Look, there is no denying it – we love science fiction here at DeepMarket. Mind you, we are more than willing to allow for certain suspension of disbelief when it is required to make the story more interesting or even possible. For instance, most space related sci-fi has some type of faster than light propulsion systems. Golly, who wants to watch a story where it takes several generations for a ship to just get to the nearest star beyond good old Sol? We suspend our disbelief and enjoy the story.

Language is one of those areas that sci-fi has to cheat, like it does with faster than light travel. Here on earth there are thousands – if not tens of thousands – human languages and dialects. Translating between all these languages is hard – really hard. So, it seems that some sci-fi just ignore the problem - like in the Serenity/Firefly universe (although they seem to be multi-lingual without any technological assistance). Or they have to come up with a technology that explains the incredible ability of multiple species to converse in multiple languages and still understand each other. Star Trek has their Universal Translators and Farscape has their Translator Microbes. These technologies allow the users to understand and communicate in languages that they had never encountered before.

Yet, this is one area that is difficult to truly suspend disbelief. I am sure anyone who has learned a foreign language would also agree – not to mention the poor scientists that are working on natural language processing today. It is hard enough to extract meaning and understanding from one language and the systems that do it well are not learning systems – they are generally programmed from the top down. It is just the way the domain is at this moment. Not to say that good learning systems can’t be developed in the realm of language – just pointing out that the top-down systems still have the edge for the moment. I invite NLPers to correct me – this is a difficult domain!

So, when a story has someone waiting for their handheld (if not invisible) universal translator instantly learning a language never encountered up to that time – it seems hard to believe. So, let’s move beyond sci-fi and back in the practical. One reason this universal translation/learning/understanding process seems so extraordinary is that there is no feedback loop. Some alien life form is talking and the technology is able to derive meaning from it – in an unsupervised manner. Unsupervised basically means that the technology does not get graded and has no idea that the translations it is offering up are correct. Granted, I am oversimplifying the concept of unsupervised learning and these techniques have a place in language learning, but I doubt an effective language learning system could be developed without some type of feedback.

This leads me to my practical application – stock chart patterns. Dare I say that chart patterns are a type of language - the language of the markets? Not unlike trying to decipher ancient hieroglyphics of some lost civilization, trying to understand the language of the markets without some form of feedback is a sure way to waste a good deal of time. However, it seems that is what chartists do when they are looking at patterns in charts of stock prices. There is an alphabet of the market – Head and Shoulders – Wedges – Triangles – Rectangles – are all patterns in charts that perhaps are better described as hieroglyphics. These patterns are supposed to describe and help the analyst to understand the future direction of the market. This is the clash of science and art. Describing the chart patterns are generally pretty straight forward – however, actually recognizing “valid” chart patterns is an art.

Or, is recognizing chart patterns an art? This week we will look at some chart patterns and see if we can come up with some methods of finding patterns in stock data and then determining the resulting price movement after the pattern was recognized. We are trying to put the feedback loop into the process and chip away a little at the art of chart pattern recognition.