We bring a scientific, empirical mind-set to investing. We act on evidence and strive to model and understand all aspects of the investment universe.
Technology is at the heart of everything we do at Man AHL.
State-of-the-art quantitative trading strategies necessitate state-of-the-art technology at all stages; from market data acquisition and initial research through to model implementation and trade execution.
All of our production systems use Linux. We are heavy users of Python and its full scientific stack including numpy, scipy, pandas and scikit-learn.
While most of our codebase is written in pure Python, when we push the performance boundaries of Python we use Cython/C/C++ as required. We implement the systems that need the highest data throughput in Java.
Man AHL’s engineering culture is open and collaborative: we try to help and learn from one another. We have a flat hierarchy with a 'no-attitude` feel, where discussions are always about ideas and never about titles.
We hold monthly internal seminars, bi-annual “FedEx days” (where we take projects we’re passionate about from idea to completion in 24 hours). All technologists are encouraged to attend the major conferences on the topics they are passionate about, be it databases, programming languages, DevOps, OS's, or machine learning.
We don’t believe in any barrier between technology and the wider business. As such, technologists have opportunities to learn and contribute to all parts of the business. We have regular internal courses and seminars on research and finance topics. Technologists at Man AHL have researched, back tested and implemented profitable trading models.