An island of clarity in a sea of pseudo-science.

books:


	• Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals David R Aronson Wiley, 2006 - 544 pages average customer review:based on 40 reviews view larger image
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highly recommended

TA as a scientific endeavor, it's about time!!

EBTA is a great book for any technical analysis (TA) based trading system developer. This book attempts to put technical analysis on a more rigorous scientific footing that will allow it to flourish as an empirical science.

As I see it, this book makes two main points:

1) Traditional TA techniques are often defined so vaguely that they are properly classified as pseudoscience. Pseudoscience is characterized by claims that are so vague, they cannot be falsified. When a TA claim cannot be falsified, it cannot be subjected to scientific scrutiny, and so it can be perpetuated even when it holds no merit in predicting market movements.

Therefore, any TA techniques that one uses should be programmable (in a computer language) so they can be backtested and either falsified or tentatively supported.

2) Even if a backtested trading rule/strategy performs well in a backtest, this is merely a necessary and not a sufficient condition for believing this rule/strat will perform well in the future. Market movements are governed, at best, by a combination of randomness and predictable market structure. If one is not careful, backtested rules/strats may merely represent curve-fitting to the random elements of market movement that will not repeat themselves in the future. If/when one trades based on such overfitted strategies, one is almost sure to lose money.

To combat this, one must submit backtesting results to rigorous statistical tests in order to reject results that are likely to be examples of such overfitting. Along these lines, the author gives a very clear introduction to basic statistical concepts/procedures, describes several out-of-sample testing methods, and explains 2 other advanced methods (White's Reality Check and the Monte Carlo Permutation method), that can all be used to analyze backtesting results. Again, applying these methods to backtesting results can help the researcher reject those results that are likely to be examples of overfitting due to data-mining efforts.

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The author goes into great detail about how and why the procedures he presents should be used, and he also includes a backtesting case study clearly illustrating how to apply the methods he has introduced earlier in the text.

The clarity and explicitness of the entire book is a refreshing change from most books on TA. The author provides enough detail for anyone interested to implement any and all the procedures he describes (including White's Reality Check and the Monte Carlo Permutation method) for themselves.

If you are looking for a book that will provide you with a specific trading strategy or an in-depth description of relevant data-mining techniques, look elsewhere, but don't ignore this book even in those cases.

For, no matter what particular systems/strategies you create/discover, this book will show you how to properly analyze your results. If your results pass the tests this book suggests you use in such analyses, you will be much more likely to make money going forward as opposed to being fooled by randomness.

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Best Investment Book I Ever Read

I have been groping for a way to apply mathematical rigor to my back testing of investment strategies for a couple of years now. This book provides all of the answers I was looking for, in detail. In a field where people rely more on superstition and old wives' tales than sound evidence, this book is a breath of fresh air.

The author is quite exhaustive in his treatment of the subject matter. Most technical textbooks leave out important steps required to duplicate procedures, leaving the reader frustrated. This one gives every step.

Portions of the book are a bit redundant. I think some editing to reduce the redundancy and page count would have been beneficial.

Warning: some mathematics required. If you have a Bachelor's degree in math, science or engineering, you should be okay.

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Accessible, relevant & thought provoking!

I was conflicted in writing a review of this text - it provides information and method's that I would've preferred were not accessible to market participants... with the exception of myself, naturally. That said, the book deserves recognition as required reading for anyone actively engaged in searching for predictive "signals" using historical data. Professor Aronson provides foundation and statistical methods for establishing that the signals are significant and NOT an expected consequence of searching large enough spaces.

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.

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An island of clarity in a sea of pseudo-science.

A well done treatise on the application of the scientific method to the field of TA. Highly recommended unless you would rather believe in fairy-tales ;)

reviews: page 1, 2, 3, 4, 5, 6, 7, 8

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.

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