[R-sig-finance] bayesian signal classifier

paul sorenson sourceforge at metrak.com
Mon Nov 28 22:18:47 CET 2005


I would be interested in the paper thanks.  Unfortunately my level of 
expertise is not high in these matters.

I may have just misunderstood yours and Krishna's response, the kind of 
paradigm I am thinking is:

	- User selects signals he/she wants to monitor.

	- When the user makes a buy/sell decision, the classifier then looks at 
the parameters of those signals and classifies the conditions for that 

	- The user continues to train the classifier in this way, analogously 
to training a spam filter.

	- The classifier then can start emitting buy/sell signals based on the 
training.  Ie it is personalized to that users previous choices.

I only mentioned Bayesian methods because the most effective spam 
filtering I have used is apparently based on that method 


Guy Yollin wrote:
> 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.
>>R-sig-finance at stat.math.ethz.ch mailing list
> _______________________________________________
> R-sig-finance at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance

More information about the R-sig-finance mailing list