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Regression

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 fundamental extension to the standard method: volume as a second weighting dimension . The result is a regression curve that gravitates toward price levels where real market participation occurred — not toward price levels where a clock happened to tick. █ WHY KERNEL REGRESSION IS MACHINE LEARNING The term machine learning describes algorithms that derive structure from data rather than from manually specified rules. Kernel regression satisfies this definition formally. The estimator computes: ŷ = Σ [ w(i) × close ] / Σ where each weight w(i) is determined by a kernel function — not by the programmer. The model decides, from the data, how much each historical bar should influence the current estimate. This is the same mathematical family as K-Nearest Neighbors, which weights neighbors by proximity. It is cited as a foundational non-parametric ML method in Bishop (2006) and Hastie et al. (2009), and is described as an attention mechanism in deep learning literature — the same concept behind transformer models. The claim is accurate, not cosmetic. █ THE CORE INNOVATION — VOLUME WEIGHTING Every existing Nadaraya-Watson implementation on TradingView uses a pure time kernel: • Standard NW: w(i) = K(i/h) This means a bar with 10,000 shares traded and a bar with 10,000,000 shares traded receive identical weight if they are the same number of bars away. A thin overnight drift and a high-volume institutional session influence the regression equally. That is statistically incorrect — volume is a direct measure of how much informational content a price bar carries. This script uses a volume-weighted kernel: • This script: w(i) = vol_norm(i) × K(i/h) where vol_norm(i) is the bar's volume normalized against the peak volume in the lookback window, raised to a configurable power exponent. The regression estimate is therefore: ŷ = Σ [ vol_norm(i) × K(i/h) × close ] / Σ High-volume bars anchor the curve. Low-volume bars — thin sessions, overnight drift, holiday trading — contribute minimally. The regression finds where the market actually agreed on price, not just where the clock recorded a tick. █ THREE KERNEL FUNCTIONS All three apply the same volume weighting. The choice controls how rapidly influence decays with time distance: • Rational Quadratic (default) — heavier tail than Gaussian. Bars from 40–60 periods ago still contribute meaningfully if they had high volume. Best for daily and weekly charts where old high-volume levels remain structurally relevant. • Gaussian — standard bell curve decay. Weight drops sharply with distance. Best for intraday charts where recency matters more than historical anchors. • Epanechnikov — hard cutoff at the bandwidth boundary. Anything beyond h periods receives zero weight. Produces the most locally sensitive regression. Best for fast charts requiring tight responsiveness. █ SIGNAL LOGIC The envelope bands are placed at a configurable multiple of ATR, standard deviation, or a fixed percentage above and below the regression line. Three band width methods are available to match different volatility contexts. Two signal modes are available: • Reversion mode (default) — a signal fires when price crosses back through the band after an extension. The ▲ label appears on the bar where price returns inside the lower band. The ▼ label appears on the bar where price returns inside the upper band. This confirms reversion has begun rather than anticipating it. • Extension mode — enable Signal on extension close to fire a signal the moment price closes outside a band. This is an early warning — useful for alerts before the reversion bar arrives. Additional signal filters: minimum bars between signals to prevent repeat firing, optional slope direction gate so signals only fire when the regression slope agrees with the signal direction. █ WHAT YOU SEE ON THE CHART Regression line The volume-weighted fair value curve. Cyan when slope is rising, magenta when falling. This is where the model estimates price should be given the recent history of high-participation price levels. Envelope bands Upper and lower boundaries built from ATR, standard deviation, or a fixed percentage. The upper band is tinted red — resistance zone. The lower band is tinted green — support zone. Bar coloring — 4 states • Bright red — price closed above the upper band. Extended, statistically stretched above fair value. • Bright green — price closed below the lower band. Extended, statistically stretched below fair value. • Dim silver — price inside bands, regression rising or falling, i.e normal bullish or bearish context. The contrast between fully saturated outside-band bars and dimmed inside-band bars makes overextension immediately visible without reading the scale. Signal labels ▲ REVERT or ▼ REVERT with VW=XX% showing the volume weight of the signal bar. A signal at VW=85% fired on a high-participation bar. A signal at VW=9% fired on a thin bar — lower confidence. Signal bar highlighting Two additional layers available: a background flash on the signal bar and a thick vertical line through the bar's full range. Both are independently toggleable. The vertical line uses width=4 — the maximum Pine Script allows — making the signal bar visually distinct even when zoomed out. Dashboard Displays: current regression value, slope direction, band width, Bar Vol Weight meter (▰▰▰▱▱▱) showing how much influence the current bar has on the regression, active kernel type, volume weighting status, percentage distance from the regression midline, and non-repainting mode status. █ NON-REPAINTING When Non-Repainting Mode is enabled (default), all calculations use a bar offset. The current bar's close does not enter its own regression estimate. Historical signals visible on closed bars will not change as new bars form. Disable this to see a predictive (repainting) version where the current bar participates in its own estimate — useful for visual exploration but not recommended for backtesting or alerts. █ HOW TO USE Core use case — mean reversion This is a mean reversion tool. It works best when price is oscillating rather than trending directionally. The recommended workflow: 1 — Confirm a ranging regime with a separate regime classifier before acting on signals. 2 — Wait for price to reach or pierce the upper or lower band (bars turn bright red or green). 3 — Check the VW% in the signal label. Higher volume weight on the signal bar = higher confidence. 4 — Enter on the reversion signal (▲ or ▼ label). Stop beyond the wick of the signal bar. 5 — Target the regression midline as the primary exit. The % from mid dashboard row tracks progress in real time. Timeframe guidance The volume-weighting advantage increases with timeframe because higher timeframes produce more meaningful volume data per bar. H4 and Daily are the strongest timeframes for this tool. For intraday use, reduce the Volume Weight Power to 0.3–0.5 to soften the impact of individual volume spikes. Quick-start settings by asset class • Stocks daily: Window=100, Bandwidth=8, Vol Power=1.0, ATR×2.0 • Crypto daily: Window=80, Bandwidth=6, Vol Power=0.7, ATR×1.8 • Forex H4: Window=100, Bandwidth=10, Vol Power=1.0, ATR×1.5 • Indices H1: Window=120, Bandwidth=12, Vol Power=0.8, Stdev×2.0 █ SETTINGS REFERENCE Kernel Settings • Lookback Window — number of historical bars in the regression. Larger = smoother, more lag. • Bandwidth (h) — controls how fast kernel weight decays with time. Higher = older bars still contribute. • Kernel Type — Gaussian / Rational Quadratic / Epanechnikov. See kernel section above. • RQ Alpha (α) — Rational Quadratic only. Lower = smoother mixture of length scales. • Non-Repainting Mode — uses offset. Recommended ON for backtesting. Volume Weighting • Enable Volume Weighting — toggle the core innovation on or off. OFF = standard NW. • Volume Normalization Window — peak volume reference window. Match or exceed the lookback window. • Volume Weight Power — exponent on the volume weight. 1.0 = linear. 2.0 = quadratic. 0.5 = softer. • Volume Weight Floor — minimum weight for any bar. Prevents zero-volume bars from being ignored entirely. Envelope Bands • Band Width Method — ATR (volatility-adaptive), Stdev (statistical), or Percent (fixed). • ATR Length — period for ATR calculation. • ATR / Stdev Mult — multiplier applied to ATR or standard deviation. • Percent Offset % — used when Percent method is selected. Signals • Signal on band crossover — enable signals on band cross events. • Signal on extension close — fire signal when price closes outside a band (early warning mode). • Require slope change — only signal when regression slope direction agrees. • Min bars between signals — gap guard to prevent repeat signals. Visuals • Dashboard — regression stats and live metrics table. • Signal labels — ▲/▼ REVERT labels with volume weight percentage. • Band fill — fill between upper and lower bands. • Background flash — bright background color on signal bars. • Vertical line on signal bar — thick line through full bar height at signal. • Large dot marker — additional plotchar layer on signal bars. • Dashboard position — Top Right / Top Left / Bottom Right / Bottom Left. █ ALERTS Seven alert conditions are available: • Long signal — reversion through lower band • Short signal — reversion through upper band • Any signal — either direction • Extended below lower band — early warning before reversion fires • Extended above upper band — early warning before reversion fires • Regression slope turned bullish • Regression slope turned bearish █ DISCLAIMER This indicator is a decision-support tool. It does not constitute financial advice and does not guarantee future results. Past statistical patterns do not predict future price behavior. Always use proper risk management. Method: Nadaraya-Watson Kernel Regression (Non-Parametric ML) Innovation: Volume × Time Kernel Weighting Kernels: Gaussian · Rational Quadratic · Epanechnikov Signals: Mean Reversion (band crossover or extension) Repainting: Configurable — non-repainting mode available
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Dots3Red tarafından
Regression Trend Reversal Signals & Forecasts [AlgoAlpha]🟠 OVERVIEW Regression Trend Reversal Signals & Forecasts combines multiple regression methods into a single trend and reversal framework. It allows traders to choose between Linear Regression, Theil-Sen Regression, LOESS smoothing, Nadaraya-Watson smoothing, Polynomial Regression, and a Kalman Filter to estimate the underlying price path. The selected regression line acts as the center of a dynamic channel. The channel width is based on the standard deviation of the distance between price and the regression line, allowing it to adapt to changing market conditions. The script also identifies potential reversal conditions when price extends beyond the channel and then shows signs of rejection. In addition, it can project the current regression slope forward to provide a simple forecast of the current trend path. 🟠 CONCEPTS Regression Line — A statistical estimate of the underlying price trend. Different methods can be selected, ranging from straight-line regressions to adaptive smoothing techniques. Theil-Sen Regression — A robust regression method that uses median slopes from all point pairs, reducing the influence of outlier price spikes. LOESS Regression — A locally weighted regression that fits nearby observations more heavily than distant observations to create a smooth trend curve. Nadaraya-Watson Smoothing — A kernel-weighted averaging method that estimates trend by assigning larger weights to recent observations. Kalman Filter — A recursive estimation method that continuously updates the trend estimate as new prices arrive. Polynomial Regression — A curved regression model that can capture non-linear trend structures using higher-order polynomial functions. Regression Deviation Bands — Channel boundaries calculated from the standard deviation of price relative to the regression line. Regression Slope — The rate of change of the regression estimate used to determine trend direction and forecast projections. 🟠 FEATURES Regression Channel — Dynamic bands expand and contract based on how far price deviates from the regression line. Trend Flip Signals — Generates directional markers when the regression trend changes from rising to falling or from falling to rising. Reversal Signals — Marks potential bullish and bearish reversals when price extends beyond the channel and begins rejecting those extremes. Forecast Projection — Extends the current regression slope into future bars and optionally displays projected channel boundaries. 🟠 HOW TO USE Select a regression method that matches the market behavior you want to analyze. Linear and Theil-Sen are suited to directional trends, while LOESS, Nadaraya-Watson, and Kalman provide smoother adaptive estimates. Use the regression line as the primary trend reference. Rising regression values indicate strengthening conditions, while falling values indicate weakening conditions. Monitor the channel boundaries for extended price movement away from the regression estimate. Watch for bullish reversal markers below the lower band after downside extensions and bearish reversal markers above the upper band after upside extensions. Use trend flip signals as confirmation that the regression slope has changed direction. Compare price location within the channel to gauge whether price is trading near trend equilibrium or at an extreme deviation. Use the forecast projection as a continuation estimate of the current regression slope rather than a prediction of future market behavior. 🟠 CONCLUSION Regression Reversal Signals combines multiple regression techniques, adaptive deviation channels, reversal detection, and forward projections into a single framework. By allowing traders to switch between several trend estimation methods, it provides different perspectives on trend structure and price deviation. The indicator helps identify trend direction, potential reversals, and areas where price has moved unusually far from its estimated path.
