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Entropy (Fiedor/Kontoyiannis) - Part 2 of Fiedor's Theory
This indicator estimates the Shannon entropy of a price series using a Markov chain model of binary returns, following the approach of Fiedor (2014) and Kontoyiannis (1997).
% of Max shows current entropy as a percentage of its theoretical maximum (1 bit for binary up/down moves).
Percentile ranks the current entropy against historical values in the chosen lookback window.
High entropy suggests price movement is less predictable by frequentist models; low entropy implies more structure and predictability.
Use this as an informational oscillator, not a trading signal.
This is a visualization of Part 1 of Fiedor's Theory. The same entropy logic is already embedded in Part 1 however the second pane is a nice reminder of why it works.
% of Max shows current entropy as a percentage of its theoretical maximum (1 bit for binary up/down moves).
Percentile ranks the current entropy against historical values in the chosen lookback window.
High entropy suggests price movement is less predictable by frequentist models; low entropy implies more structure and predictability.
Use this as an informational oscillator, not a trading signal.
This is a visualization of Part 1 of Fiedor's Theory. The same entropy logic is already embedded in Part 1 however the second pane is a nice reminder of why it works.
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開源腳本
本著TradingView的真正精神，此腳本的創建者將其開源，以便交易者可以查看和驗證其功能。向作者致敬！雖然您可以免費使用它，但請記住，重新發佈程式碼必須遵守我們的網站規則。
免責聲明
這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。