Vector Exponential (e^x) (EXP)

EXP applies the exponential function e^x to each value in a data series, where e ≈ 2.71828. This transformation creates exponential growth/decay patterns - small changes in input create large changes in output for positive values. EXP is the inverse of LN (natural logarithm) and is fundamental in calculations involving compounding, exponential moving average mathematics, or transforming log-scale data back to linear scale.

Developers use EXP when building indicators requiring exponential weighting (EMA formulas use exponential concepts), converting log-transformed data back to original scale, or creating nonlinear transformations with growth characteristics. Particularly useful for volatility indicators with exponential decay, custom smoothing algorithms with exponential weights, or building indicators on log-returns data. Combine with LN for log/antilog transformations, or use for implementing custom exponentially-weighted calculations. Essential for understanding and customizing exponential moving averages.