Quantitative Trading : How to Build Your Own Algorithmic Trading Business

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 11/17/2008
  • Publisher: Wiley
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While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.

Author Biography

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.

Table of Contents

Prefacep. xi
Acknowledgmentsp. xvii
The Whats, Whos, and Whys of Quantitative Tradingp. 1
Who Can Become a Quantitative Trader?p. 2
The Business Case for Quantitative Tradingp. 4
Scalabilityp. 5
Demand on Timep. 5
The Nonnecessity of Marketingp. 7
The Way Forwardp. 8
Fishing for Ideasp. 9
How to Identify a Strategy That Suits Youp. 12
Your Working Hoursp. 12
Your Programming Skillsp. 13
Your Trading Capitalp. 13
Your Goalp. 16
A Taste for Plausible Strategies and Their Pitfallsp. 17
How Does It Compare with a Benchmark and How Consistent Are Its Returns?p. 18
How Deep and Long Is the Drawdown?p. 21
How Will Transaction Costs Affect the Strategy?p. 22
Does the Data Suffer from Survivorship Bias?p. 24
How Did the Performance of the Strategy Change over the Years?p. 24
Does the Strategy Suffer from Data-Snooping Bias?p. 25
Does the Strategy "Fly under the Radar" of Institutional Money Managers?p. 27
Summaryp. 28
Backtestingp. 31
Common Backtesting Platformsp. 32
Excelp. 32
TradeStationp. 32
High-End Backtesting Platformsp. 35
Finding and Using Historical Databasesp. 36
Are the Data Split and Dividend Adjusted?p. 36
Are the Data Survivorship Bias Free?p. 40
Does Your Strategy Use High and Low Data?p. 42
Performance Measurementp. 43
Common Backtesting Pitfalls to Avoidp. 50
Look-Ahead Biasp. 51
Data-Snooping Biasp. 52
Transaction Costsp. 60
Strategy Refinementp. 65
Summaryp. 66
Setting Up Your Businessp. 69
Business Structure: Retail or Proprietary?p. 69
Choosing a Brokerage or Proprietary Trading Firmp. 71
Physical Infrastructurep. 75
Summaryp. 77
Execution Systemsp. 79
What an Automated Trading System Can Do for Youp. 79
Building a Semiautomated Trading Systemp. 81
Building a Fully Automated Trading Systemp. 84
Minimizing Transaction Costsp. 87
Testing Your System by Paper Tradingp. 89
Why Does Actual Performance Diverge from Expectations?p. 90
Summaryp. 92
Money and Risk Managementp. 95
Optimal Capital Allocation and Leveragep. 95
Risk Managementp. 103
Psychological Preparednessp. 108
Summaryp. 111
A Simple Derivation of the Kelly Formula when Return Distribution Is Gaussianp. 112
Special Topics in Quantitative Tradingp. 115
Mean-Reverting versus Momentum Strategiesp. 116
Regime Switchingp. 119
Stationarity and Cointegrationp. 126
Factor Modelsp. 133
What Is Your Exit Strategy?p. 140
Seasonal Trading Strategiesp. 143
High-Frequency Trading Strategiesp. 151
Is It Better to Have a High-Leverage versus a High-Beta Portfolio?p. 153
Summaryp. 154
Conclusion: Can Independent Traders Succeed?p. 157
Next Stepsp. 161
A Quick Survey of MATLABp. 163
Bibliographyp. 169
About the Authorp. 173
Indexp. 175
Table of Contents provided by Ingram. All Rights Reserved.

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