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

Genetic algorithm


Genetic algorithm (GA) is a search-based optimization technique whose fundamentals come from the theory of genetics and natural selection. It is used to solve optimization problems in research and machine learning areas which are very difficult and time-consuming solutions by alternative methods.

Optimization is the process of finding a solution which is better when compared to all other alternative solutions. It takes the space of all the possible solutions as search space, and then finds a solution which is most suited to the problem.

In GA, possible candidate solutions constitute the population and they recombine and mutate to produce new children, and this process is repeated over various generations. Each possible candidate solution is given a fitness value based upon the objective function. The fitter probable candidates are given preference for recombination and mutation to yield fitter candidate solutions.

Some of the most important terminology associated with GA...