Algorithmic Trading and Quantitative Strategies
1st Edition
Published on 12. August 2020
400 pages
978-1-4987-3719-7 (ISBN)
Description
Algorithmic Trading and Quantitative Strategies provides an in-depth overview of this growing field with a unique mix of quantitative rigor and practitioner's hands-on experience. The focus on empirical modeling and practical know-how makes this book a valuable resource for students and professionals.
The book starts with the often overlooked context of why and how we trade via a detailed introduction to market structure and quantitative microstructure models. The authors then present the necessary quantitative toolbox including more advanced machine learning models needed to successfully operate in the field. They next discuss the subject of quantitative trading, alpha generation, active portfolio management and more recent topics like news and sentiment analytics. The last main topic of execution algorithms is covered in detail with emphasis on the state of the field and critical topics including the elusive concept of market impact. The book concludes with a discussion on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings.
A git-hub repository includes data-sets and explanatory/exercise Jupyter notebooks. The exercises involve adding the correct code to solve the particular analysis/problem.
The book starts with the often overlooked context of why and how we trade via a detailed introduction to market structure and quantitative microstructure models. The authors then present the necessary quantitative toolbox including more advanced machine learning models needed to successfully operate in the field. They next discuss the subject of quantitative trading, alpha generation, active portfolio management and more recent topics like news and sentiment analytics. The last main topic of execution algorithms is covered in detail with emphasis on the state of the field and critical topics including the elusive concept of market impact. The book concludes with a discussion on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings.
A git-hub repository includes data-sets and explanatory/exercise Jupyter notebooks. The exercises involve adding the correct code to solve the particular analysis/problem.
Reviews / Votes
"This work does a marvelous job of emphasizing the dual significance of determining the fair value of an asset as well as designing the optimal way to interact with the markets. Optimizing valuation is equally important to optimizing order execution. Both skills must be mastered to avoid selection bias and capturing value. This book must be read!"~Peter J. Layton, Principal, Blackthorne Capital Management, LLC
"An outstanding and timely synthesis of the state of art algorithmic trading ideas. I will recommend it to all who is serious on the foundations."
~Guofu Zhou, Frederick Bierman and James E. Spears, Professor of Finance, Olin Business School, Washington University in St. Louis "This work does a marvelous job of emphasizing the dual significance of determining the fair value of an asset as well as designing the optimal way to interact with the markets. Optimizing valuation is equally important to optimizing order execution. Both skills must be mastered to avoid selection bias and capturing value. This book must be read!"
-Peter J. Layton, Principal, Blackthorne Capital Management, LLC
"An outstanding and timely synthesis of the state of art algorithmic trading ideas. I will recommend it to all who is serious on the foundations."
-Guofu Zhou, Frederick Bierman and James E. Spears, Professor of Finance, Olin Business School, Washington University in St. Louis
"This book is a rigorous yet practical introduction to the subject and takes the reader to some advanced concepts in quantitative algo trading. [...] What really makes the book stand out in terms of the learning experience for the interested reader is its no-nonsense attitude to hands-on-learning. The authors have the ethos that algo trading is not a spectator sport and that the best way to learn is to get their hands dirty and find out for themselves. In this spirit, each section contains a set of practical examples and exercises with data available in the corresponding GitHub repository. [...]."
-Gordon Lee, in Quantitative Finance, January 2023
"I enjoyed reading this excellent book. I strongly recommend this book to mathematical and applied statisticians generally and to portfolio analysts in particular."
-Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, June 2023
More details
Edition
1. Auflage
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
ISBN-13
978-1-4987-3719-7 (9781498737197)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
08/2020
1st Edition
Chapman & Hall/CRC
€205.80
Shipment within 15-20 days
Person
Raja Velu is a professor of Finance and Analytics in Whitman School of Management at Syracuse University. He served as a Technical Architect at Yahoo! in the Sponsored Search Division and was a visiting scientist at IBM-Almaden, Microsoft Research, Google and JPMC. He has also held visiting positions at Stanford's Statistics department, Indian School of Business, the National University of Singapore, and Singapore Management University.
Maxence Hardy is a Managing Director and the Head of eTrading Quantitative Research for Equities and Futures at J.P.Morgan, based in New York. Mr. Hardy is responsible for the development of agency algorithmic trading strategies for the Equities and Futures divisions globally.
Daniel Nehren is a Managing Director and the Head of Statistical Modelling and Development for Equities at Barclays. Based in New York, Mr. Nehren is responsible for the development of algorithmic trading and analytics products. Mr. Nehren has more than19 years of experience in equity trading working for some of the most prestigious financial firms including Citadel, J.P Morgan, and Goldman Sachs.
Maxence Hardy is a Managing Director and the Head of eTrading Quantitative Research for Equities and Futures at J.P.Morgan, based in New York. Mr. Hardy is responsible for the development of agency algorithmic trading strategies for the Equities and Futures divisions globally.
Daniel Nehren is a Managing Director and the Head of Statistical Modelling and Development for Equities at Barclays. Based in New York, Mr. Nehren is responsible for the development of algorithmic trading and analytics products. Mr. Nehren has more than19 years of experience in equity trading working for some of the most prestigious financial firms including Citadel, J.P Morgan, and Goldman Sachs.
Content
1. Trading Fundamentals. 2. Univariate Time Series Models 3. Multivariate Time Series Models 4. Advanced Topics5. Statistical Trading Strategies and Back-Testing 6. Dynamic Portfolio Management and Trading Strategies 7. News Analytics: From Market Attention and Sentiment to Trading 8. Modeling Trade Data 9. Market Impact Models 10. Execution Strategies 11. The Technology Stack 12. The Research Stack.
