AUTONOMOUS-FOX
AUTONOMOUS-FOX
LABORATORIES
About us
What we do....
We’re Autonomous-Fox Laboratories, an independent deep learning research lab focused on advancing AI for financial market modelling. Founded in 2025, the company is in its early stages operationally, but its research foundation is not. Our work builds on more than 12 years of applying machine learning to financial markets, spanning systematic trading, portfolio construction, statistical arbitrage, and market making.
We are building a general-purpose modelling layer for financial markets: one that learns and maintains coherent representations of market behaviour as conditions evolve, capturing structure at a depth and generality that narrow, task-specific models cannot achieve. This layer provides a reusable foundation across the trading and execution stack, from alpha generation through to execution, risk, and portfolio management.
Some are bulls, others bears. We are foxes. We craft intelligent systems to understand markets across all conditions.
Understanding market behaviour at a depth that has never been possible.
Embracing Complexity
Markets are not just a collection of independent prices. They are living networks of interaction, where assets influence one another through relationships that shift, strengthen, and dissolve as conditions change. Most models ignore this. They treat each asset in isolation, learn narrow patterns, and hope for the best. We think the connections between assets are where the real information lives. Our models are built to discover those connections and track how they evolve, not assume they stay the same.
Going Deeper Than Ever Thought Possible
Over the past decade, deep learning has revolutionised entire industries — while finance sat on the sidelines watching and waiting. AI has transformed vision, language, and biology, however financial modelling was left behind. The reason is simple: markets pose a much harder problem. Data is messy, relationships unstable, and structure always changing. Deep learning wasn’t built for that world — not then. But now that’s changing. Advances in machine learning and neural methods more generally, are finally giving us tools that can understand markets on their own terms, allowing us to build systems that can learn meaning, not just basic patterns. In 2025, the gap between deep learning capability and market reality is closing fast, and a new generation of quantitative trading models is about to emerge. At AF Labs, we intend to be right at the front of that renaissance.
Behaviours Over Statistics
Traditional statistics cling to assumptions of stability and order that markets rarely honour. They describe what has already happened and infer that it will happen again. Strip away the assumptions, and you're left with noise, not foresight. We view markets as a collection of assets exhibiting quantifiable behaviours. Behaviours constitute the very language of the market, and how those behaviours play off one another over time is the story. Deep learning is the mechanism through which we can finally learn to interpret that language.
A Model for All Markets
A Model for All Markets
We remain cautious using the F-word. We're not quite there yet. But our approach to behavioural modelling is deliberately agnostic to any single asset, market, or downstream task.
Rather than fitting to surface patterns, our models seek the latent structures that govern how assets behave and how those behaviours evolve and interact over time, forming a framework capable, in principle, of generalising across the entire market ecology.
In this view, behaviours form the vocabulary of the market's language, their dynamics form the sentences, and their interactions become the conversations through which structure emerges, followed by meaning for us to reason with.
We are building toward a foundation model for financial markets.
Experience and Expertise
16+
years modelling financial markets
12+
Years of Experience in Machine Learning
3
interconnected research streams
>80%
of capital directed to R&D
2025
the year we started building
Help Shape the Foundation
We are looking for exceptional researchers to join a small, focused team at a founding level. We are hiring senior research scientists, including co-founder level appointments, and doctoral-level researchers with deep expertise in representation learning, sequence modelling, probabilistic modelling, geometric DL, or related areas.
We value rigour, curiosity, and the ability to work across theory and application. If you hold a PhD in machine learning, computer science, mathematics, statistics, or a related field, and you want to work on questions that the rest of the field hasn't figured out how to ask yet, we would like to hear from you.