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Over the past two decades, numerous articles in respected academic journals have approached technical analysis in a scientifically rigorous and intellectually honest manner, and now, Evidence-Based Technical Analysis looks to continue down this path. Organized into two parts, this valuable resource first establishes the methodological, philosophical, and statistical foundations of evidenced-based technical analysis (EBTA), and then demonstrates this approach—by using twenty-five years of historical data to test 6,400 binary buy/sell rules on the S&P 500.
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 these pages, expert David Aronson details this new type of technical analysis that—unlike traditional technical analysis—is restricted to objective rules, whose historical profitability can be quantified and scrutinized.
Filled with in-depth insights and practical advice, Evidence-Based Technical Analysis 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. Experimental results presented in the book will show you that data mining—a process in which many rules are back-tested and the best performing rules are selected—is an effective procedure for discovering useful rules/signals. However, since the historical performance of the rules/signals discovered by data mining are upwardly biased, new statistical tests are required to make reasonable inferences about future profitability. Two such tests, one of which has never been discussed anywhere heretofore, are described and illustrated.
If you want to use technical analysis to navigate today's markets, you must first abandon the subjective, interpretive methods traditionally associated with this discipline, and embrace an approach that is scientifically and statistically valid. Grounded in objective observation and statistical inference, EBTA is the approach to technical analysis you need to succeed in your trading endeavors.
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Most Helpful Customer Reviews
138 of 146 people found the following review helpful:
5.0 out of 5 stars
Great book,
By Mike Carr, CMT (Cheyenne WY) - See all my reviews
This review is from: Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals (Hardcover)
In this thought-provoking work, David Aronson tests more than 6,400 technical analysis rules and finds that none of them offer statistically significant returns when applied to trading the S&P 500. This result, presented at the end of his work, is not disappointing to dedicated students of technical analysis who draw from the book not a new trading technique but instead take away a new, and more effective, approach to system development and trading. Those seeking the single best indicator or day trading pattern will be disappointed after reading Evidence-Based Technical Analysis, just as they will be disappointed in their trading until they advance beyond seeking the Holy Grail of Trading.Most books and articles about technical analysis focus on applying a specific technique in pursuit of success in the markets. This one is different in that it outlines an entirely new process of thinking, and through the application of this new thought process, success can be attained. Part I of Evidence-Based Technical Analysis is called, "Methodological, Psychological, Philosophical, and Statistical Foundations" and Aronson uses this title as an outline to define the processes which should underlie system development. The scientific method changed the world, and made the advances of modern society possible. Until now, technical analysis has been considered more of an art than a science to many practitioners and escaped the scrutiny of the scientific method. With recent advances in computing power and analysis software, it is now possible for virtually anyone to search through years of data and identify seemingly profitable trading rules. Aronson presents the scientific method, combined with the philosophy of science as explained by Karl Popper, as an antidote to this very real danger. Well designed experiments in any scientific inquiry are based upon a verifiable hypothesis grounded in detailed observations. Popper contributed the concept of falsification to this framework, which readily lends itself to mechanical trading system design. As Aronson writes, "Popper's central contention was that a scientific inquiry was unable to prove a hypothesis to be true. Rather, science was limited to identifying which hypotheses were false." In technical analysis, we can never prove that if the NYSE Advance-Decline Line reaches a new high, the Dow Jones Industrial Average will always be higher thirty days later. But, we can test this hypothesis to see if it is not true. This simple example illustrates the beginning of Aronson's scientific approach to the markets. Many of the dangers of data mining and curve fitting are grounded in psychology, and Aronson thoroughly explains many of the common problems that can contribute to inaccurate observations. Carefully studying his sections on logic and psychology should lead to better market observations, which should lead to profitable systems. The chapters on statistical analysis are worth more than the price of the book in itself. Aronson presents a clear primer on statistics, and leaves the reader with all they need to understand how to design a statistically valid experiment. In what may very well be a publishing first, he presents clear, detailed and understandable descriptions of bootstrap and Monte Carlo randomization methods. This book is well-researched and presents actionable ideas to advance the study of technical analysis. Although none of the rules Aronson tested proved to be statistically significant, he helpfully devotes a section to explaining the limitations of his test results. Armed with this information, and the knowledge provided in the rest of the book, the thoughtful analyst can develop better insights into the market and perform better backtests to identify profitable strategies.
42 of 48 people found the following review helpful:
5.0 out of 5 stars
A new (and needed) approach to technical analysis.,
By Sam Levine, CFA (The Hamptons, NY United States) - See all my reviews
This review is from: Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals (Hardcover)
Professor Aronson's book is a fascinating read for anyone frustrated with the current state of technical research and a must-read for those new to the field. I believe the Market Technicians Association should include it in its Chartered Market Technician curriculum.After a few years of studying and using technical analysis, I was left with the distinct feeling that there was an elephant in the room: most of the methods used by market technicians haven't been rigorously examined for risk-adjusted performance. Elaborate and often contradictory theories and strategies have been presented by saying "my personal experience has been..." or something similar. Eventually, TA began to seem more a religious choice rather than a science of observing and predicting the markets (let alone successful investing). Aronson's book follows a structure that is designed to break through generations of instruction from pontificating gurus. He discusses the reason TA's rules are suspect, provides a brief history of empiricism ("the scientific method") and then delves into descriptive and inductive statistics to move the field forward. Those readers fortunate enough to have an undergraduate background in philosophy and statistics will find the reading somewhat basic but the application of these fields to a critical appraisal of TA refreshing. Finally, he applies his rigorous testing to a large set of TA rules. Key takeaway: The way to develop and test strategies going forward.
81 of 97 people found the following review helpful:
1.0 out of 5 stars
There is better books available,
This review is from: Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals (Hardcover)
This book doesn't test 6400 binary rules,unless you see a Price/Moving average cross with a period of 1 to 200 as 200 rules. I see it as 1 rule with 1 optimizing parameter. A lot of stuff get repeated and the book shouldn't be longer than 200 pages. There is a lot to learn if you are a novice trader and if you've never tried to develop a trading system. I think this book isn't worth half the selling price. Rather buy "The Encyclopedia of Trading Strategies (Hardcover)" by Jeffrey Owen Katz. You will learn something about GA and NN. Don't waste your time on this book. I'm selling mine.
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