Case Study
Agora Quantum: Autonomous AI Hedge Fund
9 agents debating over 8,000 securities daily
A fully autonomous trading platform where specialized AI agents with distinct investment philosophies debate positions through structured rounds. Running 24/7 on a Raspberry Pi.
!Single-Model Bias
Traditional AI trading systems use one model with one perspective. But markets are complex—different strategies work in different conditions. A single viewpoint creates blind spots.
- Single-model systems missing diverse perspectives
- No systematic debate or challenge of positions
- High infrastructure costs for sophisticated trading
- Lack of transparency in AI decision-making
Multi-Agent Consensus
Nine specialized agents with distinct investment philosophies debate through three structured rounds. Trades only execute when multiple perspectives reach consensus.
- 9 agents: 4 Analysts, 2 Researchers, 1 Trader, 1 Risk Manager, 1 Fund Manager
- 3-round debate protocol with structured challenges
- Black-Litterman portfolio optimization
- Running entirely on Raspberry Pi
Architecture
The system uses LangGraph for agent orchestration with PostgreSQL for state and Prometheus for monitoring.
Timeline
Key Lessons
Multi-agent debate surfaces insights single models miss
Structured consensus prevents overconfident positions
Sophisticated AI doesn't require expensive infrastructure
Transparency through debate logs builds trust in autonomous systems