Evidence-Based Technical Analysis Applying The Scientific Method And Statistical Inference To Tradin
Download File --->>> https://shoxet.com/2sXDaY
DAVID ARONSON is an adjunct professor at Baruch College, where he teaches a graduate- level course in technical analysis. He is also a Chartered Market Technician and has published articles on technical analysis. Previously, Aronson was a proprietary trader and technical analyst for Spear Leeds & Kellogg. He founded Raden Research Group, a firm that was an early adopter of data mining within financial markets. Prior to that, Aronson founded AdvoCom, a firm that specialized in the evaluation of commodity money managers and hedge funds, their performance, and trading methods. For free access to the algorithm for testing data mined rules, go to www.evidencebasedta.com. Permissions Request permission to reuse content from this site
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.
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.
DAVID ARONSON is an adjunct professor at Baruch College, where he teaches a graduate- level course in technical analysis. He is also a Chartered Market Technician and has published articles on technical analysis. Previously, Aronson was a proprietary trader and technical analyst for Spear Leeds & Kellogg. He founded Raden Research Group, a firm that was an early adopter of data mining within financial markets. Prior to that, Aronson founded AdvoCom, a firm that specialized in the evaluation of commodity money managers and hedge funds, their performance, and trading methods. For free access to the algorithm for testing data mined rules, go to www.evidencebasedta.com.
The first part deals with philosophical questions of scientific knowledge. It discusses technical analysis and analysis as tactics from the perspective of philosophy, methodology, and logic, and overall focuses more on theoretical and philosophical issues and their implications for practice.
The book begins with a definition of the basic concepts of technical analysis and attempts to define the whole subject from the point of view of logic. It discusses philosophical, methodological, statistical, and psychological issues in the analysis of financial markets and emphasizes the importance of scientific thinking, judgment, and reasoning.
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.
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 a data mining case study the author evaluates more than 6,400 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. Author information David Aronson is an adjunct professor at Baruch College, where he teaches a graduate- level course in technical analysis. He is also a Chartered Market Technician and has published articles on technical analysis. Previously, Aronson was a proprietary trader and technical analyst for Spear Leeds & Kellogg. He founded Raden Research Group, a firm that was an early adopter of data mining within financial markets. Prior to that, Aronson founded AdvoCom, a firm that specialized in the evaluation of commodity money managers and hedge funds, their performance, and trading methods.
One of the key assumptions of quantitative trading strategy evaluation is that Type II errors (missed discoveries) are preferable to Type I errors (false discoveries). However, practitioners have known for a long time that the statistical properties of some genuine trading strategies are often indistinguishable from those of random trading strategies. Therefore, any adjustments to statistics to guard against p-hacking increase Type II error unless the power of the test is high. At the same time, the power of the test is limited by insufficient samples and changing market conditions. Furthermore, genuine strategies with statistical properties that are similar to those of random strategies may overfit due to favorable market conditions but fail when market conditions change. These facts severely limit the effectiveness of quantitative claims about trading strategy evaluation. Practitioners have instead resorted to Monte Carlo simulations and stochastic modeling in an effort to increase the chances of identifying robust trading strategies, but these methods also have severe limitations due to changing market conditions, selection bias, and data snooping. In this paper, we present two examples that demonstrate the limitations of quantitative evaluation of trading strategies, and we claim that the most effective way of guarding against overfitting and selection bias is by limiting the applications of backtesting to a class of strategies that employ similar but simple predictors of price. We claim that determining when market conditions change is, in many cases, fundamentally more important than any quantitative claims about trading strategy evaluation.
Technical Analysis Primer for trading Stocks, Bonds & Forex | Udemy Udemy Technical Analysis Primer for trading Stocks, Bonds & ForexLearn the building blocks of technical analysis & create your own trading strategy
Technical Analysis Using Elliott Wave Theory | Udemy Udemy Technical Analysis Using Elliott Wave Theory | UdemyLearn advanced technical analysis for financial trading in stock market, commodities and forex - futures & options
By a practitioner, for practitioners. The Art and Science of Technical Analysis explores the academic theory of technical analysis in the context of actually making profitable trades. The author uses statistical analysis to challenge some of the less reliable approaches, showing how to spot true patterns rather than random movements. He also offers an exploration of how trader psychology fits into the picture.
This one can be seen as a sequel to Japanese Candlestick Charting Techniques further down on our list. The author uncovers further Japanese technical analysis techniques and shows how to combine them with traditional strategies to maximize trading effectiveness. Just as its predecessor, the book works great as a step-by-step guide with detailed illustrations and actionable advice.
Here, the author offers a more rigorous take on technical analysis. The book shows how one can apply the scientific method along with statistical tests to better understand and evaluate technical trading signals. The methods it describes rely on advanced mathematical concepts, so the book is probably not well suited for the casual reader.
Possibly the most comprehensive entry on the list. This book is both an academic exploration of technical analysis and a truly thorough compilation of pattern recognition methods. From traditional techniques to new confidence tests using methods Kagi, Renko, Kase, DeMark, and other indicators, this title is a must-have resource for the serious trader.
Although this conditional structure of efficiency tests has several advantages in comparison to traditional tests, it introduces some additional complications in terms of statistical inference. The incorporation of conditional information is accomplished through the use of an additional set of instruments in the estimation and testing procedures. We need estimators that allow for the incorporation of this additional information in the parametric structure of the model, which in fact corresponds to the use of additional moment conditions. Thus, we are restricted to moment estimators with the possibility of overidentification, that is, a number of moment conditions greater than the number of fixed parameters of the model. The natural candidate for this problem is the GMM estimator [2], which appears as a generalization of the method of moments method for the case of overidentification. As the GMM estimators do not impose any restrictions on the data distribution, only being based on assumptions about the moments, this method is widely used in finance. In this article, we discuss the use of generalized empirical likelihood estimators [3], which can be seen as a generalization of the GMM estimators, where we use a non-parametric estimate of the likelihood function as a weighting function for the construction of the expected value of the moment conditions. 2b1af7f3a8