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VWAP Regime AI [AxeAlgo]OVERVIEW VWAP Regime AI is an anchored VWAP (Volume-Weighted Average Price) with standard-deviation bands, enhanced by a native, from-scratch k-means clustering engine that classifies recent market volatility into three regimes — Low, Medium, and High — and adapts the indicator's behavior based on which regime is currently active. At its foundation this is the same tool institutional desks use every day: a running volume-weighted average price with bands around it, used to judge where "fair v

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IQ Trend Beams [TradingIQ]🔹 OVERVIEW IQ Trend Beams is a trend assistant that draws your trendlines the way a disciplined chartist would - and then holds them accountable. It maintains two channels, support and resistance , each always showing one working line. A line is born forming : it moves and re-shapes freely, polished every bar by a perceptual score toward the line a skilled trader would actually draw. When its geometry settles and it has earned enough tangency credit, it locks - and from that moment th

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AI K-Means Clustering [TradingFinder] Machine Learning Zones 🔵 Introduction K-Means clustering is an unsupervised machine learning algorithm that groups similar data points around repeatedly updated cluster centers. Each observation is assigned to its nearest center, the centers are recalculated, and the process continues until the clusters converge. In financial market analysis, this structure can separate recurring patterns in price movement, trend direction, volume pressure, and volatility without depending entirely on fixed thresholds. As a result,

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by TradingFinder

NLMS Adaptive Trend Filter [BackQuant]NLMS Adaptive Trend Filter Overview The NLMS Adaptive Trend Filter is a machine learning inspired trend-following indicator built around one of the most important adaptive filtering algorithms in signal processing: the Normalized Least Mean Squares (NLMS) filter . Unlike traditional moving averages that use fixed weighting schemes, the NLMS filter continuously learns from incoming market data and updates its internal coefficients in real time. Rather than assuming that price behavior rem

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Neural Weight Oscillator (Zeiierman)█ Overview The Neural Weight Oscillator (Zeiierman) is an adaptive multi-factor oscillator that combines structured decision-making with dynamic market learning. The script analyzes three core market behaviors: Trend, Mean Reversion, and Momentum. Instead of treating these components equally, the oscillator uses the Best-Worst Method (BWM) to determine which market behavior should have the greatest influence under current market conditions. An adaptive training layer then studies histori

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NeuraLib Expansion: Advanced Model Layers NeuraLib_Models is the companion model expansion for NeuraLib . NeuraLib provides the runtime: tensors, graph execution, datasets, scalers, losses, optimizers, training, inference, and validation tools. NeuraLib_Models builds on that foundation with higher-level neural architectures that are difficult and repetitive to write by hand. The purpose of this expansion is to keep the main NeuraLib runtime clean, compact, and general, while giving researchers ready-to-use model families for sequenc

Library

by Alien_Algorithms

NeuraLib: A Native AI and Deep Learning Runtime 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 classi

Library

by Alien_Algorithms

Liquidity Surge Forecast with Markov Chains [TechnicalZen]Clear direction from Markov Chains confirmed projections. Publishing this v3 with all the enhancements users desired and more. Thank you for your feedback. What This Is A 3D liquidity-and-momentum visualization that tells you where the market is heading right now, how long the current state is likely to hold, and when the next regime change is expected — all backed by a 2nd-order Markov chain that learns from your chart's own history. Two independent systems — Money Flow (MFI-driven)

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by TechnicalZen

Uptrick: ML Kernel Regression Introduction This indicator applies Nadaraya-Watson kernel regression, a non-parametric machine learning estimator, directly to price data in order to produce a smooth, noise-reduced representation of the market's underlying trend. Unlike moving averages that apply equal or linearly decaying weights, this method uses a Gaussian kernel function to assign weights based on how far back in time each bar sits relative to the current one. Bars closer in time receive exponentially higher weights, wh

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ML Trend Architect [TechnicalZen]ML Trend Architect A regime visualization and forecasting engine that combines machine learning confidence scoring with curved 3D glass facades to map market structure and anticipate regime transitions in real-time. What It Does This indicator identifies directional regime changes using a dual-engine approach — an impulse momentum engine and a DX/ADX directional movement engine — then wraps them in curved 3D visual envelopes that show regime strength, direction, and conviction at a glance.

