NEHAL PATEL GMO OCTOBER 21, 2008 ProActive for Algorithmic Trading
Algorithmic Trading at GMO What we do: Introduction to our business High-level grid/distributed computing goals ProActive
What we do Our Business: Invest money for clients Methodology: “Algorithmic Trading” Computers Math (some ) Finance Our Team: 12 members based in Boston and Minsk Educational background: Math, Physics, Computer Science Work Experience: Finance, Academics, Biotech, Internet startups
Current Environment Trades Currencies and futures on 24 hour, continuous basis ~4000 trades a day, $10 billion, 9 banks Two data centers, ~200 nodes, dozens processes
Motivation Basic Problem: How can you make money using computers for investing? Basic Wisdom Trade frequently, but smartly /Law of Large Numbers Ideas last for months not years Agility, Creativity Find the right edge Math and CS are equally important Embrace complexity, don’t sacrifice robustness
Methodology Mathematics Machine Learning/Data mining (Like Biotech, Web) Dynamic Programming (Like Airline Scheduling) Computer Science Java (~750,000 lines) Emphasis on infrastructure Utilize open source Data Sets: 4-5 Terabytes Real-time, Event processing: ~30K messages/s
Algorithmic Trading at GMO What we do: Introduction to our business High-level computing goals ProActive
Distributed Computing Tasks Real-time Stochastic Control Complex Event Processing Real Time Statistical Analysis Data Mining Large Datasets Batch Processing Historical Simulation
Distributed Computing Goals Style Distributed Objects living in a sea of multicast data Lightweight Agile Features Scalability P2P / Data Push-Pull Same Code both simulation and production
Algorithmic Trading at GMO What we do: Introduction to our business High-level distributed computing goals ProActive
Attractive Features POJOS Separation of Concerns High Speed simulation Toolset Focus Rapid Deployment Dynamic byte code
ProActive Consulting with ActiveEon Low Level Architecture Topics: Performance More Transparency for POJOs