CaiSheng

CaiSheng is unique. Most trading bots try to predict whether a stock goes up or down. But short-term directional forecasting is noisy. I built CaiSheng with a fundamentally different philosophy: CaiSheng don't guess stock direction. Instead, CaiSheng is an autonomous options trading desk that captures predictable volatility mispricings, variance risk premiums, and post-earnings implied volatility crush across US equities. At CaiSheng's core is a neuro-symbolic multi-agent architecture structured like an institutional trading courtroom. In Tier 1, two opposing specialist agents debate each setup: a Long Volatility Advocate hunts underpriced earnings jump variance, while a Short Volatility Advocate calculates post-event IV collapse. In Tier 2, an independent Model-Risk Critic cross-examines both theses, auditing quote freshness, spread widths, and temporal data leakage with unoverrideable NO_TRADE veto authority. Where CaiSheng truly shines is security and risk governance. I enforce a strict fail-closed boundary: no language model ever touches broker credentials or makes raw execution calls. Once an idea survives debate, pure deterministic Python math takes over. Our symbolic engine recalculates Black-Scholes Greeks and runs the trade through a 20-point deterministic risk gate. It programmatically bans naked options, caps maximum portfolio loss strictly to $500 per trade, enforces buying power limits, and mandates delta neutrality (|Net Δ| ≈ 0.0). Approved strategies route natively through Alpaca as atomic Level-3 Multi-Leg orders (OrderClass.MLEG), completely eliminating leg-out execution risk. CaiSheng integrates Alpaca's FastMCP Server V2 for real-time tool discovery and leverages our CLI for preflight verification. An autonomous dual-ledger background runner continuously reconciles local SQLite audit logs against live broker fills, guaranteeing zero orphaned orders and cryptographic provenance.