AUTONOMOUS-FOX
Our Mission
Deep learning transformed language, vision and biological sciences. A central breakthrough was learning reusable representations that many different applications could build on. Bringing that breadth of capability to financial markets is the challenge we are taking on.
We are building a general-purpose modelling layer that learns how assets behave, individually and in relation to one another, and how that behaviour changes as market conditions evolve. Our ambition is one model whose representations can support multiple trading strategies simultaneously, alongside execution, risk and market making.
Our mission is to advance a new generation of AI that can uncover the structure of complex systems—where countless changing relationships combine to produce behaviour that humans cannot reason through. We are pursuing that ambition in financial markets, developing the scientific foundations for a deeper understanding of how markets work.
Why This Matters
Much quantitative research starts with a specific trading question and builds a model to answer it. We are pursuing a broader objective: learning the underlying market structure from which many different strategies can draw information.
A strategy may capture its own signals well while leaving wider market relationships outside its scope. A shared representation could bring that context into its decisions without requiring each strategy to model the wider system independently. Each would retain its own objectives, signals and trading decisions, while drawing on a common foundation of research.
This requires sustained fundamental research whose usefulness extends beyond any single strategy. It is difficult to make that work a primary objective within organisations focused on immediate trading performance. We have built the lab around it.
Everything begins with behaviour. In language, words acquire meaning through their arrangement and context. In biology, amino acids combine into proteins whose structure determines how they function. In both, identifiable building blocks provide a starting point for learning more complex organisation. Financial markets have observable prices and transactions, but no equivalent, established vocabulary for describing how they behave. Our research asks what those building blocks should be, and how their combinations and interactions give rise to the behaviour of the wider market.
Structure matters more than scale alone. Larger models do not automatically produce deeper understanding. What matters is what a model learns, which distinctions it preserves and whether it can separate meaningful structure from noise.
Depth over breadth. We pursue a small number of hard problems with the attention they require.
Research first. We develop original AI methods, with scientific progress judged by rigorous evidence and its usefulness established through application.
Built to compound. We pursue advances that can strengthen the common foundation on which many strategies depend.
