Crate sliding_features

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Expand description

The sliding_features crate provides modular, chainable sliding windows for various signal processing function and technical indicators

Structs

Add View a to b
Shannon entropy sliding window over values, where a positive / negative values are interpreted as true / false
John Ehlers Center of Gravity Indicator from: https://mesasoftware.com/papers/TheCGOscillator.pdf
Provides a float value to other views
Cumulative Sliding Window with a chained view
Divide View a by b
EMA - Exponential Moving Average
Echo always return the last value just like an echo
Greater Than or Equal Will allow values >= clipping_point tough and clip other values to the clipping point
A sliding High - Low Normalizer
Lower Than or Equal filter, which only allows values lower than the specified clipping point through
Multiply View a by b
John Ehlers Noise elimination technology using kendall correlation from http://www.mesasoftware.com/papers/Noise%20Elimination%20Technology.pdf
A PolarizedFractalEfficiency indicator with output range [-1.0 and 1.0] rather than [-100, 100] it is also possible to use a custom moving average instead of the default EMA in the original
Rate of Change Indicator
Relative Strength Index Indicator
SMA - Simple Moving Average
Subtract View a from b
Applies the Tanh function to the output of its View component
Variance Stabilizing Centering Transform Sliding Window
Variance Stabilizing Transform uses the standard deviation to normalize values
Welford online algorithm for computing mean and variance on-the-fly over a sliding window

Traits

The most important Trait, defining methods which each sliding feature needs to implement