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

Chapter 6.  Trading Using Machine Learning

In the capital market, machine learning-based algorithmic trading is quite popular these days and many companies are putting a lot of effort into machine learning-based algorithms which are either proprietary or for clients. Machine learning algorithms are programmed in such a way that they learn continuously and change their behavior automatically. This helps to identify new patterns when they emerge in the market. Sometimes patterns in the capital market are so complex they cannot be captured by humans. Even if humans somehow managed to find one pattern, humans do not have the tendency to find it efficiently. Complexity in patterns forces people to look for alternative mechanisms which identify such complex patterns accurately and efficiently.

In the previous chapter, you got the feel of momentum, pairs-trading-based algorithmic trading, and portfolio construction. In this chapter, I will explain step by step a few supervised and unsupervised...