Algorithmic Trading: A Practitioner's Guide

Algorithmic Trading has grown dramatically, from a tool used by only the most sophisticated traders to one used daily by virtually every major investment firm and broker. Trading algorithms today have permeated trading in most asset classes, not only traditional assets like stocks, but also more exotic assets like cryptocurrencies.This book aims to provide portfolio managers, traders, and other finance professionals with in-depth information about how trading algorithms actually work in practice. The book provides detailed coverage of:• Single-order algorithms, such as Volume-Weighted Average Price (VWAP), Time-Weighted Average Price (TWAP), Percent of Volume (POV), and variants of the Implementation Shortfall algorithm.• Multi-order algorithms, such as Pairs Trading and Portfolio Trading algorithms.• Smart routers, including “smart market,” “smart limit,” and dark aggregators.• Trading performance measurement, including trading benchmarks, “algo wheels,” trading cost models, and other measurement issues.

Jeff Bacidore is the President and Founder of The Bacidore Group, a leading trading-related research and consulting firm. The Bacidore Group's clients include some of the leading global brokerages, investment managers, trading venues, and fintech firms.

Prior to founding The Bacidore Group, Jeff was the Head of Algorithmic Trading at both Goldman Sachs and ITG (now Virtu), the Head of Research and Consulting in Credit Suisse's Advanced Execution Services (AES) group, Head of Risk (Fundamental Equities) at Citadel, and Head of Research at the New York Stock Exchange. Jeff began his career as a visiting Assistant Professor of Finance at the University of Michigan Ross School of Business, and taught in the Undergraduate and Executive Education Programs at the Indiana University Kelley School of Business.

Jeff has over 20 years of experience in algorithmic trading and quantitative finance across multiple asset classes. He has designed and implemented algorithms ranging from low latency, machine learning-enhanced smart routers through multi-asset, multiday portfolio trading algorithms.

In addition, he has created cutting-edge trading analytics and models that use both traditional statistical methods and machine learning techniques. His research has been presented at the NYSE, Nasdaq, SEC, and numerous professional and academic conferences and and featured in leading business publications including the Wall Street Journal, Bloomberg, and The New York Times. Jeff has also testified before the U.S. House of Representatives on market structure issues. His written work has been published in numerous academic and practitioner journals.

Jeff has a B.A. in Economics from Knox College, and M.S. and Ph.D. degrees in Finance from Indiana University. He also attended the University of Chicago Business School as a University of Chicago Business Fellow.

For additional details, please visit bacidore.com.