Shield-AI Autonomous Risk Guardrails Hedging Agent

Most AI trading agents chase alpha. Shield-AI defends the downside. Hand an LLM a brokerage key and tell it to pick winners, and you have a leveraged guess, not a quant system: no covariance matrix, no tail estimate, no loss budget. Shield-AI inverts that design. Deterministic mathematics defines the boundary; the language model only operates inside it. A Python quant engine built on NumPy, SciPy and statsmodels continuously recomputes the paper portfolio's risk surface from Alpaca Market Data: a 99% one-day Portfolio Value-at-Risk (parametric, historical and Monte Carlo), Conditional VaR, GARCH(1,1) volatility forecasts, and net beta-weighted Delta. These are hard numbers, not model opinions. The LLM stays asleep until a guardrail actually breaks. On breach, two agents wake: a News Sentiment Agent scores the severity, event type and persistence of the shock from the Alpaca News API, and a Risk-Manager Agent fuses that qualitative signal with the quantitative breach to select a hedge — instrument, strike, expiry and contract size. Every proposal passes a deterministic policy filter (maximum hedge cost, buying-power check, no naked short exposure) before it can reach the market. Execution runs through Alpaca's official MCP Server, using get_option_chain and get_option_snapshot to price the chain, then place_option_order to establish a protective put or a multi-leg collar. Post-fill VaR is recomputed and written to an immutable audit log. A Streamlit dashboard exposes live risk metrics alongside the agent's full reasoning trail, with human override on every decision. Shield-AI treats risk management, not prediction, as the killer app for agentic finance.