sec-ai 0.5.1.post10

A comprehensive open-source toolkit for AI-powered analysis and interpretation of SEC EDGAR filings, providing valuable insights for investors, fintech developers, and researchers.

 

sec-ai

Essentials ➔      
Health ➔             
Quality ➔            
Distribution ➔   
Community ➔    

A comprehensive open-source toolkit for AI-powered analysis and interpretation of SEC EDGAR filings, providing valuable insights for investors, fintech developers, and researchers.

See Demo | Read Docs | Ask Questions | Report Bugs | Roadmap

Overview

sec-ai is an open-source project designed to provide a comprehensive toolset for analyzing and interpreting data from SEC filings. Utilizing advanced AI technologies, this project aims to serve a wide range of users, from individual investors to researchers and regulatory bodies.

The project leverages alphanome-ai/sec-parser for its data extraction needs, an essential component that simplifies the parsing of SEC EDGAR HTML documents into a structured and analyzable format.

• Explore the Demo
• Read the Documentation
• Ask questions in Discussions
• Report bugs in Issues
• Check out the Roadmap

Installation

Open a terminal and run the following command to install sec-ai:

pip install sec-ai

Usage

import sec_ai as sa

# TODO: Examples are coming soon!

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

Contributing

Contributing to sec-ai is a rewarding way to improve this open-source project. Whether you are a user interested in expanding your knowledge or a developer who wants to dive deeper into the codebase, we have comprehensive guides to get you started.

• User Guide: If you are new to sec-ai and would like to get started, please refer to the Quickstart User Guide.

• Developer Guide: For those interested in contributing to sec-ai, the Comprehensive Developer Guide provides an in-depth walkthrough of the codebase and offers examples to help you contribute effectively.

Both guides are interactive and allow you to engage with the code and concepts as you learn. You can run and modify all the code examples for yourself by cloning the repository and running the respective notebooks in a Jupyter environment.

Alternatively, you can run the notebooks directly in your browser using Google Colab.

Note Before contributing, we highly recommend familiarizing yourself with these guides. They will help you understand the structure and style of our codebase, enabling you to make effective contributions.

Best Practices

Importing modules

1. Standard: import sec_ai as sa
2. Package-Level: from sec_ai import SomeClass
3. Submodule: from sec_ai import sub_module
4. Submodule-Level: from sec_ai.sub_module import SomeClass

Note The root-level package sec_ai 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-ai, avoid deep or chained imports such as sec_ai.sub_module.internal_utils import SomeInternalClass.

License

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