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

Hilbert transformation


Hilbert transformation is another technique to transform time series and R uses the seewave package for this. This package can be installed using install.packages() and loaded into the workspace using the library() command:

> model <-  hilbert(dji, 1) 

The first parameter is the time series object which you would like to transform, and the second parameter is the sampling frequency of the wave. In the preceding example, I used dji as time series and sampling frequency as 1 to calculate the Hilbert transformation.

If you would like to know the output of the model then you should use the following code:

> summary(model) 
      V1          
 Length:2555 
 Class :complex 
 Mode  :complex 

The preceding output mentions the length of input data series is 2555 and the type of output variable named model is complex.

As the output is complex, we can extract real and imaginary values using the following code:

>rp<- Re(model)   
&gt...