In everyday usage, we equate the word information with meaning. This concept of information is somewhat counter-intuitive. In this context, information content is really about quantifying predictability, or conversely, randomness. Measured in bits, Shannon Entropy is a measure of the information content of data, where information content refers more to what the data could contain, as opposed to what it does contain. They also beat the casinos by counting cards at Blackjack.Д«etween Information Theory and digital circuit design (and less so his gambling escapades), ShannonвЂ™s work essentially ushered in the digital world we find ourselves in today. Not being one to let his genius go to waste, he and his buddy Ed Thorpe secretly used wearable computers to beat roulette in Vegas casinos by synchronising their computers with the spins of the wheel. Later, around 1948, he discovered Information Theory, which leverages his unique-at-the-time understanding that computers could express numbers, words, pictures, even audio and video as strings of binary digits. Most notably, he was the first to describe the theory of electrical circuit design (in his MasterвЂ™s thesis at the age of 21, no less). Shannon (the man, not the entropy) was one of those annoying people that excels at everything he touches. Specifically, weвЂ™re going to tinker with the concept of Shannon Entropy. The purpose of this post is to scratch the surface of the markets from an information theoretic perspective, using tools developed by none other than the father of the digital age, Claude Shannon. Before you commit your precious time to read this post on Shannon Entropy, I need to warn you that this is one of those posts that market nerds like myself will get a kick out of, but which probably wonвЂ™t add much of practical value to your trading.

Shannon entropy