering is applied. capitalize (bool, optional): Indicates whether the cryptocurrency names should be capitalized for matching. Default is True. Returns: pd.DataFrame: A DataFrame containing cryptocurrency data matching the specified input criteria.
With this information in hand, and having seen the available options within Understanding the available options, we can specify the selection as follows. Which returns a total of 5 combination of cryptocurrencies that include the ETH.
cryptos.select(crypto="ETH")
name | cryptocurrency | currency | summary | exchange | market | |
---|---|---|---|---|---|---|
symbol | ||||||
ETH-BTC | Ethereum BTC | ETH | BTC | Ethereum (ETH) is a cryptocurrency . Users are... | CCC | ccc_market |
ETH-CAD | Ethereum CAD | ETH | CAD | Ethereum (ETH) is a cryptocurrency . Users are... | CCC | ccc_market |
ETH-EUR | Ethereum EUR | ETH | EUR | Ethereum (ETH) is a cryptocurrency . Users are... | CCC | ccc_market |
ETH-GBP | Ethereum GBP | ETH | GBP | Ethereum (ETH) is a cryptocurrency . Users are... | CCC | ccc_market |
ETH-USD | Ethereum USD | ETH | USD | Ethereum (ETH) is a cryptocurrency . Users are... | CCC | ccc_market |
If you wish to collect data from all money markets you can use the following:
moneymarkets = fd.Moneymarkets()
moneymarkets.select()
name | category | currency | market | exchange | |
---|---|---|---|---|---|
symbol | |||||
AABXX | SEI Daily Income Trust Government Fund | NaN | USD | us_market | NAS |
AAFXX | American Funds U.S. Government | NaN | USD | us_market | NAS |
AALXX | Thrivent Money Market Fund | Thrivent Mutual Funds | USD | us_market | NAS |
AAOXX | American Beacon U.S. Government Money Market S... | NaN | USD | us_market | NAS |
AARXX | AARP Money Market Fund | NaN | USD | us_market | NAS |
... | ... | ... | ... | ... | ... |
WRNXX | Ivy Government Money Market Fund | Ivy Funds | USD | us_market | NAS |
WTLXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WTPXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WTRXX | 100% Treasury Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WUCXX | Municipal Cash Management Money Market Fund | Wells Fargo Advantage Money Market Funds | USD | us_market | NAS |
1364 rows × 5 columns
This returns approximately 3.000 different money markets. Find a more elaborate explanation with help(fd.select_moneymarkets)
:
help(moneymarkets.select)
Help on method select in module financedatabase.Moneymarkets: select(category: str = '', capitalize: bool = True, exclude_exchanges: bool = True) -> pandas.core.frame.DataFrame method of financedatabase.Moneymarkets.Moneymarkets instance Returns all moneymarkets when no input is given and has the option to give a specific combination of moneymarkets based on the category defined. Args: category (str, optional): If filled, gives all data for a specific category. Default is an empty string. capitalize (bool, optional): Whether the category needs to be capitalized. Default is True. exclude_exchanges (bool, optional): Whether you want to exclude exchanges from the search. If False, you will receive multiple times the product from different exchanges. Default is True. Returns: indices_df (pd.DataFrame): Returns a DataFrame with a selection or all data based on the input.
