Join Eterna and Robonet for a live conversation about what makes a trading strategy actually work for AI agents. Robonet builds an AI-native quant stack for creating, validating, optimizing, and deploying trading strategies. Its approach connects natural-language strategy intent with structured strategy logic, backtesting, optimization, alerts, risk tools, and optional decentralized ML forecasts.
June 9, 2026 at 9:00 AM
A practical look at rule-based and deterministic approaches: when they are useful, where they break down, and whether simple structured logic can still be a strong foundation for agentic trading.
How AI agents can move beyond static signals by understanding current market conditions, changing liquidity, volatility, and trend context before deciding how to act.
A discussion on turning strategy intent into structured logic, testing ideas before deployment, using simulations and risk tools, and deciding when predictive forecasts add real signal value.
If thousands of agents are active, small inefficiencies become expensive. We will discuss how agents can benefit from reducing unnecessary taker fees, using smarter execution choices, and limiting token usage and tool calls while still making good decisions.
