Practical Applications in Quantitative Finance

About the course

This is a hands-on, practical course on Quantitative Finance with applications using R and Python, two of the most widely used open-source software for data analysis. This course aims to attract undergraduate students that are aiming to translate theoretical concepts learned on the core finance courses at FGV-EAESP to practical applications that can guide decision making.

The course will be structured in topics that are of interest to Finance practitioners, aiming to include, but not limited to: collecting and organizing financial data, equity valuation, sensitivity analysis and simulation, portfolio optimization, and backtesting. As a final evaluation, students are expected to deliver a capstone data application project showcasing at least one of the topics covered in the course. All applications will be hosted online to enable students to use the capstone project as a showcase of their acquired skills. The course will also host guest presentations from leading industry practitioners and software developers focused on financial applications using open-source languages, where students can interact, ask questions, and get to know more about the possibilities of applying programming, data science, and data analysis skills in the financial industry.

1. Getting started

To make things easier, ensure to install these packages in your computer and load it at the beginning of every session - I’ll make sure to update this list whenever needed throughout the sessions:

# Package names
packages <- c("tidyverse","tidyquant","tidymodels","xts", "glue","scales", "ggthemes","highcharter")

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())

if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

# Load all packages
lapply(packages, library, character.only = TRUE)
2. Using Quarto

In this course, you’ll be assigned with three data cases, where you’ll need to manipulate code and write your insights altogether. I want to encourage you to give Quarto a try.

To install Quarto, follow this link and choose your Operating System. RStudio will automatically locate it and make it as an option whenever creating a new file with Ctrl+N. Why you should give Quarto a try:

  1. It has multi-language support (Python, R, Julia, JavaScript), parses equations and mathematical notations via pandoc, and integrates seamlessly with GitHub
  2. Advanced document formatting and output options: you can choose pdf, html, docx, or even a reveal.js presentation (like the ones from this course!)
  3. It is easy, intuitive, and lets you focus on the most important aspect of your work
3. Replications

Along with the slides, each lecture will also contain a replication file, in .qmd format, containing a thorough discussion for all examples that have been showcased. This file, that will also be posted on eClass®, can be downloaded and replicated on your side.

To do that, download the file, open it up in RStudio, and render the Quarto document using the Render button (shortcut: Ctrl+Shift+K).

You can find the permanent links to the replication files (.qmd) below:

  1. Bridging Finance with Programming - access here
  2. Collecting, Organizing, and Manipulating Financial Data - access here
  3. Manipulating Time Series Data - access here
  4. Data Visualization - access here

Contents

Below you can find the persistent links to all lectures of the course. As they are continuously updated with fixes and new implementations, you might expect some changes from time to time in the contents of each file.

Note
  1. Hit F for full-screen mode
  2. If you are interest in getting a .pdf version of the slides, hit E to switch to print mode and then Ctrl + P