q is a programming language for array processing, developed by Arthur Whitney on the basis of Kenneth E. Iverson’s APL. The kdb+ database built on top of q is a de facto standard technology for dealing with rapidly arriving, high-frequency, big data.
kdb+/q has taken the world of electronic, including algorithmic, trading by storm. It is used by numerous sell-side and buy-side institutions, including some of the most successful hedge funds and electronic market makers.
Beyong the world of electronic trading, kdb+/q is used in retail, gaming, manufacturing, telco, IoT, life sciences, utilities, and aerospace industries.
Your course will take you through the foundations of kdb+/q and explain why it is a language of choice for Big Data, high-frequency data, and real-time event processing.
We shall explain how to work with tables and q-sql effectively, how to set up tickerplants, real-time, and historical instances, and how to apply kdb+/q to machine learning problems.
We shall consider advanced applications to tree-based regression and classification, random forests, deep learning, Google DeepMind and Monte Carlo search, producing demonstrations on real-life data examples.
- Paul Alexander Bilokon, PhD
- Jan Novotny, PhD
The course will take place on Level39.
|08:30 – 09:00||Registration and welcome|
|09:00 – 10:00||Lecture 1: Data science and machine learning crash course|
|10:00 – 10:30||Tutorial 1|
|10:30 – 11:00||Coffee break|
|11:00 – 12:00||Lecture 2: Tree-based regression and classification, random forests|
|12:00 – 12:30||Tutorial 2|
|12:30 – 13:30||Lunch|
|13:30 – 14:30||Lecture 3: Neural networks in kdb+/q|
|14:30 – 15:00||Tutorial 3|
|15:00 – 15:30||Coffee break|
|15:30 – 16:30||Lecture 4: Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search|
|16:30 – 17:00||Tutorial 4|
|17:00 – 18:00||Lab|
- Data science and machine learning crash course
- Tree-based regression and classification, random forests
- Neural networks in kdb+/q
- Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search
Your course is designed to be self-contained. However, if you would like to read up on the content before, during, and/or after the course, we recommend the following books:
- Jeffry A. Borror. q For Mortals Version 3: An Introduction to q Programming. q4m LLC, 2015.
- Nick Psaris. Q Tips: Fast, Scalable and Maintainable Kdb+. Vector Sigma, 2015.
- Jan Novotny, Paul Bilokon, Aris Galiotos, Frederic Deleze. Machine Learning and Big Data with kdb+/q. Wiley, 2019.