Algorithmic Trading Certificate (ATC): A Practitioner’s Guide
Level up your career: Understanding advanced trading strategies, Impact of Machine Learning and methods for research into new alpha sources.
12 Lecture Weeks
Final Project + Certificate
Regional & Group Discount:
Global regional & group discount fee structures.
Goals of Class
- 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.
8 - 10 Hours Weekly: Time Commitment
Weekly recorded lectures accessible any time, from any global location, in your educational portal.
Students will have the opportunity to apply what they learn in hands-on projects throughout the course.
Dedicated Faculty Support available every step of the way. Weekly seminar & student forum.
Students are awarded the prestigious Algorithmic Trading Certificate from WBS Training.
- Module 1: Introduction
- Module 2: Statistics and Time Series
- Module 3: Features and Factors
- Module 4: Trend Following
- Module 5: Carry and Volatility Strategies
- Module 6: Machine Learning and other New Techniques
- Module 7: Trading and Execution
- Module 8: Backtesting and Performance Measurement
- Module 9: Allocation and Risk Management
This course is for:
Discretionary Traders / Risk Managers – Understand the mechanics of the market and develop the tools to devise and manage new and improved algorithmic strategies of different types including multi-asset strategies. Learn the importance of allocation frameworks, execution models and performance testing. Recognise pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders.
Algorithmic Traders / Quants – Appreciate when commonly-used strategies work and when they don’t. Understand the statistical properties of strategies and discern the mathematically proven from the empirical. Expand your technology toolkit to incorporate the latest techniques including open-source tools and models from other areas of the quant industry.
Academics / Students / Data Scientists – Gain familiarity with the broad area of algorithmic trading strategies. Master the underlying theory and mechanics behind the most common strategies. Acquire a solid understanding of the principals and context necessary for new academic research into the large number of open questions in the area.
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).