Experiment design

We replayed 45 days of Hyperliquid BTC-PERP ticks. Control group: RSI/MACD bots with no governance. Treatment: HyperAgent Analyst → Boss → Executor, trained to uphold VaR < 1.5% and to publish thought logs.

Risk and performance delta

Legacy bots breached VaR eleven times and recorded an annualized Sharpe of 0.48. HyperAgent breached once (auto-paused immediately) and posted 1.91 Sharpe in this replay. The 48-hour window documented in the previous article illustrates the same discipline in live logs; past results, replayed or live, do not predict future performance.

Governance telemetry

Because each HyperAgent role writes decisions to Redis (`agent:logs:{user}`) and SQLite, compliance teams can audit prompts, edges, and overrides. Legacy bots gave us exactly zero artifacts beyond candle prints.

Cost efficiency

Multi-role agents dynamically downshift models. The system routes to optimal models based on task complexity while Executor runs deterministic Python. This keeps inference spend predictable even when desks run 24/7.

Integration path

Inside `/console/agents` you can import a JSON strategy, attach prompts, and set escalation contacts. HyperAgent keeps all custody trustless yet finally gives you the intelligence layer Hyperliquid traders have been requesting.