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AlgoAlpha tarafından
11
Polynomial/Linear Regression Volume Profile [BigBeluga]Polynomial/Linear Regression Volume Profile is a state-of-the-art charting framework that blends advanced statistical modeling with localized volume distribution analysis. By evolving past traditional, static horizontal volume profiles, this indicator dynamically curves the volume profile matrix around mathematical trend baselines, giving you a hyper-localized view of value zones, support, and resistance across the trend’s lifecycle. Equipped with a switchable Ordinary Least Squares (OLS) calculation engine, traders can analyze price distribution relative to a straight path (Linear) or an adaptive structural arc (Polynomial). 🔵 RECURSIVE REGRESSION BASELINES Adaptive Curve Fitting Engine: Choose between a straight-line trend tracking framework (Linear) or an advanced second-degree curved path (Polynomial). This non-linear baseline curves dynamically to track real institutional momentum shifts, avoiding the lag or rigid delays typical of standard moving averages. Symmetric Grid Segmentation: The indicator slices the regression space into dynamic parallel layers above and below the center line. These tracking cells act as a structural map of the trend, automatically expanding or contracting based on the mathematical bounds of the lookback period. Standard Deviation Wave Bands: Plots dedicated tracking envelopes at 1, 2, and 3 Standard Deviations. This maps statistical extremes instantly, highlighting key valuation zones directly on the chart. 🔵 CURVED ORDER FLOW PROFILE Dynamic Trend-Anchored Volume Profile: Traditional volume profiles are anchored strictly to vertical price grids. This framework bends the profile horizontally along the path of the regression curve. This ensures volume is localized directly relative to the trend's value matrix rather than arbitrary static prices. Dynamic Point of Control Matrix (POC): The tool calculates cumulative transaction weights across each regression row. The absolute highest volume cluster is highlighted across the entire lookback window as a vivid Point of Control (POC) baseline, serving as a primary target magnet for price discovery. Gradient Density Mapping: Volume bins are colored with a responsive heat-map gradient. Low-volume zones fade into deep baseline tones, while high-volume institutional interest areas light up dynamically, reflecting heavy positional accumulation. 🔵 DATA INTERFACE & CONTROLS Regression Matrix Dashboard (Top-Right): A neat information center providing live metrics, including current trend direction (Bullish/Bearish), the numerical value of the POC level, the exact transactional volume resting at that key node, and structural $\pm3\text{ SD}$ channel limits. Precision Profile Scaling: Adjust the profile width parameters to limit or extend how far back profile bins stretch across your chart space to prevent layout clutter. Complete Style Personalization: Individualized visual controls allow you to switch line architectures (Solid, Dashed, Dotted) across baselines, boundaries, and POC paths. 🔵 STRATEGIC APPLICATION Trading the Trend Value Nodes: Treat the dynamic POC line as a trend anchor. In a strong bullish trend, pullback entries occurring at a highly concentrated, heat-mapped POC node represent low-risk, high-probability entry criteria. Mean Reversion at Statistical Boundaries: When price extends completely out to the dynamic outer channel limit and volume density in that outer bin thins out, look for a swift mean-reversion snapback toward the baseline. Volume Profile Breakouts: Low-volume zones (gaps in the curved profile) indicate price levels that the market skipped quickly due to high momentum. If price breaks past a thick volume node into a low-volume zone, it is likely to sprint quickly toward the next major heat-mapped node. Structural Regime Tracking: Use the upper-right dashboard to instantly evaluate macro status. If the matrix shifts between Bullish and Bearish while price hovers consistently near a high-volume POC, it implies heavy institutional distribution is occurring before the next major expansion. Polynomial/Linear Regression Volume Profile redefines volume structure. By wrapping the laws of order flow directly around mathematical curves, it gives trend traders an elite perspective to trade with precision, statistical logic, and institutional order flow visibility.
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BigBeluga tarafından
15
Structural Inefficiency Radar [JOAT]Structural Inefficiency Radar Introduction Structural Inefficiency Radar measures regression fair value, residual z-score, path asymmetry, volatility clustering, and repair/extension force. This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used. Core Concepts 1. Regression Fair Value A linear regression spine represents statistical fair value. 2. Residual Z-Score Distance from fair value is normalized by residual standard deviation. 3. Path Asymmetry Upward and downward path movement are compared to detect directional distortion. 4. Repair and Extension Force Deviation, slope, asymmetry, and clustering determine whether price is extending or repairing. z = (close - fairValue) / residualDeviation Features Fair-value spine Inner and outer inefficiency bands Residual z-score Repair and extension scoring E+ and E- labels plus repair markers Input Parameters Regression and residual windows Deviation gate Repair quality gate Cooldown Bands, candles, and HUD toggles How to Use This Script Use the spine and bands to frame structural stretch. Extension labels indicate directional inefficiency; gold markers indicate fair-value repair. Limitations The script uses historical OHLCV data and cannot know future prices. Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting. Model outputs should be interpreted with market context, risk controls, and independent analysis. No visual state should be treated as a certain trade outcome. Originality Statement SIR is original in combining regression residuals, path asymmetry, clustering, repair force, and extension force. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment. -Made with passion by jackofalltrades
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officialjackofalltrades tarafından
Bitcoin Compressing Power Law ChannelBitcoin Compressing Power Law Channel Most Bitcoin power-law channels draw bands of a fixed width around a long-term trendline. This one is different: the channel width is not constant. It starts wide and compresses exponentially as Bitcoin matures, modeling the idea that long-term volatility around the trend tends to shrink over time. That decaying width is the core idea of this indicator. Why a compressing channel A standard power-law channel assumes the spread between its upper and lower bounds stays the same across Bitcoin's entire history. In practice, an asset's relative volatility tends to fall as it grows larger and more liquid. This indicator captures that by letting the channel narrow over time toward a configurable floor, so the bounds reflect a maturing market rather than a permanently fixed range. How it works The model assumes log(price) scales linearly with log(days since the genesis block), producing a fair-value trendline: logFair = intercept + slope * log10(days). A lower bound is offset below that line, and the upper bound is placed above the lower bound at a distance set by the channel width. The width itself is the original part: width = minWidth + startWidth * exp(-decaySpeed * yearsSinceGenesis). Early in Bitcoin's history the exponential term is large and the channel is wide. As years pass, that term shrinks toward zero and the width converges to a minimum floor (minWidth). The result is a channel whose envelope tightens over time instead of staying fixed. What it plots Three lines in price space (upper, middle, lower) with a shaded fill between the upper and lower bounds. Optionally, a 200 SMA of the current timeframe and a 200 SMA from the weekly timeframe, each toggleable. The weekly SMA is requested from a higher timeframe with lookahead disabled, so it does not repaint using future data. A normalized "Decay Channel Oscillator" is exposed in the Data Window, showing where the current close sits within the channel on a 0 to 1 scale (0 = lower bound, 1 = upper bound). Inputs Every model parameter is adjustable: the genesis date, the power-law intercept and slope, the lower offset, the initial and minimum channel widths, and the decay speed that controls how fast the channel compresses. Colors for each line, the fill, and both SMAs are configurable. How to use it Apply it to a Bitcoin chart on a longer timeframe such as Daily or Weekly, where a power-law model is most meaningful. The middle line is the model's central estimate; the upper and lower lines describe the expected long-term range, narrowing as time goes on. The Data Window oscillator lets you read how stretched price is within the channel numerically. Parameters and calibration The default intercept and slope are starting values that approximate Bitcoin's historical power-law fit. They are not fixed truths. You should re-evaluate them and adjust them, along with the offset, widths, and decay speed, to suit your own analysis and the data range you are studying. Different calibrations will move the channel and change how aggressively it compresses. Limitations and cautions This is a model, not a prediction. The power-law relationship is an empirical observation that may break down at any time, and the decay parameters are assumptions, not facts. The compressing width is a hypothesis about volatility maturing over time; it may not hold. This indicator is built for Bitcoin and is not intended for other assets. Nothing here forecasts future prices, and the past behavior of the channel does not guarantee anything about how price will behave going forward. The code is open-source under the Mozilla Public License 2.0. You are welcome to study it and build on it.
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AppTradingRobots tarafından
1
Kinetic Inertia Field [JOAT]Kinetic Inertia Field Introduction Kinetic Inertia Field models price like a noisy particle using velocity, acceleration, jerk, kinetic energy, potential displacement, and equilibrium deviation. This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used. Core Concepts 1. Velocity and Acceleration Log returns are normalized by volatility to create velocity, then differentiated into acceleration and jerk. 2. Kinetic Energy Inverse volatility acts as a mass proxy and squared velocity creates energy context. 3. Equilibrium Displacement A regression/VWAP blend creates a fair path and ATR-normalized displacement. 4. Inertia Field Energy, acceleration, and displacement combine into inertial up, inertial down, or elastic state. kineticEnergy = 0.5 * mass * velocity * velocity Features Velocity, acceleration, and jerk model Kinetic and potential energy scoring Regression/VWAP equilibrium Energy rails and impulse trace K+ and K- labels plus snapback markers Input Parameters Velocity smoothing Volatility memory Equilibrium horizon Energy and inertia gates Cooldown and display toggles How to Use This Script Use K+ and K- as confirmed high-energy state changes. Gold markers show elastic snapback conditions. Limitations The script uses historical OHLCV data and cannot know future prices. Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting. Model outputs should be interpreted with market context, risk controls, and independent analysis. No visual state should be treated as a certain trade outcome. Originality Statement KIF is original in applying kinetic energy, potential displacement, and inertia scoring to price-state analysis. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment. -Made with passion by jackofalltrades
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officialjackofalltrades tarafından
Orion Regression Field [JOAT]Orion Regression Field Introduction Orion Regression Field builds a weighted regression valuation field with standard error bands, curvature options, compression detection, and reprice signals. This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used. Core Concepts 1. Weighted Regression Recent bars can receive more influence while still preserving a full model window. 2. Standard Error Bands Inner and outer bands show statistical distance from the modeled path. 3. Curvature and Confidence Optional curvature and R2-style confidence control when the field is considered reliable. 4. Pinch and Reprice Compression and outer-band reactions create filtered reprice events. mid = weightedRegression(close, len); band = standardError * multiplier Features Weighted regression midline Inner and outer SEE bands Optional curvature Pinch shading and projection field Filtered reprice labels Input Parameters Regression window and projection bars Curvature toggle and weight floor Minimum R2 confidence Inner and outer band multipliers Pinch ratio and cooldown How to Use This Script Use the field as statistical fair-value context. Outer band interaction means stretch, not automatic reversal. Limitations The script uses historical OHLCV data and cannot know future prices. Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting. Model outputs should be interpreted with market context, risk controls, and independent analysis. No visual state should be treated as a certain trade outcome. Originality Statement Orion is original in combining weighted regression, curvature, standard error fields, compression context, and filtered reprice logic. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment. -Made with passion by jackofalltrades
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officialjackofalltrades tarafından
Helios Pullback Framework [JOAT]Helios Pullback Framework Introduction Helios Pullback Framework evaluates pullback quality using ALMA trend stack, regression trajectory, pullback depth, wick recovery, liquidity capture, compression, and HTF bias. This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used. Core Concepts 1. ALMA Trend Stack Fast and slow ALMA lines define execution and institutional spines. 2. Regression Trajectory A regression envelope provides dynamic path context rather than static zones. 3. Pullback Quality Pullback depth, wick recovery, phase impulse, and stretch are scored. 4. Liquidity Capture Confirmed sweeps of recent highs or lows contribute only when aligned with bias. score = bias + pullDepth + wickRecovery + sweep + compression + htf Features ALMA trend stack Regression trajectory envelope Pullback depth and wick recovery scoring Liquidity capture markers Sparse P+ and P- labels Input Parameters ALMA lengths Volatility and trajectory length HTF and optional session gate Signal score, cooldown, and max stretch Display toggles How to Use This Script Use P+ and P- labels as pullback-quality events inside an existing bias. Gold circles mark liquidity capture without full confirmation. Limitations The script uses historical OHLCV data and cannot know future prices. Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting. Model outputs should be interpreted with market context, risk controls, and independent analysis. No visual state should be treated as a certain trade outcome. Originality Statement Helios is original in replacing supply/demand boxes with a cleaner model that scores pullback elasticity, trajectory, and liquidity capture. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment. -Made with passion by jackofalltrades
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officialjackofalltrades tarafından
Predictive Breakout Channels | GainzAlgoAbout the Indicator The Predictive Breakout Channels indicator is a predictive machine-learning engine designed to map institutional market structure and calculate the statistical probability of impending breakouts. Instead of relying on traditional lagging indicators, the system dynamically anchors itself to major market pivots using a rolling Linear Regression Channel framework. By evaluating a combination of localized trend correlation, relative strength, institutional volume distribution, and variance metrics, the engine projects real-time target zones while simultaneously calculating a directional probability score directly on the chart the moment a breakout occurs. Dynamically anchors to institutional pivot structures Uses a rolling Linear Regression Channel Evaluates trend correlation, RSI, and variance metrics Projects real-time ATR-based target zones Calculates breakout probability scores directly on-chart Designed to distinguish genuine breakouts from fakeouts The Core Theory of Breakouts Markets spend the majority of their time consolidating rather than trending. During these equilibrium phases, liquidity pools accumulate on both sides of the range while volatility compresses beneath the surface. A breakout represents the structural transition from equilibrium into expansion. When institutional order flow aggressively consumes localized liquidity, price breaches structural boundaries and volatility rapidly expands outward. The challenge for traders has never been identifying that a breakout occurred — the real challenge is determining whether the move has enough structural backing to sustain itself or whether it is simply a liquidity trap designed to reverse shortly afterward. The Predictive Breakout Channels engine was specifically designed to address that exact problem. The Logic Engine — ANOVA & The Power of Variance To help solve the fakeout problem, this engine incorporates ANOVA, short for Analysis of Variance. Originally developed by legendary statistician Ronald Fisher, ANOVA has historically served as one of the foundational statistical tools used throughout medical research, behavioral science, and high-level quantitative analysis. Its purpose is to determine whether differences between groups of data are statistically meaningful or simply random noise. In this indicator, that same statistical framework is adapted directly to price action. The engine continuously evaluates the structural differences between groups of candle data — including highs, lows, and closes — in real time in order to measure the quality and significance of underlying market expansion. The Niche Secret — F-Statistic & Volatility Compression Quantitative modeling revealed a particularly powerful characteristic regarding variance measurements inside the ANOVA engine. When the raw ANOVA F-Statistic becomes drastically elevated, or when the standardized Z-Score breaches extreme thresholds such as 2 standard deviations, it often signals a state of hyper-compressed market consolidation. Think of it like winding a mechanical spring tighter and tighter. As variance compresses to rare statistical extremes, market energy begins building beneath the surface. Eventually that stored pressure releases through aggressive volatility expansion. This variance surge acts as a leading indicator for impending volatility before the actual breakout even occurs. However, variance alone cannot determine directional bias. Because of this, the engine layers in additional confirmation modules such as RSI and Trend Correlation Length to help determine whether institutional momentum is favoring bullish or bearish continuation. Indicator Settings & Customization The system is fully modular, allowing traders to fine-tune the engine based on their preferred asset class, timeframe, or trading style. Anchored LinReg Channel Settings: Customize left and right pivot lookbacks alongside standard deviation multipliers to control how the channel dynamically anchors itself to price structure. ANOVA Confirmation: Fine-tune the lookback period and baseline Z-Score thresholds required for breakout validation. Feature Filters: Adjust RSI and Trend Correlation baseline lengths to make directional probability scoring more aggressive or more selective. High Variance Alert Label: Disabled by default. When enabled, the engine plots visual warning labels whenever variance compression reaches statistically elevated levels. Include HTF Trend Filter: Controls whether breakout signals are filtered using higher timeframe trend conditions. The Strategic Dilemma — Higher Timeframe Trend Filtering The indicator includes a dedicated HTF Trend Filter toggle that leverages higher timeframe EMA spreads to determine whether lower timeframe breakout signals align with broader institutional trend conditions. Choosing whether to enable this filter depends entirely on the type of market environment you prefer trading. 1. HTF Filter ON — Trend Following Regime Filters out a significant amount of lower timeframe noise Produces fewer but statistically stronger breakout signals Aligns entries with broader institutional money flow Increases overall follow-through probability However, because the engine becomes heavily biased toward the macro trend, it may intentionally suppress counter-trend reversals or early-stage trend shifts. 2. HTF Filter OFF — Agile / Mean-Reversion Regime Allows the engine to react dynamically in both directions Captures sharp intraday reversals more aggressively Performs well in swinging or range-bound environments Increases breakout frequency substantially The tradeoff is naturally higher exposure to lower timeframe noise and shorter average continuation during counter-trend conditions. How to Trade with the Indicator When price closes outside the Linear Regression Channel while simultaneously satisfying the statistical validation criteria, the engine prints a breakout entry signal alongside a projected probability score. At the same time, the system projects 4 distinct ATR-based Target Zones labeled T1 through T4. Aggressive Traders: May choose to execute immediately on the breakout close while targeting T2 or T3 with structural stops positioned back within the channel. Conservative Traders: May choose to use the Probability Score as a filter or wait for a localized retest of the broken channel boundary before entering. The High Variance Play When the High Variance Alert label appears, traders should avoid impulsively chasing the immediate candle. Instead, the label should be treated as an early warning that volatility expansion is rapidly approaching. The preferred approach is to wait for the subsequent confirmed breakout signal, then trade the resulting momentum expansion into the projected target zones. High variance does not predict direction It predicts volatility expansion Directional confirmation comes afterward through breakout validation Wrapping It Up The Predictive Breakout Channels indicator bridges quantitative data science with classic market microstructure principles. By treating volatility as a measurable statistical property rather than a visual guessing game, the engine helps traders identify where the market is coiling, estimate the probability of expansion, and navigate breakout environments using structured statistical confirmation instead of emotion. Whether used for momentum continuation, volatility expansion, or intraday breakout trading, the system was designed to provide traders with a clearer framework for distinguishing meaningful expansion from market noise.
