Implement machine learning, time-series analysis, algorithmic trading and more
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.
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, time-series analysis, predictive modeling, and econometric analysis
- Build and analyze quantitative finance models using real-world examples
- How real-life examples should be used to develop strategies
- Performance metrics to look into before deciding upon any model
- Deep dive into the vast world of machine-learning based trading
- Get to grips with algorithmic trading and different ways of optimizing it
- Learn about controlling risk parameters of financial instruments