Quantitative Trading with R

Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective

Quantitative Trading with R offers readers a glimpse into the daily activities of quants/traders who deal with financial data analysis and the formulation of model-driven trading strategies.

Based on the author’s own experience as a quant, lecturer, and high-frequency trader, this book illuminates many of the problems that these professionals encounter on a daily basis. Answers to some of the more relevant questions are provided, and the easy-to-follow examples show the reader how to build functional R computer code in the process.

Georgakopoulos has written an invaluable introductory work for students, researchers, and practitioners alike. Anyone interested in applying programming, mathematical, and financial concepts to the creation and analysis of simple trading strategies will benefit from the lessons provided in this book. Accessible yet comprehensive, Quantitative Trading with R focuses on helping readers achieve practical competency in utilizing the popular R language for data exploration and strategy development.

Engaging and straightforward in his explanations, Georgakopoulos outlines basic trading concepts and walks the reader through the necessary math, data analysis, finance, and programming that quants/traders rely on. To increase retention and impact, individual case studies are split up into smaller modules. Chapters contain a balanced mix of mathematics, finance, and programming theory, and cover such diverse topics such as statistics, data analysis, time series manipulation, back-testing, and R-programming.

In Quantitative Trading with R, Georgakopoulos offers up a highly readable yet in-depth guidebook. Readers will emerge better acquainted with the R language and the relevant packages that are used by academics and practitioners in the quantitative trading realm.

Table of Contents

Chapter 1 An Overview
Chapter 2 Tools of the Trade
Chapter 3 Working with Data
Chapter 4 Basic Statistics and Probability
Chapter 5 Intermediate Statistics and Probability
Chapter 6 Spreads, Betas and Risk
Chapter 7 Backtesting with Quantstrat
Chapter 8 High-Frequency Data
Chapter 9 Options
Chapter 10 Optimization
Chapter 11 Speed, Testing, and Reporting