Shannonentropy

Volume-Weighted Information Rate [AlgoPoint]🧠 The Mathematical Engine VWIR doesn't just track where the price is going; it measures the **quality** of the move using three core pillars: 1. Shannon Entropy (Information Density): By calculating the probability distribution of log returns, the script measures market "uncertainty." Low entropy indicates that the market has reached a consensus, meaning "clear information" is flowing into the price. 2. Volume-Weighted Conviction: We multiply information clarity by relative volume. If a m

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Bernoulli Process - Binary Entropy Function This indicator is the Bernoulli Process or Wikipedia - Binary Entropy Function . Within Information Theory, Entropy is the measure of available information, here we use a binary variable 0 or 1 (P) and (1-P) (Bernoulli Function/Distribution), and combined with the Shannon Entropy measurement. As you can see below, it produces some wonderful charts and signals, using price, volume, or both summed together. The chart below shows you a couple of options and some critical details on the indicator

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Shannon Entropy V2 Version 2, Shannon Entropy This update includes both a deadband (Plotting Optional) and PercentRank Indicating. Here is a unique way of looking at your price & volume information. Utilize the calculated value of "Shannon Entropy". This is a measure of "surprise" in the data, the larger the move or deviation from the most probable value, the higher the new information gain. What I think is so interesting about this value, is the smoothness that it displays the information without using moving av

Indicator

Shannon Entropy Here is a unique way of looking at your price & volume information. Utilize the calculated value of "Shannon Entropy". This is a measure of "surprise" in the data, the larger the move or deviation from the most probable value, the higher the new information gain. What I think is so interesting about this value, is the smoothness that it displays the information without using moving averages. There is a lot of meat on this bone to be incorporated in other scripts. H = -sum(prob(i) * log_base2(p

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