Halls-Moore Michael L. Successful Algorithmic Trading

Wiley, 2009. — 339 p. — ISBN: 0470563761, 9780470563762. A hands-on guide to the fast and ever-changing world of high-frequency, algorithmic trading Financial markets are undergoing rapid innovation due to the continuing proliferation of computer power and algorithms. These developments have created a new investment discipline called high-frequency trading. This book covers all...

John Wiley, 2013. — 225 p. This book is a practical guide to algorithmic trading strategies that can be readily implemented by both retail and institutional traders. It is not an academic treatise on financial theory. Rather, I hope to make accessible to the reader some of the most useful financial research done in the past few decades, mixing them with insights I gained from...

Wiley, 2017. — 254 p. The book is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy...

N.-Y.: Wiley, 2014. - 284 p. "Award-winning trader Kevin Davey explains how he evolved from a discretionary to a systems trader and began generating triple-digit annual returns. An inveterate systems developer, Davey explains the process of generating a trading idea, validating the idea through statistical analysis, setting entry and exit points, testing, and implementation in...

Chapman and Hall, 2016. — 379 p. — ISBN: 978-1498706483. The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and...

NY: QuantStart, 2016. — 160 p. There are hundreds of textbooks, research papers, blogs and forum posts on time series analysis, econometrics, machine learning and Bayesian statistics. Nearly all of them concentrate on the theory. What about practical implementation? How do you use that method for your strategy? How do you actually program up that formula in software?