A laptop with R and R Studio installed on it.
Privileges to install packages on your laptop.
An internet connection.
This course covers the fundamentals of data wrangling in R’s tidyverse ecosystem. All of the lessons are structured around real world equity data. The goal of this course is to get you up and running as quickly as possible doing actual finance related analysis. Towards this end we discuss a variety of tidyverse functions including: mutate, summarize, filter, and group_by. We also discuss how compose these functions efficiently with the pipe operator (%>%).
Tutorial 01: a brief introduction to financial data wrangling in R
Tutorial 02: R programming basics
Tutorial 03: data frame - the analysis work horse
Tutorial 04: the summarize() function and calculating volume weighted average price (VWAP)
Tutorial 05: subsetting data with indexing and slicing
Tutorial 06: tidyverse and the pipe operator (%>%)
Tutorial 07: filtering data frames with dplyr
Tutorial 08: calculating daily returns with vectorized operations
Tutorial 09: group_by() - unleashing the power of summarize() and mutate()
Tutorial 10: a closer look at the Rstudio IDE
Tutorial 11: working with scripts and projects
Capstone Project 1: The Leverage Effect in Monthly ETF Returns and Volatility
Financial data analysis can become complex quickly. And as the complexity of your tasks grow, you will need additional tools in your tidyverse toolbox. This course builds on the Basic Wrangling course, and provides these additional tools. All examples will involved real-world options data. We discuss how to write function and how functions can be used to keep your code more organized. We also discuss the concepts of the tidy/relational data, which underpin the tidyverse philosophy, and show how to join tables together with the inner_join and left_join functions. The course ends with an introduction to advanced iteration techniques using the purrr package.
Tutorial 01: writing functions
Tutorial 02: understanding relational (tidy) data
Tutorial 03: joining data frames with inner_join() and left_join()
Tutorial 04: advanced iteration with the purrr package
Tutorial 05: a deeper look at the tidyquant package
Capstone Project 2: Comparing Investment Strategies via Data Computation
Visualization is an essential tool in financial data analysis. It is an invaluable for exploring data to arrive at conclusions, and is an unparalleled means of communicating your conclusions to other, be they a team of experts, or a group of non-expert decision makers. In the course, we introduce the ggplot2 package and demonstrate a variety of plot types that are ubiquitous in finance. We’ll show how to make quick plots for your own analysis, and then how to polish those plots for persenting to others.
Tutorial 01: ggplot and the grammar of graphics
Tutorial 02: line graphs with geom_line()
Tutorial 03: bar charts with geom_bar()
Tutorial 04: scatter plots with geom_point()
Tutorial 05: small multiples with facet_wrap() and facet_grid()
Capstone Project 3: Comparing Investment Strategies via Visualization
This course is designed specifically for option professionals who want to apply R to their business. This course will cover the essential packages, patterns, and techniques for analyzing options data. All course work and examples will involve real-world equity option data from our data partners at Delta Neutral. After an in-depth look at the options data structer, we will introduce the bizdays and fOptions packages, which are essential for calculating prices and greeks on vanilla options. Then we will discuss typical coding patterns for calculating and grouping option PNLs. The course concludes with a coverage of machine learning technique that are relevant to modeling volatility, including regression, PCA, and GARCH.
Tutorial 01: an introduction to options analysis in R
Tutorial 02: getting the know the Delta Neutral data set
Tutorial 03: calculating business days with bizdays
Tutorial 04: prices and greeks with fOptions
Tutorial 05: implied volatities with fOptions
Tutorial 07: calculating and grouping option trade pnls
Tutorial 08: regression and PCA
Tutorial 09: GARCH modeling with rugarch
Capstone Project 4: A Correlation Analysis with the CBOE Volatility Indexes