8/4/2026 at 6:36:02 PM
by Mzzzzz
8/4/2026 at 9:40:17 PM
>Quant Trading RL Envs to Teach LLMs ResearchOh my Current Thing. This this enough current things?
by languagelearner
8/4/2026 at 9:30:42 PM
One thing I always think about whenever someone talks about solving investment is "and then what?"Say there's a crystal ball, wouldn't everyone use such crystal ball? Wouldn't crystal ball become illegal? Wouldn't crystal ball nullify the effects of things?
What am I missing, can someone from this field educate me on how this stuff scales?
by hmokiguess
8/4/2026 at 9:37:18 PM
this seems to be a common misconception, our envs use market data, but the goal is not (only) trading. Market data just happens to be a good source of hard data science tasks.Re trading: I’d argue there is no such thing as solving investment nor is there “the one profitable strategy”. Every decision from personal risk appetite to trading horizon changes what is the optimal strategy for you and there are multiple strategies that make money.
Also note that even the most profitable alphas are no crystal balls. Someone else mentioned 5% correlation to future return - depending on horizon and data such level of correlation can make 9 figure PnL and is by no means easy to achieve
by RuiWang0811
8/4/2026 at 8:29:15 PM
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?by cromwellian
8/4/2026 at 9:27:40 PM
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.by RuiWang0811
8/4/2026 at 7:06:06 PM
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.by ak_111
8/4/2026 at 7:21:04 PM
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...
by Mzzzzz
8/4/2026 at 7:43:15 PM
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.by ak_111
8/4/2026 at 9:30:46 PM
we do affine transformations of the data, so all return/ pnl measures are still the same as with untransformed data. The transformation doesn’t change the conditional distribution of the data, which is what alphas ultimately measureby RuiWang0811
8/4/2026 at 7:01:28 PM
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...by Mzzzzz
8/4/2026 at 8:54:10 PM
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?by jjallen
8/4/2026 at 9:28:23 PM
not sure about your background, the trace shows the feature engineering the LLMs didby RuiWang0811