Explanation of the Code:
Weight Array for the Filter:
The b array stores weights based on a beta distribution formula.
The weights are normalized using the sum (den) to ensure the filter operates properly.
Filter Calculation:
The filt value is calculated using the source (src) values weighted by the b array.
The filter smooths the data based on the input length and lag parameters.
Dynamic Gradient Colors:
The css array stores 200 gradient colors from red to green.
Each gradient step is dynamically calculated using RGB values:
Red (r) decreases as green (g) increases.
Blue (b) remains constant at 0 for simplicity.
Dynamic Color Selection:
The RSI of the filtered value (filt) is normalized to a 0-1 range.
The normalized RSI is used to pick a color from the css array, which gives the dynamic gradient effect.
Filter Plot:
The filt value is plotted with the dynamically selected color based on the gradient.
Features:
Customizable Gradient:
The gradient can be adjusted by modifying the r, g, and b formulas.
Currently transitions from red (low RSI) to green (high RSI).
Lag Parameters:
alpha and beta control the lag distribution, providing flexibility in smoothing the data.
Efficient and Compact:
The code dynamically generates the gradient and computes the filter without manually defining large arrays.
Example Use:
Apply this script to a chart to visualize a smoothed line (filt) with a gradient color representing its relative strength (RSI-based).
This code is now concise, dynamic, and easier to customize. Test it on different timeframes and let me know if you need additional features or enhancements! 😊