We are LLMQuant, an open-source community focusing on AI, LLM (large language model) and Quantitative Finance. We aim to leverage AI to investment research with feasible collection of techniques and solutions.
Join the community and explore the latest AI use case in quantitative research with us, you will receive the event updates and access our best AI4Quant solutions.
You can contribute to our community by sharing your use case of AI in quantitative finance. We strongly appreciate the code contribution to our github project repositories.
Apply our best AI4Quant solutions in production environment, which are verified by experienced quantiative researchers and AI experts.
Structured, AI-readable financial data for LLMs.
Our flagship infrastructure project transforms how large language models consume and understand financial information.
We clean, tag, and structure financial documents such as earnings reports, filings, M&A news, and macro data into LLM-optimized formats.
Highlights:
• Real-time and historical data APIs
• Metadata-rich parsing of SEC filings, headlines, transcripts
• RAG-ready formats for chat agents and custom LLMs
• On-premise or cloud enterprise deployment
Open-source quantitative investment knowledge.
Quant-Wiki.com is our community-driven resource library of strategies, tools, and research insights. It bridges the gap between academic theory and real-world quant practices.
Highlights:
• Strategy code libraries and tutorials
• Glossaries, whitepaper reviews, and market analysis
• Open access and community contributions
• Ideal for learners, researchers, and practitioners
AI-assisted research with agents and financial experts.
MarketPulse enables intelligent alpha discovery via co-piloted workflows between humans and AI. From macro analysis to earnings summaries, it bridges insight with automation.
Highlights:
• AI agents summarize, monitor, and analyze markets
• Merger arb and event-driven monitoring tools
• Co-published research dashboards
• Deep insights for hedge funds and analysts
Next-gen AI trading simulation and backtesting platform.
Magents.ai allows LLM-native strategy testing using natural language or Python, simulating complex market environments with multi-agent interactions.
Highlights:
• Event-driven and LLM-defined trading logic
• Modular backtesting environments with latency/slippage
• Supports stocks, crypto, and derivatives
• Designed for researchers, funds, and algo developers
