Inside the Black Box

Auteur

RISHI K NARANG is the Founding Principal of T2AM and manages the firm's investment activities. Rishi began his career as a Global Investment Strategist for Citibank Alternative Investment in 1996. He then co-founded Tradeworx, Inc., a quantitative hedge fund manager, in 1999 and acted as its President until his departure in 2002. For three years, he was the Co-Portfolio Manager and a Managing Director at Santa Barbara Alpha Strategies before founding T2AM, LLC in 2005. He is Chair of the Board of Directors of Village Health Works, and has acted as an Advisor to DARPA, Planet Labs, AngelList, and numerous others. Mr. Narang completed his BA in Economics from the University of California at Berkeley.

Texte du rabat

In the newly revised Third Edition of Inside the Black Box: A Simple Guide to Systematic Investing, veteran algorithmic trader and investment manager Rishi K Narang delivers an easy-to-follow and insightful presentation of how quantitative and algorithmic trading strategies work in the real world. Offered in non-mathematical terms, the author discusses the most recent developments in the field since the publication of the previous edition in 2013.

You'll find compelling anecdotes and real-world trading stories that help illustrate the hands-on ideas explained within. You'll also discover explanations of the evolution of quantitative trading and the emergence of "Quant 3.0," explore the explosion in machine learning for alphas, signal mixing, data extraction, and execution, and examine the proliferation of alt data and learn how to use it appropriately.

Inside the Black Box demonstrates the relationship between discretionary investing and the overall quantitative and high-frequency trading market, along with newer, simpler, and more effective approaches to portfolio optimization. It reviews the emergence and growth of platforms and pods as a dominant force in the sector and as a new pathway for talent entering the space.

The author answers foundational and important questions about quantitative and high-frequency trading, including: How do quants capture alpha? What is the real level of discretion in quant trading? What is the difference between theory-driven systems and data mining strategies? And how do quant traders model risk?

Perfect for aspiring and practicing quantitative traders everywhere, Inside the Black Box is a jargon-free and accessible treatment of a complex and timely subject. It's sure to remain the gold standard for non-mathematicians seeking to understand the ins and outs of a lucrative trading strategy.

Contenu

Foreword xi Preface to the Third Edition xiii

Acknowledgments xv

PART ONE The Quant Universe

CHAPTER 1 Why Does Quant Trading Matter? 3

1.1 The Benefit of Deep Thought 8

1.2 The Measurement and Mismeasurement of Risk 9

1.3 Disciplined Implementation 10

1.4 Summary 11

Notes 11

CHAPTER 2 An Introduction to Quantitative Trading 13

2.1 What Is a Quant? 14

2.2 What Is the Typical Structure of a Quantitative Trading System? 17

2.3 Summary 20

Notes 20

PART TWO Inside the Black Box

CHAPTER 3 Alpha Models: How Quants Make Money 23

3.1 Types of Alpha Models: Theory-Driven and Data-Driven 25

3.2 Theory-Driven Alpha Models 28

3.3 Data-Driven Alpha Models 47

3.4 Implementing the Strategies 52

3.5 Blending Alpha Models 64

3.6 Summary 68

Notes 69

CHAPTER 4 Risk Models 71

4.1 Limiting the Amount of Risk 73

4.2 Limiting the Types of Risk 76

4.3 Risk Management, Outside of Risk Models 81

4.4 Summary 82

Notes 84

CHAPTER 5 Transaction Cost Models 85

5.1 Defining Transaction Costs 86

5.2 Types of Transaction Cost Models 91

5.3 Summary 96

Notes 97

CHAPTER 6 Portfolio Construction Models 99

6.1 Rule-Based Portfolio Construction Models 100

6.2 Portfolio Optimizers 104

6.3 Output of Portfolio Construction Models 121

6.4 How Quants Choose a Portfolio Construction Model 123

6.5 Summary 123

Notes 125

CHAPTER 7 Execution 127

7.1 Order Execution Algorithms 129

7.2 Trading Infrastructure 138

7.3 Summary 140

Notes 141

CHAPTER 8 Data 143

8.1 The Importance of Data 144

8.2 Types of Data 146

8.3 Sources of Data 149

8.4 Cleaning Data 152

8.5 Storing Data 158

8.6 Summary 159

Notes 160

CHAPTER 9 Research 161

9.1 Blueprint for Research: The Scientific Method 161

9.2 Idea Generation 163

9.3 Testing 166

9.4 Summary 186

Note 187

PART THREE A Practical Guide for Investors in Quantitative Strategies

CHAPTER 10 Risks Inherent to Quant Strategies 191

10.1 Model Risk 191

10.2 Regime Change Risk 196

10.3 Exogenous Shock Risk 200

10.4 Contagion, or Common Investor, Risk 202

10.5 How Quants Monitor Risk 209

10.6 Summary 211

Notes 211

CHAPTER 11 Criticisms of Quant Trading 213

11.1 Trading Is an Art, Not a Science 214

11.2 Quants Cause More Market Volatility by Underestimating Risk 215

11.3 Quants Cannot Handle Unusual Events or Rapid Changes in Market Conditions 221

11.4 Quants Are All the Same 223

11.5 Only a Few Large Quants Can Thrive in the Long Run 224

11.6 Quants Are Guilty of Data Mining 228

11.7 Summary 231

Notes 231

CHAPTER 12 Evaluating Quants and Quant Strategies 233

12.1 Gathering Information 234

12.2 Evaluating a Quantitative Trading Strategy 236

12.3 Evaluating the Acumen of Quantitative Traders 239

12.4 The Edge 241

12.5 Evaluating Integrity 244

12.6 How Quants Fit into a Portfolio 246

12.7 Summary 249

Notes 251

PART FOUR High-Speed and High-Frequency Trading

CHAPTER 13 An Introduction to High-Speed and High-Frequency Trading 255

Notes 259

CHAPTER 14 High-Speed Trading 261

14.1 Why Speed Matters 262

14.2 Sources of Latency 270

14.3 Summary 280

Notes 281

CHAPTER 15 High-Frequency Trading 283

15.1 Contractual Market Making 283

15.2 Non-Contractual Market Making 288

15.3 Arbitrage 289

15.4 Fast Alpha 291

15.5 HFT Risk Management and Portfolio Construction 293

15.6 Summary 295

Note 296

CHAPTER 16 Looking to the Future of Quant Trading 297

16.1 Business Models 297

16.2 Machine Learning and Artificial Intelligence 301

16.3 Expansion into More Asset Classes and Markets 302

16.4 Digitalization and Datasets 303

16.5 Man and Machine 304

16.6 Conclusion 305

Appendix: Controversy Regarding High-Frequency Trading 307

A.1 Does HFT Create Unfair Competition? 308

A.2 Does HFT Lead to Front-Running or Market Manipulation? 311

A.3 Does HFT Lead to Greater Volatility or Structural Instability? 317

A.4 Does HFT Lack Social Value? 324

A.5 Regulatory Considerations 325

A.6 Summary 327

Notes 328

About the Author 329

Index 331