Kernel

Directional Kernel Filter [BackQuant]Directional Kernel Filter Overview Directional Kernel Filter is a trend overlay built from Gaussian-weighted price smoothing with an additional directional weighting term. The indicator calculates fast and slow versions of the filter. Trend is determined by their relative position: Fast Kernel above Slow Kernel = bullish. Fast Kernel below Slow Kernel = bearish. The purpose of the directional weighting is simply to give slightly more influence to historical observations whose local

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KERNEL BANDS [vault]KERNEL BANDS A non-parametric kernel regression centreline wrapped in adaptive residual sigma bands, with a full trade management layer on top: filtered entries, an exit engine that reports results in pips, a reversal radar, a dead-zone shield, session and momentum context, and a multi-timeframe screener. Everything is confirmed on bar close and nothing repaints. A moving average assumes price came from a fixed-form equation (linear in lag, weighted in lag, and so on). Kernel regression make

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Recursive Kernel Trend [QuantAlgo]🟢 Overview The Recursive Kernel Trend is a trend-following indicator built on a recursive residual estimator with adaptive rate scheduling. It applies one of six selectable filter structures to a residual-corrected recursion, modulates the update rate according to efficiency and volatility conditions, and confirms directional state through slope persistence. The result is a responsive yet controlled trend line that adapts its tracking behavior to market regime while filtering noise-driven fl

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Nadaraya-Watson Trend [QuantAlgo]🟢 Overview The Nadaraya-Watson Trend indicator estimates a smooth, adaptive trend path by applying non-parametric kernel regression directly to price. For each bar it weights historical values inside a configurable lookback window with a chosen kernel function, normalizes those weights, and returns a single endpoint estimate that forms the plotted trend line. Bandwidth and kernel type control how aggressively recent bars dominate the estimate, optional residual bands express how far price is

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GBR Micro Kernel Trend [Laplace Smoothing]The GBR Micro Kernel Trend is the ultimate solution for lower-timeframe scalpers (1m-5m) who are tired of traditional moving averages whipping them out of trades. Instead of basic averaging, this indicator utilizes a Nadaraya-Watson Kernel Regression (with a Laplace probability distribution) to dynamically smooth out market noise. The result is a buttery-smooth trend line that doesn't blindly hug the candles, allowing the price to breathe during minor pullbacks. 🔥 Core Features: Laplace Smoot

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Iterative Locally Periodic Envelope The Iterative Locally Periodic Envelope is a phase-conditioned kernel estimator with temporal locality and endogenous dispersion modeling, implemented as a Nadaraya–Watson estimator under a locally periodic kernel. The locally periodic kernel defines similarity through cyclical phase alignment modulated by temporal proximity. Observations contribute to the estimator based on both their position within a repeating cycle structure and their recency, emphasizing structural recurrence with sensitiv

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by SensitiveSuit

Iterative Periodic Envelope The Iterative Periodic Envelope is a phase-conditioned kernel estimator with endogenous dispersion modeling, implemented as a Nadaraya–Watson estimator under a canonical periodic kernel. The periodic kernel defines similarity through cyclical phase alignment rather than temporal proximity or multi-scale distance decay. Observations contribute to the estimator based on their position within a repeating cycle structure, emphasizing structural recurrence over linear time dependence. The indicator

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by SensitiveSuit

Meridian Lens PRO 🟦 Meridian Lens PRO is a multi-kernel trend indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). Three independently configurable kernel lines — Fast, Medium, and Slow — cover the full reactivity spectrum from scalping to position trading, each accepting any of the eight kernel families and three filter modes exposed by the library. The visual layer applies volume-intensity-adaptive coloring, gradient-filled trailing bands, 3-layer neon glow signal ar

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KernelLens 🟦 KernelLens is a professional kernel regression library for Pine Script v6, providing eight mathematically rigorous Nadaraya–Watson estimators, a three-mode filter layer, a unified string dispatcher, and a suite of trading utilities — all built from the ground up on correct non-parametric statistics. Unlike existing Pine smoothing libraries — which inherit a decade-old loop-bound bug that silently reduces every kernel window to a handful of bars, regardless of the bandwidth parameter — Kernel

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Iterative Rational Quadratic Channel The Iterative Rational Quadratic Channel is a kernel-based smoothing and state estimation framework that applies a Rational Quadratic kernel regression to price data, combined with a rolling standard deviation envelope to construct adaptive dynamic channel boundaries. Unlike exponential kernel methods that prioritize recent data at the expense of historical context, the rational quadratic kernel introduces a heavy-tailed weighting structure that preserves multi-scale memory in price dynamics. T

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by SensitiveSuit

Exponential Nadaraya Watson kernel regression [Jamallo](2025) Intro Nadaraya-Watson (N-W) kernel regression is a non-parametric smoothing technique that estimates the underlying trend of a price series without assuming any fixed model shape (like a straight line or curve). Unlike a simple moving average which weights bars equally, or an EMA which applies a fixed exponential decay, N-W regression derives its curve by computing a weighted average of all prices within a lookback window — where the weights are determined by a kernel function. The m

