Reduce cost of implementation and time to market of algo-trading strategy deployment by using our cutting edge machine learning, backtesting, risk management and trade execution tools.
Increase trading profitability without adding undue risk by utilising our deep neural network tool to discover high performing strategies.
Expand strategy R&D through our deep expertise in data management, machine learning and trade expression.
We work with your data or use our proprietary data to train our models and demonstrate performance through backtesting and live simulation.
Matthew began his 15 year career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions and tech firms in quantitative trading, risk modeling and machine learning. He holds a PhD in Applied Mathematics from Imperial College, a MS in Parallel and Scientific Computation with distinction from the University of Reading and has held postdoctoral and visiting professor appointments at Stanford University and University of California, Davis respectively. He has published numerous academic papers, been featured in Bloomberg Markets and the Financial Times as an expert in AI and finance, and serves on the program committee of multiple computational finance workshops.