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David Aronson of Hood River Research on statistically sound machine learning for algorithmic trading

Developing Predictive-Model-Based Trading Systems Using TSSB

David Aronson, President of Hood River Research, kindly provided this excerpt from his book, ‘Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB.’

This book explores key topics like:

  • How to estimate future performance with rigorous algorithms
  • How to evaluate the influence of good luck in backtests
  • How to detect overfitting before deploying your system
  • How to estimate performance bias due to model fitting and selection of seemingly superior systems
  • How to use state-of-the-art ensembles of models to form consensus trade decisions
  • How to build optimal portfolios of trading systems and rigorously test their expected performance
  • How to search thousands of markets to find subsets that are especially predictable
  • How to create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility

In this excerpt, David introduces TSSB (Trading System Synthesis & Boosting), and lays out two approaches to automated trading. Download the excerpt here.



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