edgar-parser

Structured financial data from SEC EDGAR filings.

Give your Python application access to income statements, balance sheets, cash flows, and qualitative sections from 10-Q, 10-K, and 8-K filings — parsed directly from SEC EDGAR with no third-party data vendor required.

This isn't a raw EDGAR scraper. The library handles XBRL namespace resolution, fiscal period matching, current vs. prior period alignment, sign normalization, and fuzzy metric lookup — so you get clean, analysis-ready data.

What it does

Core Extraction (parse_filing)

• Parses iXBRL facts from 10-Q and 10-K filings on SEC EDGAR
• Resolves XBRL namespaces, units, scales, and dimensional qualifiers
• Enriches facts with presentation roles and negated-label sign flipping

Period Matching (match_filing)

• Aligns current and prior period values for each financial line item
• Handles quarterly, full-year, and 4Q (derived annual) modes
• Zip-matching with adaptive fallback to fuzzy matching for edge cases

8-K Earnings Extraction (earnings_8k) — optional, requires anthropic or openai

• Extracts financial data from 8-K earnings press releases using Claude (primary) with OpenAI GPT-4o fallback
• Automatic fallback when 10-Q/10-K is not yet available for a period
• Same output schema as core extraction for seamless integration

Filing Sections (section_parser)

• Parses qualitative sections from 10-K/10-Q filings (Risk Factors, MD&A, Business, etc.)
• 8-K earnings release parsing via source="8k" — extracts narrative and tables from Item 2.02 exhibits
• Summary and full-text modes with configurable word limits

High-Level Tools (tools)

• get_financials(ticker, year, quarter) — all facts for a filing period
• get_filings(ticker, year, quarter) — list available filings (10-Q, 10-K, 8-K)
• get_metric(ticker, year, quarter, metric_name) — single metric lookup with fuzzy matching
• get_filing_sections(ticker, year, quarter, source="8k") — qualitative section text (10-K/10-Q default, or 8-K earnings release)

Install

pip install edgar-parser

For 8-K earnings extraction (uses Claude API with OpenAI fallback):

pip install "edgar-parser[llm]"

Quick start

from edgar_parser.tools import get_financials, get_metric

# Get all financial facts from Apple's Q1 2025 10-Q
result = get_financials("AAPL", 2025, 1)
for fact in result["facts"][:5]:
    print(f"{fact['metric']}: {fact['current_value']}")

# Look up a single metric
revenue = get_metric("AAPL", 2025, 1, "Revenue")
print(f"Revenue: {revenue['value']} {revenue['unit']}")

Or use the lower-level API for more control:

from edgar_parser import parse_filing, match_filing

# Parse raw XBRL facts
parsed = parse_filing("AAPL", 2025, 1, full_year_mode=False)

# Match current vs. prior periods
matched = match_filing(parsed)
print(matched.head())

Key functions

Requirements

• Python 3.10+
• No API key needed — data comes directly from SEC EDGAR (public)
• Optional: ANTHROPIC_API_KEY environment variable for 8-K extraction
• Optional: OPENAI_API_KEY environment variable for 8-K fallback when Anthropic API is unavailable

See also

• edgar-mcp — MCP server that exposes EDGAR financial data as AI agent tools. If you're building with Claude Code or another MCP-compatible agent, use this instead.