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

Walk forward testing


Walk forward testing is used in quant finance for identifying the best parameters to be used in a trading strategy. The trading strategy is optimized on a subset of sample data for a specific time window. The rest of the unused data is kept separate for testing purposes. The testing is done on a small window of unused data with the recorded results. Now, the training window is shifted forward to include the testing window and the process is repeated again and again till the testing window is not available.

Walk forward optimization is a method used in finance for determining the best parameters to use in a trading strategy. The trading strategy is optimized with in-sample data for a time window in a data series. The remainder of the data is reserved for out-of-sample testing. A small portion of the reserved data following the in-sample data is tested with the results recorded. The in-sample time window is shifted forward by the period covered by the out-of-sample test...