* move old example notebooks to v3 folder, update loadHistoricalPriceData and add findSymbols * Create README.md * Update README.md * Update README.md * Update README.md * Vertical diagram instead * important classes * core dependencies * folder structure * devtools * swap order * fix github pages deploy issue * development index * delete expectations for developers * devtools * obbject stuff * add * installation * hub sync * idk if i committed already * add usage page for financial statements * add link to reference page * syntax * numbers * nearly --> more than * easier to read code blocks * codespell * removing mention to provider * add 'orientation' for to_dict * info card for output user preference * put it in a code block * correct user_settings.json example * syntax corrections * fix sentence * get rid of floating point mathc * get rid of other floating point math * add companion example notebook for financial statements, and new section * historical prices usage page * codespell * add commitment of traders section to usage * add a note * codespell * get rid of equals sign * Cleanup docs. with @the-praxs * install * obbject extend * spell * HeadTitle thingies * Update obbject_extensions.md * Update obbject_extensions.md * typo * Update architectural_considerations.md * point to the TET pattern * optional vs required * reviewing the patterns. removed the --pre flag under the assumption this will be the final docs for the release cc @deeleeramone * add a walkthrough * spelling * Add more in the introduction * validator examples * update Copper-to-Gold notebook example for V4 * Update README.md * QA Caveats w @the-praxs * info on class outputs * merge add data sources and add data points * disclaimer on cookiecutter * add missing import to example * slight syntax change * missing code block * missing import * update intrinio example syntax * intrinio syntax update * urls * core and provider merged * example syntax * grammar * Update API banner link * quick-fix-docs cc @minhhoang1023 * Update pip install command for v3 * remove --pre note block * update landing image --------- Co-authored-by: Henrique Joaquim <h.joaquim@campus.fct.unl.pt> Co-authored-by: Luqman <luqazino@gmail.com> Co-authored-by: James Maslek <jmaslek11@gmail.com> Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: Pratyush Shukla <ps4534@nyu.edu>
16 KiB
Vendored
title, sidebar_position, description, keywords
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| Finding Symbols | 3 | This page provides comprehensive information about finding stocks in the with the OpenBB Platform. Search companies from different sources, and filter results. This guide is intended to introduce some methods for searching, screening, and discovery. |
|
import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
Finding the ticker symbol, security identifier, the sector, and other metadata is easy if you know where to look. This guide is intended to introduce some methods for searching, screening, and discovery.
:::note
For maximum coverage and functionality, install OpenBB with [all] packages.
The examples on this page will assume that the OpenBB Platform has been installed, the environment is active, and it has been imported into a Python session.
from openbb import obb
If the installation is fresh, or an extension was just installed, the Python interface will need to be rebuilt. It will only take a few moments to complete. :::
The simplest way to find a ticker for a company is with a simple fuzzy query.
Search the SEC
Perform a quick search using the openbb-sec provider.
Find a Company
Use an empty string, "", to return the complete list - over 10,000.
all_companies = obb.equity.search("", provider="sec")
len(all_companies.results)
Out: 10840
The SEC sorts this list by market cap. Applying the to_df() method to all_companies will show Apple on top
all_companies.to_df().head(10)
| symbol | name | cik |
|---|---|---|
| AAPL | Apple Inc. | 320193 |
| MSFT | MICROSOFT CORP | 789019 |
| GOOGL | Alphabet Inc. | 1652044 |
| AMZN | AMAZON COM INC | 1018724 |
| NVDA | NVIDIA CORP | 1045810 |
| META | Meta Platforms, Inc. | 1326801 |
| BRK-B | BERKSHIRE HATHAWAY INC | 1067983 |
| TSLA | Tesla, Inc. | 1318605 |
| LLY | ELI LILLY & Co | 59478 |
| V | VISA INC. | 1403161 |
:::tip This endpoint can be used to map a ticker symbol to a CIK number. :::
Find an Institution
Some reporting companies, like investment trusts and insurance companies, do not have a ticker symbol directly associated with them. Filers in the US will have a CIK number, used to retrieve documents from the SEC.
