In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise. █ OVERVIEW We have introduced the concept of Hurst Exponent in our previous open indicator Hurst...

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My first try to implement Full Hurst Exponent. The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It...

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The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases. The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It quantifies the relative tendency of a time series...

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This is a simplified version of the Hurst Exponent indicator. In the meantime, I'm working on the full version. It's computationally intensive, so it's a challenge to squeeze it to PineScript limits. It will require some time to optimize it, so I decided to publish a simplified version for now. The Hurst exponent is used as a measure of long-term memory of time...

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- Hurst Exponent Signal Filter - The Hurst Exponent Signal Filter is meant to be used with an external signal source, this can be any indicator with a signal plot output (-1 Sell / 1 Buy) It filters out a lot of noisy signals and improves the performance of many indicators. - Example: How to Use - 1. Add a trend Indicator like Trend Index MTF to your...

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Library "HurstExponent" Library to calculate Hurst Exponent refactored from Hurst Exponent - Detrended Fluctuation Analysis demean(src) Calculates a series subtracted from the series mean. Parameters: src : The series used to calculate the difference from the mean (e.g. log returns). Returns: The series subtracted from the series mean ...

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