Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

andreyma Books Reviews

Review Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments



This book was published to carry out two purposes. The first one, it administers education and implements learning so that readers may understand the relevance of meticulous and accessible trading methodologies crucial for trading system evaluation. This is necessary as theories are the bases for application. Though readers do not have enough mathematical knowledge, one can simply subscribe to learning as techniques were presented in a comprehensive manner using real-life market statistics, with insights rendered in the simplest ways possible. 

Its second purpose heavily highlights the importance of Trading System Synthesis & Boosting for the development and assessment of trading systems. Education present inside this primer goes beyond the previous knowledge previously presented in other software development guides. Familiarization and mastery of these strategies will guarantee a trading system that will last and proven to have optimum results even though the system operates in the long run. 

Going deeper to what this book offers, it also emphasizes matters including:

  • Performance assessment and estimation through meticulous algorithms 
  • Evaluation of good luck in mock operations
  • Identification of overfitting prior to system deployment
  • Development trading system portfolios and put them under test to assess the performance
  • The building of trading platforms that highly appeal to specific market trends 

About the Author 

David Aronson is one of the people to have introduced machine learning and nonlinear trading system development and signal boosting. His experiences in the field were honed as he began pursuing the industry back in 1979. Back in 1992, acquired licensure as a Chartered Market Technician granted by The Market Technicians Association. He also endeavored in teaching with a specialization in technical analysis, data mining, and predictive analysis.

Table of Contents 

  • Introduction 
  • A Simple Standalone Trading System 
  • A Simple Filter System
  • Common Initial Commands
  • Reading and Writing Databases
  • Creating Variables
  • Screening Variables
  • Models 1: Fundamentals 
  • Models 2: The Models 
  • The Basic Tree Model
  • A Forest of Trees
  • Boosted Trees 
  • Operation String Models 
  • Split Linear models for Regime Regression
  • Committees 
  • Oracles
  • Testing Methods
  • Permutation Training 
  • Transforms 
  • Complex Prediction Systems
  • Graphics 
  • Finding Independent Predictors 
  • Market Regression Classes
  • Developing a Stand-Alone System