Oxford Algorithmic Trading Programme
- 6 weeks
- Short programme
About the programme
Learn how to integrate AI, robo-advisers and cryptocurrency into your systematic trading strategy.
In a world where trading moves beyond a pace for humans to keep up, an understanding of algorithmic trading models becomes increasingly beneficial.
The programme is intended for professionals working in the broader financial services industry and for technologists designing systematic trading architecture, infrastructure and solutions.
It equips you with a comprehensive understanding of the rules that drive successful algorithmic trading strategies and hedge funds, as well as a grounded introduction to financial theory and behavioural finance.
Delivered in partnership with online learning provider, GetSmarter, you will be part of a community learning together through a dedicated online campus.
An introduction to the programme
What our alumni say
The course was a turning point in my career. Using what I’d learned from the content... I opened my own investment firm... I recommend you take the course and open your eyes to the future of investments.
On completion of this programme, you’ll walk away with:
- The ability to illustrate the methodologies used to model trading strategies for different types of financial markets.
- An understanding of the fundamentals of classical and behavioural finance and how theoretical trading models are applied in practice.
- The ability to formulate a view on the relationship between emerging technologies and the future of systematic trading.
- Guidance from leading industry experts and Oxford Saïd faculty, and access to the official Oxford Executive Education Alumni group on LinkedIn.
Welcome to your online campus.
Applications to join the programme will be accepted until the end of the orientation module.
Introduction to classic and behavioural finance theory.
Review the fundamentals of classical and behavioural finance, and how theoretical trading models are applied.
Systematic trading and the state of the investment industry.
Interpret the historical and current state of systematic trading as well as the key challenges and opportunities faced by the industry.
Technical analysis and methods for trading system design.
Illustrate the processes used to model automated trading systems for different types of financial markets.
Building an algorithmic trading model.
Assess the efficacy of an algorithmic trading model within a live environment or real-world market circumstance.
Evaluation criteria for systematic models and funds.
Be able to assess whether a trading model or fund is worth investing in based on key evaluation criteria.
Future trends in algorithmic trading.
Formulate a view on the relationship between emerging technologies and the future of systematic trading.
The thinking behind the programme
There are no other standard courses in this subject in the world. The programme has been designed in collaboration with the Oxford MAN Institute for Quantitative Finance to provide a pragmatic, non-technical exploration of the world of algorithmic trading, demystifying the subject.
The programme is based on the four principles established by Programme Director Nir Vulkan, to guide you through the process of evaluating an algorithmic trading model. You will benefit from the latest insights of both financial experts and behavioural specialists drawn from across the University of Oxford and the investment industry.
Utilising Oxford’s unique blend of AI, Behavioural and Finance specialisms, the programme comprehensively explores both the human and technological factors of this rapidly evolving area, putting you at the forefront of available learning.
Babak Mahdavi-Damghani, Consultant at EQRC and doctoral researcher at the University of Oxford, shares an insight into the programme and the online learning experience.
Options for organisations
If you are looking to integrate Oxford online programmes with your organisation’s Learning & Development strategy, we have tailored solutions to help deliver an innovative learning experience across teams.