Predictive accuracy and explanatory power are key criteria for evaluating scientific knowledge. Although the scientific method is not guaranteed to extract gold from the mountains of market data, an unscientific approach is almost certain to produce fool’s gold. Subjective TA is akin to religion, based on faith rather than evidence. While proponents may offer cherry-picked examples of success, these anecdotes cannot compensate for the lack of objective, statistical validation. Subjective TA is not even wrong. Statements that can be qualified as wrong (untrue) at least convey cognitive content that can be tested.
Examples include classical chart pattern analysis, hand-drawn trend lines, and Elliott Wave Principle. Customers find the book well-referenced and detailed, with one customer highlighting its excellent analysis of technical trading and statistics. This point is considered by Aronson to be the most important of all factors. He argues that the larger the sample of data obtained, the smaller the negative impact of the other factors. When optimizing an existing strategy, pay attention to the parameter ranges and the number of steps.
What is data mining bias in Evidence-Based Technical Analysis?
While the scientific method doesn’t guarantee success, it significantly increases the chances of extracting valuable insights from market behavior. Evidence-Based Technical Analysis thematically addresses the issue of statistical analysis in the context of strategy development and the issue of data mining that users are concerned about. StrategyQuant x is de facto a sophisticated data mining tool that needs to be deployed and set up in a way that reduces the risk that strategy performance is actually a product of chance.
Much of popular or traditional TA stands where medicine stood before it evolved from a faith-based folk art into a practice based on science. You can read this eBook on any device that supports DRM-free EPUB or DRM-free PDF format. Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day. Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact. View all O’Reilly videos, virtual conferences, and live events on your home TV. From here we can directly influence the sample size and we also have Monte Carlo tests available directly in StrategyQuant X.
The propositions of subjective TA offer no such thing. These What If Cross Checks allow you to test the performance of the strategy without the most profitable or the most profitable trades. If the results of the strategy are unreasonably different, you need to be careful. Aronson proves the conclusions presented in the following section by experimentally running the data-driven trading rules for the S&P 500 Index over the period from 1328 to 2003. The exact procedure can be found on page 292.
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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
Factors Influencing the Degree of Data Mining Bias
This self-serving interpretation of events reinforces overconfidence and hinders learning from mistakes. People tend to overestimate their abilities and knowledge, a phenomenon known as the overconfidence bias. This is especially pronounced in difficult or impossible tasks, such as predicting short-term market trends. The publisher has supplied this book in encrypted form, which means that you need to install free software in order to unlock and read it. You can read this ebook online in a web browser, without downloading anything or installing software.
What statistical methods are emphasized in Evidence-Based Technical Analysis?
According to Aronson, the greater the variability of strategy performance metrics in the databank, the greater the risk of bias from data mining. To analyze the results of the entire databank, you can use a custom analysis or export the database and analyze it externally in Excel or Python. The stronger the correlation between the rules tested, the smaller will be the magnitude of the bias.
- Aronson is known for his skepticism towards conventional technical analysis techniques and his advocacy for evidence-based methods.
- In other words, when the set of rules tested has similar degrees of predictive power, the data-mining bias will be larger.
- It is based on analysts’ personal interpretations and is difficult to prove from a historical perspective through backtesting.
- Compelling stories, with vivid details and emotional appeal, can be more persuasive than objective facts.
- In this case, the larger the values and ranges you specify, the greater the risk of data mining bias.
- We rely on the availability heuristic to estimate the likelihood of future events.
His work focuses on applying scientific methods and statistical analysis to trading strategies, challenging traditional subjective approaches. Aronson is known for his skepticism towards conventional technical analysis techniques and his advocacy for evidence-based methods. His research and writing have significantly contributed to the advancement of objective technical analysis in financial markets. Evidence-Based Technical Analysis challenges conventional technical analysis, advocating for a scientific, objective approach.
It is based on analysts’ personal interpretations and is difficult to prove from a historical perspective through backtesting. In contrast, the objective TA is based on the use of backtesting methods and the use of objective statistical analysis of backtesting results, according to Aronson. Data mining, the process of searching for patterns in large datasets, can lead to an upward bias in the observed performance of selected rules. This bias occurs because the winning rule may have benefited from good luck during the back test, which is unlikely to repeat in the future.
Beyond Technical Analysis: How to Develop and Implement a Winning Trading System, 2nd Edition
- This problem is not easy to understand, because the state of your database depends on many factors.
- Objective evidence, obtained through rigorous scientific methods, is the only reasonable basis for asserting that an analysis method has value.
- The use of scientific methods in technical/quantitative analysis is the basic theme of the entire book.
- In the context of StrategyQuant X, we can apply the problem of multiple comparisons wherever we are looking for a large number of indicators/conditions/settings of a particular strategy in a large spectrum.
- It involves formulating testable hypotheses, collecting objective data, and using statistical analysis to evaluate the evidence.
It is based on the reasonable notion that the more easily we can bring to mind a particular class of events, the more likely it is that such events will occur in the future. The Representativeness Heuristic. The representativeness heuristic, which involves judging the probability of an event based on its similarity to a stereotype, can lead to the illusion of trends and patterns in random data. Humans are natural storytellers, and narratives have a powerful influence on our beliefs. Compelling stories, with vivid details and emotional appeal, can be more persuasive than objective facts. The hindsight bias distorts our perception of past events, making them seem more predictable than they actually were.
Conversely, the lower the correlation (i.e., the greater the degree of statistical independence) between rules returns, the larger will be the data-mining bias. This makes sense because increased correlation among the rules has the consequence of shrinking the effective number of rules being back-tested. It is the ethical and legal responsibility of all analysts to make recommendations that have a reasonable basis and not to make unwarranted claims.
In other words, when the set of rules tested has similar degrees of predictive power, the data-mining bias will be larger. If the number of building blocks is very low, you will not realize the potential of data mining; on the contrary, if the number of building blocks is very high, you risk a large data mining bias. These factors can also be eliminated by a high number of trades or by multi-market testing. Subjective TA, according to Aronson, does not use repeatable scientific methods and procedures.
In the context of StrategyQuant X, we can apply the problem of multiple comparisons wherever we are looking for a large number of indicators/conditions/settings of a evidence based technical analysis particular strategy in a large spectrum. 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. 14This refers to the variation in true merit (expected return) among the rules back-tested. The lower the variation, the greater the data-mining bias.
I often see from clients strategies with 6 conditions and lookback periods of 25. There is a real risk of data mining bias with these combinations. This phenomenon can be measured by analyzing the variability of the results in the database.