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hmokiguess 51 minutes ago [-]
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?
RuiWang0811 44 minutes ago [-]
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
hmokiguess 27 minutes ago [-]
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?
Mzzzzz 19 minutes ago [-]
Quant trading is an extremely high margin business itself. Quant shops are printing billions and have on average much higher profits per employee than tech companies. So it is definitely a business worth doing.
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.
feelingsonice 35 minutes ago [-]
I'm not fully clear on this. Is this a quant trading benchmark for LLMs or a RL env?
Mzzzzz 27 minutes ago [-]
It is both. We can use the same setup for both RL and Benchmarking.
cromwellian 2 hours 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 54 minutes 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 3 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 3 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.
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.
RuiWang0811 51 minutes ago [-]
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 measure
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?
Mzzzzz 25 minutes ago [-]
It is the raw trace, so it is the most complete records but hard for human to read. We showcased some features they found in this research blog post: https://edotenv.com/blog/alpha-autoresearch
RuiWang0811 53 minutes ago [-]
not sure about your background, the trace shows the feature engineering the LLMs did
languagelearner 41 minutes ago [-]
>Quant Trading RL Envs to Teach LLMs Research
Oh my Current Thing. This this enough current things?
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
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.
In addition, we did not observe such behaviour in our traces. An example: https://hub.harborframework.com/jobs/af0299f9-a3bb-44ea-8ced...
Oh my Current Thing. This this enough current things?