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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 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

Portfolio construction


Investors are interested in reducing risk and maximizing return of their investment and creating a portfolio does this job provided we have constructed it by keeping in mind the investor risk-return profile. I will guide you through creating an efficient frontier that can help you to measure risk with respect to your return expectation. For that, I will start extracting data for four securities. The first line of code creates a new environment to store data; the next few lines are for symbols list, data starting date, and extracting data using getSymbols():

>stockData<- new.env() 
> symbols <- c("MSFT","FB","GOOG","AAPL")
>start_date<- as.Date("2014-01-01")
>getSymbols(symbols, src="yahoo", env=stockData, from=start_date)
> x <- list()

The next for loop stores individual stock data in a list, and calculates the day's gain and a data frame consisting of closing prices of all stocks in portfolio:

>for (i in 1:length(symbols)) {
  x[[i]]...
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