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GainzAlgo tarafından
215
Helix Regime Corridor [JOAT]Helix Regime Corridor Introduction Helix Regime Corridor is an open-source regime and entry-state indicator built around adaptive ATR bands, structural CHoCH events, regression context, transition memory, and risk rails. It is designed to show when price is moving under bullish control, bearish control, or a less decisive transition state. The script solves the problem of treating every trend flip the same. It requires directional control, structure, volatility, and confluence scoring before marking a confirmed shift. Core Concepts 1. Adaptive Regime Corridor The central trend average and surrounding ATR bands adapt to current market volatility. The corridor shows whether price is operating above, below, or inside the active control area. 2. Confirmed Holds The script can require price to hold beyond the corridor for a configurable number of bars. This helps reduce single-candle fakeouts. 3. CHoCH-Assisted Structure Pivot highs and lows identify structural change. Bullish or bearish CHoCH events can support a new regime shift when price breaks prior structure. 4. Regression Context An optional regression field provides broader directional context and helps identify whether the active shift is aligned with the recent linear price path. 5. Transition Memory The script tracks transition behavior over a configurable memory window. This adds a probability-style context layer without claiming certainty. Features Adaptive ATR corridor: Volatility-adjusted bands frame bullish and bearish control. Regime shift detection: Identifies confirmed changes into bullish or bearish control. CHoCH structure: Optional structure-assisted entries using pivot confirmation. Confluence scoring: Signals require score alignment rather than a single moving-average cross. Risk rails: Entry, stop, TP1, TP2, and TP3 references can be drawn for confirmed shifts. Candle tinting: Bars can be colored by current state. Optional regression channel: Adds broader context when desired. HUD: Shows regime, score, transition, ADX/ATR, regression, structure, and bias. Alerts: Long shift, short shift, bull regime, bear regime, bullish CHoCH, and bearish CHoCH. Input Parameters Core: Operating Preset, Adaptive Average Length, ATR Length, ATR Band Multiplier, Confirmed Hold Bars. Structure and Signals: Pivot Left, Pivot Right, Regression Context Length, Transition Memory, Directional Strength Length, Minimum Shift Score, Allow CHoCH-Assisted Entries, Signal Cooldown Bars. Risk and Visuals: Structure Stop Lookback, Stop ATR Buffer, TP1 R, TP2 R, TP3 R, Rail Projection Bars, Adaptive Bands, Trend Cloud, Regression Channel, Candle Tint, Pivot Marks, HUD. How to Use This Indicator Step 1: Read the regime Use the corridor and dashboard to identify whether price is under bullish control, bearish control, or a weaker transitional state. Step 2: Wait for score confirmation Long and short shifts are only marked when the configured confluence score is reached. Step 3: Compare structure CHoCH events help explain whether a shift is supported by structural change or only by band behavior. Indicator Limitations Pivot structure confirms after the required right-side bars complete. Adaptive bands can lag sharp reversals because they are volatility-based. Transition memory summarizes recent behavior; it does not forecast future price. Risk rails do not account for spread, slippage, or order execution. Originality Statement Helix Regime Corridor is original in its combination of adaptive ATR control bands, hold confirmation, CHoCH structure, regression context, transition memory, confluence scoring, and risk rails. The components are combined to classify regime and produce cleaner shift states rather than simply plotting a trend band. Disclaimer This script is for educational and informational use only. It is not financial advice and does not recommend any trade. Trend and regime models can fail in ranges, news events, and abnormal volatility. Always manage risk independently. Made with passion by jackofalltrades
Pine Script® göstergesi
officialjackofalltrades tarafından
SwingRegress Volatility Analytics [MarkitTick]💡 A comprehensive, multi-dimensional charting tool designed to fuse structural market analysis, statistically derived linear regression pathways, and volatility compression mechanics into a single, cohesive interface. By dynamically adapting its calculations to the latest shifts in market structure—specifically Change of Character (CHoCH) events—this script offers an adaptive mapping of price action, trend trajectory, and potential breakout zones directly on the primary chart. ● ✨ Originality and Utility Traditional linear regression tools often require manual anchoring or rely on fixed lookback periods that fail to adapt to rapidly unfolding price dynamics. The distinct utility of this script lies in its self-adjusting structural anchoring mechanism. By automatically locking the regression baseline to the most recent significant pivot high or pivot low immediately following a structural break, the channel remains mathematically and contextually relevant to the current market regime. Furthermore, this tool eliminates the need for separate sub-chart oscillators by integrating a sophisticated Smart Volatility Squeeze engine. This engine compares price variance against true range to identify periods of extreme price compression, overlaying these signals directly within the active regression pathway. The result is a unified, chart-centric view of both directional trend geometry and kinetic energy build-up, allowing for a more focused and uncluttered analytical process. ● 🔬 Methodology and Concepts The underlying logic of this script is driven by three core mathematical engines operating in tandem: • Pivot Discovery and Market Structure The script continuously scans incoming price data to identify localized extremes, defined as Pivot Highs and Pivot Lows. A candidate bar is confirmed as a pivot only if it remains unbroken for a user-defined number of bars both prior to and following its occurrence. Once confirmed, these pivots establish the market structure. If the closing price breaks beyond the most recent opposing pivot, a Change of Character (CHoCH) is triggered, officially shifting the trend state. • Anchored Linear Regression Upon the confirmation of a new CHoCH, the script calculates a fresh Linear Regression Channel (LRC). The anchoring point is the origin pivot of the newly established trend. The script uses the Ordinary Least Squares (OLS) method to compute the slope and intercept of the best-fit line through the closing prices of the current regime. It then calculates the standard error of the estimate (standard deviation of the residuals) to project upper and lower variance bands parallel to the mid-line. • Volatility Squeeze Mechanics To identify volatility compression, the script employs a comparative analysis between standard deviation and Average True Range (ATR). It calculates a Bollinger Band (representing standard deviation) and a Keltner Channel (representing ATR) around a moving average baseline. A "squeeze" is structurally confirmed when the outer limits of the Bollinger Bands contract entirely within the boundaries of the Keltner Channels. This signifies that historical variance has dropped substantially below the average true range, often preceding a dynamic expansion in price movement. ● 🎨 Visual Guide The visual interface is highly detailed and structurally color-coded to provide immediate contextual awareness without cluttering the chart. • Current Anchored LRC Mid Line: A solid Neon Cyan line representing the true mean of the current trend regime. Band 1: A dashed Soft Cyan line mapping the first standard deviation threshold. Band 2: A dotted Deep Azure line mapping the secondary, outer standard deviation extreme. • Previous Anchored LRC Mid Line: A solid Magenta line representing the historical mean of the preceding trend. Band 1: A dashed Soft Magenta line for the historical inner variance. Band 2: A dotted Blue-Violet line for the historical outer variance. • Swing Point Zones Swing High Boxes: Translucent red zones originating from a confirmed pivot high, drawing forward to act as dynamic resistance until broken by price action. Swing Low Boxes: Translucent green zones originating from a confirmed pivot low, acting as dynamic support until structurally invalidated. • Volatility Squeeze Candles Cyber Gold Candles: When the market enters a state of extreme volatility compression (Bollinger Bands inside Keltner Channels) and is actively trading within the current or previous LRC pathway, the candles are painted a vibrant gold to highlight imminent kinetic release. • Heads-Up Dashboard Display Located in the top right corner, this self-updating data matrix provides critical real-time telemetry: Structure Regime: Displays the active directional bias (Bullish, Bearish, or Neutral). Last CHoCH: Indicates the direction and age (in bars) of the most recent structural shift. Squeeze Intensity: A visual block-bar measuring the depth of the volatility compression. ATR (14): The current absolute value of the Average True Range. Dist to Swings: The percentage distance between the current price and the nearest Swing High/Low. Risk/Reward Quality: A dynamic measurement of potential risk versus structural reward. LRC Window Age: The duration of the current regression channel in bars. LRC Position: Indicates whether price is currently trading inside the active regression channel, the previous channel, or is entirely unanchored. ● 📖 How to Use The primary application of this tool is identifying high-probability continuation or mean-reversion setups following structural confirmation. When a CHoCH event occurs, wait for the new Linear Regression Channel to populate. This channel defines your trading parameters. A high-probability setup manifests when price pulls back to the inner or mid-line of the active LRC, accompanied by the appearance of Cyber Gold squeeze candles. This visual confluence suggests that price is compressing directly at the statistical mean of the new trend, building energy for a move in the direction of the underlying structural regime. Conversely, if price approaches the outer standard deviation bands (Deep Azure) without structural confirmation of a breakout, it suggests the market is statistically overextended, offering a potential mean-reversion opportunity back toward the Neon Cyan mid-line. Note on Mechanics: Because the pivot discovery process requires a defined number of bars to confirm a swing high or low, there is an inherent lookback period. The swing zones will only appear after the pivot has been structurally verified. Furthermore, the linear regression channel recalculates its slope dynamically as new price data is added to the active regime, meaning the exact angle of the channel adapts in real-time until a new CHoCH locks it into history as the "Previous LRC." ● ⚙️ Inputs and Settings The configuration panel is logically divided into primary analytical modules to allow for precise user calibration. • Current Anchored CHoCH LRC Adjust the sensitivity of the pivot discovery engine by modifying the Left and Right Pivot Bars. You can also customize the multipliers for the primary and secondary standard deviation bands, as well as toggle their visibility and modify line weights. • Previous Anchored CHoCH LRC Allows for the toggling of the historical channel, providing context on how the previous trend failed. Color and visibility settings are fully adjustable here. • Swing Points & Zones Settings Toggle the structural resistance and support boxes on or off, and customize their respective color opacities for a cleaner chart overlay. • Smart Volatility Squeeze (BB vs KC) Tune the underlying volatility engine. You can adjust the lookback length for the variance baseline, as well as the specific deviation multipliers for both the Bollinger Band boundaries and the Keltner Channel limits. • Webhook Execution Configuration Input exact JSON payload action names for algorithmic execution routing (Long, Short, Close Long, Close Short). • Dashboard Settings Customize the background and text colors of the heads-up data matrix to match your specific chart theme. ● 🔍 Deconstruction of the Underlying Scientific and Academic Framework The mathematical foundation of this script is anchored heavily in econometrics and statistical probability theory. At its core, the linear regression calculation utilizes the Ordinary Least Squares (OLS) estimator. This formula determines the line of best fit through a sequence of time-series data points by minimizing the sum of the squared differences (residuals) between the observed closing prices and the values predicted by the linear model. The slope of this line represents the average rate of change per unit of time, mathematically quantifying the drift of the active regime. The parallel bands wrapping the regression line are derived by calculating the standard error of the estimate. Assuming the residuals are normally distributed (Gaussian distribution), one standard deviation captures approximately 68 percent of the price variance, while two standard deviations capture roughly 95 percent. When price moves beyond these outer bands, it represents a statistically significant deviation from the mean, inherently increasing the probabilistic likelihood of mean reversion. The volatility squeeze mechanic operates on the principle of variance compression. Bollinger Bands are a derivative of standard deviation, making them highly reactive to short-term variance. Keltner Channels utilize the Average True Range (ATR), which measures absolute periodic volatility independent of a central mean. When the standard deviation of price contracts to such a degree that the Bollinger Bands fall entirely within the ATR-based Keltner Channels, it statistically confirms a state of anomalous energy compression. In financial academia, periods of artificially suppressed variance are overwhelmingly followed by periods of geometric expansion, providing the theoretical basis for breakout execution. ⚠️ Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Pine Script® göstergesi
MarkitTick tarafından
2