moneymarkets.select(category="Wells Fargo Funds Trust")
name | category | currency | market | exchange | |
---|---|---|---|---|---|
symbol | |||||
GVIXX | Government Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
MMIXX | National Tax-Free Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
NWGXX | Government Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
NWIXX | Cash Investment Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
NWMXX | National Tax-Free Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
NWTXX | 100% Treasury Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
PISXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
PIVXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
PRVXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
SHIXX | Heritage Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
SHMXX | Heritage Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
STGXX | Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFAXX | Cash Investment Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFFXX | Government Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFGXX | Government Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFIXX | Cash Investment Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFJXX | Heritage Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFNXX | National Tax-Free Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFQXX | Cash Investment Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WFTXX | 100% Treasury Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WGAXX | Government Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WHTXX | Heritage Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WMOXX | Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WMPXX | Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WNTXX | National Tax-Free Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WOTXX | 100% Treasury Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WTLXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WTPXX | Treasury Plus Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
WTRXX | 100% Treasury Money Market Fund | Wells Fargo Funds Trust | USD | us_market | NAS |
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
equities = fd.Equities()
# Search Multiple Columns
equities.search(summary='automotive', currency='USD', country='Germany')
name | summary | currency | sector | industry_group | industry | exchange | market | country | state | city | zipcode | website | market_cap | isin | cusip | figi | composite_figi | shareclass_figi | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
symbol | |||||||||||||||||||
AFRMF | Alphaform AG | Alphaform AG, together with its subsidiaries, ... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Feldkirchen | 85622 | NaN | Nano Cap | NaN | NaN | NaN | NaN | NaN |
AUUMF | Aumann AG | Aumann AG manufactures and sells specialized m... | 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 | Bayerische Motoren Werke AG, together with its... | 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 | BASF SE operates as a chemical company worldwi... | 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 | Beiersdorf Aktiengesellschaft engages in the m... | 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 |
BDRFY | Beiersdorf Aktiengesellschaft | Beiersdorf Aktiengesellschaft engages in the m... | 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 |
BFFAF | BASF SE | BASF SE operates as a chemical company worldwi... | 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 |
BMWYY | Bayerische Motoren Werke Aktiengesellschaft | Bayerische Motoren Werke AG, together with its... | 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 |
BYMOF | Bayerische Motoren Werke Aktiengesellschaft | Bayerische Motoren Werke AG, together with its... | 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 |
COVTY | Covestro AG | Covestro AG develops, produces, and markets po... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Leverkusen | 51373 | http://www.covestro.com | Large Cap | DE0006062144 | NaN | NaN | NaN | NaN |
CTTAF | Continental Aktiengesellschaft | Continental Aktiengesellschaft develops produc... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Hanover | 30165 | http://www.continental.com | Large Cap | US2107712000 | 210771200 | NaN | NaN | NaN |
CTTAY | Continental Aktiengesellschaft | Continental Aktiengesellschaft develops produc... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Hanover | 30165 | http://www.continental.com | Large Cap | US2107712000 | 210771200 | NaN | NaN | NaN |
CVVTF | Covestro AG | Covestro AG develops, produces, and markets po... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Leverkusen | 51373 | http://www.covestro.com | Large Cap | DE0006062144 | NaN | NaN | NaN | NaN |
