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

Converting data to time series


A time series is a sequence of data points where each data point is associated with a particular time.

For example, the adjusted close of a stock is the closing price of a stock on a particular day. The time series data is stored in an R object called a time series object and it is created by using the function ts() in R.

The basic syntax of ts is given here:

ts(data, start, end, frequency) 

Here:

  • data: It is a vector or matrix containing the data values

  • start: It is the starting point or time of first observation

  • end: It is the time point of last observation

  • frequency: It is the number of data points per unit time

Let us consider a vector which is given by the following code:

> StockPrice<
-c(23.5,23.75,24.1,25.8,27.6,27,27.5,27.75,26,28,27,25.5) 
> StockPrice 

Now convert it into a time series object, which can be done with the following code:

> StockPricets<- ts(StockPrice,start = c(2016,1),frequency = 12)   
> StockPricets  

The output is as...