Company Information
Below, you'll find a list of endpoints dedicated to company information. Each endpoint generates a Pandas DataFrame. The references for the output are displayed in the command line interface. For a more user-friendly view of the output, you can consult the Demo Notebook.
Company Profile
A summary of important company information, including price, beta, market capitalization,description, headquarters, and more
company_profile(symbol='AAPL', api_key=TOKEN)
Output
>>> company_profile(symbol='AAPL', api_key=TOKEN)
symbol price beta volAvg mktCap ... defaultImage isEtf isActivelyTrading isAdr isFund
0 AAPL 180.71 1.286802 58009821 2825256201042 ... False False True False False
[1 rows x 36 columns]
Excecutive Compensation
executive_compensation(symbol='AAPL', api_key=TOKEN)
Output
>>> executive_compensation(symbol='AAPL', api_key=TOKEN).head()
cik symbol companyName ... all_other_compensation total url
0 0000320193 AAPL Apple Inc. ... 17137 26251824 https://www.sec.gov/Archives/edgar/data/320193...
1 0000320193 AAPL Apple Inc. ... 18337 27150352 https://www.sec.gov/Archives/edgar/data/320193...
2 0000320193 AAPL Apple Inc. ... 37684 26272371 https://www.sec.gov/Archives/edgar/data/320193...
3 0000320193 AAPL Apple Inc. ... 61191 27020811 https://www.sec.gov/Archives/edgar/data/320193...
4 0000320193 AAPL Apple Inc. ... 19783 27151798 https://www.sec.gov/Archives/edgar/data/320193...
[5 rows x 15 columns]
Compensation Benchmark
compensation_benchmark(year='2022', api_key=TOKEN)
Output
>>> compensation_benchmark(year='2022', api_key=TOKEN)
industryTitle year averageCompensation
0 ADHESIVES & SEALANTS 2022 4.262500e+05
1 AGRICULTURAL PROD-LIVESTOCK & ANIMAL SPECIALTIES 2022 3.123146e+05
2 AIR COURIER SERVICES 2022 7.712610e+05
3 AIRCRAFT 2022 1.964232e+06
4 AIRCRAFT & PARTS 2022 5.297654e+05
.. ... ... ...
133 WHOLESALE-LUMBER & OTHER CONSTRUCTION MATERIALS 2022 5.684227e+05
134 WHOLESALE-MACHINERY, EQUIPMENT & SUPPLIES 2022 5.230000e+05
135 WHOLESALE-MEDICAL, DENTAL & HOSPITAL EQUIPMENT... 2022 5.640770e+05
136 WHOLESALE-MISCELLANEOUS NONDURABLE GOODS 2022 2.423333e+05
137 WOOD HOUSEHOLD FURNITURE, (NO UPHOLSTERED) 2022 4.471890e+05
[138 rows x 3 columns]
Company Notes
company_notes(symbol='AAPL', api_key=TOKEN)
Output
>>> company_notes(symbol='AAPL', api_key=TOKEN)
cik symbol title exchange
0 0000320193 AAPL 1.000% Notes due 2022 NASDAQ
1 0000320193 AAPL 1.375% Notes due 2024 NASDAQ
2 0000320193 AAPL 0.000% Notes due 2025 NASDAQ
3 0000320193 AAPL 0.875% Notes due 2025 NASDAQ
4 0000320193 AAPL 1.625% Notes due 2026 NASDAQ
5 0000320193 AAPL 2.000% Notes due 2027 NASDAQ
6 0000320193 AAPL 1.375% Notes due 2029 NASDAQ
7 0000320193 AAPL 3.050% Notes due 2029 NASDAQ
8 0000320193 AAPL 0.500% Notes due 2031 NASDAQ
9 0000320193 AAPL 3.600% Notes due 2042 NASDAQ
Employee Count
employee_count(symbol='AAPL', api_key=TOKEN)
Output
>>> employee_count(symbol='AAPL', api_key=TOKEN).head()
symbol cik acceptanceTime ... filingDate employeeCount source
0 AAPL 0000320193 2022-10-27 18:01:14 ... 2022-10-28 164000 https://www.sec.gov/Archives/edgar/data/320193...
1 AAPL 0000320193 2021-10-28 18:04:28 ... 2021-10-29 154000 https://www.sec.gov/Archives/edgar/data/320193...
2 AAPL 0000320193 2020-10-29 18:06:25 ... 2020-10-30 147000 https://www.sec.gov/Archives/edgar/data/320193...
3 AAPL 0000320193 2019-10-30 18:12:36 ... 2019-10-31 137000 https://www.sec.gov/Archives/edgar/data/320193...
4 AAPL 0000320193 2018-11-05 08:01:40 ... 2018-11-05 132000 https://www.sec.gov/Archives/edgar/data/320193...
[5 rows x 9 columns]
Screener (Stock)
The simplest way to get the list of stocks that meet your criteria. You can query all data without providing any query parameters, default limit is 1000 or add query parameters to filter the results.
