Pioneering the Future of Machine Learning for the Financial Markets
Our Expertise
Learning Representations of Financial Markets
We are pioneering novel methods of representation learning tailored for low-signal, high-noise environments. Representation learning is the foundation of modern artificial intelligence: it allows machines to uncover hidden structure in raw information and transform it into concise, meaningful representations. Instead of relying on hand-crafted rules, models learn to detect the underlying patterns that make sense of the observed complexity.
This principle powers today’s most advanced systems—from image recognition and speech understanding to large language models that map human knowledge itself. By transforming raw data into meaningful abstractions, representation learning has enabled AI to achieve what was once thought impossible.
In financial markets and other complex systems, the challenge is even greater: patterns are fleeting, signals are buried in noise, and outcomes hinge on subtle interdependencies. Advancing representation learning in this setting means building models that can perceive structure where others see only randomness—unlocking deeper insight, sharper forecasts, and more resilient strategies.
Complex systems and the importance of context
We are advancing new approaches to contextual learning, enabling models to adapt their understanding based on surrounding information. Contextual learning is the ability of AI systems to interpret data not in isolation, but in relation to what comes before, after, and around it. Just as meaning in language depends on context, so too does the significance of patterns in complex data.
This principle has driven many of AI’s most important breakthroughs. It allows language models to hold conversations that flow naturally, vision systems to interpret scenes rather than objects in isolation, and recommendation engines to anticipate needs rather than simply react to past choices. By leveraging context, machines move from basic pattern recognition toward deeper comprehension.
In financial markets and other adaptive systems, context is everything: the same signal can imply opportunity or risk depending on the surrounding conditions. Developing stronger forms of contextual learning means equipping models to recognise these shifting relationships, discern regime changes, and capture the dynamics that govern complex, interconnected environments.
A Model for All Markets?
Building on advances in representation and contextual learning, we are developing a large-scale framework for modelling the structure and temporal evolution of interconnected financial markets. Markets are not isolated price series but adaptive networks of interacting behaviours, where the actions of one component has the potential to reverberate across the whole.
Traditional approaches struggle with this interdependence, often treating signals as either independent or static. They are also ill-equipped for environments where information is scarce, and valuable insights are buried in a large volume of uninformative data.
Our models, in contrast, are specifically designed for these low-signal settings. They uncover latent structures, discover changes in dependencies, and trace how information propagates through networks of assets, industries, and sectors. This allows our models to identify valuable patterns and predictive signals even when traditional methods fail.
The outcome is a framework that represents markets as complex, evolving systems of interacting behaviours. By modelling these interactions, we gain structured insight into how systemic change unfolds, uncover hidden channels of influence, and create a foundation for a range of downstream tasks — in our primary use case, forecasting the divergence and convergence of future asset prices, but equally applicable to broader analyses of market structure and adaptive strategy design.
Neural Arbitrage Engine
Our goal.
Statistical arbitrage has long been a cornerstone of quantitative finance. By exploiting, in the most elegant of ways, short-term mispricings and price convergence between related assets, it systematically discovers opportunities grounded in well-defined rules and historical relationships. Yet its very elegance is also its greatest weakness: strategies built on fixed statistical patterns are easily replicated, and once widely adopted, their edge rapidly erodes.
Our Neural Arbitrage Engine is designed to move beyond these limitations. It serves as the alpha-generation system at the heart of systematic trading strategies. Rather than depending on static correlations or surface-level metrics, it anticipates how assets behave in relation to one another within an evolving market network.
A key innovation is its ability to detect transient interdependencies — relationships that emerge, evolve, and dissolve across markets and instruments. By modelling and inferring these shifting relationships through message passing and adaptive network inference, the engine captures opportunities that conventional approaches overlook.
This deeper understanding of market structure provides a foundation not only for trade selection but for the design of fully adaptive trading systems. In contrast to traditional statistical arbitrage, which is limited and imitable, the Neural Arbitrage Engine delivers a more robust, resilient, and forward-looking approach to systematic trading.
Our Research made Public
At Autonomous-Fox, our research is proprietary, and the core methods that we develop are not disclosed. From time to time, however, when we develop innovations that do not directly affect our intellectual property, we plan to share by publishing to the machine learning community. We also plan on maintaining a public GitHub, where selected research code and tools will be released.