Online Course Running Time: 12 Hours
About the course:
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.
Beyond 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
Schedule
| Day 1 | Day 2 | |
| Lecture 1: Foundations of kdb+ and the q programming language | Lecture 1: Introduction to Data science and machine learning crash course | |
| Tutorial 1 | Tutorial 1 | |
| Lecture 2: Working with tables and q-sql | Lecture 2: Tree-based regression and classification, random forests | |
| Tutorial 2 | Tutorial 2 | |
| Lecture 3: Big data in kdb+/q | Lecture 3: Neural networks in kdb+/q | |
| Tutorial 3 | Tutorial 3 | |
| Lecture 4: Tickerplant architecture for data captures | Lecture 4: Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search | |
| Tutorial 4 | Tutorial 4 | |
| Lab | Lab |
Syllabus
- Foundations of kdb+ and the q programming language
- Working with tables and q-sql
- Big data in kdb+/q
- Tickerplant architecture for data captures
- 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
Instructors
Jan Novotny: 
Jan is a former front office quant at HSBC in the eFX markets working on predictive analytics and alpha signals. Prior to joining HSBC team, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano. He co-authored number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.
Paul Bilokon: Founder, CEO, Thalesians
Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.