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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
Author Profile Icon Dr. Param Jeet
Dr. Param Jeet
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Table of Contents (10) Chapters Close

Preface 1. Introduction to R 2. Statistical Modeling FREE CHAPTER 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

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...

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