SuperTrend Logistic Regression | Flux ChartsGENERAL OVERVIEW The SuperTrend Logistic Regression indicator combines the classic SuperTrend trend-following tool with a self-training logistic regression model that assigns a probability percentage to every SuperTrend flip. Each time the SuperTrend changes direction, the indicator evaluates the market conditions at the flip across 14 features spanning candle structure, trend context, and technical indicators, then outputs a probability score between 0% and 100% representing how similar the current setup is to past flips that resolved profitably on the same chart. The model trains itself in real time using past SuperTrend signals as labeled examples. Each historical flip becomes a training example: the features captured at the flip are paired with the outcome at the next opposite flip, producing a continuously growing dataset that the model uses to refine its weights. The probability displayed on each new flip reflects what the model has learned about this specific instrument and timeframe, not a universal assumption about what makes a good signal. screenshot: Full chart showing SuperTrend line with multiple flips and probability labels 🔹What is the purpose of the indicator? The indicator addresses a common problem with SuperTrend: not every flip leads to a sustained trend. Many flips occur during choppy or transitional conditions and reverse quickly, producing losing signals. By scoring each flip with a probability, traders get a quantitative sense of how much confidence the model has in that specific signal based on similar past setups. The indicator does not change the SuperTrend calculation itself, so every flip is still detected and visualized. The probability score adds a filtering layer that helps traders separate high-confidence flips from low-confidence ones. 🔹What is the theory behind the indicator? The indicator is built on the idea that market behavior around trend reversals is not random. Certain combinations of candle structure, volatility conditions, volume patterns, and prior trend context tend to precede trends that follow through, while different combinations tend to precede trends that reverse quickly. Logistic regression is a statistical method well-suited to learning these relationships. Given a set of input features and historical outcomes, it produces a set of weights that map feature combinations to probability estimates. In this indicator, each SuperTrend flip becomes a data point. The features at the flip are recorded, and the outcome is determined at the next opposite flip. If price moved in the signal's direction (higher for bull, lower for bear), the signal is labeled as a win. Otherwise, it is labeled as a loss. The model continuously retrains on this growing dataset, adjusting weights so that feature patterns historically associated with wins produce higher probabilities, and feature patterns associated with losses produce lower probabilities. SUPERTREND LOGISTIC REGRESSION FEATURES SuperTrend Core Logistic Regression Model Candle Features Trend Context Features Technical Features Separate Bull and Bear Models Exponential Decay Weighting Minimum Sample Gating Probability Labels and Filtering Gradient-Colored SuperTrend Visualization Optional Momentum Dots Alerts SUPERTREND CORE The SuperTrend is the foundation of every signal. All detection, training, and prediction is tied to SuperTrend flips. The core SuperTrend calculation in this indicator is standard: ATR multiplied by a user-defined factor produces the trailing line, and a flip occurs when price crosses through the line, changing the direction state. 🔹How SuperTrend is calculated The SuperTrend is derived from Average True Range (ATR) with a user-configurable period and factor. When the calculated line is below price, the indicator is in an uptrend state and the line acts as dynamic support. When the line is above price, the indicator is in a downtrend state and the line acts as dynamic resistance. A flip occurs at the exact bar where price crosses through the line, changing the direction from bullish to bearish or vice versa. 🔹Why SuperTrend was chosen SuperTrend is one of the most widely used trend-following tools on TradingView. It produces clean, deterministic signals with well-understood behavior. Rather than reinventing or modifying the underlying calculation, this indicator treats SuperTrend as a signal source and adds a separate statistical layer on top. This keeps the core behavior familiar to traders already using SuperTrend and ensures that every flip is still detected, regardless of the probability score. 🔹SuperTrend Inputs Two inputs control the SuperTrend calculation: Factor (the ATR multiplier, default 3.0) and ATR Period (the number of bars in the ATR calculation, default 10). These correspond to the standard SuperTrend parameters. Changing these values affects where flips occur, which in turn affects what signals the model trains on. LOGISTIC REGRESSION MODEL Logistic regression is a statistical model that predicts binary outcomes from a set of input features. It takes each feature, multiplies it by a learned weight, sums the results, adds a bias term, and passes the total through a sigmoid function that maps the output to a probability between 0 and 1. The model learns the weights by comparing its predictions against actual outcomes and adjusting through gradient descent. 🔹What is Logistic Regression? Logistic regression works by finding the set of weights that best separates winning signals from losing signals in feature space. A large positive weight on a feature means that higher values of that feature are associated with winning outcomes. A large negative weight means higher values are associated with losing outcomes. A weight near zero means the feature does not discriminate between winners and losers on this chart. The sigmoid function is what turns the weighted sum into a probability. It outputs values near 0 when the weighted sum is very negative, values near 1 when the weighted sum is very positive, and values near 0.5 when the weighted sum is near zero. This gives the output a natural probability interpretation. 🔹How the model is trained Training happens every time a new SuperTrend flip occurs. The indicator looks at all past resolved signals (signals that have already seen their next opposite flip and therefore have a known outcome) and runs gradient descent over them. Gradient descent calculates how much each weight should change to reduce the model's prediction error, then applies those changes iteratively. The indicator runs multiple training epochs on each update to ensure the weights converge reasonably well to the current data. L2 regularization is applied to prevent any single weight from becoming extreme. Weight clipping further constrains weights to a reasonable range, avoiding instability when training data is thin or features are noisy. Together, these make the model more robust on small sample sizes. 🔹Outcome evaluation For training to work, every historical signal needs a win/loss label. The indicator uses flip-to-flip evaluation: a bull signal is considered a win if the close at the next bear flip is higher than the close at the bull flip. A bear signal is considered a win if the close at the next bull flip is lower than the close at the bear flip. This matches the natural lifecycle of a SuperTrend trade, where entry and exit are both on flips. 🔹A Winning Signal Example A winning bull signal forms when a bull flip occurs and, by the time the next bear flip occurs, the close at the bear flip is higher than the close at the bull flip. The probability label on the bull flip reflects the model's confidence at entry, while the outcome is confirmed at the next flip. 🔹A Losing Signal Example A losing signal forms when price fails to move in the signal's direction by the next opposite flip. For example, a bull flip where price immediately reverses and ends lower than entry when the next bear flip occurs. CANDLE FEATURES Candle features describe the shape and volume of the flip candle itself, compared against recent averages. These are the most direct, local features the model uses. All candle features are toggleable in the "Candle Features" input group and are normalized to a range centered at zero, where zero means the feature value matches the recent average. 🔹Body Body measures the size of the flip candle's body (the absolute distance from open to close) relative to the average body size over the recent lookback window. A value above zero means the flip candle had a larger body than recent candles, suggesting stronger conviction. A value below zero means the body was smaller than usual, suggesting indecision. The model learns whether strong-bodied flips tend to produce winning trends on this chart. 🔹Upper Wick Upper Wick measures the length of the upper wick (the distance from the body top to the high) relative to recent average upper wicks. Long upper wicks can indicate rejection at highs, while short upper wicks suggest price accepted the top of the candle cleanly. The model learns how upper wick behavior at flips correlates with outcomes. 🔹Lower Wick Lower Wick measures the length of the lower wick (the distance from the body bottom to the low) relative to recent average lower wicks. Long lower wicks can indicate rejection at lows, while short lower wicks suggest clean acceptance. This feature is disabled by default because it historically showed the weakest predictive signal across tested instruments. 🔹Range Range measures the total high-to-low range of the flip candle relative to the recent average range. A wide-range flip candle indicates volatility expansion, while a tight-range candle suggests contraction. The model learns whether flips during volatility expansion tend to perform differently from flips during contraction. 🔹Volume Volume compares the flip candle's volume to the average volume over the recent lookback window. Higher than average volume at a flip generally indicates stronger participation, while lower than average volume suggests weak conviction. The model learns how volume conviction at flips relates to trend outcomes. 🔹Delta Volume Delta Volume estimates the net buying versus selling pressure within the flip candle by sampling 1-minute lower timeframe bars. The indicator distributes each 1-minute bar's volume to either the bullish or bearish side based on whether that bar closed up or down, then outputs the net direction as a feature value from fully bearish to fully bullish. This gives the model a finer-grained view of what happened inside the flip candle beyond the aggregate close. TREND CONTEXT FEATURES Trend context features describe the market conditions leading into the flip, not just the flip candle itself. These are calculated from the lookback window before the flip. All trend context features are toggleable in the "Trend Features" input group. 🔹Momentum Momentum counts consecutive candle direction leading into the flip. Each bullish candle increments the streak in the positive direction, and each bearish candle increments it in the negative direction. The value is capped so very long streaks do not dominate. A strongly positive momentum value before a bull flip indicates buying was already building; a strongly negative value before a bull flip indicates a sharp reversal from recent selling. The model learns which regime tends to produce better bull outcomes. 🔹Pre-Flip Trend Pre-Flip Trend averages the signed candle bodies (close minus open) over the recent lookback window, normalized by the average range. This gives a broader picture of whether the market was drifting up, drifting down, or chopping sideways before the flip. Unlike momentum, which only counts direction, pre-flip trend captures the magnitude of the directional bias. 🔹ATR Slope ATR Slope compares the current ATR value to the ATR value from the lookback period ago. A positive slope means volatility is expanding into the flip, often associated with stronger follow-through. A negative slope means volatility is contracting, often associated with weaker signals. The model learns whether volatility expansion at the flip is a positive or negative factor for the specific instrument. 🔹Volume Trend Volume Trend compares the average volume of recent bars to the average volume of bars further back. A positive value means participation is increasing; a negative value means it is fading. This feature is disabled by default because it historically showed weak signal across tested instruments. 🔹ST Distance ST Distance measures how far price was from the SuperTrend line before the flip, normalized by ATR. A value near zero means the flip was a tight cross; a larger absolute value means price was well above or below the line and had to travel significantly to trigger the flip. The model learns whether tight crosses or aggressive breaks produce better outcomes. 🔹Trend Duration Trend Duration measures how many bars the previous trend lasted before flipping. Short previous trends might indicate choppy conditions where flips come and go quickly. Longer previous trends might signal genuine exhaustion at reversal. The model learns how previous trend length relates to the success of the current flip. TECHNICAL FEATURES Technical features bring in classic technical indicator readings at the flip point. These are toggleable in the "Technical Features" input group. 🔹RSI RSI (Relative Strength Index) is calculated with the same lookback as the other features and then centered around the 50 level. Values near the oversold end push the feature toward -1, values near the overbought end push it toward +1, and values near 50 are near zero. The model learns whether overbought or oversold RSI readings at flips correlate with different outcomes on the specific chart. On some instruments, flips at extreme RSI readings perform well; on others, they perform poorly. The model discovers this from the data. 🔹BB Position BB Position measures where price sits within Bollinger Bands at the flip. Price at the lower band gives a value of -1, price at the basis gives 0, and price at the upper band gives +1. The Bollinger Bands are calculated with the same lookback as the other features. This feature captures mean-reversion versus breakout context: a flip near the lower band is different from a flip near the upper band, and the model learns which band positions tend to precede successful trends. SEPARATE BULL AND BEAR MODELS The indicator maintains completely independent models for bull signals and bear signals. Each has its own set of weights, its own training dataset, and its own prediction logic. 🔹Why separate models? A single model that assumes features mean opposite things for opposite directions would be an oversimplification. For example, a long lower wick on a bull flip might indicate strong buyer defense, while a long lower wick on a bear flip might indicate weak sellers. These are different setups with different implications, and forcing them into one model with flipped signs would blur the signal. By training separate bull and bear models, the indicator gives each direction room to learn its own relationships. A feature that strongly predicts bull wins might be irrelevant or even negatively correlated for bear wins, and the separate models can capture this without interference. 