Tired of sifting through endless financial reports of 100+ pages, struggling to extract meaningful insights?

📊 EDGAR-CRAWLER is an open-source & optimized toolkit that retrieves key information from financial reports. It can crawl any report found in the SEC EDGAR database, the web repository for all publicly traded companies in the USA.

Most importantly, apart from downloading EDGAR filings like other standard toolkits, EDGAR-CRAWLER can also preprocess and convert them from lengthy and unstructured documents into clean and easy-to-use JSON files.

📥🕷️ Business Documents Crawling: Utilize the power of the edgar_crawler.py module to effortlessly crawl and download financial reports for every publicly-traded company within your specified years.

🔍📑 Item Extraction: Extract and clean specific text sections such as Risk Factors or Management's Discussion & Analysis from 10-K documents (annual reports) using the extract_items.py module. Get straight to the heart of the information that matters most.

📚 Academics: Enhance your NLP research in economics & finance or business management by accessing and analyzing financial data efficiently.

💼 Professionals: Strengthen financial analysis, strategic planning, and decision-making with comprehensive, easy-to-interpret financial reports.

🛠 Developers: Seamlessly integrate financial data into your models, applications, and experiments using our open-source toolkit.

• 2023-01-16: EDGAR-CORPUS, the biggest financial NLP corpus (generated from EDGAR-CRAWLER), is available as a HuggingFace 🤗 dataset card. See Accompanying Resources for more details.
• 2022-10-13: Updated documentation and fixed a minor import bug.
• 2022-04-03: EDGAR-CRAWLER is available for Windows systems too.
• 2021-11-11: We presented EDGAR-CORPUS, our sister work that started it all, at ECONLP 2021 (EMNLP Workshop) at the Dominican Republic.

• Install
• Usage
• Citation
• Accompanying Resources
• Contributing
• License

• Before starting, it's recommended to create a new virtual environment using Python 3.8. We recommend installing and using Anaconda for this.
• Install dependencies via pip install -r requirements.txt

• Before running any script, you should edit the config.json file, which configures the behavior of our 2 modules.

  • Arguments for edgar_crawler.py, the module to download financial reports:
    • start_year XXXX: the year range to start from (default is 2021).
    • end_year YYYY: the year range to end to (default is 2021).
    • quarters: the quarters that you want to download filings from (List).
      Default value is: [1, 2, 3, 4].
    • filing_types: list of filing types to download.
      Default value is: ['10-K', '10-K405', '10-KT'].
    • cik_tickers: list or path of file containing CIKs or Tickers. e.g. [789019, "1018724", "AAPL", "TWTR"]
      In case of file, provide each CIK or Ticker in a different line.
      If this argument is not provided, then the toolkit will download annual reports for all the U.S. publicly traded companies.
    • user_agent: the User-agent (name/email) that will be declared to SEC EDGAR.
    • raw_filings_folder: the name of the folder where downloaded filings will be stored.
      Default value is 'RAW_FILINGS'.
    • indices_folder: the name of the folder where EDGAR TSV files will be stored. These are used to locate the annual reports. Default value is 'INDICES'.
    • filings_metadata_file: CSV filename to save metadata from the reports.
    • skip_present_indices: Whether to skip already downloaded EDGAR indices or download them nonetheless.
      Default value is True.
  • Arguments for extract_items.py, the module to clean and extract textual data from already-downloaded 10-K reports:
    • raw_filings_folder: the name of the folder where the downloaded documents are stored.
      Default value s 'RAW_FILINGS'.
    • extracted_filings_folder: the name of the folder where extracted documents will be stored.
      Default value is 'EXTRACTED_FILINGS'.
      For each downloaded report, a corresponding JSON file will be created containing the item sections as key-pair values.
    • filings_metadata_file: CSV filename to load reports metadata (Provide the same csv file as in edgar_crawler.py).
    • items_to_extract: a list with the certain item sections to extract.
      e.g. ['7','8'] to extract 'Management’s Discussion and Analysis' and 'Financial Statements' section items.
      The default list contains all item sections.
    • remove_tables: Whether to remove tables containing mostly numerical (financial) data. This work is mostly to facilitate NLP research where, often, numerical tables are not useful.
    • skip_extracted_filings: Whether to skip already extracted filings or extract them nonetheless.
      Default value is True.

• To download financial reports from EDGAR, run python edgar_crawler.py.

• To clean and extract specific item sections from already-downloaded 10-K documents, run python extract_items.py.

  • Reminder: We currently support the extraction of 10-K documents.

An EDGAR-CRAWLER paper is on its way. Until then, please cite the relevant EDGAR-CORPUS paper published at the 3rd Economics and Natural Language Processing (ECONLP) workshop at EMNLP 2021 (Punta Cana, Dominican Republic):

@inproceedings{loukas-etal-2021-edgar,
    title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round",
    author = "Loukas, Lefteris  and
      Fergadiotis, Manos  and
      Androutsopoulos, Ion  and
      Malakasiotis, Prodromos",
    booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.econlp-1.2",
    pages = "13--18",
}

Read the paper here: https://aclanthology.org/2021.econlp-1.2/

• [EDGAR-CORPUS on Zenodo] EDGAR-CORPUS: The biggest corpus for financial NLP research, built from EDGAR-CRAWLER - https://zenodo.org/record/5528490
• [EDGAR-CORPUS on HuggingFace 🤗 datasets] -https://huggingface.co/datasets/eloukas/edgar-corpus/
• [Financial Word2Vec Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS - https://zenodo.org/record/5524358

PRs and contributions are accepted.

Please use the Feature Branch Workflow.

Please create an issue on GitHub instead of emailing us directly so all possible users can benefit from the troubleshooting.

Please see the GNU General Public License v3.0.