UConn Quant is an automated equity trading system that implements a rules-based momentum strategy on the S&P 500. The project was motivated by a practical observation: the quantitative, risk-managed approach to portfolio management used at institutional desks is largely inaccessible to individual investors, who are left choosing between passive index exposure and discretionary stock picking. Our goal was to build a working version of that institutional workflow and wrap it in an interface a non-specialist can actually use. The core signal is the momentum factor originally documented by Jegadeesh and Titman (1993). On the first trading day of each month, the system ranks every S&P 500 constituent by its trailing 12-month return, skipping the most recent month to filter out short-term reversal, and selects the top quintile. Positions are sized by inverse-volatility weighting subject to a 5% single-name cap and a 30% sector cap. Five independent risk overlays run before any order is placed: a liquidity filter on 20-day average dollar volume, a 200-day trend filter on SPY, a macro regime classifier that jointly reads VIX, the 10-2 yield spread, and BAA credit spreads, a 12% annualized volatility target drawn from Barroso and Santa-Clara (2015), and a staged drawdown circuit breaker keyed to the trailing peak. The backend is deployed on AWS. EventBridge triggers a monthly Lambda execution that pulls pricing data from S3, macroeconomic series from FRED, and account state from Alpaca Markets. A React dashboard surfaces live performance, sector exposure, and user-configurable risk parameters.