Quantitative Trading

How to Build Your Own Algorithmic Trading Business
2. Auflage 2021
ISBN: 978-1-119-80006-4
Verlag: Wiley John + Sons

Buch, Englisch, 256 Seiten, Format (B × H): 159 mm x 227 mm, Gewicht: 471 g

Reihe: Wiley Trading Series

Master the lucrative discipline of quantitative trading with this insightful handbook from a master in the field

In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.

You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as:

* Updated back tests on a variety of trading strategies, with included Python and R code examples
* A new technique on optimizing parameters with changing market regimes using machine learning.
* A guide to selecting the best traders and advisors to manage your money

Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a place in the libraries of individual investors interested in exploring a career at a major financial institution.

Autoren/Hrsg.

Chan, Ernest P.

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Börse, Rohstoffe

Inhaltsverzeichnis

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

Über Autor(innen)

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.