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

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]]...