AI Trading Agent
A production multi-agent LLM system that runs continuous market analysis across timeframes. Each agent has a specific role — macro context, sentiment, execution, risk advisory — and they coordinate through a shared context store. Every decision is scored, logged, and reversible.
TECH STACK
The Problem
Traditional alerting is dumb. You set thresholds, you get notifications. But markets don't move in isolation — context matters, regimes shift, and a static rule set can't reason about nuance.
The Approach
Built a multi-agent LLM system where each agent owns a slice of the analysis. Agents share state through a context engine and coordinate via a message bus. The system runs autonomously, with hard guardrails and a complete audit trail for every decision.
Key Features
Autonomous Reasoning
Agents run macro and micro analysis on a continuous loop, no human in the loop required for assessment.
Confidence Scoring
Every assessment includes a confidence score so downstream systems can weight responses appropriately.
Full Audit Trail
Every decision, prompt, and response is logged. Nothing happens without a paper trail.
Guardrails
Hard constraints prevent the agent from taking actions outside its defined risk envelope.