Multi-Agent Architecture for Systematic Trading

Introduction

Modern systematic trading operations involve numerous concurrent processes: signal generation, risk assessment, execution optimisation, position monitoring, and portfolio rebalancing. Traditional monolithic systems handle these as tightly coupled modules. A multi-agent architecture offers superior modularity, fault tolerance, and scalability.

Agent Taxonomy

Our framework defines five core agent types:

1. Signal Agents

Specialised models that generate trading signals from specific data sources or strategies. Each signal agent operates independently with its own:

Data subscriptions

Model parameters

Signal emission frequency

Confidence scoring

2. Risk Agents

Monitor portfolio-level and position-level risk metrics in real-time:

VaR and CVaR calculations

Correlation regime monitoring

Drawdown tracking

Exposure limits enforcement

3. Execution Agents

Handle order management and trade execution:

Optimal execution scheduling (TWAP, VWAP, implementation shortfall)

Order splitting and venue selection

Slippage monitoring

Fill rate optimisation

4. Monitoring Agents

Provide system health and performance oversight:

Strategy performance attribution

Data quality validation

System latency monitoring

Alert generation

5. Orchestration Agent

The meta-agent that coordinates all sub-agents:

Priority arbitration between competing signals

Portfolio-level constraint enforcement

Agent lifecycle management

Conflict resolution

Communication Protocol

Agents communicate through an event-driven message bus with:

Typed message schemas

Priority queuing

Guaranteed delivery

Audit logging

Fault Tolerance

The multi-agent design provides natural fault isolation. If a signal agent fails:

Other signal agents continue operating

Risk agents automatically reduce exposure for the affected strategy

The orchestration agent can restart or replace the failed agent

No single point of failure cascades to the entire system

Conclusion

Multi-agent architectures represent the next evolution of systematic trading infrastructure. By decomposing complex trading operations into specialised, autonomous agents, we achieve superior reliability, scalability, and adaptability.

Neuground designs and implements these architectures for proprietary trading firms and institutional clients who require production-grade autonomous trading systems.