🔹Implementation Each direction has its own array of signals and its own weights array. When a bull flip occurs, the bull signals array is updated and the bull model is retrained. When a bear flip occurs, the bear signals array and bear model are updated separately. The two models never share state, and their predictions are based only on their own training data. EXPONENTIAL DECAY WEIGHTING Not all training examples are equally relevant. Recent signals reflect current market conditions, while older signals may reflect regimes that no longer apply. The indicator addresses this with exponential decay weighting during training. 🔹How decay works Each resolved signal is assigned a weight based on its age. The newest resolved signal gets the highest weight (1.0 after normalization), and older signals get progressively smaller weights based on a decay factor. A decay factor close to 1 means old and new signals are weighted roughly equally. A decay factor close to 0 means recent signals dominate and old signals are effectively ignored. 🔹Why decay is important Markets change. A feature that strongly predicted wins six months ago might have weak or reversed predictive power now. Without decay weighting, the model would be slow to adapt to new conditions because old data would dilute the influence of recent outcomes. With decay weighting, the model naturally updates its weights to reflect the most recent dynamics while still using historical data to maintain stability. PROBABILITY LABELS AND FILTERING When a new flip passes the minimum sample gate, the indicator calculates the probability from the current feature values and the trained weights. This is displayed as a colored triangle label at the flip bar. 🔹Label Appearance Bull flip labels appear below the bar as upward-pointing green triangles with the probability percentage inside. Bear flip labels appear above the bar as downward-pointing red triangles with the probability percentage inside. The colors are partially transparent so the labels do not obscure price action. 🔹High Probability Example A high probability label means the model found the current flip's features similar to past flips that resolved as wins. For example, a bull flip with strong pre-flip momentum, expanding volatility, positive delta volume, and price well above the SuperTrend line might receive a probability above 65% if those conditions have historically preceded successful bull trends. 🔹Low Probability Example A low probability label means the model found the current flip's features similar to past flips that resolved as losses. Low probability signals might share characteristics with flips that occurred during choppy conditions or weak momentum regimes. 🔹Min Probability Filter A Min Probability input lets users hide labels below a confidence threshold. Setting it to 0 shows every signal. Setting it to 60 only shows labels where the model estimates at least 60% probability of a profitable outcome. This filter only affects visual display and alerts. The SuperTrend line and color change still appear on every flip, and the model still trains on every signal regardless of the filter. 🔹Interpreting the probability The probability represents the model's estimate that the current flip will resolve profitably by the next opposite flip, based on how similar past flips performed. A 70% label does not guarantee a win; it means the model finds this flip's conditions more similar to past winners than past losers. Probabilities should be interpreted relative to the base rate (the overall percentage of past flips that won) rather than as absolute guarantees. VISUAL CUSTOMIZATION The indicator includes several visual elements that help traders see the SuperTrend state and the probability labels clearly. 🔹Gradient-Colored SuperTrend The SuperTrend line is plotted with a color gradient based on how far current price has moved from the line since the most recent flip. The color intensifies as price extends further in the trend direction, giving a visual indication of how developed the trend is at any point. Fills between the body midpoint and the SuperTrend line reinforce this gradient effect. 🔹Momentum Dots An optional momentum dots display overlays circles on the SuperTrend line with colors that shift between yellow and the trend color (green or red) based on price position. This provides a secondary visual cue for trend strength. The Enable Momentum Dots input toggles this display on or off. INPUTS 🔹SuperTrend Factor: ATR multiplier used in the SuperTrend calculation. Default is 3.0. Increasing this value makes the SuperTrend less sensitive and produces fewer, wider flips. Decreasing it makes the SuperTrend more sensitive and produces more frequent flips. ATR Period: Number of bars used in the ATR calculation. Default is 10. Larger periods smooth the ATR, while smaller periods make it more reactive. 🔹Display Enable Momentum Dots: Toggles the momentum dots overlay on the SuperTrend line. Default is on. 🔹Filters Min Probability %: Minimum probability required for a signal label to appear on the chart. Default is 0 (show all signals). Setting this to a higher value hides lower-confidence signals. 🔹Candle Features Body: Enable body size feature. Default is on. Upper Wick: Enable upper wick feature. Default is on. Lower Wick: Enable lower wick feature. Default is off. Range: Enable candle range feature. Default is on. Volume: Enable volume feature. Default is on. Delta Vol: Enable delta volume feature using 1-minute LTF data. Default is on. 🔹Trend Features Momentum: Enable consecutive candle direction feature. Default is on. Pre-Flip Trend: Enable average directional body feature. Default is on. ATR Slope: Enable volatility expansion feature. Default is on. Volume Trend: Enable volume building feature. Default is off. ST Distance: Enable distance from SuperTrend line feature. Default is on. Trend Duration: Enable previous trend length feature. Default is on. 🔹Technical Features RSI: Enable RSI feature. Default is on. BB Position: Enable Bollinger Band position feature. Default is on. ALERTS The indicator includes alert conditions for the following events: Bull Flip: Fires on a confirmed bullish SuperTrend flip that passes the Min Probability filter. Bear Flip: Fires on a confirmed bearish SuperTrend flip that passes the Min Probability filter. Users can configure alerts from the TradingView alerts menu and choose which condition to subscribe to. IMPORTANT NOTES 🔹Non-Repainting Behavior Signals are detected and probability labels are calculated on the bar where the SuperTrend flip confirms. The probability value is based only on feature values at the time of the flip and the model weights as of that bar. The outcome label used for training is only recorded after the next opposite flip has occurred, so the current bar's prediction never uses future data. 🔹Probability Interpretation The probability represents the model's learned estimate from past data on the current chart. It is not a guaranteed win rate. Markets can shift in ways the model has not yet seen, and sample size is always a limitation. Probabilities should be used as one input among many in a trading decision, not as a standalone signal. UNIQUENESS The SuperTrend Logistic Regression indicator takes a distinct approach to SuperTrend enhancement. Rather than altering the SuperTrend calculation itself, it preserves the classic SuperTrend behavior and adds a statistical layer on top that scores each flip independently. Every flip is still detected, so users never miss signals; the probability only determines what gets visually emphasized. The logistic regression implementation uses proper gradient descent with L2 regularization, weight clipping, and exponential decay weighting, making the training process stable and adaptive. Weights persist across bars and are refined with each new resolved signal rather than being recalculated from scratch. Separate bull and bear models learn direction-specific feature relationships independently, avoiding the oversimplification of assuming features have opposite meanings for opposite directions. All 14 features are toggleable, spanning candle structure, trend context, and technical indicators, so users can customize which market characteristics feed into the model. The probability is backed by a concrete, testable definition: the likelihood that the current flip will resolve profitably by the time the next opposite flip occurs, based on how similar past flips performed. This matches how SuperTrend is naturally traded on flip-to-flip cycles. Minimum sample gating ensures that probability labels only appear once the model has enough training data to make meaningful predictions, preventing misleading early signals. Together, these choices make the indicator a disciplined, data-driven extension of SuperTrend that adapts to each instrument and timeframe it runs on, rather than applying a one-size-fits-all scoring system.
Pine Script® göstergesi
fluxchart tarafından
Iterative Epanechnikov ChannelThe Iterative Epanechnikov Channel is a kernel-based smoothing and state estimation framework that applies an Epanechnikov kernel regression to price data, combined with a rolling standard deviation envelope to construct adaptive dynamic channel boundaries. Unlike kernels with infinite support that allow distant historical observations to influence the estimate, the Epanechnikov kernel uses a compact weighting structure that strictly limits influence to a finite window. This ensures that only relevant, recent price information contributes to the regression, resulting in a more localized and structurally responsive estimate of price behavior. The resulting channel is structurally responsive while remaining statistically efficient, making it particularly effective for tracking short-to-intermediate trend development, identifying localized overextension, and adapting quickly to evolving conditions. Its primary utility is as a state estimation and structural tracking tool for price behavior, rather than a persistence-weighted regime model. TRADING USES The Epanechnikov Channel is best interpreted as a localized structural filter; within a multi-model framework, it captures the immediate structural state of price, helping distinguish early transitions, active trends, and short-term disequilibrium relative to slower, memory-weighted estimators. Trend Detection The channel basis line (Epanechnikov smoothed price) provides a responsive representation of underlying market direction. Sustained movement above or below the basis reflects directional continuation, while rapid shifts in the basis can indicate emerging changes in trend. Structural Responsiveness Due to the compact support of the Epanechnikov kernel, only recent price data contributes to the estimate. This produces sharper turning points and faster adaptation to new information, allowing the channel to respond efficiently to changes in market structure. Mean Reversion Context Because the estimator is more localized, price interacts with the channel boundaries more frequently. These interactions represent short-term deviations from the estimated state: - Upper band: localized overextension - Lower band: localized underextension This makes the channel well-suited for mean reversion frameworks and volatility-based entry timing. State Estimation The channel functions as a continuous estimator of market state: - The basis represents the inferred local price state - The envelope represents dynamic volatility dispersion around that state Compared to heavy-tailed kernels, the Epanechnikov-based state estimate is more sensitive to current conditions and less influenced by distant history, providing a clearer view of present market structure. Volatility & Risk Context The rolling standard deviation envelope expands and contracts based on realized volatility, providing a contextual risk framework. Wider channels indicate increased uncertainty and dispersion, while tighter channels indicate compression and lower variance conditions. THEORY The Epanechnikov kernel is a quadratic, compact-support kernel used in Nadaraya–Watson nonparametric regression, introduced by V. A. Epanechnikov (1969, Non-Parametric Estimation of a Multivariate Probability Density, Theory of Probability & Its Applications) as the mean squared error–optimal bounded kernel; in this implementation it is applied causally (non-repainting) and centered at the current bar using only historical data, with the original startAtBar offset removed to maintain proper kernel alignment with the estimation point. It is defined as: K(u) = 3/4 (1 − u²), for |u| ≤ 1 Where: ---> u represents normalized distance from the current observation ---> ℓ (lookback) defines the window over which the kernel operates Unlike Gaussian kernels, which apply exponentially decaying weights over an infinite range, the Epanechnikov kernel assigns zero weight to all observations outside its finite support. This produces a strictly localized estimator that is both computationally efficient and statistically optimal in a mean squared error sense among bounded kernels. Because the kernel is centered on the current observation and evaluated using only past data, the implementation remains causal and non-repainting while preserving the essential structure of kernel regression. The rolling standard deviation complements this by measuring dispersion around the estimated state, forming a volatility-adaptive envelope. Rather than acting as a strict statistical confidence interval, it provides a dynamic representation of market expansion and contraction. The Epanechnikov kernel is a localized smoothing estimator rather than a structural similarity model, thus dispersion is defined using price-based volatility rather than kernel-weighted variance, providing a stable and interpretable envelope consistent with its role as a reactive state estimator. The iterative implementation processes data sequentially (bar-by-bar), ensuring computational efficiency and making the indicator suitable for real-time use without repainting. CALIBRATION Calibration determines the balance between responsiveness, noise, and structural clarity. Length (Lookback) - Lower (8–16): More responsive, increased sensitivity to short-term structure - Medium (20–40): Balanced for swing trading and intermediate regimes - Higher (50–64+): Smoother output, reduced noise, slower response to turning points Smoothing Mode (Single vs Double Pass) Controls the tradeoff between responsiveness and stability: Single Pass: - Pure Epanechnikov regression - Maximum responsiveness - Faster detection of structural changes - Increased sensitivity to noise Double Pass: - Applies the kernel regression twice - Reduces variance and smooths fluctuations - Produces cleaner structural output - Introduces additional lag This parameter allows users to tune the indicator based on whether early signal detection or stability is preferred. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices.
