Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

Review Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

by TIMOTHY MASTERS & DAVID ARONSON

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

Description

Timothy Master’s and David Aronson’s “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments” is highly regarded as an advanced textbook that delves into how to adequately test and evaluate a trading system before it is released. With clear and concise examples as well as for instructions, this manual goes through all the stages of testing and evaluation. Further illustrated with relevant pictures as well as the use of real-life examples, “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments” makes it easy for those with the training to understand immediately. This book also provides guidance and reference on how the Trading System Synthesis and Boosting (TSSB) work in thorough detail. All in all, this guide is great reference material for those interested in the development of trading systems. 

About the Authors

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 groundbreaking 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: TimothyMasters.info

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
  • Committees
  • 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

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