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

What we know

We are Rui and Michael and we’re building EdotEnv (https://edotenv. com): self-improving RL environments from Quant Trading workflows.

Why this counts

With all the benchmaxxing around, evals saturate and become meaningless for model comparison. Useful benchmarks should increase in difficulty as models advance.

What precedes this

Back in our Quant jobs, Michael and I saw that the market has exactly this property: markets became more efficient as people profited from trading inefficiencies, making new profitable strategies harder to find and old ones decay over time

Where this fits in Signal Ledger

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The lens

Back in our Quant jobs, Michael and I saw that the market has exactly this property: markets became more efficient as people profited from trading inefficiencies, making new profitable strategies harder to find and old ones decay over time. In our environments, we give LLMs a quant trading workflow and evaluate their performance on out-of-sample data: build predictive features/ models.