AEGIS - Autonomous Adaptive Portfolio Hedge Agent

AEGIS is an autonomous quantitative multi-agent system designed for the Alpaca Hackathon 2026. It transforms portfolio hedging from a static, reactive manual chore into a continuous, closed-loop protection system. By monitoring live Alpaca paper portfolios, AEGIS dynamically assesses market risk, evaluates competing options hedge structures, enforces deterministic risk gates, executes multi-leg orders, and adapts protection as regimes shift. The Core Problem & Solution Traditional hedging relies on rigid rules (e.g., "buy puts if the index drops 5%"), which bleed cash through theta decay and ignore volatility surfaces. AEGIS autonomously resolves five fundamental questions: What systemic and asset-specific risks exist in the portfolio? How much downside protection is required and for what horizon? Which options structures (Protective Put, Bear Put Spread, Zero-Cost Collar) fit best? Which structure optimizes the protection-to-cost trade-off? When should the hedge be rolled, trimmed, or closed? Architecture & Multi-Agent Pipeline Built on Python 3.11, FastAPI, and LangGraph, the system executes through distinct phases: Analysis Council: Gathers beta, sector concentration, IV percentiles, and news into a typed HedgeContext. Strategy Manager: Simulates candidate hypotheses—Puts, Spreads, Zero-Cost Collars, and an explicit No-Hedge baseline. Deterministic Risk Gate: A strict mathematical firewall that validates Value-at-Risk (VaR), Greeks limits, margin sufficiency, and slippage before execution. Alpaca Multi-Leg Execution: Submits atomic multi-leg orders directly to the Alpaca API. Adaptive Rebalancing Loop: Continually monitors delta drift and volatility shifts to roll, adjust, or remove protection.