Evidence-Based Technical Analysis


  • Pages: 526
  • Format: PDF
  • Published Date: 2007


Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals

Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.

Author’s Introduction:

Technical analysis (TA) is the study of recurring patterns in financial market data with the intent of forecasting future price movements. It is comprised of numerous analysis methods, patterns, signals, indicators, and trading strategies, each with its own cheerleaders claiming that their approach works.

Much of popular or traditional TA stands where medicine stood before it evolved from a faith-based folk art into a practice based on science. Its claims are supported by colorful narratives and carefully chosen (cherry picked) anecdotes rather than objective statistical evidence.

This book’s central contention is that TA must evolve into a rigorous observational science if it is to deliver on its claims and remain relevant. The scientific method is the only rational way to extract useful knowledge from market data and the only rational approach for determining which TA methods have predictive power.

I call this evidence-based technical analysis (EBTA). Grounded in objective observation and statistical inference (i.e., the scientific method), EBTA charts a course between the magical thinking and gullibility of a true believer and the relentless doubt of a random walker.

This book is organized in two sections. Part One establishes the methodological, philosophical, psychological, and statistical foundations of EBTA. Part Two demonstrates one approach to EBTA: testing of 6,402 binary buy/sell rules on the S&P 500 on 25 years of historical data. The rules are evaluated for statistical significance using tests designed to cope with the problem of data-mining bias.


  • Objective Rules and Their Evaluation
  • The Illusory Validity of Subjective Technical Analysis
  • The Scientific Method and Technical Analysis
  • Statistical Analysis
  • Hypothesis Tests and Confidence Intervals
  • Data-Mining Bias: The Fool’s Gold of Objective TA
  • Theories of Nonrandom Price Motion
  • Case Study of Rule Data Mining for the S&P 500
  • Case Study Results and the Future of TA