Algorithmic Trading Certificate (ATC): A Practitioner’s Guide

Starts Monday 30th October. 

This course is an up-to-date version of the course, Algorithmic Trading Strategies. Nick taught a version of this course at University College London, to Computational Finance and Risk Management PhD and MSc students from 2015 – 2023, and online through QuantsHub and other platforms. With over 500 students having successfully completed earlier versions of this course, and the curriculum continually being re-jigged, it seemed an appropriate time for a larger update to broaden the perspective and make the course more applied, with the goal of having students be able to implement methods, models and frameworks themselves. Changes to previous courses include:

• In addition to looking at the returns of QIS, Risk-premia or Factor-based strategies (e.g., trend-following, carry, etc and equities momentum, value, etc factors), this course explicitly considers larger returns-forecasting models and the value of including factors as exogenous features.
• While including much of the university course material, this course goes beyond the merely academic to focus on practical implementations. The academic literature is of interest only in that we can use it as a starting point for delving deeper into real-world applications.
• We expect a working knowledge of programming and can focus more on greater value added material

Unlike the earlier courses, the new Algorithmic Trading: Practitioners Guide course takes a hands-on approach to building trading pipelines, from data to features to modelling to allocation to execution to performance measurement, guiding the student through common practice as well as areas of innovation. It is designed to go far beyond the purely academic remit of the UCL course and the more practical online course.

Assessments
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).