4 comments

  • cromwellian 1 hour 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?
    • RuiWang0811 1 minute ago
      cofounder here - LLMs can do some model training, they train on ML competition data after all. But they do struggle with low signal to noise ratio of market data. But that’s exactly what our environments will teach.
  • ak_111 2 hours ago
    if the data is not synthetic, how do you ensure that the LLM hasn't learnt about this data for example from training on the Financial Times.
    • Mzzzzz 2 hours ago
      We do a 2 step anonymisation: 1. Mask all symbols, timestamps etc. So the agents cannot infer the assets/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/bearish biases, it cannot make use of it, as we use the transformed values.

      In addition, we did not observe such behaviour in our traces. An example: https://hub.harborframework.com/jobs/af0299f9-a3bb-44ea-8ced...

      • ak_111 1 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.
  • Mzzzzz 2 hours ago
    Here is an example rollout trace with gpt 5.6 luna. Checkout if you are interested what kind alphas the agent found XD https://hub.harborframework.com/jobs/af0299f9-a3bb-44ea-8ced...
    • jjallen 35 minutes ago
      I looked through the transcript/output of the model/run linked but didn't find anything that showed much, if any, alpha. Maybe I missed it?
      • RuiWang0811 0 minutes ago
        not sure about your background, the trace shows the feature engineering the LLMs did