EasoLab's Alpaca AI Trading Agent

Alpaca AI Trading Agent is an autonomous options trading agent that runs unattended, evaluates real market data every 15 minutes, and trades weekly SPY iron condors on a strategy backtested against a full year of real Alpaca historical option data. The architecture is a strict three-layer pipeline: an Analyst (a Featherless LLM) debates market regime and risk for the audit trail, but is advisory-only and can never approve or block a trade. A Governor does the actual thinking deterministically — real Black-Scholes math, real strike selection off the live option chain — because LLMs have documented 15-40% error rates on option math done in token space, and this project treats that as a hard constraint, not a caveat. An Executor validates the resulting order against a strict schema and submits it as one atomic multi-leg order through the Alpaca CLI. No order ever reaches the broker without passing through deterministic validation. The strategy itself — Hybrid VRP+Skew — sells premium only when two independent real-data signals agree: implied volatility priced richer than realized volatility, and put-call skew that isn't already elevated. Backtested at Sharpe 2.53, 90.9% win rate, and under 2% max drawdown. Four more aggressive, directional strategies were also researched and backtested specifically to check whether something could beat the index outright — none did once tested against real data, and the most promising one on paper was actually the worst performer once real data hit it. This isn't a backtest-only submission. It runs live on a GitHub Actions cron against a dedicated paper account, with hard risk gates (2% risk per trade, a 6% total-drawdown kill switch, spread rejection, 0-DTE auto-liquidation) and a full structured audit log committed back to the repo after every cycle — including the cycles where it decides to do nothing.