We are an early-stage UK research team developing systematic trading strategies using artificial intelligence — building scalable, data-driven investment infrastructure from the ground up.
We are a UK-based quantitative research team focused on applying AI to financial markets. Our work centres on developing and testing systematic trading strategies across multiple asset classes, with an emphasis on approaches that are robust, adaptable, and transferable across markets.
Rather than optimising for a single environment, we aim to identify patterns and structures that persist across equities, commodities, digital assets, and other accessible markets.
At our current stage, we are focused on research, experimentation, and validation — building the foundations for a scalable trading operation.
For developing trading strategies that generalise across markets and regimes.
We use machine learning to accelerate idea generation, feature discovery, and model iteration.
Strategies are designed to work across asset classes, reducing dependence on any single market regime.
Robust backtesting and stress testing to avoid overfitting and ensure real-world viability.
A small team moving quickly — testing, refining, and discarding ideas based on evidence.
Financial markets are becoming increasingly data-driven and competitive. Traditional approaches are being augmented — and in some cases replaced — by AI-enabled systems capable of processing information at scale.
Improve how trading strategies are discovered, tested, and validated before capital is put at risk.
Build systems that adapt as market conditions change, rather than relying on a single regime.
Develop domestic expertise in applied AI within financial markets — a strategically important capability.
To build a scalable, AI-native trading research and execution capability — with the right support, we plan to:
Expand from early-stage research into live deployment of production-grade systems.
Build proprietary datasets, research infrastructure, and a diversified portfolio of systematic strategies.
Establish a long-term, UK-based centre of expertise in AI-driven trading and quantitative finance.
Support would enable us to transition from early-stage research to a structured development programme.
Expanding our compute capacity and data coverage to support larger-scale experimentation and model training.
Increasing the volume and quality of experiments we can run — and the speed at which promising ideas reach production.
Hiring selectively to deepen capability across quantitative research, machine learning, and trading infrastructure.
We are seeking long-term partners who support early-stage, high-potential AI initiatives and recognise the importance of building capability from the ground up. Get in touch to start a conversation.