By some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, author Dr. Ernest Chan, a respected independent trader and consultant, will show you how.
Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Organized around the steps you should take to start trading quantitatively, this book skillfully addresses how to:
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Find a viable trading strategy that you're both comfortable with and confident in
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Backtest your strategy—with MATLAB®, Excel, and other platforms—to ensure good historical performance
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Build and implement an automated trading system to execute your strategy
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Scale up or wind down your strategies depending on their real-world profitability
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Manage the money and risks involved in holding positions generated by your strategy
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Incorporate advanced concepts that most professionals use into your everyday trading activities
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And much more
While Dr. Chan takes the time to outline the essential aspects of turning quantitative trading strategies into profits, he doesn't get into overly theoretical or sophisticated theories. Instead, he highlights the simple tools and techniques you can use to gain a much-needed edge over today's institutional traders.
And for those who want to keep up with the latest news, ideas, and trends in quantitative trading, you're welcome to visit Dr. Chan's blog, epchan.blogspot.com, as well as his premium content Web site, epchan.com/subscriptions, which you'll have free access to with purchase of this book.
As an independent trader, you're free from the con-straints found in today's institutional environment—and as long as you adhere to the discipline of quantitative trading, you can achieve significant returns. With this reliable resource as your guide, you'll quickly discover what it takes to make it in such a dynamic and demanding field.

New edition of book that demystifies quant and algo trading In this updated edition of his bestselling book, Rishi K Narang offers in a straightforward, nontechnical style-supplemented by real-world examples and informative anecdotes-a reliable resource takes you on a detailed tour through the black box. He skillfully sheds light upon the work that quants do, lifting the veil of mystery around quantitative trading and allowing anyone interested in doing so to understand quants and their strategies. This new edition includes information on High Frequency Trading. Offers an update on the bestselling book for explaining in non-mathematical terms what quant and algo trading are and how they work Provides key information for investors to evaluate the best hedge fund investments Explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager This new edition of Inside the Black Box explains quant investing without the jargon and goes a long way toward educating investment professionals.

Praise for Algorithmic Trading: "Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers." (Daren Smith, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management). "Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses." (Roger Hunter, Mathematician and Algorithmic Trader).

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.

The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market