Our Mission
At Autonomous Fox Laboratories, we envision a future where financial markets are understood through the lens of advanced machine learning. By pioneering neural modelling at the frontier of AI, we aim to redefine how complex systems are analysed and forecast. Our vision is to build adaptive financial intelligence that sets the standard for the next generation of algorithmic trading systems.
Our Focus
Only One Objective
Our research is directed at a singular objective: to build adaptive intelligent systems for large-scale, medium-frequency trading, focused entirely on market-neutral arbitrage strategies.
This clear, singular focus demands solutions that are both technically rigorous and intellectually broad. We develop this intelligence specifically for proprietary traders, quantitative hedge funds, and electronic liquidity providers.
Scale and Generalisability
The intelligence we engineer is designed for maximum scale and depth of application.
Our models are not designed to exploit basic, market-specific patterns or short-term noise. Rather, we seek to uncover universal latent structures that generalise across markets, asset classes, and time — making our systems inherently agnostic to market and asset idiosyncrasies.
Our commitment to cross-market generalisation at scale defines both the purpose and direction of our research. By deliberately avoiding sprawling, multi-strategy agendas, we focus all resources on a single, well-defined and universal challenge. Through this disciplined focus, we aim to build scalable, intelligent systems with the potential to form a new lasting foundation for systematic arbitrage.
Our Approach: Adaptive Intelligence from First Principles
To address the challenge of large-scale, medium-frequency arbitrage, we cannot rely on off-the-shelf solutions. We take the state-of-the-art in machine learning and fundamentally tailor it—developing novel custom neural architectures and methods uniquely suited for this domain.
Uncovering the Hidden Structure
The reality of financial data is our starting point. We know that actionable information is scarce, the market is non-stationary (the rules are always changing), and everything is obscured by overwhelming noise.
So Our research moves decisively beyond traditional statistical modelling. We focus on uncovering the latent structure of asset behavior, building models that learn representations which capture complex temporal and cross-sectional dependencies—like lead–lag effects—and adapt fluidly to changing market states.
This methodology is what allows us to extract faint but consistently actionable signals, transforming raw, noisy observations into structured intelligence invisible to conventional approaches.
