Quantitative Trading : How to Build Your Own Algorithmic Trading Business / Edition 1

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Overview

Praise for Quantitative Trading

"As technology has evolved, so has the ease in developing trading strategies. Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques."
—PETER BORISH, Chairman and CEO, Computer Trading Corporation

"Dr. Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, programming knowledge, and real-time system implementation to develop and run an algorithmic trading business step by step in Quantitative Trading."
—YASER ANWAR, trader

"Quantitative systematic trading is a challenging field that has always been shrouded in mystery, seemingly too difficult to master by all but an elite few. In this honest and practical guide, Dr. Chan highlights the essential cornerstones of a successful automated trading operation and shares lessons he learned the hard way while offering clear direction to steer readers away from common traps that both individual and institutional traders often succumb to."
—ROSARIO M. INGARGIOLA, CTO, Alphacet, Inc.

"This book provides valuable insight into how private investors can establish a solid structure for success in algorithmic trading. Ernie's extensive hands-on experience in building trading systems is invaluable for aspiring traders who wish to take their knowledge to the next level."
—RAMON CUMMINS, private investor

"Out of the many books and articles on quantitative trading that I've read over the years, very few have been of much use at all. In most instances, the authors have no real knowledge of the subject matter, or do have something important to say but are unwilling to do so because of fears of having trade secrets stolen. Ernie subscribes to a different credo: Share meaningful information and have meaningful interactions with the quantitative community at large. Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike."
—STEVE HALPERN, founder, HCC Capital, LLC

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Product Details

  • ISBN-13: 9780470284889
  • Publisher: Wiley
  • Publication date: 11/17/2008
  • Series: Wiley Trading Series , #381
  • Edition number: 1
  • Pages: 208
  • Sales rank: 678,917
  • Product dimensions: 6.10 (w) x 9.10 (h) x 0.90 (d)

Meet the Author

Ernest P. Chan, PhD, is a quantitative trader and consultant who advises clients on how to implement automated statistical trading strategies. He has worked as a quantitative researcher and trader in various investment banks including Morgan Stanley and Credit Suisse, as well as hedge funds such as Mapleridge Capital, Millennium Partners, and MANE Fund Management. Dr. Chan earned a PhD in physics from Cornell University.

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Table of Contents

Preface.

Acknowledgments.

Chapter 1: The Whats, Whos, and Whys of Quantitative Trading.

Who Can Become A Quantitative Trader?

The Business Case for Quantitative Trading.

Scalability.

Demand on Time.

The Nonnecessity of Marketing.

The Way Forward.

Chapter 2: Fishing for Ideas.

How to Identify a Strategy That Suits You.

Your Working Hours.

Your Programming Skills.

Your Trading Capital.

Your Goal.

A Taste for Plausible Strategies and Their Pitfalls.

How Does It Compare with a Benchmark and How Consistent Are Its Returns?

How Deep and Long is the Drawdown?

How Will Transaction Costs Affect the Strategy?

Does the Data Suffer from Survivorship Bias?

How Did the Performance of the Strategy Change Over the Years?

Does the Strategy Suffer from Data-Snooping Bias?

Does the Strategy “Fly under the Radar” of Institutional Money Managers?

Summary.

Chapter 3: Backtesting.

Common Backtesting Platforms.

Excel.

MATLAB.

TradeStation.

High-End Backtesting Platforms.

Finding and Using Historical Databases.

Are the Data Split- and Dividend-Adjusted?

Are the Data Survivorship Bias Free?

Does Your Strategy Use High and Low Data?

Performance Measurement.

Common Backtesting Pitfalls to Avoid.

Look-Ahead Bias.

Data-Snooping Bias.

Transaction Costs.

Strategy Refinement.

Summary.

Chapter 4: Setting up Your Business.

Business Structure: Retail or Proprietary?

Choosing a Brokerage or Proprietary Trading Firm.

Physical Infrastructure.

Summary.

Chapter 5: Execution Systems.

What an Automated Trading System Can Do for You.

Building a Semiautomated Trading System.

Building a Fully Automated Trading System.

Minimizing Transaction Costs.

Testing Your System by Paper Trading.

Why Does Actual Performance Diverge from Expectations?

Summary.

Chapter 6: Money and Risk Management.

Optimal Capital Allocation and Leverage.

Risk Management.

Psychological Preparedness.

Summary.

Appendix: A Simple Derivation of the Kelly Formula when Return Distribution is Gaussian.

Chapter 7: Special Topics in Quantitative Trading.

Mean-Reverting versus Momentum Strategies.

Regime Switching.

Stationarity and Cointegration.

Factor Models.

What Is Your Exit Strategy?

Seasonal Trading Strategies.

High-Frequency Trading Strategies.

Is it Better to Have a High-Leverage versus a High-Beta Portfolio?

Summary.

Chapter 8: Conclusion: Can Independent Traders Succeed?

Next Steps.

Appendix: A Quick Survey of MATLAB.

Bibliography.

About the Author.

Index.

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