obb.regulators.sec.institutions_search("Berkshire Hathaway").to_df()
| name | cik |
|---|---|
| BERKSHIRE HATHAWAY ENERGY CO | 0001081316 |
| BERKSHIRE HATHAWAY FINANCE CORP | 0001274791 |
| BERKSHIRE HATHAWAY HOME STATE INSURANCE CO. | 0000829771 |
| BERKSHIRE HATHAWAY INC /DE/ | 0000109694 |
| BERKSHIRE HATHAWAY INC/DE | 0000109694 |
| BERKSHIRE HATHAWAY INC | 0001067983 |
| BERKSHIRE HATHAWAY LIFE INSURANCE CO OF NEBRASKA | 0001015867 |
| LMZ & BERKSHIRE HATHAWAY CO | 0001652795 |
Find a Filing
Search for filings by CIK or ticker symbol.
homestate_filings = obb.equity.fundamental.filings(cik="0000829771", provider="sec")
homestate_filings.to_df().iloc[-1]
| 2023-11-14 | |
|---|---|
| type | 13F-NT |
| link | https://www.sec.gov/Archives/edgar/data/0000829771/000095012323010929/xslForm13F_X02/primary_doc.xml |
| report_date | 2023-09-30 |
| accepted_date | 2023-11-14 16:15:06+00:00 |
| act | 34 |
| primary_doc_description | |
| primary_doc | xslForm13F_X02/primary_doc.xml |
| accession_number | 0000950123-23-010929 |
| file_number | 028-02226 |
| film_number | 231406391 |
| is_inline_xbrl | 0 |
| is_xbrl | 0 |
| size | 2960 |
| complete_submission_url | https://www.sec.gov/Archives/edgar/data/0000829771/0000950123-23-010929.txt |
| filing_detail_url | https://www.sec.gov/Archives/edgar/data/0000829771/0000950123-23-010929-index.htm |
Or, search by form type.
obb.equity.fundamental.filings("AAPL", type="4", provider="sec").to_df().iloc[-1]
| 2023-11-14 | |
|---|---|
| type | 4 |
| link | https://www.sec.gov/Archives/edgar/data/0000320193/000032019323000109/xslF345X05/wk-form4_1700004649.xml |
| report_date | 2023-11-10 |
| accepted_date | 2023-11-14 18:31:09+00:00 |
| primary_doc_description | FORM 4 |
| primary_doc | xslF345X05/wk-form4_1700004649.xml |
| accession_number | 0000320193-23-000109 |
| is_inline_xbrl | 0 |
| is_xbrl | 0 |
| size | 5066 |
| complete_submission_url | https://www.sec.gov/Archives/edgar/data/0000320193/0000320193-23-000109.txt |
| filing_detail_url | https://www.sec.gov/Archives/edgar/data/0000320193/0000320193-23-000109-index.htm |
Screen Markets
Screeners provide a targeted search, a tool for comparison and discovery. Find stocks from around the world with the screener endpoint, and the openbb-fmp provider.
Find Stocks From India
results = obb.equity.screener(country="IN", provider="fmp").to_df()
len(results)
Out: 1821
Search by Sector
results = obb.equity.screener(country="IN", sector="Financial Services", provider="fmp").to_df()
len(results)
Out: 190
results.iloc[0]
| symbol | HDFCBANK.NS |
|---|---|
| name | HDFC Bank Limited |
| market_cap | 11344796293939 |
| sector | Financial Services |
| industry | Banks—Regional |
| beta | 0.714285 |
| price | 1505.1 |
| last_annual_dividend | 19.0 |
| volume | 11850413 |
| exchange | NSE |
| exchange_name | National Stock Exchange of India |
| country | IN |
| is_etf | False |
| actively_trading | True |
Search by Industry
results = obb.equity.screener(country="IN", industry="manufacturing").to_df()
len(results)
Out: 119
results.iloc[0]
| symbol | PAGEIND.NS |
|---|---|
| name | Page Industries Limited |
| market_cap | 418222172840 |
| sector | Consumer Cyclical |
| industry | Apparel Manufacturing |
| beta | 0.462 |
| price | 37495.6 |
| last_annual_dividend | 300.0 |
| volume | 12166 |
| exchange | NSE |
| exchange_name | National Stock Exchange of India |
| country | IN |
| is_etf | False |
| actively_trading | True |
Search by Exchange
Some countries, like America, have multiple exchanges. Narrow the search by combining two or more parameters. The example below finds the companies listed on the American Stock Exchange (AMEX) that are domiciled in China.
