Insights on AI-powered trading and alternative data
How institutional investors evaluate and integrate alternative data APIs into trading dashboards, risk systems, and research platforms. Covers API requirements, integration patterns, and vendor selection.
How to evaluate and integrate stock prediction APIs into trading systems. Covers API requirements, integration patterns, and practical Python examples for building prediction-powered applications.
Learn why confidence intervals matter more than point estimates in stock predictions. How to interpret prediction ranges, use uncertainty for position sizing, and avoid the trap of false precision.
Learn what stock prediction models are, how they work, and the different approaches—from technical analysis to machine learning. Understand the strengths and limitations of each method.
Learn what SEC Form 13F is, how to interpret institutional holdings data, and how investors use hedge fund positions to inform their own strategies.
Understand when to use alternative data versus traditional fundamental analysis, how they complement each other, and how to integrate both into your investment process.
Learn how to combine insider transactions, sentiment, options flow, and analyst ratings into a unified scoring system using Python and the FinBrain API.
Discover how institutional investors use satellite imagery, sentiment analysis, insider filings, and other alternative data to gain an edge—and how retail investors can access similar signals.
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.