Developed an AI-powered investment research platform at the Y Combinator Full-Stack Hackathon. AlphaHedge orchestrates a parallel multi-agent council to simulate institutional-grade equity research workflows in under 2 minutes.
Three autonomous analyst agents are prompted concurrently: a fundamental analyst evaluating 10-Q / 10-K filings, balance sheets, cash flow statements, debt structure, and valuation metrics; a quantitative analyst operating on raw OHLCV data, technical indicators, volatility and beta, factor exposures, and probabilistic risk-adjusted models; and a sentiment analyst synthesizing news and PR flow, social media buzz, analyst ratings and price targets, corporate catalysts, short interest, and narrative trend mapping.
A final judge agent reviews all analyst memos and produces a unified investment recommendation tailored to the user's risk tolerance, time horizon, and portfolio size collected during onboarding. Implemented retrieval-augmented Q&A using Google Gemini and managed real-time persistence with Supabase. Implemented an interactive knowledge layer that parses complex financial terminology into styled tooltips for immediate context.
built with love @ yc full-stack hackathon, hosted by sim.ai & loveable @ y combinator, sponsored by stripe, brex & supabase