Description
Book SynopsisQuantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.
Trade Review"Through the lens of an expert practitioner, Harry provides a treatise on how to develop a robust quantitative trading strategy using 'R'. This is the first book written that has covered the ability of 'R' software to provide the infrastructure for an algorithmic trading system. Harry has written an instant classic that the professional and novice will find inherently useful. There is an inordinate amount of working 'R' code that the reader can deploy instantly or lever to develop more exotic functions and scripts. With this book, there is no need for expensive software development or a MATLAB license. Download the R software and you can begin building profitable strategies immediately. Harry has spawned an entire new generation of hedge fund managers with this seminal work." - Ed Zarek, Quantitative Options Trader, Chicago Volatility Group
"This is a superb text for aspiring quantitative traders. Financial math and computing concepts are introduced and developed simultaneously. The text guides readers through a set of R programming exercises that culminate in several data-based trading strategies. The conversational writing style and practitioner perspective will resonate with many readers." - Steven Todd, Associate Dean for Faculty and Research, Former Finance Department Chairperson, and Associate Professor Finance, Quinlan School of Business, Loyola University Chicago
"Quantitative Trading with R translates complicated topics into straightforward concepts. I'm using it as a reference and Belvedere has already incorporated some of the material into our classes." - Thomas Hutchinson, Managing Partner, Belvedere Trading, LLC
Table of Contents1 An Overview 2 Tools of the Trade 3 Working with Data 4 Basic Statistics and Probability 5 Intermediate Statistics and Probability 6 Spreads, Betas and Risk 7 Backtesting with Quantstrat 8 High-Frequency Data 9 Options 10 Optimization
