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