[Last update Dec 2022]

This is a compound course on time series analysis, financial engineering and algorithmic trading featuring Python programming. Originally it was for night training sessions for new-hire in my previous institution (hedge fund), all quantitative analysts and macro analysts are supposed to have more than working knowlege of time series modelling, so this training session will discuss and demonstrate the underlying mathematical foundations, modelling and Python-related skills.

The tutorial splits in several parts, the Part I discusses various topics in time series analysis, the Part II will cover the classic financial engineering, the Part III discusses algorithmic trading implementation. Also there are some supplementary tutorials about algorithms and data structures as a starter reference for more complicated quant developing topics.

All trainees are required to have solid knowledge of linear algebra, probability theory, statistics and basic econometrics. All analysts are researchers too, with these knowledge, you can freely read institutional or academic researches and internalize them into your own analystical framework.

The attendees must have working knowledge of linear algrebra, statistics and probability theory, and ideally advanced econometrics skills too.

And also the attendees are assumed to have constant exposure of

• Python
• NumPy
• Matplotlib
• Statsmodels
• Pandas

It is advised that you download all material and browse in your own computer, since nbviewer has persistent LaTeX rendering errors.

Chapter 0 - Dates and Time in Python
Chapter 1 - Time Series Manipulation
Chapter 2 - Lag Operator and Difference Equation
Chapter 3 - Simple and Log Returns
Chapter 4 - Stationary Processes and Trend Removal
Chapter 5 - ARMA Models
Chapter 6 - ARCH and GARCH Models
Chapter 16 - Implementing Technical Indicators

Chapter 0 - Wiener Process and Random Walk
Chapter 1 - Bond Valuation and Modern Portfolio Theory
Chapter 2 - Capital Assets Pricing Model (CAPM)
Chapter 3 - Options Pricing
Chapter 4 - Rates Modeling
Chapter 5 - Value at Risk (VaR)