DDAIF | Daimler AG | Daimler AG, together its subsidiaries, develop... | USD | Consumer Discretionary | Automobiles & Components | Automobiles | PNK | OTC Bulletin Board | Germany | NaN | Stuttgart | 70372 | http://www.daimler.com | Large Cap | NaN | NaN | NaN | NaN | NaN |
DMLRY | Daimler AG | Daimler AG, together its subsidiaries, develop... | USD | Consumer Discretionary | Automobiles & Components | Automobiles | PNK | OTC Bulletin Board | Germany | NaN | Stuttgart | 70372 | http://www.daimler.com | Large Cap | NaN | NaN | NaN | NaN | NaN |
DUERF | Durr Aktiengesellschaft | Dürr Aktiengesellschaft, together with its ... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Bietigheim-Bissingen | 74321 | http://www.durr-group.com | Mid Cap | US2668881061 | 266888106 | NaN | NaN | NaN |
DURYY | Durr Aktiengesellschaft | Dürr Aktiengesellschaft, together with its ... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Bietigheim-Bissingen | 74321 | http://www.durr-group.com | Mid Cap | US2668881061 | 266888106 | NaN | NaN | NaN |
EGKLF | ElringKlinger AG | ElringKlinger AG develops, manufactures, and d... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Dettingen an der Erms | 72581 | http://www.elringklinger.com | Small Cap | US2901591022 | 290159102 | NaN | NaN | NaN |
ELLRY | ElringKlinger AG | ElringKlinger AG develops, manufactures, and d... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Dettingen an der Erms | 72581 | http://www.elringklinger.com | Small Cap | US2901591022 | 290159102 | NaN | NaN | NaN |
EVKIF | Evonik Industries AG | Evonik Industries AG engages in the specialty ... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Essen | 45128 | http://corporate.evonik.com/en/ | Large Cap | US3005031097 | 300503109 | NaN | NaN | NaN |
EVKIY | Evonik Industries AG | Evonik Industries AG engages in the specialty ... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Essen | 45128 | http://corporate.evonik.com/en/ | Large Cap | US3005031097 | 300503109 | NaN | NaN | NaN |
FUPBY | Fuchs Petrolub SE | Fuchs Petrolub SE develops, produces, and sell... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Mannheim | 68169 | http://www.fuchs.com/group | Mid Cap | DE000A3E5D64 | NaN | NaN | NaN | NaN |
FUPEF | Fuchs Petrolub SE | Fuchs Petrolub SE develops, produces, and sell... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Mannheim | 68169 | http://www.fuchs.com/group | Mid Cap | DE000A3E5D64 | NaN | NaN | NaN | NaN |
FUPPF | Fuchs Petrolub SE | Fuchs Petrolub SE develops, produces, and sell... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Mannheim | 68169 | http://www.fuchs.com/group | Mid Cap | DE000A3E5D64 | NaN | NaN | NaN | NaN |
HELKF | Henkel AG & Co. KGaA | Henkel AG & Co. KGaA, together with its subsid... | USD | Consumer Staples | Household & Personal Products | Household Products | PNK | OTC Bulletin Board | Germany | NaN | Dusseldorf | 40589 | http://www.henkel.com | Large Cap | US42550U2087 | 42550U208 | NaN | NaN | NaN |
HENKY | Henkel AG & Co. KGaA | Henkel AG & Co. KGaA, together with its subsid... | USD | Consumer Staples | Household & Personal Products | Household Products | PNK | OTC Bulletin Board | Germany | NaN | Dusseldorf | 40589 | http://www.henkel.com | Large Cap | US42550U2087 | 42550U208 | NaN | NaN | NaN |
HENOF | Henkel AG & Co. KGaA | Henkel AG & Co. KGaA, together with its subsid... | USD | Consumer Staples | Household & Personal Products | Household Products | PNK | OTC Bulletin Board | Germany | NaN | Dusseldorf | 40589 | http://www.henkel.com | Large Cap | US42550U2087 | 42550U208 | NaN | NaN | NaN |
HENOY | Henkel AG & Co. KGaA | Henkel AG & Co. KGaA, together with its subsid... | USD | Consumer Staples | Household & Personal Products | Household Products | PNK | OTC Bulletin Board | Germany | NaN | Dusseldorf | 40589 | http://www.henkel.com | Large Cap | US42550U2087 | 42550U208 | NaN | NaN | NaN |
HLKHF | HELLA GmbH & Co. KGaA | HELLA GmbH & Co. KGaA, together with its subsi... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Lippstadt | 59552 | http://www.hella.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
HLLGY | HELLA GmbH & Co. KGaA | HELLA GmbH & Co. KGaA, together with its subsi... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Lippstadt | 59552 | http://www.hella.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
IFNNF | Infineon Technologies AG | Infineon Technologies AG designs, develops, ma... | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | PNK | OTC Bulletin Board | Germany | NaN | Munich | 85579 | http://www.infineon.com | Large Cap | US45662N1037 | 45662N103 | NaN | NaN | NaN |
IFNNY | Infineon Technologies AG | Infineon Technologies AG designs, develops, ma... | USD | Information Technology | Semiconductors & Semiconductor Equipment | Semiconductors & Semiconductor Equipment | PNK | OTC Bulletin Board | Germany | NaN | Munich | 85579 | http://www.infineon.com | Large Cap | US45662N1037 | 45662N103 | NaN | NaN | NaN |
