Inizia

Kernelregression

Nadaraya-Watson Kernel Flow [NeBlok]NADARAYA-WATSON KERNEL FLOW OVERVIEW Nadaraya-Watson Kernel Flow is a non-repainting trend and envelope tool built on Nadaraya-Watson kernel regression, a statistical smoothing method from 1964. Instead of weighting bars equally (SMA) or by a fixed decay (EMA), a kernel assigns each past bar a weight based on its distance from the current bar using a chosen mathematical curve. The result is a smooth estimate of price with a shape you control. This is an original implementation written from t
Indicatore
di NeBlok
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
Indicatore
di curtal
TURKS Engine - Tiered Unit Risk KernelMost position-sizing tools tell you what to hold after the move. This one tells you the price at which it changes, before price gets there. TURKS Engine decides how much of a long position to hold. Exposure is a graded function of where the close sits against four moving averages, so it moves in rungs rather than switching on and off. That function is monotone in price, which means it inverts: every rung has exactly one price. The panel solves for those prices and prints them today — a ladder o
Indicatore
di Daymenion
TURKS - Tiered Unit Risk Kernel StrategyTURKS decides how much of a long position to hold. Exposure is a graded function of where the close sits against four moving averages (20/50/100/200), so it moves in rungs rather than switching on and off. That function is monotone in price, which means it inverts: every rung has exactly one price. The panel prints those prices before they are reached — a ladder of levels at which the position gets larger or smaller, readable today. Long-only, 0 to 1, no shorting and no leverage. 📊 THE RESULT
Strategia
di Daymenion
1
NW Volume Profile - Kernel-Smoothed [Dots3Red]📊 NW VOLUME PROFILE - KERNEL-SMOOTHED A volume profile answers a different question than a normal chart. Instead of "how much traded today," it asks "how much traded at each price." This version applies Nadaraya-Watson kernel smoothing to that profile before reading any level off it — turning a jagged, noisy histogram into the actual underlying distribution of where volume concentrated. 🎯 WHY THIS MATTERS A raw volume profile is built from independent price bins — each one only knows its o
Indicatore
di Dots3Red
Breakout & Retest Entry Signals & the Break-vs-RetestOVERVIEW "Wait for the retest" is the most repeated piece of advice in breakout trading. It is also, as far as I can tell, completely untested by the people who repeat it. This tool settles it — on your instrument, with your settings — by taking BOTH entries on the SAME breaks and grading them against the same control: PER-TRADE EDGE expectancy vs control · n Break entry +0.09R +0.02R · n=412 Retest — CLEAN
Indicatore
di Market_Logic_India
Machine Learning Price Bands Kernel Regression SignalsOVERVIEW Every "AI band" on this platform draws two lines and asserts them. None of them can tell you how often price actually stays inside. This one can — because it is built on a method that comes with a MATHEMATICAL COVERAGE GUARANTEE, and then it CHECKS WHETHER IT KEPT THE PROMISE, live, on your chart: Coverage (empirical vs nominal) 89.1% vs 90% n = 20,266 Is the miss REAL? -0.9 pp z = -4.3 (real) Verdict undercove
Indicatore
di Market_Logic_India
Support & Resistance Confluence Levels, Hold Record & ReversalOVERVIEW Four lines. Two above price, two below. That is the whole chart. Every other support/resistance tool has the opposite problem. It finds forty levels and leaves you to work out which two matter. Forty levels is not information, it is wallpaper — and a level you have to hunt for is a level you will not trade. So this tool inverts the design. It does not ask "where are the levels?" It asks: WHICH LEVEL WOULD I ACTUALLY TRADE, RIGHT NOW, AND WHY SHOULD I BELIEVE IT? A price is onl
Indicatore
di Market_Logic_India
[Quadapt] Machine Learning Trader Machine Learning Trader A multi-component Pine Script v6 trading assistant combining a kernel-regression MLMA trend model, dual-length signal envelopes, order block context, retest scoring, adaptive Fibonacci targets, stop loss projection, signal quality filtering, and optional webhook automation. ## Description Machine Learning Trader is a full-featured market structure and signal confluence indicator built around a Machine Learning Moving Average (MLMA) trend model. It is designed to hel
Indicatore
di QuadaptTrader
16
Adaptive Lorentzian Classification [Quantum Algo]Quantum ML Engine — Adaptive Lorentzian Classification █ OVERVIEW Quantum ML Engine is a machine-learning classifier that predicts the direction of price over a configurable horizon using an Approximate Nearest Neighbors (ANN) search across historical feature vectors. Instead of relying on a single oscillator, it compares the current bar's "fingerprint" — a vector of up to six normalized features — against thousands of past bars, finds the most similar market conditions, and lets those histo
Indicatore
di Quantum-Algo
True Trend Oscillator + Price EnvelopeTrue Trend Oscillator + Price Envelope is a Pine v6 rebuild and expanded interpretation of the original True Trend Oscillator concept by wbburgin. The original idea is simple but powerful: • Convert ATR into a stochastic volatility / trend-energy reading • Smooth that Stoch ATR reading with a Rational Quadratic Kernel • Use RSI as the directional pressure input • Build separate Bull Trend and Bear Trend lines from that shared volatility engine In this rebuild, I kept the original spirit of th
Indicatore
di MYNAMEISBRANDON
2
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
Libreria
di a_jabbaroff
2
Machine Learning SupertrendThe 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
Indicatore
di Julien_Exe
2
Machine Learning Price Target Prediction Signals [AlgoAlpha]Introducing the Machine Learning Price Target Predictions, a cutting-edge trading tool that leverages kernel regression to provide accurate price targets and enhance your trading strategy. This indicator combines trend-based signals with advanced machine learning techniques, offering predictive insights into potential price movements. Perfect for traders looking to make data-driven decisions with confidence. What is Kernel Regression and How It Works Kernel regression is a non-parametric mach
Indicatore
di AlgoAlpha
20
Kernel Regression Envelope with SMI OscillatorThis script combines the predictive capabilities of the **Nadaraya-Watson estimator**, implemented by the esteemed jdehorty (credit to him for his excellent work on the `KernelFunctions` library and the original Nadaraya-Watson Envelope indicator), with the confirmation strength of the **Stochastic Momentum Index (SMI)** to create a dynamic trend reversal strategy. The core idea is to identify potential overbought and oversold conditions using the Nadaraya-Watson Envelope and then confirm these
Strategia
di nguyentd010
2
Cosine Kernel Regressions [QuantraSystems]Cosine Kernel Regressions Introduction The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a unique approach to market analysis. This indicator employs Kernel Regressions using bespoke tunable Cosine functions in order to smoothly interpret a variety of market data, providing traders with incredibly clean insights into market trends. The CKR is particularly useful for traders looking to understand underlying trends without the 'noise' typical in raw price mo
Indicatore
di Cryptosystems
26
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
Libreria
di GoemonYae
Triple Confirmation Kernel Regression Overlay [QuantraSystems]Kernel Regression Oscillator - Overlay Introduction The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends. This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones. It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence
Indicatore
di Cryptosystems
46
Triple Confirmation Kernel Regression Base [QuantraSystems]Kernel Regression Oscillator - BASE Introduction The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends. This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones. It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence to the
Indicatore
di Cryptosystems
20