Node.js SDK for SEC & EDGAR data — API access and bulk dataset downloads for 20M+ filings, insider trades, 13F holdings, financial statements, exec comp, enforcement actions, material contracts, registration statements & prospectuses, bond & credit agreements, investment advisers, proxy voting, XBRL-to-JSON, real-time streaming, full-text search...

Python SDK for SEC & EDGAR data — API access and bulk dataset downloads for 20M+ filings, insider trades, 13F holdings, financial statements, exec comp, enforcement actions, material contracts, registration statements & prospectuses, bond & credit agreements, investment advisers, proxy voting, XBRL-to-JSON, real-time streaming, full-text search...

A free MCP server to analyze and extract insights from public filings, earnings transcripts, financial metrics, stock market data, private market transactions, and deep web-based research within Claude Desktop and other popular MCP clients.

🏛️ Open-source toolkit for accessing SEC EDGAR financial data with Python and TypeScript/JavaScript SDKs. Features comprehensive parsing for 10-K, 10-Q, 8-K forms, XBRL financial data extraction, and real-time company filings retrieval.

A set of tools for dealing with SEC EDGAR corporate filings

EDGAR Analytics – Python Library for Extracting, Analyzing, and Forecasting SEC EDGAR Filings. Streamline your financial analysis with comprehensive metrics, growth rates, and automated reporting capabilities.

Streams new SEC Filings in real-time using Phoenix LiveView

A curated list of tools, data sources, libraries, and resources for working with SEC filings (13F, 10-K, 10-Q, 8-K)

High performance heatmap trading terminal

AI-powered autonomous equity research agent. Multi-source analysis (Yahoo Finance, SEC 10-K, Reddit, news) with LangGraph orchestration, hybrid RAG, and real-time market data. Generates professional research reports via Telegram bot or REST API. Deployed on Azure Container Apps.

AI agent toolkit for accessing and analyzing SEC EDGAR filing data. Build intelligent agents with LangChain, MCP-use, Gradio, Dify, and smolagents to analyze financial statements, insider trading, and company filings.

This project predicts corporate default probability using sentiment analysis of annual reports and financial data from U.S. companies. Key steps included data preprocessing, textual analysis, feature selection, and testing machine learning models. A Random Forest classifier was identified as the best model, trained, optimized, and exported for use.

AI-powered financial analysis platform that tracks SEC filings to reveal what institutional investors, activist funds, and insiders thinks about publicly traded stocks. All the moves are captured here. Alpha before the alpha!

Real-Time SEC 8-K Filing Analyzer for Financial Modeling

Stock market news API, developed by traders and investors for traders and investors.

An open-source, self-hosted mini Bloomberg Terminal for AI agents — SEC filings, institutional holdings, insider trading, congressional trades, and short data from a single self-hosted stack.

Scrapes ticker info from various sites

A Python tool for extracting stock repurchase program data from SEC 10-Q and 10-K filings

Financial Data Anomaly Detection and Forecasting Project Overview This project involves the development of a machine learning model to analyze financial data, detect anomalies, and forecast future trends. Using a variety of models from scikit-learn, such as RandomForestRegressor, KMeans, and IsolationForest.