KUKAF | KUKA Aktiengesellschaft | KUKA Aktiengesellschaft, an automation company... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Augsburg | 86165 | http://www.kuka.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
KUKAY | KUKA Aktiengesellschaft | KUKA Aktiengesellschaft, an automation company... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Augsburg | 86165 | http://www.kuka.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
LNNNY | LEONI AG | LEONI AG, together with its subsidiaries, prov... | USD | Industrials | Capital Goods | Electrical Equipment | PNK | OTC Bulletin Board | Germany | NaN | Nuremberg | 90402 | http://www.leoni.com | Small Cap | NaN | NaN | NaN | NaN | NaN |
LNXSF | LANXESS Aktiengesellschaft | LANXESS Aktiengesellschaft, a specialty chemic... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Cologne | 50569 | http://lanxess.com | Mid Cap | US5165571051 | 516557105 | NaN | NaN | NaN |
LNXSY | LANXESS Aktiengesellschaft | LANXESS Aktiengesellschaft, a specialty chemic... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Cologne | 50569 | http://lanxess.com | Mid Cap | US5165571051 | 516557105 | NaN | NaN | NaN |
LPKFF | LPKF Laser & Electronics AG | LPKF Laser & Electronics AG, together with its... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Garbsen | 30827 | http://www.lpkf.com | Small Cap | DE0006450000 | NaN | NaN | NaN | NaN |
MKGAF | MERCK Kommanditgesellschaft auf Aktien | MERCK Kommanditgesellschaft auf Aktien operate... | USD | Health Care | Pharmaceuticals, Biotechnology & Life Sciences | Pharmaceuticals | PNK | OTC Bulletin Board | Germany | NaN | Darmstadt | 64293 | http://www.merckgroup.com | Large Cap | US5893392093 | 589339209 | NaN | NaN | NaN |
MKKGY | MERCK Kommanditgesellschaft auf Aktien | MERCK Kommanditgesellschaft auf Aktien operate... | USD | Health Care | Pharmaceuticals, Biotechnology & Life Sciences | Pharmaceuticals | PNK | OTC Bulletin Board | Germany | NaN | Darmstadt | 64293 | http://www.merckgroup.com | Large Cap | US5893392093 | 589339209 | NaN | NaN | NaN |
NGRRF | Nagarro SE | Nagarro SE, a digital engineering company, pro... | USD | Information Technology | Software & Services | IT Services | PNK | OTC Bulletin Board | Germany | NaN | Munich | 81677 | http://www.nagarro.com | Small Cap | DE000A3H2200 | NaN | NaN | NaN | NaN |
OSAGF | OSRAM Licht AG | OSRAM Licht AG provides various lighting produ... | USD | Industrials | Capital Goods | Electrical Equipment | PNK | OTC Bulletin Board | Germany | NaN | Munich | 80807 | http://www.osram-group.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
OSAGY | OSRAM Licht AG | OSRAM Licht AG provides various lighting produ... | USD | Industrials | Capital Goods | Electrical Equipment | PNK | OTC Bulletin Board | Germany | NaN | Munich | 80807 | http://www.osram-group.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
PSSWF | PSI Software AG | PSI Software AG develops and sells software sy... | USD | Information Technology | Software & Services | Software | PNK | OTC Bulletin Board | Germany | NaN | Berlin | 10178 | http://www.psi.de | Small Cap | DE000A0Z1JH9 | NaN | NaN | NaN | NaN |
SCFLF | Schaeffler AG | Schaeffler AG, together with its subsidiaries,... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Herzogenaurach | 91074 | http://www.schaeffler.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
SFFLY | Schaeffler AG | Schaeffler AG, together with its subsidiaries,... | USD | Consumer Discretionary | Automobiles & Components | Auto Components | PNK | OTC Bulletin Board | Germany | NaN | Herzogenaurach | 91074 | http://www.schaeffler.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
SGLFF | SGL Carbon SE | SGL Carbon SE engages in the manufacture and s... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Wiesbaden | 65201 | http://www.sglcarbon.com | Small Cap | NaN | NaN | NaN | NaN | NaN |
SLGRF | SLM Solutions Group AG | SLM Solutions Group AG provides metal-based ad... | USD | Industrials | Capital Goods | Machinery | PNK | OTC Bulletin Board | Germany | NaN | Lubeck | 23560 | http://www.slm-solutions.com | Small Cap | DE000A111338 | NaN | NaN | NaN | NaN |
SZGPY | Salzgitter AG | Salzgitter AG, together with its subsidiaries,... | USD | Materials | Materials | Metals & Mining | PNK | OTC Bulletin Board | Germany | NaN | Salzgitter | 38239 | http://www.salzgitter-ag.com | Small Cap | US7958422021 | 795842202 | NaN | NaN | NaN |
TKAMY | thyssenkrupp AG | thyssenkrupp AG operates in the areas of autom... | USD | Industrials | Capital Goods | Building Products | PNK | OTC Bulletin Board | Germany | NaN | Essen | 45143 | http://www.thyssenkrupp.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
TYEKF | thyssenkrupp AG | thyssenkrupp AG operates in the areas of autom... | USD | Industrials | Capital Goods | Building Products | PNK | OTC Bulletin Board | Germany | NaN | Essen | 45143 | http://www.thyssenkrupp.com | Mid Cap | NaN | NaN | NaN | NaN | NaN |