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SensitiveSuit tarafından
Sigma Channel [JOAT]JOAT Sigma Channel Introduction JOAT Sigma Channel is an open-source regression-based channel overlay built to identify the best-fit directional corridor across a search range of candidate windows. It is designed to show whether price is traveling normally inside a fitted corridor, stretching away from it, reclaiming it, or moving into stress territory. The problem it solves is model selection. A fixed-length regression channel can look excellent in one regime and poor in the next. If the market accelerates, slows down, or shifts its dominant drift horizon, a static window becomes less useful. Sigma Channel solves that by searching a range of possible windows and selecting the strongest available fit. That fit becomes the active corridor. Around it the script builds multiple sigma tiers. These tiers create a richer price-state map than a single upper and lower band. The result is a more useful framework for reading acceptance, stretch, reclaim, and exhaustion. Core Concepts 1. Multi-Window OLS Search The script evaluates multiple candidate regression windows and scores them by fit quality. for len = minWindow to maxWindow by stepWindow = f_model(len) 2. Sigma-Tier Envelope Stack The fitted centerline is surrounded by inner, core, outer, and stress layers. 3. Slope and Quality Diagnostics The chosen model exposes both slope and fit quality. 4. Reclaim and Acceptance States The script identifies when price reclaims or accepts back inside the corridor after extension. 5. Stress-State Shading The most extreme statistical layer is visually emphasized. 6. Forward Projection The active model projects forward to keep the corridor useful at the right edge. 7. Residual Awareness Residual behavior helps judge whether the active model remains representative. 8. Gradient Candle Context Bar coloring transitions with the current statistical state. Features Adaptive regression search: scans multiple windows instead of using one fixed length Inner, core, outer, and stress bands: multiple sigma tiers for normal and abnormal travel Slope-aware corridor logic: distinguishes positive and negative drift Reclaim and acceptance events: return-to-channel behavior is identified Stress shading: abnormal statistical extension is highlighted Forward projection: the active corridor extends to the chart edge Gradient candle tinting: bar color reflects the current z-state Dashboard: slope, quality, window, and deviation state are summarized Input Parameters Model Search: Minimum Window Maximum Window Window Step Minimum R-Squared Projection Bars Deviation Envelope: Inner Sigma Core Sigma Outer Sigma Stress Sigma Filter Stack / Visual System: Bias EMA ATR Length Momentum RSI Use EMA Bias Gate Use RSI Gate Use Volume Gate Tint Bars Show Projection Show Dashboard How to Use This Indicator Step 1: Check the active slope and whether the corridor is upward, downward, or balanced. Step 2: Check fit quality before trusting the active statistical path. Step 3: Read whether price is inside the core corridor, at the outer layer, or in stress territory. Step 4: Watch reclaims back into the channel after extension. Step 5: Use the projection to organize the near-future chart space. Indicator Limitations The selected best-fit window can change as the market evolves Linear regression becomes less representative in strongly nonlinear markets Statistical extension does not guarantee immediate reversal The indicator measures deviation from a model, not directional certainty Originality Statement This script is original in the way it combines adaptive regression selection, multi-tier sigma structure, reclaim logic, stress-state shading, and forward projection into one corridor framework. The goal is not just to draw a regression channel. The goal is to keep the active model responsive while preserving statistical context. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Regression fit and statistical deviation do not predict future price with certainty. Markets can remain extended or break away from the active fit entirely. Best Use Cases Reading whether price is behaving normally inside a fitted directional corridor Identifying statistical stretch and stress states Watching for reclaim behavior after extension Combining statistical deviation with separate structure or liquidity analysis Interpretation Notes The channel should be trusted more when fit quality is stronger and the slope is clear. Core travel is generally more normal than outer-band travel. Stress states are especially useful when they coincide with separate structural or participation warnings. The projection should be read as a continuation of the current best-fit model, not as a forecast guarantee. Publication Notes This script is intended to be published with a clean chart where the corridor, the active slope, and at least one reclaim or extension state are obvious. The example chart should help the viewer understand how the band hierarchy works. Avoid combining it with unrelated overlays in the publication image. -Made with passion by jackofalltrades
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officialjackofalltrades tarafından
[ A L P H A X ] Slope Spectrum ProAlphaX Slope Spectrum Pro — Multi-Period Regression Oscillator, Adaptive Signal Line, Momentum Acceleration, Statistical Regime Detection, Classic & Hidden Divergence & Conviction Dashboard AlphaX Slope Spectrum Pro is a professional-grade trend momentum oscillator built on a proprietary multi-period linear regression engine that scans across an entire range of lookback periods simultaneously and synthesizes them into a single adaptive oscillator value per bar. Rather than relying on a fixed-period momentum calculation, Slope Spectrum Pro measures the statistical slope of price across every period in the scan range and combines them using inverse-variance weighting — giving more influence to periods whose regression fits are more consistent, and less to those where price has been noisy. The result is a momentum oscillator that is simultaneously responsive and robust. On top of this engine sits a complete analytical layer: an adaptive signal line that changes speed with market conditions, a momentum acceleration histogram, statistical regime detection bands, classic and hidden divergence detection, regime-filtered entry signals with main chart overlay, and a live 10-row conviction dashboard. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📸 Visual Overview AlphaX Slope Spectrum Pro in the oscillator pane — gradient-colored slope line with gradient fill, acceleration histogram in the background, adaptive signal line, regime threshold bands, divergence markers, bull/bear signal dots, main chart triangles, and the conviction dashboard ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 The Slope Engine — Multi-Period Regression Core At the foundation of Slope Spectrum Pro is a multi-period linear regression scanner . On every bar, the engine runs a complete set of linear regressions across every period from your configured minimum to maximum, stepping at your chosen interval. With default settings of Min Period 10, Max Period 100, and Step Size 5, this means 19 independent regressions are computed and synthesized per bar. Each regression fits a straight line to the logarithm of price over the lookback window and extracts the slope of that fit — a dimensionless measure of directional momentum at that timescale. Positive slope means upward momentum. Negative slope means downward momentum. Steeper slope means stronger momentum. Inverse-Variance Weighting: When Inverse-Variance Weighting is enabled, each regression period's slope is weighted by the inverse of its residual variance — how consistently price tracked the regression line over that lookback. A period where price followed its regression cleanly gets high weight. A period where price bounced erratically around the fitted line gets low weight. The result is that cleaner, more consistent trend periods contribute more to the final oscillator value than noisy, choppy periods — the oscillator becomes naturally less sensitive to random price fluctuations and more responsive to genuine directional momentum. What the oscillator value means: A value above zero means the weighted regression slope is net positive — more periods are trending upward than downward across the scanned range. A value below zero means the weighted slope is net negative — dominant downward trend momentum. The magnitude reflects how steep the consensus slope is. A large positive value means strong, consistent upward momentum. A value near zero means flat or contested momentum. The oscillator is plotted as a gradient-colored line that transitions dynamically from bear red to bull green based on its rolling 200-bar min/max range — the stronger the current momentum relative to recent history, the brighter the color. A gradient fill between the oscillator line and the zero line reinforces the directional bias visually. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 〰 Adaptive Signal Line The signal line tracks the slope oscillator — but instead of a fixed-period EMA, Slope Spectrum Pro uses an adaptive alpha-based EMA that changes its effective length based on current market conditions. How it adapts: The adaptation measures trend strength as how many standard deviations the current oscillator value is from its recent mean. When the oscillator is far from its mean — a clear, developing trend — the signal line shortens and reacts faster, following the oscillator closely. When the oscillator is near its mean — a ranging or choppy condition — the signal line lengthens, smoothing out minor oscillations and reducing false crossovers. In a strong trend: the signal is tight. Crossovers happen quickly and reflect real momentum shifts. In chop or range: the signal is loose. It takes a more significant oscillator move to produce a crossover, filtering out noise automatically. The current effective signal length is shown live on the dashboard as EMA~N — you can see exactly how tight or loose the signal is at any moment. Adaptive speed can be toggled off for a consistent fixed-length signal. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ Momentum Acceleration Histogram Behind the oscillator line, a background acceleration histogram plots the rate of change of the slope oscillator — how fast momentum is building or fading right now. Green columns — acceleration is positive. The slope oscillator is increasing. Momentum is building in the bullish direction. Red columns — acceleration is negative. The slope oscillator is decreasing. Momentum is fading or building in the bearish direction. Reading the acceleration alongside the main oscillator gives you a two-layer picture: Oscillator above zero + green acceleration = strengthening bull momentum . The best time to enter or hold long. Oscillator above zero + red acceleration = bull momentum peaking and fading . Consider reducing exposure or preparing to exit. Oscillator below zero + red acceleration = strengthening bear momentum . Best time to hold or enter short. Oscillator below zero + green acceleration = bear momentum fading . Watch for a potential reversal or crossover signal. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Statistical Regime Detection Slope Spectrum Pro automatically classifies the current market environment into one of three regimes using a rolling statistical framework built from the oscillator's own mean and standard deviation: Bull Regime — the oscillator is above the upper band (mean + N standard deviations). Background tints subtly green. Only bullish signals fire when regime filtering is enabled. Bear Regime — the oscillator is below the lower band (mean − N standard deviations). Background tints subtly red. Only bearish signals fire when regime filtering is enabled. Range Regime — the oscillator sits between the two bands. Background is a very faint neutral gray. No directional regime is confirmed. Regime-filtered signals are suppressed entirely. Thresholds are calculated as oscillator mean ± (standard deviation × sensitivity multiplier) over the configured lookback. Increasing the sensitivity multiplier raises the bar required to enter Bull or Bear regime — only the strongest trend episodes qualify. Lowering it makes transitions more frequent. This system directly controls signal quality when Regime-Filtered Signals is enabled — crossovers opposing the active regime direction are silently blocked before they ever reach the chart. Regime bands on the oscillator — Bull threshold above, Bear threshold below, background tint reflecting the current zone ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ Divergence Engine — Classic and Hidden Slope Spectrum Pro detects four divergence types by comparing confirmed oscillator pivot highs and lows against confirmed price pivot highs and lows. All detections are confirmed on bar close only — no repainting. Classic Divergences — Reversal Signals: Classic Bull Divergence (DIV ▲) — price makes a lower low while the slope oscillator makes a higher low. Selling pressure is weakening even though price is still falling. A reversal upward may be building. Classic Bear Divergence (DIV ▼) — price makes a higher high while the slope oscillator makes a lower high. Buying pressure is weakening even though price is still rising. A reversal downward may be building. Hidden Divergences — Trend Continuation Signals: Hidden Bull Divergence (H ▲) — price makes a higher low while the oscillator makes a lower low. Price held higher ground during the pullback — the uptrend is intact and continuation upward is probable. Hidden Bear Divergence (H ▼) — price makes a lower high while the oscillator makes a higher high. Price failed to rally as high as before even as the oscillator rebounded — the downtrend is intact and continuation downward is probable. Classic divergences warn of potential reversals. Hidden divergences confirm that pullbacks within a trend are likely to resume. Both are marked on the oscillator at the pivot bar with compact DIV or H labels. The divergence pivot lookback is configurable. Hidden divergence can be toggled independently from classic divergence. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 Entry Signals — Oscillator and Main Chart Overlay Crossover signals fire when the slope oscillator crosses above or below the adaptive signal line, confirmed on bar close: Bull Signal (●) — oscillator crossed above signal line. A small green dot appears on the oscillator. Bear Signal (●) — oscillator crossed below signal line. A small red dot appears on the oscillator. Regime filtering: When enabled, bull crossovers during a Bear Regime are blocked. Bear crossovers during a Bull Regime are blocked. Only signals aligned with the current statistical regime are displayed — this single filter eliminates a significant category of false signals. Main chart overlay: When Overlay Candle Color is enabled: A ▲ green triangle appears below the bar on the main chart at every bull signal. A ▼ red triangle appears above the bar on the main chart at every bear signal. Candles on the main chart are colored using the same gradient as the oscillator — chart candles reflect the current slope momentum state at a glance. