Quantitative Trading

ERNEST P. CHAN, PhD, is an expert in the application of statistical models and software for trading currencies, futures, and stocks. He holds a doctorate in theoretical physics from Cornell University and is Managing Member of investment management firm QTS Capital Management and founder of financial machine learning firm Predictnow.ai.

• Foreword by Rishi K. Narang xiPreface to the 2 nd Edition xvPreface xixAcknowledgments xxvChapter 1: The Whats, Whos, and Whys of Quantitative Trading 1Who Can Become a Quantitative Trader? 2The Business Case for Quantitative Trading 4Scalability 5Demand on Time 5The Nonnecessity of Marketing 7The Way Forward 8Chapter 2: Fishing for Ideas 11How to Identify a Strategy that Suits You 14Your Working Hours 14Your Programming Skills 15Your Trading Capital 15Your Goal 19A Taste for Plausible Strategies and Their Pitfalls 20How Does It Compare with a Benchmark, and How Consistent Are Its Returns? 20How Deep and Long Is the Drawdown? 23How Will Transaction Costs Affect the Strategy? 24Does the Data Suffer from Survivorship Bias? 26How Did the Performance of the Strategy Change over the Years? 27Does the Strategy Suffer from Data-Snooping Bias? 28Does the Strategy “Fly under the Radar” of Institutional Money Managers? 30Summary 30References 31Chapter 3: Backtesting 33Common Backtesting Platforms 34Excel 34MATLAB 34Python 36R 38QuantConnect 40Blueshift 40Finding and Using Historical Databases 40Are the Data Split and Dividend Adjusted? 41Are the Data Survivorship-Bias Free? 44Does Your Strategy Use High and Low Data? 46Performance Measurement 47Common Backtesting Pitfalls to Avoid 57Look-Ahead Bias 58Data-Snooping Bias 59Transaction Costs 72Strategy Refinement 77Summary 78References 79Chapter 4: Setting Up Your Business 81Business Structure: Retail or Proprietary? 81Choosing a Brokerage or Proprietary Trading Firm 85Physical Infrastructure 87Summary 89References 91Chapter 5: Execution Systems 93What an Automated Trading System Can Do for You 93Building a Semiautomated Trading System 95Building a Fully Automated Trading System 98Minimizing Transaction Costs 101Testing Your System by Paper Trading 103Why Does Actual Performance Diverge from Expectations? 104Summary 107Chapter 6: Money and Risk Management 109Optimal Capital Allocation and Leverage 109Risk Management 120Model Risk 124Software Risk 125Natural Disaster Risk 125Psychological Preparedness 125Summary 130Appendix: A Simple Derivation of the Kelly Formula when Return Distribution Is Gaussian 131References 132Chapter 7: Special Topics in Quantitative Trading 133Mean-Reverting versus Momentum Strategies 134Regime Change and Conditional Parameter Optimization 137Stationarity and Cointegration 147Factor Models 160What Is Your Exit Strategy? 169Seasonal Trading Strategies 174High-Frequency Trading Strategies 186Is it Better to Have a High-Leverage versus a High-Beta Portfolio? 188Summary 190References 192Chapter 8: Conclusion 193Next Steps 197References 198Appendix: A Quick Survey of MATLAB 199Bibliography 205About the Author 209Index 211