Fundamentals of electronic trading
What you'll learn
- Learn the fundamentals of electronic markets
- Learn the levels of data granularity and platform capacity, and why they are important for your objectives
- Get to know the concepts of latency and observability
- Learn how sequential decision making helps you achieve your goals in electronic markets
- Get to know the importance of patterns and seasonalities
- Prediction is king and ML/AI is not a magic wand
- No previous experience with electronic markets is necessary, you'll learn the fundamentals
- Some experience with the concept of modern markets in financial instruments and asset classes is desirable
May 21 2023 update: technical flaws finally fixed, thanks everyone who found them!
The course is designed to give an introduction to the fundamentals of electronic trading, a lucrative, growing, and highly competitive area of finance.
The intro lecture gives and exposition of the course and the intended audience.
We discuss evolution and the current state of electronic markets, why they work and how they are designed. A short discussion about likely future development of electronic markets is given.
A thorough introduction to limit order markets, how and why they work and why the continuous double auction design is used in many electronic markets. Various representations of limit order books and their evolution is discussed with some demonstrations and animations.
We then proceed to the super important topic of rules and regulations, how they are similar and different in various asset classes and regions.
Data providers are discussed in a concise fragment.
The core of the course in the discussion of sample data sets. I walk the audience through the structure of data sets. We discuss the two representative data streams: the order book state and the messages. I discuss levels of granularity in data and give a few examples of analytics helpful in making sense of the order book evolution. This section is supported by a reference to an academic paper which gives additional insights into the inner workings of limit order markets and the evolution of the order book.
In the section on practical issues we discuss the implications of the data rate, latency and time synchronization requirements, stable patterns and their importance in designing successful products and applications in electronic trading.
We also discuss serial data, prediction and handling of rare events.
I provide a short discussion on the two major types of activities in electronic markets: market making and agency execution.
The audience is provided with a focused discussion on reinforcement learning in electronic markets.
The course ends with a short discussion on the career choice and pros and contras of careers in an academic or a commercial environments.
The course contains a few assignments and coding exercises.
The course does not require any familiarity with electronic markets, but some introductory knowledge of markets is desirable.
Who this course is for:
- Aspiring quantitative researchers and quantitative traders in electronic markets who would like to get up to speed with fundamentals of electronic trading
- Anyone who is already employed in the industry and is choosing the direction of his/her professional journey and is interested in electronic trading
- Professional developers and data scientists working in quantitative finance and thinking about specializing in electronic trading
Vacslav Glukhov has a PhD in Electrical Engineering from Stanford University in California. He also holds a PhD in Theoretical Physics from Kyiv University in Ukraine. One of his early scientific contributions was a model of the lower region of the ionosphere important for radio communication and navigation. This model is still in use today. He also studied instabilities and flares in the solar corona. His Google Scholar profile gives a glimpse of his scientific interests.
He was running a team of bioinformatics specialists at Stanford Genome Technology Center when the Center was actively participating in the global effort to sequence the human genome.
After the completion of the Human Genome project, for the next 20 years Vacslav was designing and developing algorithms for financial applications. Between 2017 and 2021 his team in JPMorgan Chase, the largest US bank, was focusing, among other products, on an innovative AI-based trading agent aimed at improving the efficiency of the bank's electronic trading services. The build up of the Center of Excellence for Robust AI in Finance was the focus his efforts before leaving finance to pursue other endeavours.
He is based in London, UK.