This project provides a pipeline to fetch, process, and store financial data from the SEC EDGAR database. It focuses on:

• Company metadata (name, CIK, ticker, SIC, etc.)
• Filings history (10-K, 10-Q, and other forms)
• Company facts and US GAAP financial metrics (Revenue, Net Income, Accounts Payable, etc.)
• It also flattens nested XBRL data into a relational SQLite database for easy querying and analysis.

The project is designed to be configurable, allowing you to track additional metrics or forms over time.

Features

• Automatically fetch company filings and facts from SEC EDGAR based on CIK
• Flatten and normalize XBRL metrics into a structured format.
• Store metadata, filings, and metrics in an SQLite database.
• Flexible configuration to track specific financial metrics.
• Supports historical analysis of key financial data over multiple years.

Installation

Clone the repository:

• git clone - https://github.com/Max1-618/Edgar-SEC-API.git
• cd Edgar-SEC-API

Create and activate a Python virtual environment:

• python -m venv .venv
• source .venv/bin/activate # Linux / Mac
• .venv\Scripts\activate # Windows

Install dependencies:

• pip install -r requirements.txt

Add your SEC API headers in config/SEC_header.py:

• headers = { "User-Agent": "Your Name your_email@example.com", }

Usage

• Prepare a CSV of company tickers and CIKs:

ticker,cik AAPL,0000320193 MSFT,0000789019 NVDA,0001045810

• Place it in the config/path

Run the ingestion script to fetch and store data:

• python ingestion.py

Check the SQLite database (DB_PATH) for:

• companies – Company metadata
• filings – Historical filings
• company_facts – Flattened financial metrics

Use extract scripts to query metrics, for example, Revenue or Accounts Payable over years.

Configuration

• You can configure which metrics to track in utils/source/source.py by editing the COMPANY_FACTS_SOURCE dictionary. For example, to track AccountsPayable:

COMPANY_FACTS_SOURCE = { ... "metrics": ["AccountsPayable"], ... }

• You can add new metrics, taxonomies, or even new tables without changing the ingestion logic.

Important Resources

• SEC EDGAR API Documentation
• XBRL US GAAP Taxonomy
• Python Requests Library
• Pandas Documentation
• SQLite Documentation

Future Enhancements

• Add visualizations of key metrics over time (Revenue, Net Income, EPS).
• Create a financial dictionary automatically by tagging metrics across companies.
• Build a web interface/dashboard to explore companies and metrics interactively.