VOLMIND trades the divergence between what an AI believes and what the options market is pricing in. A LangGraph pipeline of specialized agents — News, Fundamental, and Probability — each call an LLM to reason over live Alpaca market and news data and produce an evidence-required probability estimate; unsupported forecasts have their confidence automatically clamped. That estimate is compared against the market-implied probability computed directly from the live Alpaca option chain. A second, independent LLM pass — the Diligence Agent — stress-tests the resulting thesis for thin evidence, IV/liquidity red flags, and whether the edge clears configured thresholds, halting the pipeline before a trade is ever proposed if it doesn't hold up. Only cleared theses reach the Risk Agent, which enforces per-trade loss limits, a portfolio-wide position cap, and a daily loss circuit breaker before a real paper order is submitted. Execution isn't the finish line: a Position Monitor marks every open position to market and closes it on a take-profit, stop-loss, or days-to-expiry rule, and agent reputation is scored against real trade outcomes. Left running, VOLMIND scans its watchlist and manages its book autonomously — market-hours-gated, fail-closed to paper trading — for the length of a competition, with every decision logged with its full reasoning for an auditable trail. Built as a hackathon agent, but the same pipeline doubles as a decision-support terminal a desk could run for research and alerts, with auto-execution simply switched off.
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