High-Frequency Trading Models


  • Format: PDF
  • Pages: 338
  • Published Date: 2011


In High Frequency Trading Models, Dr. Gewei Ye describes the technology, architecture, and algorithms underlying current high frequency trading models, which exploit order flow imbalances and temporary pricing inefficiencies. Along the way, he explains how to develop a HFT trading system and introduces you to his own system for building high frequency strategies based on behavioral algorithms.


Thus, the high-frequency trading models may expand to the following:(1) existing revenue models; (2) new revenue modes, for example, high-frequency trading in derivatives markets; (3) theoretical (behavioral, financial, and quantitative) models for building unique investment strategies for high-frequency trading; and (4) computer algos for high-frequency trading and portfolio management. These four topics make up the central themes of the book.

The audience for this book includes traders, regulators, portfolio managers, financial engineers, IT professionals, graduate or senior undergraduate students in finance, investment analysts, financial advisors, investment bankers, hedge fund managers, and financial institutions. This book may be instrumental to the effort of reforming domestic or global financial systems and improving financial regulations. Imagine if financial regulators could develop a new high-frequency trading monitoring system based on the theoretical models and computer algos of this book. The monitoring system might automatically detect the preconditions of market anomalies and prevent the occurrence of undesirable anomalies. It would be especially useful for financial regulators to use computer algos to monitor and regulate trading. As a result, abnormal market behaviors like the one on May 6, 2010, could be anticipated.

The book comprises four parts: Part I describes the fundamental revenue models of high-frequency trading; Part II discusses theoretical models as a foundation of the computer algos used in high-frequency trading; Part III creates a unique model of sentiment asset pricing engine for portfolio management and high-frequency trading; Part IV discusses new models and computer algos of high-frequency trading. The four parts are illustrated in this outline.


  • High-Frequency Trading and Existing Revenue Models
  • Roots of High-Frequency Trading in Revenue Models of Investment Management
  • History and Future of High-Frequency Trading with Investment Management
  • Behavioral Economics Models on Loss Aversion
  • Loss Aversion in Option Pricing: Integrating Two Nobel Models
  • Expanding the Size of Options in Option Pricing
  • Multinomial Models for Equity Returns
  • More Multinomial Models and Signal Detection Models for Risk Propensity
  • Behavioral Economics Models on Fund Switching and Reference Prices
  • A Sentiment Asset Pricing Model
  • SAPE for Portfolio Management—Effectiveness and Strategies
  • Derivatives
  • Technology Infrastructure for Creating Computer Algos
  • Creating Computer Algos for High-Frequency Trading