Ernie Chan

Quantitative Trading

How to Build Your Own Algorithmic Trading Business

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Wiley Trading
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 208
• Erscheinungstermin: November 2008
• Gewicht: 385g
• ISBN-13: 9780470284889
• ISBN-10: 0470284889
• Artikelnr.: 24624416

Autorenporträt

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.

Inhaltsangabe

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 Un-necessity of Marketing
The Way Forward

Chapter 2. Fishing for Ideas
Where can we find good strategies?
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
Sample Size
Out-of-sample testing
Sensitivity Analysis
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 Semi-automated 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 Kelly Formula when Returns 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. A Quick Survey of MATLAB
Bibliography
About the Author
Index