David Aronson is a respected figure in the field of technical analysis and quantitative trading. He brings a unique perspective to the subject, combining academic rigor with practical experience. Aronson’s background includes a five-year stint as a proprietary trader before transitioning to academia.
Key Takeaways
Readers appreciate its rigorous methodology, statistical focus, and debunking of subjective TA myths. The book is praised for its unique perspective and valuable insights, particularly on data mining bias and statistical testing. However, some find it overly long and academic, with excessive focus on basic concepts. While considered essential reading for aspiring traders, the book’s practical trading utility is debated, with some viewing it as more theoretical than actionable. The scientific method is the only rational way to extract useful knowledge from market data and determine which TA methods have predictive power. It involves formulating testable hypotheses, collecting objective data, and using statistical analysis to evaluate the evidence.
The Scientific Method: A Rigorous Path to Knowledge
A similar experiment can be easily repeated in StrategyQuant X for any market. It considers the two main components of observed performance (strategy performance) as follows. This blog post aims to pull out the basic concepts that David Aronson works with and apply them to the topic of StrategyQuant X development. I have focused on the parts that most concern SQX users, taking into account the most common mistakes that newbies make when setting up the program. The TA expert’s role is to propose informative indicators and specify the problem to be solved by data-mining software.
- This provides for the migration of strategies between islands.
- Ivan Hudec, known as “Clonex” on the forum, is an experienced algorithmic trader, consultant, and researcher who has been trading for 15 years and using StrategyQuant X (SQX) since 2014.
- This is especially pronounced in difficult or impossible tasks, such as predicting short-term market trends.
An illusory correlation is the false perception of a relationship between a pair of variables. Ironically, more intelligent people may be more prone to the confirmation bias, as they are better able to construct rationales for their beliefs and defend them against challenges. A conflict exists between our desire for knowledge and our desire that it be delivered in the form of a good story.
The Hindsight Bias Creates Illusory Validity
The book was published in 2006 and became popular fairly quickly. Asymmetric Binary Variables. Illusory correlations are especially likely to emerge when the variables involved are asymmetric binary variables. The knowledge illusion is a false confidence in what we know—both in terms of quantity and quality. It is based on the false premise that more information should translate into more knowledge.
This bias inhibits learning and reinforces erroneous knowledge. To combat the hindsight bias, subjective practitioners should make falsifiable forecasts, clearly specifying the conditions under which their predictions would be considered wrong. This allows for objective evaluation and feedback, reducing the illusion of validity. In subjective TA, the ambiguity inherent in chart patterns and indicators is often obscured by outcome knowledge. After the fact, it’s easy to selectively notice features that seem to have predicted the outcome, while downplaying contradictory signals. The self-attribution bias further distorts our perception of reality by attributing successes to our skills and failures to external factors.
This involves a continual process of testing, refining, and discarding ideas that fail to hold up under scrutiny, leading to a progressively more accurate understanding of market dynamics. Island evolution can also have a major impact. This provides for the migration of strategies between islands. Evolutionary management can also play an important role. Especially if we restart genetic evolution with too many generations. You may end up with more correlated strategies in the databank.
What is data mining bias in Evidence-Based Technical Analysis?
- It discusses philosophical, methodological, statistical, and psychological issues in the analysis of financial markets and emphasizes the importance of scientific thinking, judgment, and reasoning.
- This evolution, termed evidence-based technical analysis (EBTA), charts a course between blind faith and relentless skepticism.
- According to Aronson, the greater the variability of strategy performance metrics in the databank, the greater the risk of bias from data mining.
- Readers appreciate its rigorous methodology, statistical focus, and debunking of subjective TA myths.
- Evolutionary management can also play an important role.
This creates a false sense of confidence in our ability to make predictions. The hindsight bias creates the illusion that the prediction of an uncertain event is easier than it really is when the event is viewed in retrospect, after its outcome is known. This book’s central contention is that TA must evolve into a rigorous observational science if it is to deliver on its claims and remain relevant.
Variation in expected returns among the rules
In the following chapters, Aronson explains the importance of rigorous statistical analysis in evaluating strategies. A representative portfolio that began in 1984 has earned a compounded annual return of 23.7%. In 1990 AdvoCom advised Tudor Investment Corporation on their public multi-advisor fund. The first part deals with philosophical questions of scientific knowledge.
Traditional technical analysis (TA) often relies on subjective interpretations and anecdotal evidence, resembling a faith-based folk art more than a rigorous science. To truly deliver on its claims of forecasting future price movements, TA must embrace the scientific method, grounded in objective observation and statistical inference. This evolution, termed evidence-based technical analysis (EBTA), charts a course between blind faith and relentless skepticism. 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. It is the subjective TA analysis that can often be based on the biases described by Aronson, but he points out that even with objective – statistical TA biases often occur unconsciously. Therefore, he proposes the use of the so-called objective TA in the form of the application of scientific methods in the analysis.
This requires domain expertise, creativity, and a deep understanding of market dynamics. A scientific hypothesis must be falsifiable, meaning that it can be tested and potentially disproven by empirical evidence. This distinguishes science from pseudoscience, which is often characterized by untestable claims and resistance to empirical challenge. The enduring appeal of the Elliott Wave Principle may be attributed to its comprehensive cause-effect story, which promises to decipher the market’s past and divine its future. However, its flexibility and loosely defined rules make it difficult to test objectively. The goal of EBTA is to create a body of knowledge about market behavior that is as reliable as possible, given the limitations of evidence gathering and the powers of inference.
Vague evaluation criteria in subjective TA facilitate the confirmation bias. By selectively noticing supportive evidence and downplaying contradictory evidence, practitioners can maintain their beliefs even in the face of poor performance. Subjective TA methods, characterized by their vagueness and reliance on private interpretations, fail to meet the criteria for legitimate knowledge. Because they cannot be objectively tested or refuted, claims of their effectiveness are essentially meaningless.
There are also live events, courses curated by job role, and more. Excellent review of Aronson’s work with respect to StratQuant. What was not explicitly explained was the concept of “degrees of freedom” as explained in Robert Pardo’s book, “Design, Testing, and Opimization of Trading Systems,” (1992) and his second edition, (2008). From the 1st edition, “Placing to many restrictions on the price data is the primary cause of overfitting” pg. In today’s blog post, I will try to summarize some important ideas from the book Evidence Based Technical Analysis by David Aronson.
It is based on knowledge of the first point. The number of correlated strategies in the StrategyQuantX can be affected by the type of building blocks used in strategy construction, but also by the setting of the genetic search for strategies. For example, if you choose only moving averages as building blocks, it is more likely that the strategies will be more correlated with each other. The use of scientific methods in technical/quantitative analysis is the basic theme of the entire book. Ivan Hudec, known as “Clonex” on the forum, evidence based technical analysis is an experienced algorithmic trader, consultant, and researcher who has been trading for 15 years and using StrategyQuant X (SQX) since 2014. Ivan offers his expertise to help others accelerate their trading projects and approach them in innovative ways.
13This refers to the presence of very large returns in a rule’s performance history, for example, a very large positive return on a particular day. In other words, more observations dilute the biasing effect of positive outliers. In general, the larger the data sample (number of trades in out of sample), the higher the statistical power of the results.