High Frequency Trading Models + Website

Overview

Accounting for over 60 percent of equity trading volume andgenerating huge profits for a number of firms, high-frequencytrading is one of the most talked about topics in the world offinance. Given the success of this approach, many institutions andindividuals are looking for ways to make high-frequency tradingwork for them.

In High-Frequency Trading Models, Dr. Gewei Ye describesthe technology, architecture, and algorithms (algos) underlyingcurrent high-frequency trading models,...

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Overview

Accounting for over 60 percent of equity trading volume andgenerating huge profits for a number of firms, high-frequencytrading is one of the most talked about topics in the world offinance. Given the success of this approach, many institutions andindividuals are looking for ways to make high-frequency tradingwork for them.

In High-Frequency Trading Models, Dr. Gewei Ye describesthe technology, architecture, and algorithms (algos) underlyingcurrent high-frequency trading models, which exploit order flowimbalances and temporary pricing inefficiencies. Along the way, heexplains how to develop a high-frequency trading system andintroduces you to his own system for building high-frequencystrategies based on behavioral algos.

Divided into four comprehensive parts, this timely guide:

  • Describes the fundamental revenue models of high-frequencytrading
  • Introduces a series of theoretical models—behavioral,financial, and quantitative—for building unique investmentstrategies for high-frequency trading
  • Develops a unique set of computer algos, called Sentiment AssetPricing Engine (SAPE), to automate the process of buildingbehavioral strategies for high-frequency trading and portfoliomanagement
  • Discusses the potential of new revenue models in derivativeswith high-frequency trading systems and the creation of computeralgos for high-frequency trading

To help solidify your understanding of the information foundhere, the author's Web site, Yeswici.com, contains ancillarymaterials of the models and computer algos mentioned throughoutthis book. Yeswici.com is also a quantitative modeling andcomputing platform for innovative investment research, whichtransfers this research into Internet and mobile applications.

High-frequency trading has quickly become a profitable path intoday's market. With the proliferation of computing power andalgos, this approach will only continue to grow. Engaging andinformative, High-Frequency Trading Models will help youstay ahead of the—curve in this hot new area and put you in abetter position to capture consistent profits along the way.

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Product Details

  • ISBN-13: 9780470633731
  • Publisher: Wiley
  • Publication date: 1/4/2011
  • Series: Wiley Trading Series, #480
  • Edition number: 1
  • Pages: 322
  • Product dimensions: 6.20 (w) x 9.00 (h) x 1.20 (d)

Meet the Author

GEWEI YE, PhD, teaches graduate-level courses on financial engineering, derivatives, and program trading strategies at Johns Hopkins University. Recently, he has released the Sentiment Asset Pricing Engine (SAPE), a Web-based strategy builder for algorithmic trading and high-frequency trading systems (http://sap.yeswici.com). Dr. Ye has been a senior architect or consultant for investment and technology companies such as CitiBank, T. Rowe Price, Federal Reserve Banks, and IBM. He has published about forty articles in peer-reviewed journals or conference proceedings and has been building financial models and computing systems for ten years. Dr. Ye earned a PhD degree from University of Tilburg, the Netherlands.

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Table of Contents

Preface.

Acknowledgments.

PART I Revenue Models of High-Frequency Trading.

CHAPTER 1 High-Frequency Trading and Existing RevenueModels.

What Is High-Frequency Trading?

Why High-Frequency Trading Is Important.

Major High-Frequency Trading Firms in the United States.

Existing Revenue Models of High-Frequency TradingOperations.

Categorizing High-Frequency Trading Operations.

Conclusion.

CHAPTER 2 Roots of High-Frequency Trading in Revenue Modelsof Investment Management.

Revenue Model 1: Investing.

Revenue Model 2: Investment Banking.

Revenue Model 3: Market Making.

Revenue Model 4: Trading.

Revenue Model 5: Cash Management.

Revenue Model 6: Mergers and Acquisitions.

Revenue Model 7: Back-office Activities.

Revenue Model 8: Venture Capital.

Creating Your Own Revenue Model.

How to Achieve Success: Four Personal Drivers.

Conclusion.

CHAPTER 3 History and Future of High-Frequency Trading withInvestment Management.

Revenue Models in the Future.

Investment Management and Financial Institutions.

High-Frequency Trading and Investment Management.

Technology Inventions to Drive Financial Inventions.

The Ultimate Goal for Models and Financial Inventions.

Conclusion.

PART II Theoretical Models as Foundation of Computer Algosfor High-Frequency Trading.

CHAPTER 4 Behavioral Economics Models on LossAversion.

What Is Loss Aversion?

The Locus Effect.

