Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB by Timothy Masters & David Aronson

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In author Timothy Masters and David Aronson’s highly-advanced manual, “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB” aims to show how to evaluate any trading system before it is implemented out in public. With step-by-step examples and illustrations that apply real market data, this book makes it convenient for readers to grasp at a certain level. Furthermore, “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments” also serves as a guide to how the Trading System Synthesis & Boosting (TSSB) works when developing and testing trading systems. A fully-detailed and comprehensive guide and manual, which is a must-have for anyone interested in the subject matter. 

About the Author

David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering. He has worked in this field since 1979 and has been a Chartered Market Technician certified by The Market Technicians Association since 1992. He was an adjunct professor of finance, and regularly taught to MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics. His ground-breaking book, “Evidence-Based Technical Analysis,” was published by John Wiley & Sons 2006.

Timothy Masters received a Ph.D. in mathematical statistics with a specialization in numerical computing. Since then, he has continuously worked as an independent consultant for government and industry. His current focus is on methods for evaluating financial market trading systems. He has authored five books on prediction, classification, and practical applications of neural networks: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995) Assessing and Improving Prediction and Classification (CreateSpace, 2013) More information can be found on his website:

 Table of Contents

  • Introduction
    • A Simple Standalone Trading System
    • A Simple Filter System
    • Common Initial Commands
    • Reading and Writing Databases
    • Creating Variables
    • Volatility Indicators
    • Indicators Involving Indices
    • Basic Price Distribution Statistics
    • Indicators that significantly involve Volume
    • Basic Price Distribution Statistics
    • Entropy and Mutual Information Indicators
    • Indicator Based on Wavelets
    • Follow-Through-Index (FTI) Indicators
    • Target Variables
    • Screening Variables
  • Models 1: Fundamentals
  • Models 2: The Models
  • Comittees
  • Oracles
  • Testing Methods
    • Permutation Training
    • Transforms
  • Complex Prediction Systems
  • Graphics
  • Finding Independent Predictors
  • Market Regression Classes
  • Developing a Stand-Alone System
  • Trade Simulation and Portfolios
  • Integrated Portfolios