About us
What we do....
We’re a newly established research lab dedicated to advancing machine learning for systematic arbitrage. Lean, independent, and focused, our work centres on developing neural models that power trading systems — systems built to infer structure, adapt to shifting market conditions, and uncover predictive signals on a large scale.
Our goal is simple: to build a new breed of neural intelligence for financial markets — the first generation of foundation models designed to power systematic trading systems.
Some are bulls, others bears. We are foxes, we craft intelligent systems to discover opportunities in all markets
Building the next generation of trading systems, powered by our proprietary neural arbitrage engine
Embracing Complexity
Markets are not just a collection of independent prices; they are adaptive webs of interaction. Our models capture interdependencies and transient relationships, uncovering mispricings that emerge when complex equilibria are temporarily disrupted.
Signal in the Noise
Market data is a living record of human behaviour under uncertainty. Every price change reflects decisions shaped by emotion, expectation, and the reflexive cascade of reactions to others’ reactions. Fear and greed play their part, but so too do envy, anticipation, hesitation, and collective momentum. While predicting exact price moves is notoriously difficult, predicting behaviour is far more tractable. The essential information is already embedded in the data — our task is to design models capable of interpreting it.
Nets not Trees
For years, financial data was viewed as too noisy, unstable, and heterogeneous for deep learning methods to be effective. Tree-based models dominated. But recent advances — in representation learning, contextual modelling, and generative architectures — have changed that. With the right approach, neural methods are no longer impractical; they are essential.
Slow, Not Shallow
We don’t try to compete with strategies chasing microseconds for an edge. Our forecasts span minutes to days. Our advantage lies in scientific rigour—identifying statistical patterns in market behaviour—not in being faster, but in being smarter.
A Model for All Markets
We hesitate to use the F-word --we're not quite there yet, but our design philosophy of behavioural modelling is deliberately agnostic to any single asset or market.
Rather than fitting to surface patterns, our models seek the latent structures that govern how systems evolve and adapt — a framework capable, in principle, of generalising across the entire market ecology.
In this view, behaviors form the vocabulary of the market's language, and their interactions are the conversations through which structure emerges, followed by comprehension.
Dare we suggest, it is a first step toward a foundation model for financial markets.
Experience and Expertise
15+
Years of Experience in quantitative financial market modelling
11+
Years of Experience in Machine Learning
100s
Trading Models Developed
100%
of researchers are PhDs
1
Objective
10+
Join the Team
We are looking for exceptional machine learning experts to join our small but growing team of research scientists at a founding level. In particular, we seek specialists in representation learning and probabilistic / generative modelling — in other words, wizards with the ability to bend neural architectures to their will.
If you hold a PhD in computer science or a related field (theorists included) and want to tackle some of the hardest problems humanity has ever faced, we’d love to hear from you.