Extract financial data from the SEC's EDGAR database.

The purpose of this project is to allow users to conveniently extract financial data from the SEC's EDGAR database. This library parses the financial data from the SGML into JSON format. This seamless conversion of filing data to application-ready data is what differentiates this library from other EDGAR libraries.

While it is essentially a web-scraper, the EDGAR filing structure that it is built on, and the formatting of the filings themselves, should remain rather static.

Will eventually upload to PyPI, but for now:

1. clone/download
2. python setup.py sdist to create distribution package (note: you must have setuptools installed)
3. pip install ./dist/sec-edgar-financials-0.0.1.tar.gz

The below example shows a basic usage of this package.

from edgar.stock import Stock

stock = Stock('AAPL')

period = 'quarterly' # or 'annual', which is the default
year = 2016 # can use default of 0 to get the latest
quarter = 1 # 1, 2, 3, 4, or default value of 0 to get the latest
# using defaults to get the latest annual, can simplify to stock.get_filing()
filing = stock.get_filing(period, year, quarter)

# financial reports (contain data for multiple years)
income_statements = filing.get_income_statements()
balance_sheets = filing.get_balance_sheets()
cash_flows = filing.get_cash_flows()

The structure of the resulting FinancialReports are shown below, using the income_statements as an example.

{
	"company": "AAPL",
	"date_filed": "2016-01-27T00:00:00",
	"reports": [
		{
			"date": "2015-12-26T00:00:00",
			"months": 3,
			"map": {
				"us-gaap_SalesRevenueNet": {
					"label": "Net sales",
					"value": 75872000000
				},
				...
			}
		},
		...
	]
}

• Note that the above is in JSON format just for the purposes of easy communication and that the actual output of the call is a FinancialReport Object from the edgar.financials module. To get the JSON, you can use FinancialReportEncoder from edgar.financials, e.g. FinancialReportEncoder().encode(financial_report).
• As we can see above, a given FinancialReport will actually contain reports for multiple periods/dates. The map in each one of these reports contains XBRL elements (e.g. "SalesRevenueNet"), with their namespace found as a prefix (e.g. "us-gaap"). More information on XBRL can be found at https://xbrl.us/data-rule/dqc_0015-le/.

The program crawls through the FilingSummary.xml file that is contained within the SGML of quarterly and annual filings, trying to find income statements, balance sheets, and statements of cash flows, which can go by different names depending on the company. As a result, there are currently constants defined in edgar.filing.Statements to capture the different synonyms for these statements and thus this will likely be the root of the issue if an error message is logged reading "could not find anything for ShortName...". A guessing or best match algorithm may be developed in the future if such issues become too frequent and maintaining the constants becomes too difficult.

US companies are required by law to file forms with the SEC and these submissions are stored in the EDGAR file system (database), which is organized by year and then by quarter. Below is the terminology we use to navigate the data available to us in this database.

• FilingInfo: Each directory in the file system has an index that keeps track of the location of the filings for all companies that submitted during the period, allowing users to navigate to the file they need.
• Filing: Refers to the text file that the company submits to the SEC, which contains the reported data in SGML format. AKA "SEC filing/document".
  • DTD: SEC documents follow a Document Type Definition that describes their SGML structure.
• Document Filings contain one or more Documents, which contain the report data in a number of potential formats (pdf, xml, xbrl, etc.)
  • Documents can have different form types such as 8-K, 10-Q, 10-K, etc.
  • 10-Q and 10-K forms are financials that are processed into their income statement, balance sheet, and statement of cash flow components.

EDGAR doesn't categorize using stock symbols, since not all companies are publicly traded, but rather by a cik value. To help reconcile the issue of mapping a stock symbol (easy for users) to a cik, created the cik-to-symbol database, edgar.data.symbols.csv, using edgar.data.symbols.py.

It should have most companies that have filed on or before November 2018. When the python file runs, it will append to what's already in place in the csv (this means new companies will have to be manually added). At this point, however, it's recommended to manually add to this file as needed as 1) it's a very resource intensive process and 2) they way it's currently coded just continues backwards from the last entry (i.e. wouldn't capture new cik/symbol combos that come along).

• Allow statements to be gathered over a period of time (so not just for a given year+quarter)
• Clean up logging + error handling
• Utilities:
  • Number of shares over time