EdotEnv
A Quant Neolab building toward RSI via quant trading
About EdotEnv
A Quant Neolab building toward RSI by leveraging markets as self-improving research environments. They work with frontier AI labs and academic groups building research harnesses, evaluation benchmarks, and post-training environments.
Public traction evidence
Each signal links to the public source used for attribution.
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LLMs can't trade & higher reasoning doesn't help.
LLMs can't trade & higher reasoning doesn't help. we ran SOTA models for a 2y period. TL;DR: they suck & reasoning doesn't help. - no model comes close to simple static baseline - more reasoning ≠ better trading - when losing money Sol trades less instead of better details 👇
- Hacker News
Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research
Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research
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our RL envs teach the hardest data science problem (quant trading), and we need 3+ years of quant experience.
absolutely agree. our RL envs teach the hardest data science problem (quant trading), and we need 3+ years of quant experience. in quant industry this is mid/senior quant depending on how well your strategies performed
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Today we are launching EdotEnv, a Quant Neolab building toward RSI.
Today we are launching EdotEnv, a Quant Neolab building toward RSI. RSI needs a loop of increasingly difficult tasks, which markets naturally are: Trading well means markets become more efficient, this makes successful trading harder. Reach out if you are interested!...
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using LLMs to find alphas, what runs out first: money or tokens? lol we had LLMs build technical features & gave them models & backtests check Sharpe vs tokens & vs cost: Grok surprisingly good! seems like @SpaceXAI @elonmusk value real world tasks >>> benchmarks https://t.co/z0g9Mk2VN1
using LLMs to find alphas, what runs out first: money or tokens? lol we had LLMs build technical features & gave them models & backtests check Sharpe vs tokens & vs cost: Grok surprisingly good! seems like @SpaceXAI @elonmusk value real world tasks >>> benchmarks...
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LLMs do not reliably beat S&P500.
LLMs do not reliably beat S&P500. we tested SOTA models on a 2y window and none of them reliably beats the S&P 500. looking at a long window like this shows why 2 week experiments are just pure noise https://t.co/WzyXz6uV6I
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I attended an auto-research hackathon @agihouse_org today.
I attended an auto-research hackathon @agihouse_org today. This is what I learned from ex-OAI VP and Meta TBD researcher: - Current benchmarks saturate too fast - Auto-research agents should think about WHAT to do, not just HOW to do it Interesting cuz that’s exactly what...