We are building the next frontier of fundamental equity.
Bayswater deploys a quantitative approach to fundamental strategies, using artificial intelligence to extract signals from complex unstructured data and transform them into adaptive strategies at a speed and scale no human team can match.
Founded by engineers and computer scientists.
Our autonomous system processes vast data and operates across three integrated levels: the portfolio adapts to the macro landscape and maintains market and factor neutrality, strategies are generated and evolved through evolutionary algorithms, and trades are executed from active strategies in real time - all governed by disciplined risk controls and researcher oversight.
Evolutionary algorithms form the foundation of our framework. Every strategy is systematically hypothesised, statistically validated, deployed under controlled risk parameters, and iteratively evolved or sunsetted according to performance data.
Data flows into three integrated systems.
Synthesises macroeconomic and market data to maintain factor neutrality. Dynamically managing exposures, hedges, and risk across the entire book.
Strategies are generated and evolved through evolutionary algorithms. Each is autonomously hypothesised, statistically validated, deployed, and iteratively evolved or sunsetted.
Processes multi-source data to identify opportunities aligned with active strategies and manages position entry, sizing, and exit.
Our platform exposes the underlying AI decision logic, equipping researchers with the visibility,
governance frameworks, and supervisory controls needed to manage risk at each level.
The demo below uses sample data to illustrate a small subset of our platform capabilities. Institutional investors interested in a comprehensive view of our systems are invited to contact us.
Previously ran long/short equity strategies at multiple hedge funds with a focus on European markets, and worked as a quantitative trader at a high-frequency trading firm.
Published research in high-frequency trading at Imperial College London, machine learning research at Queen's University Belfast, with research affiliations at the University of Oxford and UCL.
Imperial College London, MSc Computer Science
Queen's University Belfast, BEng Aerospace Engineering
Previously owned and led development of a portfolio management and trading platform at Schroders, as an engineer working closely with the Head of Investment.
Academic research on Bayesian optimisation for portfolio selection and sentiment-driven investment decisions at Imperial College London, with a mathematics-focused management background from LSE.
Imperial College London, MSc Computer Science
London School of Economics, BSc Management