Syllabus
- Provide a strong foundation in the tools and techniques used in algorithmic trading.
- Cover everything from basic programming concepts to advanced trading strategies and methods for research into new alpha sources.
- Apply everything in hands-on projects throughout the course.
12 Lecture Weeks:
- Module 1: Intro and Industry Overview
- Module 2: Data and Features
- Module 3: Statistics and Time Series
- Module 4: Machine Learning
- Module 5: Trend Following
- Module 6: Carry and Volatility Strategies
- Module 7: Mean Reversion
- Module 8: Forecasting Models and Factor Investing
- Module 9: Order Execution and Market Making
- Module 10: Portfolio Theory and Allocation
- Module 11: Backtesting and Performance
- Module 12: Risk Management
Assesment
- One written assessment at the end (PDF + Python Notebook), describing a strategy in detail: its behaviour, its rationale (with quoted references if applicable), implementation and performance and limitations and room for improvements. Marks for sensibility of coverage and exposition, for following the methodology, etc. (i.e., good performance only is not sufficient – you have to display it and explain it).
Summary
- Key takeaways
- Designing your own strategies
- Doing active research
- Sourcing and cleaning data
- Algorithmic Trading Certificate (ATC): A Practitioner’s Guide
- Keeping tech stack up-to-date
- Maintenance and Improvement
- Next steps
Final Project
- The ATC concludes with a practical final project that gives you the opportunity to implement the knowledge and skills you have acquired during the course of the programme.
Download Event PDF