VIAO | VIA optronics AG | VIA optronics AG, through its subsidiary, VIA ... | USD | Information Technology | Technology Hardware & Equipment | Electronic Equipment, Instruments & Components | NYQ | New York Stock Exchange | Germany | NaN | Nuremberg | 90411 | http://www.via-optronics.com | Nano Cap | US91823Y1091 | 91823Y109 | BBG00X5F6PX0 | BBG00X5F6PS6 | BBG00X5F6QP7 |
VJET | voxeljet AG | voxeljet AG provides three-dimensional (3D) pr... | USD | Information Technology | Technology Hardware & Equipment | Technology Hardware, Storage & Peripherals | NCM | NASDAQ Capital Market | Germany | NaN | Friedberg | 86316 | http://www.voxeljet.com | Nano Cap | NaN | NaN | NaN | NaN | NaN |
WKCMF | Wacker Chemie AG | Wacker Chemie AG provides chemical products wo... | USD | Materials | Materials | Chemicals | PNK | OTC Bulletin Board | Germany | NaN | Munich | 81737 | http://www.wacker.com | Mid Cap | DE000WCH8881 | NaN | NaN | NaN | NaN |
# Collect all Moneymarkets
moneymarkets = fd.Moneymarkets()
# Search Multiple Columns
moneymarkets.search(name="treasury fund", market="us_market")
name | category | currency | market | exchange | |
---|---|---|---|---|---|
symbol | |||||
APGXX | U.S. Treasury Fund | Cavanal Hill Funds | USD | us_market | NAS |
APJXX | U.S. Treasury Fund | Cavanal Hill Funds | USD | us_market | NAS |
APKXX | U.S. Treasury Fund | Cavanal Hill Funds | USD | us_market | NAS |
APNXX | U.S. Treasury Fund | Cavanal Hill Funds | USD | us_market | NAS |
FDCXX | Treasury Fund | Fidelity Newbury Street Trust | USD | us_market | NAS |
FDUXX | Treasury Fund | Fidelity Newbury Street Trust | USD | us_market | NAS |
FSRXX | Treasury Fund | Fidelity Newbury Street Trust | USD | us_market | NAS |
FZFXX | Treasury Fund | Fidelity Newbury Street Trust | USD | us_market | NAS |
TRIXX | Treasury Fund | State Street Institutional Investment Trust | USD | us_market | NAS |
TRVXX | Treasury Fund | State Street Institutional Investment Trust | USD | us_market | NAS |
# Collect all Funds
funds = fd.Funds()
# Search Multiple Columns
funds.search(category_group="Fixed Income", category="Bonds", summary="United States")
name | currency | summary | manager_name | manager_bio | category_group | category | family | exchange | market | |
---|---|---|---|---|---|---|---|---|---|---|
symbol | ||||||||||
ACASX | Access Capital Community Investment Fund Class A | USD | The investment seeks to invest in geographical... | Brian Svendahl | Brian Svendahl oversees our fixed income resea... | Fixed Income | Bonds | RBC Global Asset Management | NAS | us_market |
ACATX | Access Capital Community Investment Fund Class IS | USD | The investment seeks to invest in geographical... | Brian Svendahl | Brian Svendahl oversees our fixed income resea... | Fixed Income | Bonds | RBC Global Asset Management | NAS | us_market |
ACCSX | Access Capital Community Investment Fund Insti... | USD | The investment seeks to invest in geographical... | Brian Svendahl | Brian Svendahl oversees our fixed income resea... | Fixed Income | Bonds | RBC Global Asset Management | NAS | us_market |
AHIFX | American Funds American High-Income Trust Clas... | USD | The investment seeks to provide a high level o... | David A. Daigle | David A. Daigle is a fixed-income portfolio ma... | Fixed Income | Bonds | American Investments | NAS | us_market |
AHITX | American Funds American High-Income Trust Class A | USD | The investment seeks to provide a high level o... | David A. Daigle | David A. Daigle is a fixed-income portfolio ma... | Fixed Income | Bonds | American Investments | NAS | us_market |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
VUSCX | Invesco Quality Income Fund Class C | USD | The investment seeks a high level of current i... | Clint P. Dudley | Clint Dudley is a Senior Portfolio Manager for... | Fixed Income | Bonds | Invesco Asset Management | NAS | us_market |
VUSIX | Invesco Quality Income Fund Class Y | USD | The investment seeks a high level of current i... | Clint P. Dudley | Clint Dudley is a Senior Portfolio Manager for... | Fixed Income | Bonds | Invesco Asset Management | NAS | us_market |
VUSJX | Invesco Quality Income Fund R5 Class | USD | The investment seeks a high level of current i... | Clint P. Dudley | Clint Dudley is a Senior Portfolio Manager for... | Fixed Income | Bonds | Invesco Asset Management | NAS | us_market |
VUSRX | Invesco Quality Income Fund Class R | USD | The investment seeks a high level of current i... | Clint P. Dudley | Clint Dudley is a Senior Portfolio Manager for... | Fixed Income | Bonds | Invesco Asset Management | NAS | us_market |
VUSSX | Invesco Quality Income Fund Class R6 | USD | The investment seeks a high level of current i... | Clint P. Dudley | Clint Dudley is a Senior Portfolio Manager for... | Fixed Income | Bonds | Invesco Asset Management | NAS | us_market |
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