Book description
Implement machine learning, timeseries analysis, algorithmic trading and more
About This Book
 Understand the basics of R and how they can be applied in various Quantitative Finance scenarios
 Learn various algorithmic trading techniques and ways to optimize them using the tools available in R.
 Contain different methods to manage risk and explore trading using Machine Learning.
Who This Book Is For
If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required.
What You Will Learn
 Get to know the basics of R and how to use it in the field of Quantitative Finance
 Understand data processing and model building using R
 Explore different types of analytical techniques such as statistical analysis, timeseries analysis, predictive modeling, and econometric analysis
 Build and analyze quantitative finance models using realworld examples
 How reallife examples should be used to develop strategies
 Performance metrics to look into before deciding upon any model
 Deep dive into the vast world of machinelearning based trading
 Get to grips with algorithmic trading and different ways of optimizing it
 Learn about controlling risk parameters of financial instruments
In Detail
The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in toptier organizations and investment banks. This book is your goto resource if you want to equip yourself with the skills required to tackle any realworld 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.
Style and approach
This book introduces you to the essentials of quantitative finance with the help of easytounderstand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a reallife example.
Publisher resources
Table of contents

Learning Quantitative Finance with R
 Learning Quantitative Finance with R
 Credits
 About the Authors
 About the Reviewer
 www.PacktPub.com
 Customer Feedback
 Preface
 1. Introduction to R

2. Statistical Modeling
 Probability distributions
 Sampling
 Statistics
 Correlation

Hypothesis testing
 Lower tail test of population mean with known variance
 Upper tail test of population mean with known variance
 Twotailed test of population mean with known variance
 Lower tail test of population mean with unknown variance
 Upper tail test of population mean with unknown variance
 Two tailed test of population mean with unknown variance
 Parameter estimates
 Outlier detection
 Standardization
 Normalization
 Questions
 Summary
 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
Product information
 Title: Learning Quantitative Finance with R
 Author(s):
 Release date: March 2017
 Publisher(s): Packt Publishing
 ISBN: 9781786462411
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