Vector Hyperbolic Tangent (TANH)

TANH applies the hyperbolic tangent function to each value: tanh(x) = sinh(x) / cosh(x) = (e^x - e^-x) / (e^x + e^-x). TANH produces S-shaped curves bounded between -1 and +1, approaching these limits asymptotically for large |x|. This sigmoid-like function is widely used in machine learning activation functions and creates smooth bounded transformations - useful for normalizing unbounded indicators to a fixed range while preserving nonlinear relationships.

Developers use TANH for creating bounded transformations of unbounded indicators, implementing sigmoid-style normalizations, or building machine learning-inspired technical indicators. TANH is particularly valuable for converting indicators with infinite range (like momentum or ROC) into bounded -1 to +1 outputs while maintaining sensitivity near zero and damping extremes. Combine with custom momentum indicators for adaptive normalization, or use in neural network-based trading systems. Popular among quantitative developers building ML-enhanced indicators or adaptive normalization systems.