Math Transform
Vector Hyperbolic Tangent (TANH)
Vector Trigonometric Tanh
Deep Dive
Everything You Need to Know
Under the Hood
How It Works
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.
In Practice
How Traders Use It
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.
Highlights
TANH at a Glance
- Hyperbolic tangent: (e^x - e^-x) / (e^x + e^-x)
- Output bounded: -1 to +1 (sigmoid-like)
- S-shaped transformation curve
- Normalizes unbounded to bounded range
- Common in ML activation functions
- Useful for adaptive normalization
- Popular among ML/quant developers
Related
More Math Transform
Get Started
Automate TANH with Cryptorobot.ai
Build automated strategies using Vector Hyperbolic Tangent and hundreds of other indicators. Connect to your favourite exchange and let the bot execute trades 24/7 — no code required.
FAQ
Frequently Asked Questions
What is the Vector Hyperbolic Tangent (TANH) indicator?
Vector Trigonometric Tanh
How does TANH work?
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.
How do traders use TANH in their strategies?
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.
What are the key points to know about TANH?
Hyperbolic tangent: (e^x - e^-x) / (e^x + e^-x) Output bounded: -1 to +1 (sigmoid-like) S-shaped transformation curve Normalizes unbounded to bounded range Common in ML activation functions Useful for adaptive normalization Popular among ML/quant developers
Can I use TANH with Cryptorobot.ai?
Yes. Vector Hyperbolic Tangent (TANH) is available as a built-in indicator in Cryptorobot.ai. You can add it to any automated strategy using the no-code strategy builder, backtest it against historical data, and deploy it live on supported exchanges.