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

Neural network


In the previous section, I implemented a model using two classes. In reality, it might be possible that traders do not want to enter trade when the market is range-bound. That is to say, we have to add one more class, Nowhere, to the existing two classes. Now we have three classes: Up, Down, and Nowhere. I will be using an artificial neural network to predict Up, Down, or Nowhere direction. Traders buy (sell) when they anticipate a bullish (bearish) trend in some time and no investment when the market is moving Nowhere. An artificial neural network with feedforward backpropagation will be implemented in this section. A neural network requires input and output data to the neural network. Closing prices and indicators derived from closing prices are input layer nodes and three classes (Up, Down, and Nowhere) are output layer nodes. However, there is no limit on the number of nodes in the input layer. I will use a dataset consisting of prices and indicators used in the logistic...