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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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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R FREE CHAPTER 2. Statistical Modeling 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

Multi factor model


The multi factor model can be used to decompose returns and calculate risk. The factors are constructed using pricing, fundamental, and analyst estimates data. I will use Systematic Investor Toolbox for this section.

The gzcon() function creates a connection and reads data in compressed format. Once we create a connection, we also have to close the connection.

The following commands explain this:

> con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
>  source(con)
> close(con)

The following function is used to fetch Dow Jones components data from http://money.cnn.com and join() is taken from Systematic Investor Toolbox:

>dow.jones.components<- function(){
url = 'http://money.cnn.com/data/dow30/'
   txt = join(readLines(url))
   temp = gsub(pattern = '">', replacement = '<td>', txt, perl = TRUE)
   temp = gsub(pattern = '</a>', replacement = '</td>', temp, perl = TRUE) 
   temp = extract.table.from.webpage(temp, 'Volume...
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