This subject provides the opportunity to learn the tools and strategies used by investment and hedge fund managers to invest and trade in a number of financial instruments, including equities, futures, FX and ETFs in both low and high-frequency environments. Using financial data drawn from a variety of sources including Bloomberg, you will learn to model and benchmark these strategies using Python. The overall aim of this applied, research-focused subject is to explore quantitative trading strategies used to capitalise on market anomalies.
|Faculty||Bond Business School|
1. Apply advanced tools and algorithmic approaches to model risk/reward relationships. 2. Critically evaluate core issues related to trading and investing, and key trends in financial markets. 3. Critically evaluate the use of algorithmic approaches to advanced systems design.
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
Assumed Prior Learning (or equivalent):
Possess demonstrable knowledge in basic data science concepts and techniques to the level of a unit such as DTSC71-200 Data Science
Future offerings not yet planned.