Mzzzzz

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

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.

It is not only quant research. We also have live trading: https://edotenv.com/blog/long-horizon-planning

For now it is only facing LLM/Agent.

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.

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

It is both. We can use the same setup for both RL and Benchmarking.

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...

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...

test