In the late seventies, while conducting the research in computerized strategies for managed futures Aronson realized the potential of applying of artificial intelligence to the discovery of predictive patterns in financial market data. This practice, which is now gaining acceptance on Wall Street, is referred to as data mining. In Aronson founded Raden Research Group, an early adopter of data mining and non-linear predictive modeling to the development systematic trading methods.
Raden Research Group Inc. Experienced Accountant with a demonstrated history of working in the financial services industry Trading Cryptocurrencies. Strong Bitcoin Trader. LinkedIn Pinterest 0. Table of Contents. Patrick Howells. Patrick Howells Experienced Accountant with a demonstrated history of working in the financial services industry Trading Cryptocurrencies. Leave a Reply Cancel reply Comment. Enter your name or username to comment.
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Probability and statistical inference. Probability and Statistical Inference. Technical Analysis for the Trading Professional. Probability And Statistical Inference.
Introduction to Linear Models and Statistical Inference. Freud and the Scientific Method. Multivariate statistical inference and applications. Applying Contemporary Statistical Techniques. Here, EBTA relies on the findings of cognitive psychology to explain how erroneous beliefs arise and thrive despite the lack of valid evidence or even in the face of contrary evidence.
Cognitive psychologists have identified various illusions and biases, such as the confirmation bias, illusory correlations, hindsight bias, etc. Thus EBTA relies on computerized methods for identifying patterns, and combining evidence into useful trading signals. Due to recent advances in computing and data mining algorithms it becomes possible for the modern technical analyst to amplify their research efforts and find the real gold.
In other words, EBTA advocates a synergistic partnership between technical analysts and data mining computers to expand the valid base of knowledge called technical analysis. The union of humans and intelligent machines makes sense because the two entities have different but complimentary information processing abilities.
Whereas human intelligence has a limited ability to engage in complex configural reasoning, which is required to identify valid predictive variables and combine them into a mathematical function, it can pose questions and proposed candidate variables.
Whereas computer intelligence is ill equipped to pose questions and propose variables it has enormous capacities to identify relevant predictors and derive optimal combining functions. However, this new approach to technical analysis will require that human technicians abandon some tasks they now do and learn a new set of analytical skills.
While they will no longer try to subjectively evaluate complex information patterns, they will need to learn about the kinds of data transformations that produce variables that are most digestible to data mining computers.
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