NeuraLib: A Native AI and Deep Learning Runtime — Library by Alien_Algorithms

NeuraLib: A Native AI and Deep Learning Runtime — Library by Alien_Algorithms — TradingView

https://www.tradingview.com/script/GewgOj30-NeuraLib-A-Native-AI-and-Deep-Learning-Runtime/

Publish Date: 2026-04-27 18:36:00

NeuraLib is a tensor-based, auto-differentiating Machine Learning runtime built natively for Pine Script™.

It brings real Deep Learning mechanisms that power modern Artificial Intelligence systems into TradingView. Instead of relying on fixed formulas, static regressions, or rigid structures, NeuraLib gives Pine developers a different tool: a compact neural runtime that can learn from the features you feed it, using the architecture you define.

This means users are no longer limited to classical methods like Linear Regression, Logistic Regression, KNN, Naive Bayes, Kalman Filters, or Markov Chains. One can build adaptive architectures perfectly suited for custom indicators, strategies, regime detection, directional prediction, price transforms, and AI-assisted signal generation.

Using NeuraLib, one can build a model, collect market data, normalize it, run predictions, train through backpropagation, track validation behavior, and update weights directly inside TradingView.

Furthermore, it is not necessary to directly display trained variables. The process can be a part of a larger script functionality, where AI-powered decision making changes how an indicator behaves.

The goal is to make real neural network workflows usable in Pine Script without hiding the important controls, being scalable with evolving market dynamics, and abstracting away the complexity that comes with such software. The provided API is highly modular and intuitive, using chained object-oriented programming for easy readability and use. The backend is engineered with fault-tolerance in mind, providing users with sanity checks and preventing common pitfalls by default.

Think of NeuraLib as a comprehensive machine learning ecosystem, containing:

A Model Builder: Define neural networks with readable chained calls like `.input()`, `.dense()`, and `.dropout()`.
An In-Pine Training Engine: Models calculate losses, backpropagate gradients, update weights, and produce predictions directly on chart data.

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