sec-parsers 0.541

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, 20-F. More will be added soon, or you can write your own! How to write a Custom Parser in 5 minutes

Note: syntax change for find_nodes_by_title, to find_all_sections_by_title and associated functions.

URGENT: Advice on how to name functions used by users is urgently needed. SEC Parsers has started to get a lot of users, and I don't want to deprecate function names in the future. Link

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_sections_from_title(title) # finds section by title, e.g. 'item 1a'
filing.find_sections_from_text(text) # finds sections 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,encoding='utf-8')
filing.save_csv(file_name,encoding='ascii')

Additional Resources:

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

Features:

• lots of filing types
• export to xml, csv, with option to convert to ASCII
• visualization

Feature Requests:

Request a Feature

• Export to dta (Denis)
• DEF 14A, DEFM14A (Denis)
• Export to markdown (Astarag)
• Better parsing_string handling. E.g. Emphasis capitalization needs to be more dynamic for one word section titles.e.g. Governance sometimes needs to be considered same string as Risk Management and Strategy as well as Risk management and strategy

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 errors
• xml

Might be done along the way:

• Faster parsing, probably using streaming approach, and combining modules together.
• Introduction section parsing
• Signatures section parsing
• Better visualization interface (e.g. like pdfviewer for sections)

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 xml tree issue with parsing type
• look at code duplication w.r.t to style detectors, e.g. all caps and emphasis. may want to combine into one detector

• yep this is a priority. have to handle e.g. Introduction and Segment Overview as same rule. Bit difficult. Will think over.

• better function names - need to decide terminology soon.
• consider adding table of contents, forward looking information, etc

• forward looking information, DOCUMENTS INCORPORATED BY REFERENCE, TABLE OF CONTENTS - go with a bunch,

• fix layering issue - e.g. top div hides sections
• make trees nicer
• add more filing types
• 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.