LLM-based trading agents can be expensive and non-deterministic. The harder problem is identifying which strategies remain robust outside a backtest.
@roostooAI is building a system to search at scale:
> A reinforcement-learning agent factory designed to generate up to 1M agent configurations
> Live competitions evaluate them; qualifying agents can then be routed into DEX venues and vaults
The team reports 50K users on the pre-launch waitlist for its trading simulator.
Catch
@Edwardsolah at EASY Residency S4 Demo Day.