Course outline

1. Basics of Python: data types, loops, functions, list comprehensions and a glimpse of objet oriented programming. (3h)
2. Advanced Python: functional programming, generators... and an introduction to the numpy library. (3h)
3. Data Analysis with pandas: I/O operations. Working with dates/time. Financial panel data: the pandas library. (3h)
4. High-frequency Data and Visualisation: Data visualization the matplotlib library. (3h)
5. Regression. Optimization: Interpolation and curve fitting. Symbolic mathematics in Python. Principal component analysis. (3h)
6. Stochastic Processes in Python: Generating random numbers. Monte Carlo simulations. Simulating stock price paths (Brownian motion with jumps). Value-at-Risk and Expected Shortfall. (3h)
7. Option Pricing: Option pricing with binomial trees and Monte Carlo simulation. Least-Squares Monte Carlo for pricing American options. (3h)
8. Portfolio Theory. Efficient frontier. PCA Analysis. Test for normality. (3h)

2019-2020

Project (group of 2 or 3 people)

• Option pricing - monte carlo method : Black Scholes diffusion is not enough + choose a convenient option for that
• Parallelism (concurrent.futures, Joblib, Dask...)
• Dash as a service to display web pages: Form to fill the parameters, display the pricing and draw some trajectories Expected by 6th December
• Readme file is mandatory: how to install, how to use the app

Exam

to be planned

Try out collaborative tools

• course #3: https://codeboard.io/projects/134893
• https://codeshare.io/