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KDE Value Clouds [LuxAlgo]The KDE Value Clouds indicator is a quantitative tool that uses Kernel Density Estimation (KDE) to visualize the statistical distribution of price action, identifying high-density "Value Clouds" where the market has spent the most time. 🔶 USAGE The indicator highlights areas of price " fair value " by calculating the probability density of price across a user-defined lookback period. Traders can use these density clusters to identify significant support and resistance levels that are often i

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Adaptive Nadaraya-Watson (Non Repainting) [Metrify]To understand this implementation of the Nadaraya-Watson estimator, we have to look at the core equation governing non-parametric regression. This script aren't trying to average prices; we are trying to find the probability density of where price should be relative to its recent history. 1. The Kernel Physics (Bandwidth Modulation) In standard kernel regression, you have a bandwidth parameter (h). This controls the "smoothness" of the curve. If h is too low, the curve jitters with every tick

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EDUVEST Lorentzian Classification EDUVEST Lorentzian Classification - Machine Learning Signal Detection ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ ORIGINALITY This indicator enhances the original Lorentzian Classification concept by jdehorty with EduVest's visual modifications and alert system integration. The core innovation is using Lorentzian distance instead of Euclidean distance for k-NN classification, providing more robust pattern recognition in financial markets. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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by EduVest_doro

Kernel Channel [BackQuant]Kernel Channel A non-parametric, kernel-weighted trend channel that adapts to local structure, smooths noise without lagging like moving averages, and highlights volatility compressions, expansions, and directional bias through a flexible choice of kernels, band types, and squeeze logic. What this is This indicator builds a full trend channel using kernel regression rather than classical averaging. Instead of a simple moving average or exponential weighting, the midline is computed as a

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Multiple Symbol Trend Screener [Pineify]Multiple Symbol Trend Screener Pineify – Ultimate Multi-Indicator Scanner for TradingView Empower your trading with deep market insights across multiple symbols using this feature-rich Pine Script screener. The Multiple Symbol Trend Screener Pineify enables traders to monitor and compare trends, reversals, and consolidations in real-time across the biggest equity symbols on TradingView, through a synergistic blend of popular technical indicators. Key Features Monitor up to 15 symbols a

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Adaptive Quadratic Kernel Envelope This study draws a fair-value curve from a quadratic-weighted (Nadaraya-Watson) regression. Alpha sets how sharply weights decay inside the look-back window, so you trade lag against smoothness with one slider. Band half-width is ATRslow times a bounded fast/slow ATR ratio, giving an instant response to regime shifts without overshooting on spikes. Work in log space when an instrument grows exponentially, equal percentage moves then map to equal vertical steps. NearBase and FarBase define a prog

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Half Causal Estimator Overview The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output. Core Concept Traditional moving averages suffer from time lag, which can delay

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by The_Peaceful_Lizard

Machine Learning Supertrend The Machine Learning Supertrend is an advanced trend-following indicator that enhances the traditional Supertrend with Gaussian Process Regression (GPR) and kernel-based learning. Unlike conventional methods that rely purely on historical ATR values, this indicator integrates machine learning techniques to dynamically estimate volatility and forecast future price movements, resulting in a more adaptive and robust trend detection system. At the core of this indicator lies Gaussian Process Regres

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Trend Regression Kernel [IkkeOmar]Kernel by @jdehorty huge shoutout to him! This is only an idea for how I use it when trading All credit for the kernel goes to him, I did not make the kernel! I don't know how to make it more clear. I use this to assist with top-down analysis. timeframe I want to trade : timeframe to analyse with white noise and kernel: 1m : 1H 5m : 2H 15m : 4H 1H : 1D In the chart you see that I have the 1H open, I use the white noise at a "lower setting length" (55 in this case), I change the sou

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Kernels ©2024, GoemonYae; copied from @jdehorty's "KernelFunctions" on 2024-03-09 to ensure future dependency compatibility. Will also add more functions to this script. Library "KernelFunctions" This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substition/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernel

Library

Bandwidth Volatility - Silverman Rule of thumb Estimator Overview This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is

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by Traders_Endeavors

Kernel Regression Ribbon Kernel Regression Ribbon is a flexible, visually pleasing trend identification tool. Plotting 8 different kernel regressions of different types and parameters allows the user to see where levels of support and resistance are being tested, retested and broken. What’s Kernel Regression? A statistical method for estimating the best fitting curve for a dataset, in this case, a time/price chart. How’s Kernel Regression different from a Moving Average? A Moving Average is basically a simple fo

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Bollinger Bands (Nadaraya Smoothed) | Flux Charts Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: default General Purpose This script is an upgrade to the classic Bollinger Bands. The idea behind Bollinger bands is the detection of price movements outside of a stock's typical fluctuations. Bollinger Bands use a moving average over period n plus/minus the standard deviation over period n times a multiplier. When price closes above or below either band this can be considered an abnormal movement. This script allows for the classic Bolli

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