Retrieve company filings from the SEC, the US securities regulator, using Python. Extract financial statements, insider transactions, and institutional holdings to research companies and compare financial data across reporting periods.
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Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.
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Python library for accessing all SEC filings since 1994 with structured data extraction.
The SEC requires identification for API access. Always set identity before any operations:
from edgar import set_identity
set_identity("Your Name your.email@example.com")
Set via environment variable to avoid hardcoding: EDGAR_IDENTITY="Your Name your@email.com".
uv pip install edgartools
# For AI/MCP features:
uv pip install "edgartools[ai]"
from edgar import Company, find
company = Company("AAPL") # by ticker
company = Company(320193) # by CIK (fastest)
results = find("Apple") # by name search
# Company filings
filings = company.get_filings(form="10-K")
filing = filings.latest()
# Global search across all filings
from edgar import get_filings
filings = get_filings(2024, 1, form="10-K")
# By accession number
from edgar import get_by_accession_number
filing = get_by_accession_number("0000320193-23-000106")
# Form-specific object (most common approach)
tenk = filing.obj() # Returns TenK, EightK, Form4, ThirteenF, etc.
# Financial statements (10-K/10-Q)
financials = company.get_financials() # annual
financials = company.get_quarterly_financials() # quarterly
income = financials.income_statement()
balance = financials.balance_sheet()
cashflow = financials.cashflow_statement()
# XBRL data
xbrl = filing.xbrl()
income = xbrl.statements.income_statement()
text = filing.text() # plain text
html = filing.html() # HTML
md = filing.markdown() # markdown (good for LLM processing)
filing.open() # open in browser
company.name # "Apple Inc."
company.cik # 320193
company.ticker # "AAPL"
company.industry # "ELECTRONIC COMPUTERS"
company.sic # "3571"
company.shares_outstanding # 15115785000.0
company.public_float # 2899948348000.0
company.fiscal_year_end # "0930"
company.exchange # "Nasdaq"
| Form | Object | Key Properties |
|---|---|---|
| 10-K | TenK | financials, income_statement, balance_sheet |
| 10-Q | TenQ | financials, income_statement, balance_sheet |
| 8-K | EightK | items, press_releases |
| Form 4 | Form4 | reporting_owner, transactions |
| 13F-HR | ThirteenF | infotable, total_value |
| DEF 14A | ProxyStatement | executive_compensation, proposals |
| SC 13D/G | Schedule13 | total_shares, items |
| Form D | FormD | offering, recipients |
Important: filing.financials does NOT exist. Use filing.obj().financials.
filing.financials → AttributeError; use filing.obj().financialsget_filings() has no limit param; use .head(n) or .latest(n)amendments=False for multi-period analysis (amended filings may be incomplete)None before accessing optional dataLoad these when you need detailed information:
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Support experimental validation of designed proteins through a cloud laboratory. Prepare and optimize sequences, submit tests for binding, expression, thermal stability, or enzyme activity, track experiment progress, and retrieve results through the API.
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Analyze time series, observations ordered over time, using aeon in Python. Build classification, prediction, and clustering workflows, detect unusual patterns or changes, and compare methods through an interface compatible with scikit-learn.
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Create original generative art with p5.js, using code to produce patterns and motion. Receive a written artistic concept and an interactive HTML viewer with adjustable parameters, reproducible random variations, seed navigation, and PNG downloads.
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Retrieve stock prices, exchange rates, cryptocurrency prices, company financial statements, and economic indicators through the Alpha Vantage API. It also covers historical data, news sentiment, and technical indicators, which summarize price and trading patterns.
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