Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

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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 PhD 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 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:

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

Developing Predictive-Model-Based Trading Systems Using TSSB

Authored by David Aronson, Timothy Masters

This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with step-by-step examples using actual market data, and all examples are explained in plain language.

Second, this book shows how the free program TSSB (Trading System Synthesis & Boosting) can be used to develop and test trading systems. The machine learning and statistical algorithms available in TSSB go far beyond those available in other off-the-shelf development software. Intelligent use of these state-of-the-art techniques greatly improves the likelihood of obtaining a trading system whose impressive backtest results continue when the system is put to use in a trading account.

Among other things, this book will teach the reader how to:
Estimate future performance with rigorous algorithms
Evaluate the influence of good luck in backtests
Detect overfitting before deploying your system
Estimate performance bias due to model fitting and selection of seemingly superior systems
Use state-of-the-art ensembles of models to form consensus trade decisions
Build optimal portfolios of trading systems and rigorously test their expected performance
Search thousands of markets to find subsets that are especially predictable
Create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility

More information on the TSSB program can be found at TSSBsoftware dot com.

Publication Date:
148950771X / 9781489507716
Page Count:
Binding Type:
US Trade Paper
Trim Size:
7.44" x 9.69"
Black and White
Related Categories:
Business & Economics / Forecasting

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