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by TechnicalZen

AI SuperTrend Strength Forecasting Engine [TraderZen]The question that matters most in real time: "How confident should I be in this setup right now?" The ML Trend Strength Forecasting Engine was built to answer that question directly. Instead of drawing lines on a chart and leaving interpretation to the trader, it computes a numerical confidence score for every potential bull and bear setup as it forms. It evaluates the quality of the setup context, the strength of the trigger bar, the state of the broader market regime, and whether similar se

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by TechnicalZen

KNN Supertrend Horizon [LuxAlgo]The KNN Supertrend Horizon indicator is a machine learning tool that combines K-Nearest Neighbors (KNN) classification with Supertrend logic to identify high-probability trend directions and price rejection zones. 🔶 USAGE The indicator provides a comprehensive view of market trends by filtering traditional Supertrend calculations through a machine learning engine. It is designed to help traders stay on the right side of the trend while identifying potential exhaustion points through visual "

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Polynomial Regression Clustering [LuxAlgo]The Polynomial Regression Clustering indicator utilizes K-Means clustering to categorize historical price data into discrete levels and fits polynomial regression curves to each identified cluster. This tool allows traders to visualize non-linear trends within specific price regimes, providing a unique perspective on support, resistance, and price momentum. 🔶 USAGE The indicator identifies "K" number of clusters based on the vertical distribution of price over a user-defined lookback per

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Clusters Volume Profile [LuxAlgo]The Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups and generates individual volume profiles for each detected cluster. This tool provides a unique perspective on volume distribution by isolating price behaviors based on proximity rather than strictly chronological order. 🔶 USAGE The indicator identifies "clusters" of price activity within a user-defined lookback period. Each cluster is assigned a unique color and its

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Machine Learning Moving Average [BackQuant]Machine Learning Moving Average A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels. What is Percentile Clustering? Percentile clustering is a met

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Machine Learning BBPct [BackQuant]Machine Learning BBPct What this is (in one line) A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.

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Bober XM v2.0 # ₿ober XM v2.0 Trading Bot Documentation **Developer's Note**: While our previous Bot 1.3.1 was removed due to guideline violations, this setback only fueled our determination to create something even better. Rising from this challenge, Bober XM 2.0 emerges not just as an update, but as a complete reimagining with multi-timeframe analysis, enhanced filters, and superior adaptability. This adversity pushed us to innovate further and deliver a strategy that's smarter, more agile, and more powerf

Strategy

Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices. For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings. 🔶 USAGE Traders only need to set the number of trendlines (cluster

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AI Adaptive Oscillator [PhenLabs]📊 Algorithmic Adaptive Oscillator Version: PineScript™ v6 📌 Description The AI Adaptive Oscillator is a sophisticated technical indicator that employs ensemble learning and adaptive weighting techniques to analyze market conditions. This innovative oscillator combines multiple traditional technical indicators through an AI-driven approach that continuously evaluates and adjusts component weights based on historical performance. By integrating statistical modeling with machine learni

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Bars pattern ML This script implements a K-Nearest Neighbors (KNN)-based machine learning model to predict future price movements in financial markets. It analyzes past price action using Euclidean distance and selects the most similar historical patterns to estimate future price changes. Unlike traditional KNN implementations, this approach optimizes distance calculations by maintaining a dynamically updated list of the closest neighbors, ensuring efficient selection without the need for sorting. The model gen

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VWAP Bands with ML [CryptoSea]VWAP Machine Learning Bands is an advanced indicator designed to enhance trading analysis by integrating VWAP with a machine learning-inspired adaptive smoothing approach. This tool helps traders identify trend-based support and resistance zones, predict potential price movements, and generate dynamic trade signals. Key Features Adaptive ML VWAP Calculation: Uses a dynamically adjusted SMA-based VWAP model with volatility sensitivity for improved trend analysis. Forecasting Mechanism:

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Machine Learning Moving Average [LuxAlgo]The Machine Learning Moving Average (MLMA) is a responsive moving average making use of the weighting function obtained Gaussian Process Regression method. Characteristic such as responsiveness and smoothness can be adjusted by the user from the settings. The moving average also includes bands, used to highlight possible reversals. 🔶 USAGE The Machine Learning Moving Average smooths out noisy variations from the price, directly estimating the underlying trend in the price. A higher "

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Machine Learning RSI [BackQuant]Machine Learning RSI The Machine Learning RSI is a cutting-edge trading indicator that combines the power of Relative Strength Index (RSI) with Machine Learning (ML) clustering techniques to dynamically determine overbought and oversold thresholds. This advanced indicator adapts to market conditions in real-time, offering traders a robust tool for identifying optimal entry and exit points with increased precision. Core Concept: Relative Strength Index (RSI) The RSI is a well-known moment

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ANN Trend Prediction This trend indicator utilizes an artificial neural network (ANN) to predict the next market reversal within a certain range of previous candles. The larger the range of previous candles you set, the fewer reversals will be predicted, and trends will tend to last longer. The ANN is trained on the BTCUSD 4-hour chart, so using it on other assets or timeframes may yield suboptimal results. It takes three input values: the closing price, the Stochastic RSI, and a Choppiness Indicator. Based on thes

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by BerlinCode42