Oxford Algorithmic Trading Programme

Take human bias out of the equation with automated trading.


6 weeks, excluding


8-10 hours per week,
entirely online

Learning Format

Weekly modules,
flexible learning

You'll walk away with:


A comprehensive overview of algorithmic trading, including its history, benefits, challenges, rules and processes.


The skills required to plan and implement your own algorithmic trading strategies, and assess the efficacy of a trading model in a real-world market environment.


An informed view on the future of systematic trading and how it’s impacted by emerging technologies such as artificial intelligence and machine learning.


Knowledge, insights, and frameworks from University of Oxford faculty, and a host of international industry experts.

Who is this programme for?

Traders, investors, and financial professionals who want to understand what algo trading has to offer as a trading strategy.

Business leaders who want to leverage algo trading in their firms, and IT and technology professionals who want to understand the strategies behind the creation of rule-driven trading models.

MBA graduates and new hires to quantitative finance firms will be able to gain an essential introduction to the world of automated trading.

Risk, compliance, and legal professionals seeking to improve their professional knowledge of algo trading and its regulatory framework.

Programme outline

This supported and interactive online programme in algorithmic trading is the first of its kind in the world.1 Over the course of the programme, you'll engage with the following subject matter:

1 Saïd Business School (Jul, 2018).

Module 1:
Introduction to classic and behavioural finance theory
Module 2:
Systematic trading and the state of the investment industry
Module 3:
Technical analysis and the methodology of trading system design
Module 4:
Working with an algorithmic trading model
Module 5:
Evaluation criteria for systematic models
and funds
Module 6:
Future trends in algorithmic trading

This is a non-technical programme and does not require the ability to code.

Programme Director

Nir Vulkan

Associate Professor of Business Economics at Saïd Business School, University of Oxford

Nir is a leading authority on e-commerce, market design, applied research, and hedge funds. Alongside his role at Oxford Saïd, he is also a Fellow of Worcester College.

Your Guest Experts

This programme combines esteemed academics and leading industry players to ensure that you get a full 360-degree perspective on algo trading.


Co-chief Executive Officer of Man AHL
Co-founder of Aspect Capital
Co-founder and CEO of MoneyFarm


Director of Quantitative Strategies Oversight at Tudor Capital Europe LLP
Chief Scientist at Man AHL
CEO and Co-founder of Cantab Capital Partners


Associate Professor at Oxford-Man Institute of Quantitative Finance at the University of Oxford, former Quantitative Strategist at leading high-frequency-trading firms
Managing Director and Portfolio Manager, Systems Group at Tudor Investment Corporation
Director of the Oxford-Man Institute of Quantitative Finance and RAEng/Man Professor of Machine Learning at the University of Oxford


Chair of Quantitative Finance and Risk Management at IPAG Business School
Partner and Co-founder at Oxford Asset Manager
Non-Executive Director of Morgan Stanley,Public Speaker, Educator, Finance and Risk Expert
James Robert Ackerley, Associate Director, HSBC image

“This course provides an important foundation. It requires commitment, but is engaging, enjoyable, and will hopefully open doors.”

James Robert Ackerley, Associate Director, HSBC

An online education that sets you apart

This Oxford Algorithmic Trading Programme is delivered in collaboration with leaders in digital education GetSmarter. Join a global community of professionals who have already had the opportunity to:


Experience a flexible but structured approach to online education as you learn around your schedule


Enjoy a personalised and supported online learning experience


Earn a certificate of attendance from Saïd Business School, University of Oxford

View Programme Prospectus

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