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📋 Live Conviction Dashboard A real-time 10-row readout of the oscillator's full internal state, updating on every bar close: SAMPLES — number of regression periods computed per bar based on Min, Max, and Step settings. MODE — INV-VAR WEIGHTED or SIMPLE AVERAGE aggregation. OSC VALUE — live slope oscillator value to six decimal places, green above zero, red below. REGIME — ▲ BULL ZONE, ▼ BEAR ZONE, or — RANGING. Row highlighted in the corresponding color. MOMENTUM — ▲ BUILDING (acceleration positive) or ▼ FADING (acceleration negative). Row highlighted accordingly. SIGNAL — ▲ BULL CROSS / ▼ BEAR CROSS when a crossover fired this bar, or ▲ ABOVE SIG / ▼ BELOW SIG for ongoing position. SIG SPEED — current effective signal length as EMA~N, showing how tight or loose the adaptive signal is in real time. DIVERGENCE — highest-priority active divergence: ▲ BULL DIV, ▼ BEAR DIV, ▲ HIDDEN BULL, ▼ HIDDEN BEAR, or — NONE. Row highlighted when active. CONVICTION — a 0–4 confluence score. Each of the following adds 1 point: regime and oscillator sign agree; regime and acceleration agree; regime and signal side agree; any divergence is active. Score labels — LOW / MIXED (0–1), MODERATE (2), HIGH CONVICTION (3), MAX — ALL ALIGNED (4). OSC (sigma) — the oscillator value expressed as standard deviations from its recent mean. Shows statistically how far momentum has moved from neutral — +2.5s means 2.5 standard deviations into bull territory. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ Key Features 🔬 Multi-period regression engine — 19 independent regressions per bar (default) synthesized into one adaptive oscillator value ⚖ Inverse-variance weighting — consistent regression periods carry more weight; noisy periods are automatically discounted 〰 Adaptive signal line — effective EMA length speeds up in trends, slows in chop; current length shown live as EMA~N ⚡ Momentum acceleration histogram — rate-of-change of the oscillator, showing whether momentum is building or fading right now 📊 Statistical regime detection — three-zone classification (Bull / Bear / Range) using rolling mean ± standard deviation bands 🔒 Regime-filtered signals — crossovers opposing the current statistical regime are automatically suppressed ◆ Classic divergence — bull and bear reversal divergence between price pivots and oscillator pivots, bar-close confirmed ◆ Hidden divergence — trend continuation signals when price and oscillator diverge in the trend direction 🎯 Main chart overlay — bull/bear triangles on price chart and gradient candle coloring reflecting live slope momentum 🎨 Dynamic gradient oscillator color — live transition from bear to bull based on rolling 200-bar normalization 📋 Live 10-row conviction dashboard — samples, mode, osc value, regime, momentum, signal, signal speed, divergence, conviction score, sigma reading 🏆 4-point conviction scoring — real-time confluence count across regime, acceleration, signal, and divergence alignment 🔔 10 alert conditions — crossovers, classic and hidden divergences, confluence long/short, zero line crosses 🎨 14 user-configurable color inputs — every visual element independently themeable with AlphaX brand defaults ✅ Confirmed on bar close — no repainting on any signal, divergence, or regime transition ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙ Settings Reference Slope Engine Max Period — upper bound of the regression scan range (default: 100) Min Period — lower bound of the scan range (default: 10) Step Size — increment between scanned periods (default: 5). Smaller = more samples, smoother oscillator. Inverse-Variance Weighting — weight each period by its regression consistency (default: on) Source — price input for all regressions (default: close) Adaptive Signal Line Base Signal Length — signal EMA length in neutral conditions (default: 7) Adaptive Speed — toggle adaptive EMA alpha on or off Momentum Acceleration Show Acceleration Histogram — toggle the background column histogram Acceleration Smoothing — EMA smoothing period for acceleration (default: 3) Regime Detection Show Regime Bands — toggle threshold lines and background tinting Regime Lookback — rolling window for mean and standard deviation (default: 200) Regime Sensitivity (σ×) — standard deviation multiplier for thresholds (default: 0.5) Divergence Engine Show Divergences — toggle all divergence detection Divergence Pivot Length — bars on each side to confirm a divergence pivot (default: 5) Show Hidden Divergences — toggle hidden divergence independently Entry Signals Show Crossover Signals — toggle oscillator signal dots Regime-Filtered Signals — suppress signals opposing the current regime Overlay Candle Color — toggle main chart triangles and gradient candle coloring Dashboard Show Dashboard — toggle the dashboard panel Position — Top Right, Top Left, Bottom Right, Bottom Left Theme Bull Primary / Bright / Dim — three shades of the bullish color family Bear Primary / Bright / Dim — three shades of the bearish color family Neutral / Neutral Light — neutral and secondary text colors Accel Bull / Accel Bear — acceleration histogram column colors Divergence Bull / Divergence Bear — classic divergence marker colors Hidden Div Bull / Hidden Div Bear — hidden divergence marker colors ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 How to Trade with AlphaX Slope Spectrum Pro — Step by Step Step 1 — Establish the regime and directional bias Check the REGIME row. ▲ BULL ZONE = the market is in a statistically confirmed trend upward. ▼ BEAR ZONE = confirmed trend downward. — RANGING = no directional edge. Only trade in the direction of the active regime. Check OSC (sigma). A reading above +2s means deep bull territory. Below −2s means deep bear territory. Near zero = flat momentum, no edge. Step 2 — Read acceleration for entry timing In a Bull Regime, wait for the acceleration histogram to turn green (▲ BUILDING on the dashboard). This means the slope oscillator is accelerating — momentum is growing, not just present. The ideal entry timing: Bull Regime + oscillator above zero + acceleration building + oscillator above signal line. Entering while acceleration is red (▼ FADING) in a bull regime risks entering as the current wave is losing steam. Step 3 — Enter on a signal crossover A bull signal dot (oscillator crosses above signal line) with regime filtering enabled is your entry trigger — counter-trend crossovers are already blocked automatically. If Overlay is enabled, the ▲ triangle on the main chart confirms the exact entry bar. The faster the adaptive signal (lower EMA~N), the tighter the crossover timing. In strong trends the signal shortens and reacts more quickly. Step 4 — Upgrade conviction with divergence A bull signal crossover occurring simultaneously with or just after a Classic Bull Divergence (DIV ▲) = high-probability reversal setup. A bull signal crossover during an uptrend following a Hidden Bull Divergence (H ▲) = high-probability trend continuation setup. Check CONVICTION. Score of HIGH CONVICTION (3) or MAX — ALL ALIGNED (4) means multiple independent factors agree. These are the setups to prioritize. Step 5 — Exit when momentum confirms the move is ending Acceleration histogram turning red (▼ FADING) while you are long = first sign the current wave is losing force. Begin monitoring for exit. A bear signal crossover (oscillator crosses below signal line) = exit trigger. A regime shift from Bull Zone to Ranging or Bear Zone = structural move is over. Exit and reset. A Classic Bear Divergence appearing while you are in a long position = warning. Price is printing new highs the oscillator is not confirming. Reduce size or prepare to close. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔔 Alert Conditions SSP — Bull Crossover — oscillator crossed above the adaptive signal line SSP — Bear Crossover — oscillator crossed below the adaptive signal line SSP — Classic Bull Divergence — price lower low, oscillator higher low confirmed SSP — Classic Bear Divergence — price higher high, oscillator lower high confirmed SSP — Hidden Bull Divergence — price higher low, oscillator lower low (trend continuation) SSP — Hidden Bear Divergence — price lower high, oscillator higher high (trend continuation) SSP — CONFLUENCE LONG — bull crossover occurring with bull or hidden bull divergence simultaneously SSP — CONFLUENCE SHORT — bear crossover occurring with bear or hidden bear divergence simultaneously SSP — Zero Line Cross Up — oscillator crossed above zero (macro bull momentum shift) SSP — Zero Line Cross Down — oscillator crossed below zero (macro bear momentum shift) All alert messages include {{ticker}} and {{interval}} for webhook integration. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 👥 Who This Is For 🧠 Systematic and quantitative traders — the regression engine, inverse-variance weighting, and statistical regime framework provide a mathematically grounded, objective momentum reading with no arbitrary indicator parameters to tune 📈 Trend traders on any instrument and timeframe — the multi-period scan adapts naturally to any market's momentum characteristics without manual recalibration 🔍 Divergence traders — four divergence types covering both reversals and trend continuations, all confirmed on bar close ⚡ Momentum traders — the acceleration histogram adds a layer no standard oscillator provides: not just where momentum is, but whether it is growing or shrinking right now 🎯 Precision entry traders — regime filtering, adaptive signal speed, and the conviction score ensure signals are only shown when multiple independent conditions agree simultaneously 🎨 Traders who customize their charts — 14 fully user-configurable color inputs, compatible with any chart theme 🔔 Alert-driven traders and bot operators — 10 alert conditions including a dedicated confluence alert for maximum-conviction setups ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📝 Notes All signals are confirmed on bar close only. Slope Spectrum Pro does not repaint. The oscillator value is in units of log-price per bar. The absolute value is very small (typically 0.000001 to 0.001 range) — what matters is the sign, the direction of change, and where it sits relative to regime thresholds and zero. Wider Min–Max scan ranges produce a smoother oscillator capturing broader trend consensus. Narrower ranges are more reactive to short-term changes. Smaller Step Size = more periods sampled per bar = smoother result. Regime Sensitivity is the most impactful setting after the scan range. At 0.5σ (default) the regime bands trigger relatively frequently. At 1.0–1.5σ, only very strong trend episodes qualify — signals become rarer but higher quality. On very low timeframes the nested regression loop may be computationally intensive. Default settings are optimized for timeframes from 1 minute upward on standard instruments. All theme color defaults are designed for dark chart backgrounds. Adjust Theme inputs if using a light background. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠ Disclaimer This indicator is a technical analysis and visualization tool intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument. All signals are generated from historical and real-time price data using mathematical calculations — their accuracy or profitability is not guaranteed. Past performance does not guarantee future results. Always conduct your own analysis, use proper risk management, and consult a licensed financial advisor before making any trading decisions. The author accepts no responsibility for any losses incurred from the use of this indicator. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Built for traders who want to know not just where momentum is — but how it got there, how strong it is, and whether it is still building.
Pine Script® göstergesi
AlphaX-Trade tarafından
3
Bitcoin RSI Channel (2W, M)Bitcoin RSI Channel (2W, M) The Bitcoin RSI Channel is a macro-analytical tool designed to identify long-term cyclical tops and bottoms of Bitcoin by applying linear regression modeling to the Relative Strength Index (RSI). Traditionally, RSI levels like 70 and 30 are used as static markers for overbought and oversold conditions. However, as Bitcoin matures and its volatility dampens, its cyclical RSI peaks and troughs have historically followed a descending trajectory. This script accounts for that "fading" volatility by using mathematically fitted trendlines that track the actual historical extremes of Bitcoin’s momentum. Main Objectives Curve Fitting: Plots mathematically modeled linear regression lines through historical RSI peaks and bottoms, ensuring the channel remains as close as possible to the actual turning points of previous cycles. Normalized View: Provides an optional "Normalized" mode where the dynamic channel is flattened into a 0-1 "Risk Metric" for easier comparison between cycles. Main Chart Visualization: Directly projects overbought and oversold zones onto the price chart via plot coloring, allowing for a seamless confluence of price action and momentum analysis. Cycle Forecasting: Designed as a high-timeframe tool to assist in predicting global market tops and generational buying opportunities. IMPORTANT: Timeframe & Methodology Please note that the regression lines in this script are specifically modeled for the 2-Week (2W) and Monthly (1M) timeframes. These high timeframes were chosen because they significantly reduce market noise, making them superior for identifying global cycle shifts. The coefficients used in the code were derived from an approximation (fitting) of data points from 2011 through 2023. Recommendation: Always wait for the candle to close on the 2W or Monthly timeframe to confirm a signal. Intra-candle fluctuations can be volatile and may provide premature signals before the period is finalized. How to Use Identifying Extremes: When the RSI enters the green "Buy Zone" or the red "Sell Zone," it historically indicates that Bitcoin has reached extreme oversold or overbought levels. These periods suggest the proximity of a potential cyclical bottom or top. Visualizing on Price: These macro shifts are intuitively reflected through the price overlay on the main chart, providing immediate visual feedback on the current stage of the market cycle. Strategic Caution: These zones have not been infallible in the past, and this indicator should not be used in isolation. Always seek confluence with other technical or fundamental analysis before making trading decisions. User Tips Clean View: You can hide the Bitcoin price bars on your main chart (via the Chart Settings) to focus entirely on the risk-based coloring provided by the indicator overlay. Overlay Toggle: If you prefer to keep the indicator purely in its own pane, you can disable the "Plot on Main Pane" option in the script settings. Settings & Parameters View Mode: Switch between "Fit Lines" (standard RSI inside a diagonal channel) and "Normalized" (RSI converted into a Risk Metric). Sell/Buy Zone Levels: Adjustable thresholds (default 0.90 and 0.10) to define your personal risk appetite for overbought and oversold conditions. Plot on Main Pane: Toggle to enable/disable price bar colorization based on the current Risk Metric. Limitations & Disclaimer BTC Specificity: This indicator is purpose-built for Bitcoin. While you can load it on other symbols, the indicator pane will always calculate based on INDEX:BTCUSD data to maintain its cyclical logic. The main chart overlay, however, will color the plot of whatever instrument you are currently viewing. Fixed RSI Optimization: The regression model is strictly optimized using the default RSI settings (14-period, Source: Close). The linear regression lines were fitted specifically to these values; therefore, the ability to modify the RSI length or source has been intentionally omitted from the settings. Changing these parameters would render the channel irrelevant, as the trendlines would no longer align with historical market extremes. Static Modeling: The regression coefficients are "hardcoded" based on historical peaks and troughs up to 2023. As the market evolves, future cycles may require new approximations to account for shifting volatility. No Guarantees: Mathematical models describe the past but do not guarantee the future. There is no guarantee that Bitcoin will reach the upper or lower boundaries in any given cycle, or that it will stay within them. Use this tool as one part of a comprehensive trading strategy.
