London: Big Data, High-Frequency Data, and Machine Learning with kdb+/q. 27th - 28th June

Big Data, High-Frequency Data, and Machine Learning with kdb+/q Workshop

London 27th & 28th June

25% Super Early Bird Discount until Friday 10th May

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.

This event is in conjunction with The Thalesians.

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

  • Schedule
Time Day 1 Day 2
08:30 – 09:00 Registration and welcome, a tour of Level39 Registration and welcome
09:00 – 10:00 Lecture 1: Foundations of kdb+ and the q programming language Lecture 1: Introduction to Data science and machine learning crash course
10:00 – 10:30 Tutorial 1 Tutorial 1
10:30 – 11:00 Coffee break Coffee break
11:00 – 12:00 Lecture 2: Working with tables and q-sql Lecture 2: Tree-based regression and classification, random forests
12:00 – 12:30 Tutorial 2 Tutorial 2
12:30 – 13:30 Lunch Lunch
13:30 – 14:30 Lecture 3: Big data in kdb+/q Lecture 3: Neural networks in kdb+/q
14:30 – 15:00 Tutorial 3 Tutorial 3
15:00 – 15:30 Coffee break Coffee break
15:30 – 16:30 Lecture 4: Tickerplant architecture for data captures Lecture 4: Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search
16:30 – 17:00 Tutorial 4 Tutorial 4
17:00 – 18:00 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 & Senior Quantitative Consultant, BNP Paribas

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.