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Machine Trading: Deploying Computer Algorithms to Conquer the Markets

by Ernest P. Chan (2017)

Quick summary -- an in-depth PhD-level extended summary (10-30 pages) for this book is coming soon.

Machine Trading: Deploying Computer Algorithms to Conquer the Markets

By Ernest P. Chan

Quick Summary

Ernest Chan's third and most advanced book covers factor models, time-series analysis, AI/machine learning, options strategies, intraday trading and market microstructure, Bitcoin trading, and practical algorithmic system deployment.

Executive Summary

"Machine Trading" is the most advanced in Ernest Chan's trilogy of quantitative trading books (following "Quantitative Trading" and "Algorithmic Trading"). Chan covers factor models including options-derived factors, time series techniques (ARIMA, VAR, state space models), artificial intelligence and machine learning methods with emphasis on overfitting reduction, options and volatility trading strategies, intraday trading and market microstructure, Bitcoin as a new asset class, and practical matters including transitioning from proprietary trader to investment advisor.

Key Concepts and Frameworks

  1. Factor Models -- Traditional and options-derived factors for explaining and predicting returns.
  2. Time-Series Analysis -- ARIMA, VAR, and hidden-variable state space models for trading.
  3. AI/Machine Learning -- Methods that reduce overfitting including regularization and ensemble techniques.
  4. Options Strategies -- Portfolio-level options trading and volatility harvesting.
  5. Market Microstructure -- Order types, routing optimization, dark pools, and adverse selection.
  6. Bitcoin Trading -- Applying quantitative techniques to cryptocurrency markets.

Conclusion

"Machine Trading" is an essential resource for quantitative traders seeking to advance beyond basic algorithmic strategies into more sophisticated techniques including machine learning, options trading, and market microstructure analysis.