How to Use LLMs for Investment Research
A practical guide to using large language models for investment research: screening stocks, synthesizing data, and building AI-powered research workflows with real-time market data.
Insights on AI-powered trading and alternative data
A practical guide to using large language models for investment research: screening stocks, synthesizing data, and building AI-powered research workflows with real-time market data.
Learn what short interest is, how to interpret short interest ratios and days-to-cover, and why short squeezes happen when bears get crowded.
Learn what unusual options activity is, how to identify it using put/call ratios and volume data, and what it signals about institutional positioning.
LLMs have knowledge cutoffs that limit investment research. Learn how MCP bridges the gap by connecting AI assistants to live alternative data feeds.
Learn how patent filing data reveals R&D priorities, competitive positioning, and potential revenue streams for technology and pharmaceutical companies.
Learn how government contract data reveals revenue opportunities, competitive positioning, and growth catalysts for defense, tech, and healthcare companies.
Learn how corporate lobbying data can provide insights into company priorities, regulatory risks, and potential policy tailwinds for investors.
Learn how Reddit stock mentions from WallStreetBets and other subreddits can signal retail investor interest and potential stock moves.
Learn how to use LinkedIn employee counts and follower growth as alternative data signals for investment analysis and company research.
Learn how to use app store ratings, downloads, and reviews as alternative data for investment analysis and company research.
Learn how to track insider buying activity, filter for meaningful signals, and use executive purchases to inform your investment decisions.
Explore how AI and machine learning are used for stock price prediction, their limitations, and how to use AI forecasts as part of a broader strategy.