Evidence-Based Technical Analysis is a breakthrough book in that it rigorously applies the scientific method and recently developed statistical tests to determine the true effectiveness of trading strategies, rules or systems discovered by data mining. Traditional technical analysis - as currently practiced - is more like a faith-based folk art than a science, the author asserts. To move technical analysis forward, the author proposes a new type of technical analysis, which he calls: evidence-based technical analysis or EBTA. Unlike traditional technical analysis, EBTA is restricted to objective methods whose historical profitability can be quantified and then rigorously scrutinized. The author provides a new statistical methodology specifically designed for evaluating the performance of rules that are discovered by data mining, a process in which many rules are back-tested and the best performing rule(s) is selected. Experimental results presented in the book show that data mining is an effective approach for discovering useful rules. However, the historical performance of the best rule (s) is upwardly biased - a combined effect of randomness and data mining. Thus new statistical tests are needed to make reasonable inferences about the future profitability of rules discovered by data mining. Most importantly, in data mining case study the author evaluates over 6400 signaling rules applied to the S-P500 Index using these new tests. For technical analysts and traders, the book is a wake-up call to abandon subjective, interpretive methods and embrace an approach that is scientifically and statistically valid. For other traders, the rigorous testing of trading signals/rules may make their data mining efforts more productive and stimulate the development of new systems, signaling rules.