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- Hacker News
- Strong agree that static evals saturate — the decay property of markets is the genuinely useful part: historically, quant alpha decays on the order of 30-50% per year as capital crowds in, so a live market eval is self-difficultating, exactly what model comparison needs once benchmarks plateau. The hard part I'd flag is comparability: market paths are stochastic, so two runs of the same model can land on wildly different difficulty depending on the realized path — without controlling for that, the eval measures luck more than the model. Options that work: fixed-seed regime paths with resampled baselines, or bootstrapped difficulty metrics (percentile of PnL against a distribution of random strategies) instead of raw return. The second hard part is reward shaping for RL: sparse PnL rewards over multi-day horizons give terrible exploration, so you'll likely need shaped intermediate rewards (execution quality, information state) to get gradients flowing at all. Also worth publishing: variance across seeds for each model — that's the number that tells users whether a 2% delta is signal or noise. Are you planning to ship fixed seed regimes, or is path stochasticity part of the point?by modgate
- We use real historical market data for the environments. There is no parametric modelling involved.
The decay property refers to alpha that we give the agent for trade in the env - they are generated as tools.
by RuiWang0811 - I'm not fully clear on this. Is this a quant trading benchmark for LLMs or a RL env?
- It is both. We can use the same setup for both RL and Benchmarking.by Mzzzzz
- Curious as to whether you guys have shown that post-training has actually improved performance of agents in your environments.by balanbong
- We have experiments showing that agents at least can learn from the environment by overfitting on train. But we do not yet have full post train runs, mainly due to time. But follow our blog/X where we’ll regularly update our researchby RuiWang0811
- >Quant Trading RL Envs to Teach LLMs Research
Oh my Current Thing. This this enough current things?
- languagelearner, I think you need to spend more time learning languagesby RuiWang0811
- 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
- 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
- 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 - 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 - 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
- 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 - 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
- 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
- This is very cool, but who is the ideal customer here? I used to work at one of the top tier shops and we had multiple teams whose entire responsibility was building and maintaining our simulation environments.by jfrbfbreudh
- We are selling to frontier labs, e.g. OAI and Ant, and fintech companies that are trying to build agentic trading system but do not have those internal quant setups, e.g. Coinbase, Kalshi etc.by Mzzzzz
- You guys might want to look at Alphadidatic [0] and it might be worth your time to see if that model published 5 months ago still generalizes. Also, all the prompts are tuned for Claude 4.6 and I needed to throwout or rewrite all my agents, prompts, skills for Claude 5 which fortunately seems like it handles recursive self-improving agents natively.
I can't justify spending $1k - $2k a month for real time options data and compute for what is in my 401k. I guess the question I have is would your tool help me trade these strategies and more important test them on a few hundred a month?
by dataviz1000 - Very interesting. I am curious: Is the "test" part actual live trading, or is it also a backtest?
I am asking because I see the agents can do websearch, so how is Alphadidatic preventing the agents from just looking at the asset's performances and cheat?
Back to our product. It is not 2C yet. So we are building the RL Envrionments for finetuning LLM, not providing a personal trading agent. However we can also build a professional trading harness using the setup we have. If you are interested, let's keep in touch: michael.zhang@edotenv.com
by Mzzzzz