Quantitative Trading

Preface to the 2nd Edition xi Preface xv Acknowledgments xxi Chapter 1: The Whats, Whos, and Whys of Quantitative Trading 1 Who Can Become a Quantitative Trader? 2 The Business Case for Quantitative Trading 4 Scalability 5 Demand on Time 5 The Nonnecessity of Marketing 7 The Way Forward 8 Chapter 2: Fishing for Ideas 11 How to Identify a Strategy that Suits You 14 Your Working Hours 14 Your Programming Skills 15 Your Trading Capital 15 Your Goal 19 A Taste for Plausible Strategies and Their Pitfalls 20 How Does It Compare with a Benchmark, and How Consistent Are Its Returns? 20 How Deep and Long is the Drawdown? 23 How Will Transaction Costs Affect the Strategy? 24 Does the Data Suffer from Survivorship Bias? 26 How Did the Performance of the Strategy Change over the Years? 27 Does the Strategy Suffer from Data-Snooping Bias? 28 Does the Strategy "Fly under the Radar" of Institutional Money Managers? 30 Summary 30 References 31 Chapter 3: Backtesting 33 Common Backtesting Platforms 34 Excel 34 MATLAB 34 Python 36 R 38 QuantConnect 40 Blueshift 40 Finding and Using Historical Databases 40 Are the Data Split and Dividend Adjusted? 41 Are the Data Survivorship-Bias Free? 44 Does Your Strategy Use High and Low Data? 46 Performance Measurement 47 Common Backtesting Pitfalls to Avoid 57 Look-Ahead Bias 58 Data-Snooping Bias 59 Transaction Costs 72 Strategy Refinement 77 Summary 78 References 79 Chapter 4: Setting Up Your Business 81 Business Structure: Retail or Proprietary? 81 Choosing a Brokerage or Proprietary Trading Firm 85 Physical Infrastructure 87 Summary 89 References 91 Chapter 5: Execution Systems 93 What an Automated Trading System Can Do for You 93 Building a Semiautomated Trading System 95 Building a Fully Automated Trading System 98 Minimizing Transaction Costs 101 Testing Your System by Paper Trading 103 Why Does Actual Performance Diverge from Expectations? 104 Summary 107 Chapter 6: Money and Risk Management 109 Optimal Capital Allocation and Leverage

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.