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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?
Earlier quoted context omitted.
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…
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.
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?
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
Oh my Current Thing. This this enough current things?
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?
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 str…
I'm not fully clear on this. Is this a quant trading benchmark for LLMs or a RL env?
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...
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?
Earlier quoted context omitted.
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 str…
What's the margins that makes this worth chasing then? That's the part I maybe don't quite understand, why would you pour a lot of money and resources into something that is stochastic at best?
On the other hand, you could also view quant research as some very hard research problems, so training LLMs on these problems could also enhance their general research capabilities.