AI Options Wheel Agent

AI Options Wheel Agent is an auditable autonomous trading system combining LLM-based reasoning, deterministic risk controls, and Alpaca paper trading infrastructure. The project explores a key question in AI trading: how much autonomy should an AI agent have, and where should deterministic controls take over? The agent implements an Options Wheel strategy across two portfolio states: CASH and SHARES. In CASH, it evaluates cash-secured put opportunities. After assignment, it transitions to SHARES and evaluates covered-call opportunities. When shares are called away, the cycle returns to CASH. Claude acts as the reasoning layer, evaluating pre-filtered candidates and proposing structured trading actions. The model cannot bypass risk controls — every proposal passes through deterministic validation before execution. The risk layer validates collateral, exposure, contract validity, option type, quantity, quote quality, liquidity, duplicate orders, portfolio state, and covered-call cost-basis constraints. Invalid or malformed output fails closed instead of reaching the broker. Alpaca provides market data and paper-trading execution via limit orders, with an auditable decision and transaction ledger. A Streamlit dashboard provides visibility into decisions, portfolio state, performance, orders, and trading history. The system includes replay-based evaluation, baseline-versus-agent comparison, adversarial testing, and live paper-trading validation backed by an 88-test automated suite. Live validation exposed and corrected real API response-shape assumptions around option-chain data, open interest, and order parsing. The core principle: AI reasons. Deterministic systems enforce safety. Alpaca executes. The goal is not unrestricted AI control, but a verifiable, testable, auditable boundary where AI reasoning operates within deterministic safety constraints.