Develop the ability to implement your own trading strategies and demystify algorithmic trading.

While algorithmic trading has been around for a long time, the rise of machine learning and artificial intelligence (AI) has substantially accelerated its course of development. This allows trading algorithms to find market efficiencies and better recognise profitable patterns of their own accord, making trades at a very high frequency. The performance of these algorithms, therefore, often exceeds those of professional traders and financial advisors.

The Oxford Algorithmic Trading Programme from Saïd Business School, University of Oxford, 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. Over the course of six weeks, you’ll learn four principles established by Programme Director Nir Vulkan, to guide you through the process of evaluating an algorithmic trading model. You’ll also discover what biases are present in the market and how these behavioural traits create exploitable patterns. On completion, you’ll have the ability to assess whether a trading model or fund is worth investing in.

Is this programme for you?

This programme is aimed at working professionals in the trading and investment space or those not currently familiar with the algorithmic trading platform. It will be of value to anyone interested in hearing from industry leaders and top academics on the key issues facing algorithmic systematic traders. The programme will also interest professionals looking to learn about the predicted impact of AI and automation on the course of financial trading’s future. Private and part-time traders who wish to improve their trading abilities and experiment with algorithmic trading models will also benefit from the programme. This non-technical programme features optional activities for participants interested in engaging in the technical side of algorithmic trading by discovering how to build a model using Python. These activities are entirely voluntary and run through an integrated development environment. No extra software is required.