sec-parsers 0.531

A package to parse SEC filings

SEC Parsers

Parses non-standardized SEC filings into structured xml. Use cases include LLMs, NLP, and textual analysis. Package is a WIP.

Supported filing types are 10-K, 10-Q, 8-K, S-1. More will be added soon.

Installation

pip install sec-parsers

Quickstart

from sec_parsers import Filing, download_sec_filing

html = download_sec_filing('https://www.sec.gov/Archives/edgar/data/1318605/000162828024002390/tsla-20231231.htm')
filing = Filing(html)
filing.parse() # parses filing
filing.visualize() # opens filing in webbrowser with highlighted section headers
filing.find_nodes_by_title(title) # finds node by title, e.g. 'item 1a'
filing.find_nodes_by_text(text) # finds nodes which contains your text
filing.get_tree(node) # if no argument specified returns xml tree, if node specified, returns that nodes tree
filing.get_title_tree() # returns xml tree using titles instead of tags. More descriptive than get_tree.
filing.set_filing_type(type) # e.g. 'S-1'. Use when automatic detection fails
filing.save_xml(file_name)
filing.save_csv(file_name)

Additional Resources:

• quickstart
• [In Progress] Article explaining how to write custom filing parsers.
• Archive of Parsed XMLs / CSVs - Last updated 7/10/24.
• example parsed filing
• example parsed filing exported to csv.

Feature Requests:

To request features or suggest a way to improve the package please use the form below. Google Form

• Extract title of section along with its text (sharif)
• Extract subsections from section (sharif)
• Export to dta (Denis)
• option to remove special chars from document in export (bill)

Statistics

• Speed: On average, 10-K filings parse in 0.25 seconds. There were 7,118 10-K annual reports filed in 2023, so to parse all 10-Ks from 2023 should take about half an hour.

Updates

Towards Version 1:

• Most/All SEC text filings supported
• Few errrors
• xml

Might be done along the way:

• Faster parsing, probably using streaming approach, and combining modules together.
• Introduction section parsing
• Signatures section parsing

Beyond Version 1:

To improve the package beyond V1 it looks like I need compute and storage. Not sure how to get that. Working on it.

Metadata

• Clustering similar section titles using ML (e.g. seasonality headers)
• Adding tags to individual sections using small LLMs (e.g. tag for mentions supply chains, energy, etc)

Other

• Table parsing
• Image OCR
• Parsing non-html filings

Current Priority list:

• fix layering issue
• fix all caps and emphasis issue
• clean text
• Better historical conversion: handle if PART I appears multiple times as header, e.g. logic here item 1 continued.