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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
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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

xts


xts is an extensible time series object which carries all the features of a zoo object. It consists of a matrix and index which has to be time-based. There are two ways of constructing xts objects: one is by calling as.xts and another is constructing the xts object from scratch.

Construction of an xts object using as.xts

Let us read a few lines of our sample data through zoo and construct the xts object by executing the following code:

> StockData <- read.zoo("DataChap4.csv",header = TRUE, sep = ",",format="%m/%d/%Y",nrows=3) 
> matrix_xts <- as.xts(StockData,dateFormat='POSIXct') 
> matrix_xts 

This gives the following output:

Volume

Adj.Close

Return

12/12/2016

615800

192.43

0.13

12/13/2016

6816100

198.15

2.97

12/14/2016

4144600

198.69

0.27

The composition of the xts object can be given by the following code:

> str(matrix_xts) 

This generates the following output:

An xts object on 2016-12-12/2016-12-14 contains the following:

  Data: num [1:3, 1:3]...
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