bayesian signal classifier
Paul, I did some research in this area back in grad school and tested a variety of classical technical analysis indicators and their ability to forecast in-the-market or out-of-the-market periods based on classification. Its an interesting topic. Implementing a basic classification system is quite straight-forward. I would suggest looking at the following sections of V&R (4th edition): 9.1-9.3 Classification Trees 7.2 GLM with binomial data 8.10 Neural Networks If you're interested in our research report, drop me an email and I'll be happy to forward it to you. -- Guy Guy Yollin Senior Financial Engineer Insightful Corporation www.insightful.com gyollin at insightful.com
paul sorenson wrote:
The issue I am curious about is how to classify various signals (eg price, volume, MACD, etc) into to buy, sell, or hold? Assuming I could "tokenize" various attributes of signals (value, 1st, 2nd and 3rd derivatives, crossing, etc), would it be feasible to take these as inputs to a (trained) classifier which then outputs some number between 0 and 1 representing buy, hold, sell? The analogy I am thinking of is a Bayesian spam classifier. My background is in engineering and I have only basic statistics knowledge. I have been using R for a couple of years now mostly for graphic output. I have a reasonable grasp of the language but I'm not strong on the underlying theory of the statistical functions. R has a number of packages which deal with Bayesian statistics but I don't have the knowledge to join the dots from there to a classifier. Any pointers would be most welcome. cheers
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