Preface

Learning Quantitative Finance with R explains practical examples of quantitative finance in the statistical language R. This book has been written with the intention of passing knowledge to people who are interested in learning quantitative finance with R. In this book, we have covered various topics, ranging from basic level to advance level. In particular, we have covered statistical, time series, and wavelet analysis along with their applications in algorithmic trading. We have also done our best to explain some applications of machine learning, risk management, optimization, and option pricing in this book.

What this book covers

Chapter 1, Introduction to R, explains basic commands in R. It starts with the installation of R and its packages and moves on to data types, DataFrames, and loops. This chapter also covers how to write and call functions and how to import data files of various formats into R. This chapter is meant to provide a basic understanding of R.

Chapter 2, Statistical Modeling, talks about the exploratory analysis like common distribution, correlation, measure of central tendencies, outlier detection to better understand the data. It also talks about sampling and standardization/ Normalization of the data which helps in preparing the data for analysis. Further this chapter also deals with hypothesis testing and parameter estimation.

Chapter 3, Econometric and Wavelet Analysis, covers simple and multivariate linear regression models, which are the backbone of every analysis. An explanation of ANOVA and feature selection adds flavor to this chapter. We also build a few models using wavelets analysis.

Chapter 4, Time Series Modeling, in this chapter the author presents the examples to convert data in time series using ts, zoo and xts which works as the base for forecasting models. Then the author talks about various forecasting techniques like AR, ARIMA, GARCH,VGARCH etc. and its execution in R along with examples.

Chapter 5, Algorithmic Trading, contains some live examples from the algorithmic trading domain, including momentum trading and pair trading using various methods. CAPM, multifactor model, and portfolio construction are also covered in this chapter.

Chapter 6, Trading Using Machine Learning, shows how to model a machine learning algorithm using capital market data. This covers supervised and unsupervised algorithms. 

Chapter 7, Risk Management, in this chapter the author discusses the techniques to measure market and portfolio risk. He also captures the common methods used for calculation of VAR. He also gives examples of the best practices used in banking domain for measuring credit risk.

Chapter 8, Optimization, in this chapter the author demonstrates examples of optimization techniques like dynamic rebalancing, walk forward testing, grid testing, genetic algorithm in financial domain.

Chapter 9, Derivative Pricing, use cases of R in derivative pricing. It covers vanilla option pricing along with exotic options, bonds pricing, credit spread and credit default swaps. This chapter is complex in nature and require people to have some basic understanding of derivatives.

What you need for this book

First of all, you should make sure that R is installed on your machine. All the examples in this book have been implemented in R and can be executed on the R console.  R is an open source platform and can be installed free of charge for any operating system from https://www.r-project.org/. Installation guidelines are also found on this website. Once you have R on your machine, you can straightaway go to chapter 1 and  start. Each chapter explains about the required packages, shows how to install packages, and and tells the reader how to load them into the workspace.

Who this book is for

This book is written with the intent to pass knowledge to people who are interested in learning R and its application in analytics. However, we have covered examples from finance.  This book covers basic to complex finance examples, along with varying degrees of complexity of R coding. This book does not expect you to have prior R programming knowledge, however this expects you to have little bit knowledge of mathematical analytical concepts. Even if you are well versed with R, this book can still be of great help to you as it explains various live examples from the data analytics industry, in particular, capital markets.

Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: The quantmod package is used quite a few times."

A block of code is set as follows:

>getSymbols("^DJI",src="yahoo")
>dji<- DJI[,"DJI.Close"]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

corr<- rollapply(data,252,correlation ,by.column=FALSE)

For any R command we have used >, which means this command has been written on the command prompt, as  >, implies command prompt.

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Clicking the Next button moves you to the next screen."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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