I’ve been running a day trading bot for the last 4-5 months, and it’s a lot of work. It never comes down to agent skills or abilities, more so the data you can receive and how quickly you can receive it. 1m bars, MFE calculations, rvol, executable bid/ask, spread, volume, market/sector context, and then making sure none of it has look ahead leakage

I’ve been doing a fairly similar experiment, but I ended up moving in almost the opposite direction than what this framework purports. deterministic code decides what is actually legal to trade, handles sizing/risk/execution, and an LLM (nanobot architecture) only gets to rank the already valid candidate set. If the model fails or times out, deterministic ordering takes over, so only the -nth degree of data actually makes it to the non-deterministic part (haha).

The hard part hasn’t been making the agents smarter/skillset but getting clean, fast data, preserving exact order/fill lineage (Postgres) and separating bad selection from bad execution or exits without leaking future information into the analysis

The multi agent debate stuff is interesting, but if every agent is reasoning over the same stale or incomplete inputs, I’m not convinced you gain much. I’ve built PoCs for my same project, and a round robin of LLMs is just hallucination and self approval city. Better data and tighter decision boundaries seem to matter more