Book Image

Learning Quantitative Finance with R

By : Dr. Param Jeet, PRASHANT VATS
Book Image

Learning Quantitative Finance with R

By: Dr. Param Jeet, PRASHANT VATS

Overview of this book

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
Table of Contents (16 chapters)
Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

zoo


The ts object has its limitations in representing the time series. It is used for representing equally spaced data. It cannot be used to represent the daily level stock prices as stock prices are equally spaced between Monday to Friday, but it is not the same case for Friday to Monday and in case there is market holidays on weekdays. This type of unequally spaced data cannot be represented by a ts object.

zoo is flexible and fully equipped to handle unequally spaced data, equally spaced data, and numerically indexed data.

Let us first install and load the zoo library. This can be done by executing the following code:

> install.packages("zoo") 
> library(zoo) 

Now we will discuss how to represent different time series scenarios using zoo.

Please note we will be using a common dataset for all the examples.

Constructing a zoo object

In order to create a zoo object, an ordered time index and data are required. So we are going to construct a zoo object.

Let us first import a few rows of our...