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

Summary


Now let us recap what we have learned so far in this chapter:

  • How it is very important for analysts pursuing their career in financial analytics to learn R

  • Installation of R and its packages

  • The basic objects in R are character, numeric, integer, complex, and logical

  • Commonly used data types in R are lists, matrices, arrays, factors, and DataFrames

  • Reading files from external data files such as CSV and XLSX, and particularly from online sources and databases in R

  • Writing files to CSV and XLSX from R

  • Writing different types of expression, such as constant, arithmetic, logical, symbols, assignments, and so on

  • Write user-defined functions

  • Ways of calling of user defined functions and inbuilt functions

  • Running R programs from the console window and by sourcing saved files

  • The use of conditional decision-making by using if and else statements

  • The use of loops such as for and while