stock_screener(api_key=TOKEN)
Output
>>> stock_screener(api_key=TOKEN)
symbol companyName marketCap ... country isEtf isActivelyTrading
0 WEQ.TO WEQ Holdings Inc. 1534332425003000 ... None False False
1 TAGS Teucrium Agricultural Fund 41128965220000 ... US True True
2 CORN Teucrium Corn Fund 30345394100000 ... US True True
3 CANE Teucrium Sugar Fund 19628224280000 ... US True True
4 KITTW Nauticus Robotics, Inc. 9359396897200 ... US False True
.. ... ... ... ... ... ... ...
995 UMC United Microelectronics Corporation 18029326461 ... TW False True
996 AIU Meta Data Limited 18008100864 ... CN False True
997 GLE.PA Société Générale Société anonyme 17855763569 ... FR False True
998 HRL Hormel Foods Corporation 17853535054 ... US False True
999 EQT EQT Corporation 17852666023 ... US False True
[1000 rows x 14 columns]
Here is an example of adding query parameters to filter the results, for example:
stock_screener(api_key=TOKEN, limit=2000, country='US', sector='Consumer Cyclical')
Output
>>> stock_screener(api_key=TOKEN, limit=2000, country='US', sector='Consumer Cyclical')
symbol companyName marketCap ... country isEtf isActivelyTrading
0 M&M.NS Mahindra & Mahindra Limited 1948176249434 ... US False True
1 AMZN.NE Amazon.com, Inc. 1850806590395 ... US False True
2 AMZN Amazon.com, Inc. 1365354533284 ... US False True
3 AMZ.DE Amazon.com, Inc. 1306233536717 ... US False True
4 CCL.L Carnival Corporation & plc 1209229705104 ... US False True
.. ... ... ... ... ... ... ...
896 1592.TW Enterex International Limited 0 ... US False False
897 138250.KS NS Shopping Co., Ltd 0 ... US False False
898 0RT7.L Tenneco Inc. 0 ... US False False
899 0R16.L Mcdonald's Corp 0 ... US False False
900 0QZH.L Starbucks Corp 0 ... US False False
[901 rows x 14 columns]
Please check all available query parameters below:
| Query Parameter | Type | Example |
|---|---|---|
| marketCapMoreThan | number | 1000000000 |
| marketCapLowerThan | number | 1000000000 |
| priceMoreThan | number | 100 |
| priceLowerThan | number | 100 |
| betaMoreThan | number | 1 |
| betaLowerThan | number | 1 |
| volumeMoreThan | number | 10000 |
| volumeLowerThan | number | 10000 |
| dividendMoreThan | number | 1 |
| dividendLowerThan | number | 1 |
| isEtf | Boolean | true |
| isActivelyTrading | Boolean | true |
| sector | String | Consumer Cyclical, Energy, Technology, Industrials, Financial Services, Basic Materials, Communication Services, Consumer Defensive, Healthcare, Real Estate, Utilities, Industrial Goods, Financial, Services, Conglomerates |
| Industry | String | Autos, Banks, Banks Diversified, Software, Banks Regional, Beverages Alcoholic, Beverages Brewers, Beverages Non-Alcoholic, .. |
| Country | String | US, UK, MX, BR, RU, HK, CA, .. |
| exchange | String | nyse, nasdaq, amex, euronext, tsx, etf, mutual_fund, .. |
| limit | number | 10 |
Stock Grade
stock_grade(symbol='AAPL', limit=500, api_key=TOKEN)
Output
>>> stock_grade(symbol='AAPL', limit=500, api_key=TOKEN)
symbol date gradingCompany previousGrade newGrade
0 AAPL 2023-08-17 Wedbush Outperform Outperform
1 AAPL 2023-08-16 Wedbush Outperform Outperform
2 AAPL 2023-08-04 Raymond James Outperform Outperform
3 AAPL 2023-08-04 Barclays Equal-Weight Equal Weight
4 AAPL 2023-08-04 Needham Buy Buy
.. ... ... ... ... ...