obb.equity.screener(exchange="amex", country="CN").to_df()
| symbol | name | market_cap | sector | industry | beta | price | volume | exchange | exchange_name | country | is_etf | actively_trading |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AMBO | Ambow Education Holding Ltd. | 4041842 | Consumer Defensive | Education & Training Services | 0.448 | 0.1425 | 203994 | AMEX | American Stock Exchange | CN | False | True |
| ITP | IT Tech Packaging, Inc. | 2945282 | Basic Materials | Paper & Paper Products | -0.1 | 0.2926 | 14954 | AMEX | American Stock Exchange | CN | False | True |
| DXF | Dunxin Financial Holdings Limited | 1291625 | Financial Services | Credit Services | 1.632 | 0.325 | 2829238 | AMEX | American Stock Exchange | CN | False | True |
| CPHI | China Pharma Holdings, Inc. | 1024379 | Healthcare | Drug Manufacturers—Specialty & Generic | 0.875 | 0.0896 | 539863 | AMEX | American Stock Exchange | CN | False | True |
Filter by Metric
Applying some filters refines and targets the search. The example below finds listing on the NYSE domiciled in the USA, with a market cap between $100-300 billion, and exhibiting a beta value of less than 0.5
obb.equity.screener(
exchange="nyse",
mktcap_min=100000000000,
mktcap_max=300000000000,
country="us",
beta_max=0.5,
provider="fmp",
).to_df()
| symbol | name | market_cap | sector | industry | beta | price | last_annual_dividend | volume | exchange | exchange_name | country | is_etf | actively_trading |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MRK | Merck & Co., Inc. | 258192673024 | Healthcare | Drug Manufacturers—General | 0.375 | 101.75 | 2.92 | 6760568 | NYSE | New York Stock Exchange | US | False | True |
| VZ | Verizon Communications Inc. | 152314546478 | Communication Services | Telecom Services | 0.391 | 36.23 | 2.66 | 14960968 | NYSE | New York Stock Exchange | US | False | True |
Find an Index
List all indices from a source with:
indices = obb.index.available(provider="yfinance").to_df()
len(indices)
Out: 274
Filter the list down by querying the DataFrame.
indices[indices["name"].str.contains("ASX 200")]
| name | code | symbol |
|---|---|---|
| S&P/ASX 200 Index (AUD) | au_asx200 | ^AXJO |
| S&P/ASX 200 Energy Sector Index (AUD) | au_energy | ^AXEJ |
| S&P/ASX 200 Resources Sector Index (AUD) | au_resources | ^AXJR |
| S&P/ASX 200 Materials Sector Index (AUD) | au_materials | ^AXMJ |
| S&P/ASX 200 Industrials Sector Index (AUD) | au_industrials | ^AXNJ |
| S&P/ASX 200 Consumer Discretionary Sector Index (AUD) | au_discretionary | ^AXDJ |
| S&P/ASX 200 Consumer Staples Sector Index (AUD) | au_staples | ^AXSJ |
| S&P/ASX 200 Health Care Sector Index (AUD) | au_health | ^AXHJ |
| S&P/ASX 200 Financials Sector Index (AUD) | au_financials | ^AXFJ |
| S&P/ASX 200 A-REIT Industry Index (AUD) | au_reit | ^AXPJ |
| S&P/ASX 200 Info Tech Sector Index (AUD) | au_tech | ^AXIJ |
| S&P/ASX 200 Communications Sector Index (AUD) | au_communications | ^AXTJ |
| S&P/ASX 200 Utilities Sector Index (AUD) | au_utilities | ^AXUJ |
:::tip
With the openbb-yfinance extension, index time series can be loaded using the ticker symbol or short code. Non-American indices have a code beginning with the two-letter country code.
(
obb.index.market("au_utilities", provider="yfinance").to_df().tail(1)
== obb.index.market("^AXUJ", provider="yfinance").to_df().tail(1)
)
| date | open | high | low | close | volume |
|---|---|---|---|---|---|
| 2023-11-17 | True | True | True | True | True |
:::
The examples above show demonstrate the most basic ways to find ticker symbols with the OpenBB Platform. Create your own custom scripts for discovery by combining these with other methods.