This is an interesting thread for me. I think it would be possible to do some sort of day based pattern matching.. (i.e. looking at the shape of a whole days trading, and finding similar shapes hitorically) to do that you'd just need ot export the intraday chart each day in a standard size and format, and then image process it so the main shape is more clearly visible.... then downsize the image file to 100 or so pixels, and run a pattern matching analysis on that. the idea would then be to see what happened the day after the historical day who's shape matched..
Very nice link, going quickly through this, there is some nice info here, plus lots of information packed into the joone guide, it is 137 pages long.
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Take a look at Recognia: "Using patent-pending pattern recognition algorithms and neural networking technology, Recognia then systematically analyzes the pricing data for these financial instruments overnight, producing charts for each security and automatically detecting current and historical chart patterns. The technology used by Recognia is proprietary, and has been developed and validated by experts in the field of technical analysis." http://www.recognia.com/services/patternrecogn_desc.htm
Sound post. Interestingly, we come right onto Python! Of course, the url is only meant to be a set of test examples. As the Poncho says, a huge amount of references is available. As to NN's use for making a bit more money in the markets, that's altogether another subject.
You don't want a 'multi-timeframe' version. You want continuously adaptive scales in both directions. This is a well known research topic in data-mining. Wavelets have been tried a couple of times but seem to be of little use. An inherent problem is the efficient cataloguing of the patterns searched for, this linked with a quick search/retrieval method.
You might want to take a look at differential stochastic hopfield networks. I used to think that mulit-timeframe templates would be useful, but it is not straightforward to extend the idea of synchronized spikes to multiple time frames, the good news is that each template can have its own inherent timeframe so all sorts of templates operating on different timescales can be run in parallel.