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.

Quantitative Trading 2e has been fully revised and updated. It includes:

*  Updated back tests on trading strategies, including Python code

* New predictnow.ai example and new chapter on trading strategies that have performed well during Covid-19 crisis.  

* How Chan made machine learning work in investing.

* How to build a quantitative research team at a hedge fund

* How best to select the best traders/advisors to manage your money.

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.

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 109

Risk Management 120

Model Risk 124

Software Risk 125

Natural Disaster Risk 125

Psychological Preparedness 125

Summary 130

Appendix: A Simple Derivation of the Kelly Formula when Return Distribution is Gaussian 131

References 132

Chapter 7: Special Topics in Quantitative Trading 133

Mean-Reverting versus Momentum Strategies 134

Regime Change and Conditional Parameter Optimization 137

Stationarity and Cointegration 147

Factor Models 160

What is Your Exit Strategy? 169

Seasonal Trading Strategies 174

High-Frequency Trading Strategies 186

Is it Better to Have a High-Leverage versus a High-Beta Portfolio? 188

Summary 190

References 192

Chapter 8: Conclusion 193

Next Steps 197

References 198

Appendix: A Quick Survey of MATLAB 199

Bibliography 205

About the Author 209

Index 211