Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research

EdotEnv (YC S26) develops reinforcement learning environments derived from real market data to train AI agents on quantitative trading tasks, offering a non-saturating benchmark that continuously increases in difficulty as agents improve. Unlike static synthetic environments, their market-based approach teaches long-horizon planning and strategy development under realistic, adversarial conditions where successful trading makes markets more efficient and trading edges decay. The platform enables agents to use professional tools and develop their own solutions while learning to adapt to shifting market regimes over extended time horizons.