Oxford Algorithmic Trading Programme

Understand the impact of automation, AI, and machine learning on systematic trading

Start date:

Duration:

  • 6 weeks

Time commitment:

  • Short programme

Location:

  • Online

Cost:

  • £1,900

About the programme

Learn how to integrate AI, robo-advisers, and cryptocurrency into your systematic trading strategy.

In a world wherefinancial tradingmoves at a pace that humans struggle to keep up with, an understanding ofalgorithmic tradingmodels andstrategiesbecomes increasingly beneficial.

This programme is intended for professionals working in the broader financial services industry, including investors,system traders,andquantitativeanalysts, as well as for technologists designingautomated tradingarchitecture, infrastructure, and solutions.

It equips you with a comprehensive understanding of the rules that drive successfulalgorithmic trading strategiesand hedge funds, as well as a grounded introduction to financial theory and behavioural finance.

Delivered in collaboration with digital education providerGetSmarter, a 2U, Inc. brand, you will be part of a community learning together through a dedicated Online Campus.

Programme changes

Following feedback we have added new optional activities that will allow you to actively engage with and build models in Python using an Integrated Development Environment. This gives you the chance to get a better sense of the real-world construction of an algo model. If you are proficient in Python, you will be able to apply what you have learnt, our non-technical participants will gain a first-hand look into how these models are built. This bridges the gap between theoretical content, and real-world application. No extra software is required. The modules are optional and will not be graded.

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.

Vinicius Karam

Quant Strategist for Fasanara Capital, GQS

Benefits

On completion of this programme, you’ll walk away with:

  • The ability to illustrate the methodologies used to model quantitative 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.

Programme impact

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.

Curriculum

Orientation module

Welcome to your online campus.

Applications to join the programme will be accepted until the end of the orientation module.

Module 1

Introduction to classic and behavioural finance theory.

Review the fundamentals of classical and behavioural finance, and how theoretical trading models are applied.

Module 2

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.

Module 3

Technical analysis and methods for trading system design.

Illustrate the processes used to model automated trading systems for different types of financial markets.

Build a simple time series momentum model in Python and evaluate the performance of a long-only strategy using the Sharpe, Sortino, and Calmar ratios.

Module 4

Building an algorithmic trading model.

Assess the efficacy of an algorithmic trading model within a live environment or real-world market circumstance.

Module 5

Evaluation criteria for systematic models and funds.

Assess whether a trading model or fund is worth investing in based on key evaluation criteria.

In Modules 4 and 5, you will build a simple volatility-scaled time series momentum model, weight signals using different timescales, and use more sophisticated methods.

Module 6

在算法tr未来趋势ading.

Formulate a view on the relationship between emerging technologies and the future of systematic trading.

Meet the faculty

Developed from research in collaboration with theOxford MAN Institute for Quantitative Finance,该计划是由近红外光谱沿袭教授领导,一个expert across all aspects of technology, economics, and finance. You will be guided by prominent industry thought-leaders who will share their experience and in-depth subject knowledge throughout the programme.

Experts and contributors

The thinking behind the programme

There are no standard courses on 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 strategies, 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.

The CPD Certification Service

This programme is certified by The CPD Certification Service. It may be applicable to individuals who are members of, or are associated with, UK-based professional bodies.

Contact

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.