Practical Quantitative Finance with R
About this Book
The book "Practical Quantitative Finance with R - Solving Real-World Problems with R for Quant Analysts and Individual Traders will be available soon. This book will provide all the tools you need to develop professional financial applications using R. I hope this book will be useful for quant developers, quant analysts, individual traders, R programmers, and students of all skill levels.
R provides a script-programming environment that allows for rapid prototyping of an idea, provides us instant feedback, and enables the data and result visualization in an efficient manner. R is especially strong in the finance community. In this book, I will choose R as our scripting environment, mainly because R is a free open-source language, and has strong packages in quantitative finance, as well as efficient data visualization power.
The main advantage of R is that it is free, extremely flexible and extensible. R is not only free, but also open source. You can see the source code, and change it as per your own requirements. People across different disciplines around world reviewed the core of the R system and contributed to make it better. You can use R to perform data processing and analysis and to produce a variety of graphics. R has a substantial collection of packages, which are written by experts in quantitative finance. That is why, whether you are a quant analyst/developer, or individual trader, you should find a set of functions that serve your purpose. The graphic system in R is one of the most powerful tools in this era, and you have full control over every part of graphics produced in R. R is now becoming one of the platforms to implement and prototype the research work, quant models, and trading strategies. You should be able to find an R package suitable for the most recent developments in quantitative finance.
I write this book with the intention of providing a complete and comprehensive explanation of R programming and usage of the relevant R packages in quantitative finance. The book pays special attention to creating various business applications and reusable R libraries that can be directly used in real-world finance applications. Much of this book contains original work based on my own programming experience when I was prototyping quant models, pricing framework, and trading strategies in quantitative financial field.
Practical Quantitative Finance with R provides everything you need to create your own advanced applications in quantitative finance and reusable packages using R. It shows you how to use R and relevant R packages to create a variety of financial applications that range from simple market data collection, data visualization, quantitative analysis to pricing equity options and complex fixed income instruments, machine learning, trading strategy development, and portfolio optimization. I will try my best to introduce you to R programming in quantitative finance in a simple way – simple enough to be easily followed by a quant or individual trader who has basic prior experience in developing business applications using R.
This book contains:
- A complete, in-depth instruction on practical quantitative finance programming with R. After reading this book and running the example programs, you will be able to create various sophisticated business applications in quantitative finance.
- Ready-to-run example programs that allow you to explore the quantitative finance programming techniques described in the book. You can use these examples to understand how the algorithms in finance work. You can modify the code examples or add new features to them to form the basis of your own projects. Some of the example code listings provided in this book are already sophisticated programming packages in quantitative finance that you can use directly in your own real-world business applications.
- Many R functions in the sample code listings that you will find useful in your quant development. These functions include charting libraries, various quantitative analysis models, pricing engines for options and fixed income instruments, machine learning for trading strategy development and back-testing, and the other useful utility classes. You can extract these functions and plug them into your own business applications.