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Algorithmic Trading: Winning Strategies and Their Rationale

Book Description

Praise for Algorithmic Trading

"Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers."

—DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management

"Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses."

—Roger Hunter, Mathematician and Algorithmic Trader

Table of Contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication
  7. Preface
    1. The Motive
    2. A Note about Sources and Acknowledgments
  8. Chapter 1: Backtesting and Automated Execution
    1. The Importance of Backtesting
    2. Common Pitfalls of Backtesting
    3. Statistical Significance of Backtesting: Hypothesis Testing
    4. When Not to Backtest a Strategy
    5. Will a Backtest Be Predictive of Future Returns?
    6. Choosing a Backtesting and Automated Execution Platform
  9. Chapter 2: The Basics of Mean Reversion
    1. Mean Reversion and Stationarity
    2. Cointegration
    3. Pros and Cons of Mean-Reverting Strategies
  10. Chapter 3: Implementing Mean Reversion Strategies
    1. Trading Pairs Using Price Spreads, Log Price Spreads, or Ratios
    2. Bollinger Bands
    3. Does Scaling-in Work?
    4. Kalman Filter as Dynamic Linear Regression
    5. Kalman Filter as Market-Making Model
    6. The Danger of Data Errors
  11. Chapter 4: Mean Reversion of Stocks and ETFs
    1. The Difficulties of Trading Stock Pairs
    2. Trading ETF Pairs (and Triplets)
    3. Intraday Mean Reversion: Buy-on-Gap Model
    4. Arbitrage between an ETF and Its Component Stocks
    5. Cross-Sectional Mean Reversion: A Linear Long-Short Model
  12. Chapter 5: Mean Reversion of Currencies and Futures
    1. Trading Currency Cross-Rates
    2. Rollover Interests in Currency Trading
    3. Trading Futures Calendar Spread
    4. Futures Intermarket Spreads
  13. Chapter 6: Interday Momentum Strategies
    1. Tests for Time Series Momentum
    2. Time Series Strategies
    3. Extracting Roll Returns through Future versus ETF Arbitrage
    4. Cross-Sectional Strategies
    5. Pros and Cons of Momentum Strategies
  14. Chapter 7: Intraday Momentum Strategies
    1. Opening Gap Strategy
    2. News-Driven Momentum Strategy
    3. Leveraged ETF Strategy
    4. High-Frequency Strategies
  15. Chapter 8: Risk Management
    1. Optimal Leverage
    2. Constant Proportion Portfolio Insurance
    3. Stop Loss
    4. Risk Indicators
  16. Conclusion
  17. Bibliography
  18. About the Author
  19. About the Website
  20. Index