Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research
Non-saturating, market-derived RL environments that teach agents applied ML, long-horizon optimisation and adaptation under adversarial noise.
Summary
Rui and Michael are developing EdotEnv, a platform that creates self-improving reinforcement learning environments based on quantitative trading workflows. Their goal is to provide meaningful benchmarks that evolve in complexity as models improve, addressing the limitations of current evaluation methods in AI research.
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