“Any scientific statement is objective only to the extent that it has mathematics in it.”
Algorithmic Sciences researches, builds and reviews the systems that quantitative trading depends on: the procedures that decide whether a strategy is real, the infrastructure that tests it, the portfolio it joins, the orders that carry it to market, and the controls that watch it once it trades.
We started as a trading research firm, and the work for clients grew out of that. The standard did not change: an idea is treated as wrong until data it has never seen says otherwise, and a simpler system is preferred to a cleverer one that is harder to check.
The methods do not depend on the asset. We work with equities, futures, currencies, options, rates and crypto assets, from daily bars down to tick and order book data.
Ben Racz
Founder, Managing Director and Head of Quantitative Analysis
Ben Racz is the founder and managing director of Algorithmic Sciences, and its Head of Quantitative Analysis. He leads the firm’s work on institutional validation procedures and on systems for building and executing portfolios of intraday trading strategies, using statistical analysis and robustness testing.
He has managed complex software projects since 2001. His specialties are the validation of manual, semi-automated and automated trading strategies, distributed research infrastructure, execution and order-management systems, and risk management and monitoring systems.
Before Algorithmic Sciences he co-founded a data-driven business consultancy, a data-science-based wholesale trading company, and an information security consultancy. The security work covered security auditing, ISO 27001 auditing and policy, Sarbanes-Oxley compliant infrastructure operations and PCI-DSS vulnerability testing for multinational corporations.
He was one of the founding faculty of the Institute of Algorithmic Trading and Applied Computational Finance, where the founding faculty set the curriculum and the assessment standards. He is also one of the contributing authors of Humanity’s Last Exam, a benchmark of expert-written questions used to measure the reasoning of AI systems.