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Nadaraya-watson

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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QuantAlgo tarafından
23
Nadaraya-Watson Envelope [Gabremoku]Nadaraya-Watson Envelope This indicator builds a non-repainting Nadaraya-Watson envelope using a one-sided Gaussian kernel, so every value is computed from the current bar and past bars only. The goal is to provide a smoother adaptive baseline than a standard moving average while keeping the script operationally honest and suitable for live use. What makes this script different: - The central basis is a kernel-weighted Nadaraya-Watson estimate, not a classic SMA/EMA baseline. - The main enve
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Gabremoku tarafından
2
Machine Learning: Volume-Weighted Mean Reversion [Dots3Red]█ MACHINE LEARNING: VOLUME-WEIGHTED MEARN REVERSION KERNEL REGRESSION Nadaraya-Watson kernel regression is a non-parametric machine learning method. Unlike moving averages which apply fixed, predefined weights to historical bars, kernel regression derives each bar's weight from a mathematical function — the kernel — that measures how relevant that bar is to the current estimate. No hardcoded coefficients. No assumed shape. The model adapts purely from the data. This script introduces a
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Dots3Red tarafından
Nadaraya-Watson Regression Liquidity Sweeps [AlgoAlpha]🟠 OVERVIEW This script combines Nadaraya-Watson regression, momentum analysis, and liquidity level tracking into a single workflow. It measures the slope of a smoothed price regression curve, converts that slope into a normalized oscillator, and uses momentum shifts to identify areas where liquidity may be resting. The oscillator is built from the rate of change of the Nadaraya-Watson estimate rather than price itself. This allows momentum transitions to be measured relative to the underlying
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AlgoAlpha tarafından
28
Iterative Locally Periodic EnvelopeThe 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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SensitiveSuit tarafından
Iterative Periodic EnvelopeThe 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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SensitiveSuit tarafından
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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Jamallo22 tarafından
5
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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Metrify tarafından
Adaptive Quadratic Kernel EnvelopeThis 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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Julien_Exe tarafından
3
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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IkkeOmar tarafından
1
Nadaraya-Watson Envelope Strategy (Non-Repainting) Log ScaleIn the diverse world of trading strategies, the Nadaraya-Watson Envelope Strategy offers a different approach. Grounded in mathematical analysis, this strategy utilizes the Nadaraya-Watson kernel regression, a method traditionally employed for interpreting complex data patterns. At the core of this strategy lies the concept of 'envelopes', which are essentially dynamic volatility bands formed around the price based on a custom Average True Range (ATR). These envelopes help provide guidance on p
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Julien_Exe tarafından
4
Adaptive Price Channel (log scale)The field of technical analysis is consistently expanding, with numerous indicators used for market forecasting. Amongst them, a novel indicator dubbed the Adaptive Price Channel (log scale), inspired by the renowned Nadaraya-Watson Envelope (LuxAlgo) from LuxAlgo, is gaining traction for its distinctive features and versatility. Unlike its predecessor, the Adaptive Price Channel (log scale) is applicable on a logarithmic scale, thereby allowing it to be utilized on both smaller and larger timef
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Julien_Exe tarafından
5
Nadaraya-Watson Envelope (Non-Repainting) Logarithmic ScaleIn the fast-paced world of trading, having a reliable and accurate indicator can make all the difference. Enter the Nadaraya-Watson Envelope Indicator, a cutting-edge tool designed to provide traders with valuable insights into market trends and potential price movements. In this article, we'll explore the advantages of this non-repainting indicator and how it can empower traders to make informed decisions with confidence. Accurate Price Analysis: The Nadaraya-Watson Envelope Indicator operat
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Julien_Exe tarafından
5
Smart Trend EnvelopeThe "Smart Trend Envelope" indicator is a powerful tool that combines the "Nadaraya-Watson Envelope " indicator by LuxAlgo and the "Strongest Trendline" indicator by Julien_Eche. This indicator provides valuable insights into price trends and projection confidence levels in financial markets. However, it's important to note that the indicator may repaint, meaning that the displayed results can change after the fact. The "Strongest Trendline" indicator by Julien_Eche focuses on identifying the
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Julien_Exe tarafından
8
Nadaraya-Watson OscillatorThis indicator is based on the work of @jdehorty and his amazing Nadaraya-Watson Kernel Envelope, which you can see here: General Description The Nadaraya-Watson Oscillator (NWO) will give the same information as the Nadaraya-Watson Envelope, but as an oscillator off the main chart, by plotting the relationship between price and the Kernel and its bands. This also means that we can now detect divergences between price and the NWO. You can see the relationship between the two here: Y
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FractalTrade_ tarafından
16
Machine Learning: Lorentzian Classification█ OVERVIEW A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm. █ BACKGROUND In physics, Lorentzian space is perhaps best known for its role in d
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jdehorty tarafından
816
Nadaraya-Watson: Envelope (Non-Repainting)Due to popular request, this is an envelope implementation of my non-repainting Nadaraya-Watson indicator using the Rational Quadratic Kernel. For more information on this implementation, please refer to the original indicator located here: What is an Envelope? In technical analysis, an "envelope" typically refers to a pair of upper and lower bounds that surrounds price action to help characterize extreme overbought and oversold conditions. Envelopes are often derived from a simple moving
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jdehorty tarafından
24
Nadaraya-Watson non repainting [LPWN]// ENGLISH The problem of the wonderfuls Nadaraya-Watson indicators is that they repainting, @jdehorty made an aproximation of the Nadaraya-Watson Estimator using raational Quadratic Kernel so i used this indicator as inspiration i just added the Upper and lower band using ATR with this we get an aproximation of Nadaraya-Watson Envelope without repainting Settings: Bandwidth. This is the number of bars that the indicator will use as a lookback window. Relative Weighting Parameter. The al
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Lupown tarafından
17
KernelFunctionsLibrary "KernelFunctions" This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substitution/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels. Compared to Moving Averages (which are really just simple kernels themselves), these kernel functions are more adaptive and afford the user an unprecedented de
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jdehorty tarafından
11