Autorentext

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.

Klappentext

PRAISE FOR Quantitative Trading 2ND EDITION

"Ernie's timely update to his classic Quantitative Trading is extraordinary in that, despite the modernization of the content, all the fundamentals remain unchanged and have clearly stood the test of time since the first edition. If you want to be a competitive swimmer, you need to learn the fundamentals of swimming first. Trading is no different; Ernie makes the fundamentals as simple as possible, but no simpler (as Einstein would say) and strikes the perfect balance between intuition and technical depth. Those specifically interested in trading, and anyone generally interested in understanding how modern financial markets work, will benefit from reading the Second Edition of Quantitative Trading."
-CRAIG BETTS, mathematician and Founder, Solace

"As technology has evolved, so has the ease in developing trading strategies. Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques."
-PETER BORISH, Chairman and CEO, Computer Trading Corporation; Founding Partner, Tudor Investment Corporation

"Out of the many books and articles on quantitative trading that I've read over the years, very few have been of much use at all. In most instances, the authors have no real knowledge of the subject matter or do have something important to say but are unwilling to do so because of fears of having trade secrets stolen. Ernie subscribes to a different credo: Share meaningful information and have meaningful interactions with the quantitative community at large. Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike."
-STEVE HALPERN, Founder, HCC Capital, LLC

"Often the hardest part of getting started is simply knowing what questions to ask. This holds especially true for fields like quantitative trading, which are shrouded in mystery and protected by impenetrable jargon. Readers of this book will not only learn the foundations of research and strategy development, but also gain pragmatic insight into the operational sides of the business. Ernie has written the ideal guide for those looking to go from zero-to-one in their quantitative trading journey."
-COREY HOFFSTEIN, Co-founder and CIO, Newfound Research

Inhalt

Foreword by Rishi K. Narang xi

Preface to the 2 nd Edition xv

Preface xix

Acknowledgments xxv

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