Kernel-regression

CKD - Conformal Kernel Direction CKD - Conformal Kernel Direction is a clean trend-direction tool. Main timeframe: 4H. WHAT IT IS CKD answers one question: which way is the market moving right now? No entries, targets or stops. Just a smooth direction line, an adaptive channel and a clear marker (with price) where the direction flips. HOW IT WORKS 1. Smooth line. Recent closes are averaged with a Gaussian kernel: the newest candles count most, older ones fade out. Much smoother than a regular moving average. 2. Less lag. The

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by n0x6E6F6F6E65

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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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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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 Regression Toolkit This toolkit provides filters and extra functionality for non-repainting Nadaraya-Watson estimator implementations made by @jdehorty. For the sake of ease I have nicknamed it "kreg". Filters include a smoothing formula and zero lag formula. The purpose of this script is to help traders test, experiment and develop different regression lines. Regression lines are best used as trend lines and can be an invaluable asset for quickly locating first pullbacks and breaks of trends. Other features incl

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KernelFunctionsFilters Library "KernelFunctionsFilters" This library provides filters for non-repainting kernel functions for Nadaraya-Watson estimator implementations made by @jdehorty. Filters include a smoothing formula and zero lag formula. You can find examples in the code. For more information check out the original library KernelFunctions. rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar, _filter) Parameters: _src (float) _lookback (simple int) _relativeWeight (simple floa

Library

Nadaraya-Watson Envelope [LuxAlgo]This indicator builds upon the previously posted Nadaraya-Watson smoothers. Here we have created an envelope indicator based on Kernel Smoothing with integrated alerts from crosses between the price and envelope extremities. Unlike the Nadaraya-Watson estimator, this indicator follows a contrarian methodology. Please note that by default this indicator can be subject to repainting. Users can use a non-repainting smoothing method available from the settings. The triangle labels are designed so t

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Nadaraya-Watson Smoothers [LuxAlgo]The following tool smoothes the price data using various methods derived from the Nadaraya-Watson estimator, a simple Kernel regression method. This method makes use of the Gaussian kernel as a weighting function. Users have the option to use a non-repainting as well as a repainting method, see the USAGE section for more information. 🔶 USAGE 🔹 Non Repainting When Repainting Smoothing is disabled the returned indicator acts similarly to a regular causal moving average. This result

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