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Trading Agent Swarm
10 specialized agents coordinating over async message bus: +4.62% returns across 250 days of S&P 500 data.
The problem
Coordinating specialized trading roles (analysts, traders, risk managers) autonomously across a portfolio usually means either centralizing control or losing risk oversight.
- One person's trading logic becomes a single point of failure for the whole strategy.
- Centralizing decisions kills the specialization that made each role valuable in the first place.
- Risk oversight is either baked in from the start or bolted on too late to matter.
What NEO built
NEO built 10 specialized agents across 4 tiers on an async pub/sub message bus, with dual-layer risk validation (pre-trade approval and post-trade stop-loss) and backtested them on 250 days of S&P 500 data.
The result
+4.62% returns, 10 agents
The swarm returned +4.62% ($46,155 profit) on $1M capital with a 0.46% max drawdown across 86 trades.
From the blog · 8 min
Stock Trading Agent Swarm: How NEO Coordinated 10 Specialized Agents on a Simulated Portfolio
NEO built a multi-agent trading simulation with 10 specialized agents coordinating over an async message bus, achieving +4.62% returns across 250 days of S&P 500 data with an 86.9% order approval rate.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Build a swarm of specialized trading agents (analysts, traders, risk managers) coordinating over a message bus with pre-trade approval and post-trade stop-loss, and backtest it on historical data.
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