High Frequency Finance and Algorithmic Trading

About

High frequency data should be the primary object of research because practitioners determine their trading decisions by observing tick-by-tick data. This leaves the practitioner with the problems of dealing such vast amounts of data using the right quantitative tools and models. This module provides in-depth understanding on 1) how markets are organized and regulated, 2) how traders analyze the big data from the high frequency markets, 3) how to design algorithmic trading strategies and 4) how to perform risk analysis in the context of high frequency finance. In addition to the theoretical aspects, students gain practical skills needed to analyse big data in finance, design and deploy algorithmic trading strategies. Further, apply the appropriate analysis and modelling techniques for financial risk analysis in the context of high frequency finance.

Course leader

Venkata Lakshmipathi Raju Chinthalapati

Target group

To apply for the course, you must have passed a Bachelor's degree in Business Economics, Business Administration, or an equivalent degree.

Furthermore, participants must have a good understanding of quantitative and statistical techniques that are relevant to undergraduate finance/economics. For example, understanding of probability, conditional probability, random variables, probability distribution, correlation and covariance, and matrix algebra. No programming background is required, but it helps.

Fee info