A quantitative trading desk wanted to combine machine-learning signals with LLM reasoning across the S&P complex — MES futures and SPY / SPX options — without ever letting a language model make an unchecked trade. Blue Avanti architected an AWS-native platform where the engine makes the recommendation, deterministic risk validates it, and a human trader gives the final approval before any order reaches the market — with the LLM never in the tick loop and every decision auditable.
The desk wanted an LLM's reasoning about event risk, discipline, and rule conflicts — but a hallucinating or slow model anywhere near live execution is a catastrophic risk. Three problems had to be solved together.
LLMs are compelling for trading judgment, but a model that hallucinates, stalls, or simply makes a poor call cannot be allowed near live orders. The desk needed the reasoning without ever surrendering control to it.
MES, SPY, and SPX are all bets on the same index. Per-instrument limits let an unintended 3× S&P position hide across three symbols — including through options delta, 0DTE gamma, and pin risk near expiry.
Every decision had to be reconstructable — what the model saw, what the LLM advised, and why a trade was taken or blocked — for both discipline (replaying losing days) and a defensible answer to AI-governance risks like OWASP “excessive agency.”
A layered AWS-native architecture where a deterministic quant core scores every bar, a governed agent reasons only at decision points, and the engine's recommendation clears deterministic risk before a human trader gives the final approval — always.
Real-time bars and features flow through the quant stack; a SageMaker-hosted signal model (Chronos-2, benchmarked against XGBoost / LightGBM) scores each 5-minute close. Hard risk — daily stop, max size, kill switch, news lockouts — is deterministic and always enforceable.
Only when a setup crosses a confidence threshold is a Bedrock agent (AgentCore + Strands + Guardrails) invoked — advisory only. It gathers evidence through least-privilege tools and cited RAG over the trade journal and an external market-intelligence store, then returns a structured approve / defer / size-down / reject with rationale.
A deterministic portfolio risk engine on EKS converts every position — options included, via delta — into net S&P delta-equivalent exposure and caps it at the portfolio level. What clears risk is a recommendation, not an order: a human trader reviews it and gives the final approval before anything reaches the broker.
The platform's outcomes are architectural guarantees, not hopeful metrics — safety, auditability, and control that hold even when the AI fails, times out, or is simply wrong.
The agent runs only after the signal crosses a threshold, its output is advisory, deterministic risk applies hard limits, and a human trader approves every order before it reaches the market — layered defense against LLM “excessive agency.”
Every score, rationale, and risk decision is persisted, so losing sessions can be replayed bar by bar and trading discipline improved over time.
Config-driven instrument abstraction and multi-agent orchestration let the platform grow from MES to the full S&P complex without touching the safety core.
We got the judgment of an LLM without ever handing it the keys — the engine recommends, deterministic risk screens it, and a trader makes the final call — and every decision is on the record.
Blue Avanti designs agentic AI systems where deterministic controls keep final authority and every decision is auditable. Start with a free discovery call.