EDGAR

Python tool to search and retrieve corporate and financial data from the United States Securities and Exchange Commission (SEC).

What is EDGAR?

EDGAR is a database of corporate filings maintained by the SEC. These filings contain a wealth of quantitative and qualitative information on every legal entity that issues non-exempt securities in the United States. Whether you are looking to study the fundamentals of your favorite stocks, or to track the corporate webs weaved by a person or company of interest, EDGAR is the place to do it.

This tool was initially developed as part of the Bellingcat Tech Fellowship program, we hope it helps you utilise this incredible, free resource.

Installation 🪄

You can install this tool directly from the official PyPi release.

pip install edgar-tool

Usage - Text Search 🔎

What is the text search tool?

If you're interested in finding all the documents mentioning a certain person, company or phrase in the EDGAR database, you can do that via the full text search page

It isn't always easy to get the information you might need from the SEC, so this Python tool lets you download the search results to a file without having to go through all the pages of results by hand.

This is a command line tool that takes a search query, queries a server, and downloads the search results into a CSV file that can be opened in a spreadsheet program (such as Excel).

Examples

# Display help message describing all supported arguments along with their usage, aliases and eventual default values (type q to exit)
edgar-tool text_search --help

# Basic usage (defaults to searching the last 5 years of records)
edgar-tool text_search John Doe

# Basic usage with a combination of exact and partial search parameters
edgar-tool text_search \"John Doe\" Pharmaceuticals Chemicals

# Usage with date range and export to custom CSV file
edgar-tool text_search Tsunami Hazards --start_date "2021-01-01" --end_date "2021-12-31" --output "results.csv"

# Usage with a partial set of filing forms + single forms 
edgar-tool text_search Hurricane Damage --filing_form "registration_statements" --single_forms "['1-K', '1-SA']"

# Usage specifying the location of incorporation
edgar-tool text_search oil --inc_in "Egypt"

# More advanced usage specifying more arguments, with export to JSON
edgar-tool text_search Volcano Monitoring --start_date "2021-01-01" --end_date "2021-12-31" --output "results.json"\
          --filing_form "all_annual_quarterly_and_current_reports" --entity_id "0001030717" \
          --min_wait 5.0 --max_wait 7.0 --retries 3
          
# Using aliases where supported and exporting to JSONLines
edgar-tool text_search Calabarzon -s "2021-01-01" -o "results.jsonl" -f "all_annual_quarterly_and_current_reports" -r 3 -h

> [!WARNING] > Combining text search parameters with entity_id parameter seems to increase the risk of failed requests on the SEC page due to an apparent bug, we recommend to either avoid doing so (you can specify an empty string for search keywords using "" and use only entity ID) or setting the number of retries accordingly if you do so.

Detailed Feature Information

Usage - RSS Feed 📇

What is the RSS feed customized retrieval tool ?

The SEC publish a live feed of filings and this part of the tool lets you monitor particular tickers for new filings, so you can get to-the-minute updates.

The output is a CSV file containing the company and filings' metadata, which can be opened in a spreadsheet program (such as Excel).

Examples

# Display help message describing all supported arguments along with their usage, aliases and eventual default values (type q to exit)
edgar-tool rss --help

# Basic one-off usage with export to CSV
edgar-tool rss "GOOG" --output "rss_feed.csv"

# Periodic usage specifying 10 minutes interval duration, with export to JSON
edgar-tool rss "AAPL" "GOOG" "MSFT" --output "rss_feed.json" --every_n_mins 10

# Same example as above, using aliases and exporting to JSONLines (.jsonl)
edgar-tool rss "AAPL" "GOOG" "MSFT" -o "rss_feed.jsonl" -e 10

Detailed Feature Information

Table of Cleaned Financial Data 🏦

There is also a table of data containing most income statements, balance sheets, and cash flow statements for every company traded publicly in the U.S.

This table is updated intermittently and is available here for download as a .CSV file. You can open this file in Excel, use it as a data source for your own code, or use the simple Python script to access time series for the desired data points.

The quality of any programmatically produced financial dataset is not going to be as accurate or as complete as a S&P Global or Bloomberg subscription. It should, however, be of comparable accuracy to what you can find on Yahoo Finance and spans a wider time frame.

George Dyer, the former Bellingcat tech fellow who developed the first version of this tool, describes it as: "good enough use in projects such as Market Inference and Graham".

Please report any inconsistencies in the data to George and he will do his best to refine the existing method.

Development :octocat:

# Clone the repository
git clone https://github.com/bellingcat/EDGAR.git

# Change directory to the project folder
cd EDGAR

# Install poetry if you haven't already
pip install poetry

# Install dependencies
poetry install

# Run the tool
poetry run edgar-tool --help

# Run unit tests using your Poetry environment's Python interpreter
poetry run pytest

# Run unit tests with tox
poetry run tox -- run-parallel