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

4 comments

• cromwellian21 minutes ago
  I'm skeptical frontier LLMs can actually do well (e.g. alpha 5%+) without fine-tuning, especially on historical market data. Presumably you support fine-tuned models?
• Mzzzzz1 hour ago
  Here is an example rollout trace with gpt 5.6 luna. Checkout if you are interested what kind alphas the agent found XD <a href="https:&#x2F;&#x2F;hub.harborframework.com&#x2F;jobs&#x2F;af0299f9-a3bb-44ea-8ced-7b56682bf739" rel="nofollow">https:&#x2F;&#x2F;hub.harborframework.com&#x2F;jobs&#x2F;af0299f9-a3bb-44ea-8ced...</a>
• ak_1111 hour ago
  if the data is not synthetic, how do you ensure that the LLM hasn&#x27;t learnt about this data for example from training on the Financial Times.
  • Mzzzzz1 hour ago
    We do a 2 step anonymisation: 1. Mask all symbols, timestamps etc. So the agents cannot infer the assets&#x2F;time periods. 2. Mathematically transform numerical values and returns. E.g. the market return targets are not the raw market returns, but neutralised and manipulated. So even the agents have certain bullish&#x2F;bearish biases, it cannot make use of it, as we use the transformed values.<p>In addition, we did not observe such behaviour in our traces. An example: <a href="https:&#x2F;&#x2F;hub.harborframework.com&#x2F;jobs&#x2F;af0299f9-a3bb-44ea-8ced-7b56682bf739" rel="nofollow">https:&#x2F;&#x2F;hub.harborframework.com&#x2F;jobs&#x2F;af0299f9-a3bb-44ea-8ced...</a>
    • ak_1111 hour ago
      ah i thought so, interesting. I think the challenge is to do 2 while still keeping it realistic, which actually gets very close to synthetic data generation.