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Quantitative Trading: How to Build Your Own Algorithmic Trading Busi - VERY GOOD
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Numero oggetto eBay:406246567007
Specifiche dell'oggetto
- Condizione
- Brand
- Unbranded
- MPN
- Does not apply
- ISBN
- 9781119800064
Informazioni su questo prodotto
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1119800064
ISBN-13
9781119800064
eBay Product ID (ePID)
2321425967
Product Key Features
Edition
2
Book Title
Quantitative Trading : How to Build Your Own Algorithmic Trading Business
Number of Pages
256 Pages
Language
English
Topic
Finance / General
Publication Year
2021
Genre
Business & Economics
Book Series
Wiley Trading Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
13.6 Oz
Item Length
9.1 in
Item Width
6.3 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Dewey Decimal
332.64
Table Of Content
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
Synopsis
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.
LC Classification Number
HG4529.C445 2021
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