- 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.