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Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals Hardcover – November 3, 2006
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"…his book is well written and contains a great deal of information that is of value…." (The Technical Analyst, May/June 2007)
From the Inside Flap
As an approach to research, technical analysis has suffered because it is a "discipline" practiced without discipline. In order for technical analysis to deliver useful knowledge that can be applied to trading, it must evolve into a rigorous observational science.
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.
Top customer reviews
I marked it down, because it is only when you get to page 400+ that he reveals that not one of those systems worked.
If he said that on page 1, you wouldn't bother to read the book.
The previous reviewer (Useless..) that gave it one star clearly did not get the concepts of the book. Did he even read it? That review does not compute. The *only* negative I would say is that if you're just looking for how to do robust backtesting, then the extensive material on the scientific method might be a bit much (but you can always read lightly those sections). But I understand why he put it in there, since it's the entire premise of taking a different and more rigorous approach to TA.
Now back to re-reading Chapter 6... Thank you Mr. Aronson!
The book is well referenced and Aronson has done a superb job making otherwise complex concepts accessible.
If you find yourself resisting and/or violating the principals of inference described in the book, consider that you may by fueling the inefficiencies that more sophisticated groups are likely to exploit.
Anyone wishing to succeed at trading system design must first develop a skill which is surprisingly subtle and exceptionally rare: the ability to distinguish between a good result and bad result. This seems so obvious on the surface that it is overlooked by the vast majority. However, it is absolutely not trivial and this skill is a key differentiator between the successful few and the majority who continuously struggle with trading. This book contains a series of tools and concepts which, when mastered, will equip the trader with the ability to understand when they're likely to have found something real and when they are simply fooling themselves.
These techniques are also extremely useful in evaluating the ideas of other authors in a much more realistic way before attempting to deploy them in a live trading account. Because of this, Evidence-Based Trading Analysis complements a collection of trading books quite nicely and should be included in every serious trader's library.
While it is never pleasant to find out that despite having worked tirelessly on a trading system that one's methods of development and analysis are faulty (I believe this accounts for the majority of the negative reviews this book has received), it is far better to learn and apply proper methods BEFORE deploying poorly constructed models rather than discovering one's error through mounting financial losses.
In summary, while this book may deliver a painful message to some it also provides the tools that enable the reader to truly progress in a way that translates to real-world success. Anyone who wants to elevate their trading system design process from the realm of the weekend hobbyist to that of the professional absolutely must master the techniques contained in this book. I cannot recommend it highly enough.