• Intermediate or Experienced Programmer – preferably with working knowledge of Python – all examples in the class will be in Python, in Jupyter Notebooks, using Numpy and Pandas. While other languages may be considerably faster and more stable, Python is particularly noted for the speed of development, the access to a wide range of libraries (stats or ML-stack), and interoperability with other languages. Python also is a noted teaching language and a vast number of students have been educated in Python.

• Statistics or Econometrics or (Statistical) Machine Learning – We will assume strong familiarity with a lot of statistical concepts, although there will be some review of Time Series Econometrics (e.g., ARIMA models and concepts of stationarity). Understandings of basic distributions, statistical hypothesis testing and OLS will be a bare minimum.

• Basic Finance and familiarity with Economics – We will assume a basic knowledge of financial markets. Our focus will be markets which are less model-dependent (i.e., less Fixed Income and Vol, more FX, Equities, Futures), although there will be illustrations from each. We will also assume a basic understanding of utility theory from Economics. And Economics impacts all markets and knowing this will take the student a long way.

• A plus: Some familiarity with SDEs (e.g., as in Black-Scholes), and differential equations.

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.