Theory and Hypotheses.

Study 1: The Locus Effect on Inertia Equity.

Study 2: Assumption A1 and A2.

General Discussion.

Conclusion.

CHAPTER 5 Loss Aversion in Option Pricing: Integrating TwoNobel Models.

Demonstrating Loss Aversion with Computer Algos.

Visualizing the Findings.

Computer Algos for the Finding.

Explaining the Finding with the Black-Scholes Formula

Conclusion.

CHAPTER 6 Expanding the Size of Options in OptionPricing.

The NBA Event.

Web Data.

Theoretical Analysis.

The NBA Event and the Uncertainty Account.

Controlled Offline Data.

General Discussion.

Conclusion.

CHAPTER 7 Multinomial Models for Equity Returns.

Literature Review.

A Computational Framework: The MDP Model.

Implicit Consumer Decision Theory.

Empirical Approaches.

Analysis 1: Examination of Correlations and a RegressionModel.

Analysis 2: Structural Equation Model.

Contributions of the ICD Theory.

Conclusion.

CHAPTER 8 More Multinomial Models and Signal Detection Modelsfor Risk Propensity.

Multinomial Models for Retail Investor Growth.

Deriving Implicit Utility Functions.

Transforming Likeability Rating Data into ObservedFrequencies.

Signal Detection Theory (SDT).

Assessing a Fund's Performance with SDT.

Assessing Value at Risk with Risk Propensity of SDT forPortfolio Managers.

Defining Risk Propensity Surface.

Conclusion.

CHAPTER 9 Behavioral Economics Models on Fund Switching andReference Prices.

What Is VisualFunds for Fund Switching?

Behavioral Factors That Affect Fund Switching.

Theory and Predictions.

Study 1: Arbitrary Anchoring on Inertia Equity.

Study 2: Anchor Competition.

Study 3: Double Log Law.

Conclusion.

PART III A Unique Model of Sentiment Asset Pricing Engine forPortfolio Management.

CHAPTER 10 A Sentiment Asset Pricing Model.

What Is Sentiment Asset Pricing Engine?

Contributions of SAPE.

Testing the Effectiveness of SAPE Algos.

Primary Users of SAPE.

Three Implementations of SAPE.

SAPE Extensions: TopTickEngine, FundEngine, PortfolioEngine, andTestEngine.

Summary on SAPE.

Alternative Assessment Tools of Macro Investor Sentiment.

Conclusion.

CHAPTER 11 SAPE for Portfolio Management—Effectivenessand Strategies.

Contributions of SAPE to Portfolio Management.

Intraday Evidence of SAPE Effectiveness.

Trading Strategies Based on the SAPE Funds.

Case Study 1: Execution of SAPE Investment Strategies.

Case Study 2: The Trading Process with SAPE.

Case Study 3: Advanced Trading Strategies with SAPE.

Creating a Successful Fund with SAPE and High-FrequencyTrading

Conclusion.

PART IV New Models of High-Frequency Trading.

CHAPTER 12 Derivatives.

What Is a Derivative?

Mortgage-Backed Securities: Linking Major FinancialInstitutions.

Credit Default Swaps.

Options and Option Values.

The Benefits of Using Options.

Profiting with Options.

New Profitable Financial Instruments by Writing Options.

The Black-Scholes Model As a Special Case of the BinomialModel.

Implied Volatility.

Volatility Smile.

Comparing Volatilities Over Time.

Forwards and Futures.

Pricing an Interest-Rate Swap with Prospect Theory.

The Behavioral Investing Based On Behavioral Economics.

Conclusion.

CHAPTER 13 Technology Infrastructure for Creating ComputerAlgos.

Web Hosting vs. Dedicated Web Servers.

Setting Up a Dedicated Web Server.

Developing Computer Algos.

Jump Starting Algo Development with PHP Programming.

Jump Starting Algo Development with Java Programming.

Jump Starting Algo Development with C++ Programming.

Jump Starting Algo Development with Flex Programming.

Jump Starting Algo Development with SQL.

Common UNIX/LINUX Commands for Algo Development.

Conclusion.

CHAPTER 14 Creating Computer Algos for High-FrequencyTrading.

Getting Probability from Z Score.

Getting Z Scores from Probability.

Algos for the Sharpe Ratio.

Computing Net Present Value.

Developing a Flex User Interface for Computer Algos.

Algos for the Black-Scholes Model.

Computing Volatility with the ARCH Formula.

Algos for Monte Carlo Simulations.

Algos for an Efficient Portfolio Frontier.

Algos for Signal Detection Theory (SDT).

Conclusion.

Notes.

References.

About the Author.

Index.

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