AI-native quantitative trading
The trading firm where
AI agents do the quant work.
We want to build a $10B-scale trading firm run by a handful of quants, a handful of devs, and swarms of AI agents.
Team
Two quants from one of the leading systematic hedge funds.
Product
An AI-native trading infrastructure on which swarms of agents independently research, simulate, and ship strategies.
Bigger picture
A working example of AI that learns from real-world experiments. Empirical AI is a step toward AGI.
The problem
The quant trading industry is bottlenecked by high-quality human quants.
Every major firm — Citadel, Two Sigma, Jane Street, QRT, HRT, Millennium — is locked in an aggressive bidding war for the same few hundred people every year. The entire competitive dynamic of the industry is "who hires the best quants". We know this firsthand because we lived it.
AI now lets us produce that quant intelligence directly, instead of buying it on the human market — and the size of the existing bidding war is the strongest possible demand signal that what we're building is needed.
The shift
From discretionary, to algorithmic, to agent swarms.
The last big change in quant finance moved trading from discretionary to algorithmic. The next one moves it from human quants to agent swarms.
What AI can do alone today
Given a pipeline, clean data, and an instruction, it produces in minutes a strategy that takes a human weeks.
What AI cannot do alone
Build the trading framework — signal processing, simulation, live execution. And prove that simulation matches reality.
To automate quant trading you need a trading infrastructure that AIs can drive on their own — to write strategies, simulate them, iterate on the feedback, and push them live.
Our solution
An AI-native trading infrastructure × a swarm of agents.
The same kind of trading stack the big firms run on, but designed so an AI can drive it: a simulator on which agents run a research loop to converge on working strategies.