495 AAPL 2020-12-15 Cascend Buy Buy
496 AAPL 2020-12-14 Cascend Buy Buy
497 AAPL 2020-12-13 Wells Fargo Overweight Overweight
498 AAPL 2020-12-09 Wedbush Outperform Outperform
499 AAPL 2020-12-08 Wedbush Outperform Outperform
[500 rows x 5 columns]
Executives
company_executives(symbol='AAPL', api_key=TOKEN)
Output
>>> company_executives(symbol='AAPL', api_key=TOKEN)
title name ... titleSince symbol
0 Senior Vice President of People & Retail Ms. Deirdre O'Brien ... 1.676249e+09 AAPL
1 Chief Executive Officer & Director Mr. Timothy D. Cook ... NaN AAPL
2 Chief Financial Officer & Senior Vice President Mr. Luca Maestri ... NaN AAPL
3 Chief Operating Officer Mr. Jeffrey E. Williams ... NaN AAPL
4 Senior Vice President, Gen. Counsel & Sec. Ms. Katherine L. Adams ... NaN AAPL
5 Senior Vice President of Retail Ms. Deirdre O'Brien ... NaN AAPL
6 Senior Director of Corporation Accounting Mr. Chris Kondo ... NaN AAPL
7 Chief Technology Officer Mr. James Wilson ... NaN AAPL
8 Chief Information Officer Ms. Mary Demby ... NaN AAPL
9 Senior Director of Investor Relations & Treasury Ms. Nancy Paxton ... NaN AAPL
10 Senior Vice President of Worldwide Marketing Mr. Greg Joswiak ... NaN AAPL
[11 rows x 8 columns]
Company Core Information Summary
company_core_info(symbol='AAPL', api_key=TOKEN)
Output
>>> company_core_info(symbol='AAPL', api_key=TOKEN)
cik symbol exchange ... mailingAddress taxIdentificationNumber registrantName
0 0000320193 AAPL NASDAQ ... ONE APPLE PARK WAY,CUPERTINO CA 95014 94-2404110 Apple Inc.
[1 rows x 13 columns]
Market Cap
market_cap(symbol='AAPL', api_key=TOKEN)
Output
>>> market_cap(symbol='AAPL', api_key=TOKEN)
symbol date marketCap
0 AAPL 2023-10-12 2825256201042
History Market Cap
historical_market_cap(symbol='AAPL', limit=100, api_key=TOKEN)
Output
>>> historical_market_cap(symbol='AAPL', limit=100, api_key=TOKEN)
symbol date marketCap
0 AAPL 2023-10-12 2836715825940
1 AAPL 2023-10-11 2822430997200
2 AAPL 2023-10-10 2800297361460
3 AAPL 2023-10-09 2809715929860
4 AAPL 2023-10-06 2786169508860
.. ... ... ...
95 AAPL 2023-05-26 2753832424020
96 AAPL 2023-05-25 2715530245860
97 AAPL 2023-05-24 2697477989760
98 AAPL 2023-05-23 2693082657840
99 AAPL 2023-05-22 2734524358800
[100 rows x 3 columns]
All Countries
available_countries(api_key=TOKEN)
Output
>>> available_countries(api_key=TOKEN)
0
0 FK
1 RU
2 DK
3 SN
4 SI
.. ..
109 IS
110 CR
111 MK
112 KH
113 GR
[114 rows x 1 columns]
Company Rating
company_rating(symbol='AAPL', api_key=TOKEN)
Output
>>> company_rating(symbol='AAPL', api_key=TOKEN)
symbol date rating ... ratingDetailsPERecommendation ratingDetailsPBScore ratingDetailsPBRecommendation
0 AAPL 2023-10-12 S ... Strong Buy 5 Strong Buy
[1 rows x 17 columns]
Historical Rating
historical_rating(symbol='AAPL', api_key=TOKEN)
Output
>>> historical_rating(symbol='AAPL', api_key=TOKEN)
symbol date rating ... ratingDetailsPERecommendation ratingDetailsPBScore ratingDetailsPBRecommendation
0 AAPL 2023-10-12 S ... Strong Buy 5 Strong Buy
1 AAPL 2023-10-11 S ... Strong Buy 5 Strong Buy
2 AAPL 2023-10-10 S ... Strong Buy 5 Strong Buy
3 AAPL 2023-10-09 S ... Strong Buy 5 Strong Buy
4 AAPL 2023-10-06 S ... Strong Buy 5 Strong Buy
... ... ... ... ... ... ... ...
