sec-parser 0.18.0.post16

Parse SEC EDGAR HTML documents into a tree of elements that correspond to the visual structure of the document.

 

sec-parser

Essentials ➔      
Health ➔             
Quality ➔            
Distribution ➔   
Community ➔    

Parse SEC EDGAR HTML documents into a tree of elements that correspond to the visual structure of the document.

See Demo | Read Docs | Join Discussions | Report Bugs

Overview

The sec-parser project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. Semantic elements might include section titles, paragraphs, and tables, each classified for easier data manipulation. This forms a semantic tree that corresponds to the visual and informational structure of the document.

This tool is especially beneficial for Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLM) applications by streamlining data pre-processing and feature extraction.

• Explore the Demo
• Read the Documentation
• Join the Discussions to get help, propose ideas, or chat with the community
• Report bugs in Issues
• Stay updated and contribute to our project's direction in Announcements and Roadmap
• Learn How to Contribute

Key Use-Cases

sec-parser is versatile and can be applied in various scenarios, including but not limited to:

Financial and Regulatory Analysis

• Financial Analysis: Extract financial data from 10-Q and 10-K filings for quantitative modeling.
• Risk Assessment: Evaluate risk factors or Management's Discussion and Analysis sections for qualitative analysis.
• Regulatory Compliance: Assist in automating compliance checks for the legal teams.
• Flexible Filtering: Easily filter SEC documents by sections and types, giving you precisely the data you need.

Analytics and Data Science

• Academic Research: Facilitate large-scale studies involving public financial disclosures, sentiment analysis, or corporate governance evaluation.
• Analytics Ready: Integrate parsed data seamlessly into popular analytics tools for further analysis and visualization.

AI and Machine Learning

• Cutting-Edge AI for SEC EDGAR: Apply advanced AI techniques like MemWalker to navigate and extract and transform complex information from SEC documents efficiently. Learn more in our blog post: Cutting-Edge AI for SEC EDGAR: Introducing MemWalker.
• AI Applications: Leverage parsed data for various AI tasks such as text summarization, sentiment analysis, and named entity recognition.
• Data Augmentation: Use authentic financial text to train and test machine learning models.

Causal AI

• Causal Analysis: Use parsed data to understand cause-effect relationships in financial data, beyond mere correlations.
• Predictive Modeling: Enhance predictive models by incorporating causal relationships, leading to more robust and reliable predictions.
• Decision Making: Aid decision-making processes by providing insights into the potential impact of different actions, based on causal relationships identified in the data.

Large Language Models

• LLM Compatible: Use parsed data to facilitate complex NLU tasks with Large Language Models like ChatGPT, including question-answering, language translation, and information retrieval.

These use-cases demonstrate the flexibility and power of sec-parser in handling both traditional data extraction tasks and facilitating more advanced AI-driven analysis.

Getting Started

This guide will walk you through the process of installing the sec-parser package and using it to extract the "Segment Operating Performance" section as a semantic tree from the latest Apple 10-Q filing.

Installation

First, install the sec-parser package using pip:

pip install sec-parser

In order to run the example code in this README, you'll also need the sec_downloader package:

pip install sec-downloader

Usage

Once you've installed the necessary packages, you can start by downloading the filing from the SEC EDGAR website. Here's how you can do it:

from sec_downloader import Downloader

# Initialize the downloader with your company name and email
dl = Downloader("MyCompanyName", "[e-mail]")

# Download the latest 10-Q filing for Apple
html = dl.get_latest_html("10-Q", "AAPL")

Note The company name and email address are used to form a user-agent string that adheres to the SEC EDGAR's fair access policy for programmatic downloading. Source

Now, we can parse the filing into semantic elements and arrange them into a tree structure:

import sec_parser as sp

# Parse the HTML into a list of semantic elements
elements = sp.Edgar10QParser().parse(html)

# Construct a semantic tree to allow for easy filtering by section
tree = sp.TreeBuilder().build(elements)

# Find section "Segment Operating Performance"
section = [n for n in tree.nodes if n.text.startswith("Segment")][0]

# Preview the tree
print("\n".join(sp.render(section).split("\n")[:13]) + "...")

TitleElement: Segment Operating Performance
├── TextElement: The following table sho... (dollars in millions):
├── TableElement: 414 characters.
├── TitleElement[L1]: Americas
│   └── TextElement: Americas net sales decr... net sales of Services.
├── TitleElement[L1]: Europe
│   └── TextElement: The weakness in foreign...er net sales of iPhone.
├── TitleElement[L1]: Greater China
│   └── TextElement: The weakness in the ren...er net sales of iPhone.
├── TitleElement[L1]: Japan
│   └── TextElement: The weakness in the yen..., Home and Accessories.
└── TitleElement[L1]: Rest of Asia Pacific
    ├── TextElement: The weakness in foreign...lower net sales of Mac....

For more examples and advanced usage, you can continue learning how to use sec-parser by referring to the User Guide, Developer Guide, and Documentation.

What's Next?

You've successfully parsed an SEC document into semantic elements and arranged them into a tree structure. To further analyze this data with analytics or AI, you can use any tool of your choice.

For a tailored experience, consider using our free and open-source library for AI-powered financial analysis:

pip install sec-ai

Explore sec-ai on GitHub

Best Practices

Importing modules

1. Standard: import sec_parser as sp
2. Package-Level: from sec_parser import SomeClass
3. Submodule: from sec_parser import semantic_tree
4. Submodule-Level: from sec_parser.semantic_tree import SomeClass

Note The root-level package sec_parser contains only the most common symbols. For more specialized functionalities, you should use submodule or submodule-level imports.

Warning To allow us to maintain backward compatibility with your code during internal structure refactoring for sec-parser, avoid deep or chained imports such as sec_parser.semantic_tree.internal_utils import SomeInternalClass.

Contributing

For information about setting up the development environment, coding standards, and contribution workflows, please refer to our CONTRIBUTING.md guide.

License

This project is licensed under the MIT License - see the LICENSE file for details.