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

Fast Fourier transformation


Fast Fourier transformation (FFT) is used for calculating the Fourier transform of discrete time series. You need to install the relevant package fft for FFT with the help of the following code:

install.packages('fft') 

Once you install the package, you have to load this into the workspace by using the following code:

library(fft) 

Fast Fourier transform of time series can be calculated using fft, and it accepts real or complex numbers series.

In the following example, dji is a real number time series:

> model<- fft(dji)  

The variable model is a transformed series which basically consists of complex numbers, and the real and imaginary parts can be extracted using the following code:

>rp = Re(model) 
>ip = Im(model) 

The following command calculates the absolute value of the model:

>absmodel<- abs(model) 

Let me plot this and see what information the absolute value of fft has for me:

>plot(absmodel) 

Figure 3...