5573 AAPL 2001-07-09 B+ ... Strong Sell 3 Neutral
5574 AAPL 2001-07-06 B+ ... Strong Sell 3 Neutral
5575 AAPL 2001-07-05 B+ ... Strong Sell 3 Neutral
5576 AAPL 2001-07-03 B+ ... Strong Sell 3 Neutral
5577 AAPL 2001-07-02 B+ ... Strong Sell 3 Neutral
[5578 rows x 17 columns]
Analyst Estimates
analyst_estimate(symbol='AAPL', api_key=TOKEN)
Output
>>> analyst_estimate(symbol='AAPL', api_key=TOKEN).head()
symbol date estimatedRevenueLow ... estimatedEpsLow numberAnalystEstimatedRevenue numberAnalystsEstimatedEps
0 AAPL 2024-12-30 2.936333e+11 ... 5.008968 10
10
1 AAPL 2023-12-31 3.387104e+11 ... 4.810000 12
12
2 AAPL 2022-12-31 3.079185e+11 ... 4.370000 16
16
3 AAPL 2021-12-31 2.987380e+11 ... 3.400000 14
14
4 AAPL 2020-12-31 1.999940e+11 ... 2.130000 13
13
[5 rows x 22 columns]
Optional: You can spefify the period and limit of the analyst estimates as query parameters, for example:
analyst_estimate(symbol='AAPL', api_key=token, period='quarter')
Where period can be either 'quarter' or 'annual' and limit is the number of analyst estimates you want to get.
Output
>>> analyst_estimate(symbol='AAPL', api_key=TOKEN, period='quarter').head()
symbol date estimatedRevenueLow ... estimatedEpsLow numberAnalystEstimatedRevenue numberAnalystsEstimatedEps
0 AAPL 2025-03-29 1.048101e+11 ... 1.476591 18
18
1 AAPL 2024-12-30 4.885256e+10 ... 0.778242 18
18
2 AAPL 2024-09-29 7.006572e+10 ... 1.209069 14
14
3 AAPL 2024-06-29 5.875709e+10 ... 0.994242 20
20
4 AAPL 2024-03-31 8.823863e+10 ... 1.300000 20
20
[5 rows x 22 columns]
Analyst Recommendation
analyst_recommendation(symbol='AAPL', api_key=TOKEN)
Output
>>> analyst_recommendation(symbol='AAPL', api_key=TOKEN).head()
symbol date analystRatingsbuy ... analystRatingsSell analystRatingsStrongSell analystRatingsStrongBuy
0 AAPL 2023-08-01 21 ... 0 0 11
1 AAPL 2023-07-01 21 ... 1 0 11
2 AAPL 2023-06-01 20 ... 1 0 10
3 AAPL 2023-05-01 24 ... 1 0 10
4 AAPL 2023-04-01 24 ... 1 0 10
[5 rows x 7 columns]
Company Outlook
company_outlook(symbol='AAPL', api_key=TOKEN)
Output
>>> company_outlook(symbol='AAPL', api_key=TOKEN)
ratios ... symbol
0 [{'dividendYielTTM': 0.005201704388246361, 'di... ... AAPL
[1 rows x 54 columns]
Stock Peers
stock_peer(symbol='AAPL', api_key=TOKEN)
Output
>>> stock_peer(symbol='AAPL', api_key=TOKEN)
symbol peersList
0 AAPL [LPL, SNEJF, PCRFY, SONO, VZIO, MICS, WLDSW, K...
Market Open
is_market_open(api_key=TOKEN)
Output
>>> is_market_open(api_key=TOKEN)
stockExchangeName ... stockMarketHours.closingHour
0 New York Stock Exchange ... 04:00 p.m. ET
[1 rows x 8 columns]
Delisted Companies
delisted_company(api_key=TOKEN)
Output:
>>> delisted_company(api_key=TOKEN)
symbol companyName exchange ipoDate delistedDate
0 ASPY ASYMshares ASYMmetric S&P 500 ETF AMEX 2021-03-10 2023-10-12
1 ZSPY ASYMmetric Smart Alpha S&P 500 ETF AMEX 2023-02-01 2023-10-11
2 LFACW LF Capital Acquisition Corp. II NASDAQ 2018-06-29 2023-10-11
3 JIDA JPMorgan ActiveBuilders International Equity ETF AMEX 2021-07-08 2023-10-10
4 JUN Juniper II Corp. NYSE 2021-12-23 2023-10-09
.. ... ... ... ... ...
95 LCI Lannett Company, Inc. NYSE 1997-01-02 2023-06-16
96 MLAC Malacca Straits Acquisition Company Limited NASDAQ 2020-08-14 2023-06-16
97 HCNEW JAWS Hurricane Acquisition Corporation NASDAQ 2021-08-05 2023-06-16
98 HCNEU JAWS Hurricane Acquisition Corporation NASDAQ 2021-06-11 2023-06-16
99 FIHD UBS AG FI Enhanced Global High Yield ETN AMEX 2016-02-22 2023-06-15
[100 rows x 5 columns]