Pine Script® göstergesi
ChartNChill tarafından
Regression Deviation Channel [JOAT]Regression Deviation Channel Introduction The Regression Deviation Channel is an institutional-style statistical trend and execution framework built around segmented regression, deviation envelopes, premium/discount zoning, breakout qualification, and risk mapping. Instead of acting like a plain moving-average channel, it models price through a best-fit regression path, measures dispersion with RMSE, then classifies where price is trading inside that structure: discount, equilibrium, or premium. This version is designed to feel more like a desk-grade directional map than a simple overlay. It combines a frozen regression segment, internal band hierarchy, confidence scoring, Supertrend stack alignment, breakout detection, and ATR-based trade mapping into one visual structure. The goal is not just to show where price is, but whether the current move is balanced, compressed, expanding, or resolving. Why This Indicator Exists Most channels are too simple. They show boundaries but do not explain what price is doing inside those boundaries. This indicator was built to solve that by combining: Segmented Regression: Tracks the current directional price path with a proper best-fit slope Deviation Architecture: Uses RMSE to define statistically meaningful channel width Premium / Discount Zoning: Splits the channel into expensive, fair value, and cheap territory Breakout Qualification: Scores breakout quality using slope, participation, structure, and location Trend Stack Context: Adds Supertrend alignment to distinguish strong directional pressure from noise Trade Mapping: Builds clean ATR-based stop and multi-target projections after confirmed breaks The result is a regression channel that does more than draw lines. It gives context, bias, execution framing, and visual hierarchy. Core Components Explained 1. Segmented Regression Engine = f_ols(winLen) basisVal = intercept + slope * float(barsInSeg - 1) upperVal = basisVal + rmse * multiplier lowerVal = basisVal - rmse * multiplier The core engine uses manual ordinary least squares regression to calculate the channel basis. Once the segment matures, the regression values are frozen and projected forward until price resolves beyond the envelope. This “freeze and resolve” behavior keeps the channel visually stable instead of constantly shifting every bar. 2. RMSE Deviation Structure Root mean squared error defines channel width, making the envelope responsive to how tightly price is hugging the trend. Tight RMSE = cleaner trend structure Wide RMSE = unstable or volatile structure Internal bands split the envelope into inner, quarter, and outer zones These nested bands create a true structure ladder instead of a single upper/lower shell. 3. Premium / Discount Channel Arrays The channel is separated into three value areas: Premium: Upper edge territory where price is extended and expensive relative to the current regression path Equilibrium: The center band around fair value and neutral orderflow balance Discount: Lower edge territory where price is cheap relative to the active path This makes the indicator more useful for directional context: Bull channels pressing premium signal strong continuation pressure Bear channels pressing discount signal strong downside control Repeated failure to hold premium/discount can signal exhaustion or rebalancing 4. Breakout Confidence Model Breakouts are not treated equally. The indicator scores breakout quality using four ingredients: Participation: Distance from the regression basis normalized by ATR Slope Force: Strength of the normalized regression slope Location: Whether price is already pressing the outer structure Alignment: Whether price direction and Supertrend stack agree with the channel breakoutConfidence = participation + slopeForce + location + alignment This helps separate lazy drifts from high-quality channel resolution. 5. Supertrend Ribbon Stack The Supertrend layer is not there as a generic add-on. It acts as a second-order directional filter. Bull channel + bull Supertrend = higher-quality directional stack Bear channel + bear Supertrend = stronger downside stack When regression and Supertrend disagree, price is more likely in transition The fill between regression basis and Supertrend visually shows whether pressure is aligned or conflicted. 6. ATR Risk Map After a confirmed breakout, the indicator projects: 1 ATR-based stop level 3 reward targets using configurable risk-reward multiples Auto-expiring lines so stale trade maps are removed This gives the channel direct execution value instead of leaving the user to manually measure every move. Visual Elements Metallic Basis Line: Gold-toned centerline for the active regression basis Outer Deviation Shell: Main channel boundaries with glow Inner Structure Bands: Internal ladder for pressure staging Premium / Discount Fills: Separate upper and lower value zones inside the channel Equilibrium Fill: Neutral fair-value region Supertrend Ribbon: Context layer showing secondary directional alignment Iridescent Candles: Candle coloring that intensifies as control and confidence improve Breakout Markers: Compact signals for confirmed resolves Readiness Diamonds: Pre-break alignment markers when channel conditions are strong The visual hierarchy is designed so you can read the channel at a glance without relying on heavy objects or clutter. Dashboard The dashboard is intentionally compact and fixed to the right side. It shows only the highest-signal metrics: Bias Regime Flow Channel Position Confidence Compression Trend Stack Trade Map How to Use This Indicator Step 1: Identify Channel Bias Check whether the regression slope is bullish or bearish. That defines the primary directional path. Step 2: Read Value Location See whether price is trading in premium, equilibrium, or discount. This tells you whether price is extended or balanced inside the channel. Step 3: Watch Trend Stack Alignment When Supertrend and regression agree, directional pressure is cleaner. When they disagree, reduce conviction. Step 4: Monitor Confidence Use the breakout confidence score to judge whether price is merely drifting or building a meaningful resolution. Step 5: Trade the Resolve, Not the Noise Use breakout markers and ATR map levels when price exits the frozen envelope with qualified pressure. Best Practices Use higher timeframes for cleaner channel geometry Treat equilibrium as fair value, not a signal by itself Bull channels work best when premium holds and pullbacks respect the inner bands Bear channels work best when discount holds and rallies fail at internal structure High compression followed by rising confidence often precedes expansion Use the risk map for framing, not blind automation Indicator Limitations Regression is still a model of recent price, not a guarantee of future direction Sudden event-driven moves can invalidate the frozen segment quickly Premium and discount are relative to the current channel, not absolute market value High breakout confidence can still fail in thin or news-driven markets Short segments increase responsiveness but also increase noise Technical Implementation Built in Pine Script v6 using: Manual OLS regression RMSE deviation envelopes Segment freeze-and-resolve logic Internal quarter and inner bands Premium/discount channel zoning Supertrend stack integration Breakout confidence scoring ATR-based stop and target map Compact institutional dashboard Originality Statement This indicator is original in how it treats a regression channel as a full market-state framework instead of a static overlay. The value is not just in plotting upper and lower lines, but in combining: Segment freezing Internal value zoning Directional stack confirmation Breakout qualification Execution mapping Each layer contributes different information: regression defines path, RMSE defines structure, premium/discount defines value, Supertrend defines stack, and confidence defines quality. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Regression channels, premium/discount zones, and breakout scores are analytical tools, not guarantees of market outcome. All trading decisions remain the responsibility of the user. -Made with passion by officialjackofalltrades
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officialjackofalltrades tarafından
Artemis Regression Bands🟦 Artemis Regression Bands is a kernel-driven volatility envelope indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). A single kernel estimate — selectable from eight classical kernel families — anchors the Fair Value line. Around it, three residual-standard-deviation bands (±1σ, ±2σ, ±3σ) fan outward with either Linear or Exponential spacing, producing a statistically grounded envelope far cleaner than the classical close-stdev approach used by legacy Bollinger-style indicators. A four-gate Romb signal engine overlays buy / sell diamond markers when price pokes through the outermost enabled σ boundary and reverses back inside. 🟦 HOW IT WORKS Artemis calls the KernelLens library's unified dispatcher once per bar to build the Fair Value line, then queries three additional library exports to derive the band widths, slope direction, and residual σ: ``` fair = kl.estimate (type, src, ℓ, α, period, phase, filter) sigma = kl.confidenceBand(src, fair, window) slopeVal = kl.slope (fair, 1) trendSt = kl.trendState (fair, 1) dev = baseMult · sigma upper1 = fair + 1·dev lower1 = fair − 1·dev upper2 = fair + 2·dev lower2 = fair − 2·dev upper3 = fair + k3·dev lower3 = fair − k3·dev (k3 = 3 Linear | 4 Exp) ``` The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, division-by-zero guards, and input validation internally. Artemis contains zero kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Artemis imports the published KernelLens library and uses the following exports: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on the user's Kernel Type dropdown. Called once per bar to produce the Fair Value line. | | `kl.confidenceBand()` | Rolling standard deviation of the (source − Fair Value) residual. Drives the band half-widths on every bar. | | `kl.slope()` | Discrete first derivative of the Fair Value line. Feeds trend flip alerts. | | `kl.trendState()` | Ternary classifier (+1 rising / −1 falling / 0 flat) of the Fair Value line. Drives the slope-adaptive color, the kernel trend confluence filter, and the dashboard Trend row. | Every regression computation — kernel weight evaluation, NA-safe summation, bandwidth-aware loop termination, residual stdev, finite-difference slope — is delegated to the library. The indicator itself only orchestrates the four library calls and layers the visual pipeline on top. 🟦 EIGHT KERNEL FAMILIES A single Kernel Type dropdown selects any of the eight kernels shipped with the KernelLens library. Each is a different mathematical smoother with its own statistical character: | Kernel | Formula | Best For | |---|---|---| | Rational Quadratic | (1 + d² / (2·α·ℓ²))^(−α) | Multi-scale mixer; α controls stretch. Recommended default. | | Gaussian / RBF | exp(−d² / (2·ℓ²)) | Canonical smoother; infinitely differentiable. | | Periodic | exp(−2·sin²(π·d/p) / ℓ²) | Resonates with a known repetition distance p. | | Locally Periodic | Periodic × Gaussian | Seasonal patterns with slow trend drift. | | Epanechnikov | (3/4)·(1 − u²), \|u\| ≤ 1 | MSE-optimal; compact support, no tail contamination. | | Tricube | (70/81)·(1 − \|u\|³)³, \|u\| ≤ 1 | LOWESS standard; near-Gaussian compact profile. | | Triangular | (1 − \|u\|), \|u\| ≤ 1 | Simplest compact kernel; cheapest to compute. | | Cosine | (π/4)·cos(π·u/2), \|u\| ≤ 1 | Raised-cosine; smooth boundary transition. | Because the dropdown feeds the library's `kl.estimate()` dispatcher directly, every kernel inherits the same three-mode filter layer (No Filter / Smooth / Zero Lag) and the same non-repainting guarantees — there is no special case per kernel in Artemis. 🟦 FILTER LAYER A second dropdown applies an optional post-processing layer on top of the raw Nadaraya–Watson estimate: | Filter | Formula | Trade-off | |---|---|---| | No Filter | ŷ = ŷ_raw | Single-pass kernel. Rawest output, most reactive. | | Smooth | ŷ = K(ŷ_raw) | Double-pass — kernel applied to its own output. Cleaner line, slightly more lag. | | Zero Lag | ŷ = 2·ŷ_raw − K(ŷ_raw) | Ehlers de-lagging identity — sharpens edges without adding lag. | The filter is resolved entirely inside `kl.estimate()`, so switching modes incurs no runtime cost beyond the extra kernel pass. 🟦 RESIDUAL-σ BAND ENGINE Artemis bands are statistically grounded on the residual standard deviation — not on raw close stdev as in classical Bollinger indicators. The residual is computed as: ``` residual = src − fair sigma = ta.stdev(residual, window) // via kl.confidenceBand() ``` Because Fair Value is already an unbiased local estimate of the source, the residual is a zero-mean noise series and its stdev captures **only the portion of price variance that the kernel could not explain**. This produces three benefits over the classical approach: 1. **Tighter bands in trending regimes** — close-stdev widens during strong trends because the trend itself inflates the variance; residual-σ does not, because the kernel absorbs the trend. 2. **Faster reaction to volatility regime changes** — residual-σ tightens as soon as the kernel fits well, and widens the instant the market breaks out of the kernel's neighborhood. 3. **True statistical interpretation** — under the assumption of locally Gaussian residuals, ±1σ / ±2σ / ±3σ enclose approximately 68 % / 95 % / 99.7 % of near-term price variation. The traditional close-stdev envelope carries no such interpretation. A dedicated Residual σ Window input controls the lookback; typical values range from 50 (reactive, scalping) to 300 (stable, position trading). 🟦 BAND SPACING MODES Two spacing presets shape the outward fan of the three σ bands: | Mode | Multipliers | Character | |---|---|---| | Linear | 1·, 2·, 3· | Classical Bollinger-style uniform steps. Predictable, symmetric. | | Exponential | 1·, 2·, 4· | Fibonacci-flavored — outer band (4σ) is reserved for genuine blow-off excursions. | Base Multiplier scales all three bands uniformly (default 1.0). The formula is: ``` band_level = fair ± (baseMult · k · sigma) k ∈ {1, 2, k3} ``` where k3 resolves to 3 in Linear mode and 4 in Exponential mode. Every band has an independent visibility toggle, so minimalist users can run ±1σ only, swing traders ±3σ only, or any combination. 🟦 FOUR-GATE ROMB SIGNAL ENGINE The Romb engine prints buy / sell diamond markers when price pokes through the outermost enabled σ band and reverses back inside. Four sequential gates protect against false entries: | Gate | Logic | Purpose | |---|---|---| | 1 — Crossover | `ta.crossunder(high, triggerUp)` / `ta.crossover(low, triggerDn)` | Detects the reversal back through the outer band. | | 2 — Warm-up | Residual σ computable for N consecutive bars | Blocks signals during the early kernel-settlement window. | | 3 — Confluence | Fair Value slope aligns with the reversal direction | Optional PRO filter — Sell Romb requires falling kernel, Buy Romb requires rising kernel. | | 4 — Cooldown | Minimum bar gap since the last same-side Romb | Prevents signal clustering on a single extended poke-and-reverse sequence. | A Signal Mode toggle layers on top: - **Confirmed** — signals fire only on `barstate.isconfirmed`; zero repaint on closed bars. - **Realtime** — signals fire live on the current open bar; faster reaction, may vanish if price reverses before close. Each confirmed signal is rendered as a two-layer neon diamond: - **Halo** — `size.small`, 40 % transparent theme hue (glow layer). - **Core** — `size.tiny`, fully opaque theme hue (bright center). The halo renders first so the core sits cleanly on top, producing a sharp luminous marker that reads instantly even on dense price charts. 🟦 ADAPTIVE OUTER-BAND TRIGGER The Romb engine does not hard-code the ±3σ band as the signal trigger. Instead, it resolves the outermost currently-enabled band on every bar: ``` triggerUp = show3 ? upper3 : show2 ? upper2 : show1 ? upper1 : na triggerDn = show3 ? lower3 : show2 ? lower2 : show1 ? lower1 : na ``` The result is an envelope that respects the user's visibility choices: | Visible Bands | Romb Fires At | |---|---| | ±1σ + ±2σ + ±3σ | ±3σ (default) | | ±1σ + ±2σ | ±2σ | | ±1σ only | ±1σ | | All off | no signals | Diamond positioning follows the same trigger, so the glyph always floats ~0.3σ outside whatever envelope is actually drawn on the chart. The behavior matches user intent: the band I can see is the band that fires signals. 🟦 NON-REPAINTING BEHAVIOR Artemis inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. A single Phase input (default 2) shifts the kernel center into the past by that many bars: - **Phase = 0** — live estimate, flickers on the current bar (real-time only; history is immutable). - **Phase = 1** — 1-bar lag, non-repainting once the bar is confirmed. - **Phase = 2** — recommended balance between freshness and stability (default). - **Phase = 3+** — extra margin against erratic ticks, higher lag. Historical repainting never occurs at any phase value. The library contains no `request.security` calls, no lookahead, and no array rotation that could leak future data. Every historical bar's plotted Fair Value, band, and Romb signal is final once confirmed. 🟦 VISUAL PIPELINE **σ Band Outlines** — Three upper bands (±1σ / ±2σ / ±3σ) in progressively lighter `thBear` hues, three lower bands in progressively lighter `thBull` hues. Hidden bands collapse to na via their individual visibility toggles; the outline widths share a single Band Line Width input. **Tapered Gradient Fills** — Six fills drawn between the Fair Value line and each σ band. Opacity scales progressively from ±1σ (densest, most opaque) to ±3σ (lightest, most transparent), creating a halo that mirrors the statistical density of price residuals under normality. Master Fill Opacity input (0 = invisible, 100 = fully opaque) scales all three fills uniformly. **Fair Value Line** — Slope-adaptive color resolver swaps between `thBull` (rising kernel) and `thBear` (falling kernel). Flat bars retain the previous color so the line never flashes neutral on a perfectly horizontal tick. Width is user-controlled (1–5 px). **Romb Diamonds** — Two-layer neon glow at the adaptive trigger band; halo + core rendering described above. **Bar Coloring** — Optional theme-aware candle coloring driven by the Fair Value slope. Off by default; when enabled it paints every bar with the active theme's bull / bear hue based on the current trend state. 🟦 THEME SYSTEM Twelve cohesive color palettes drive every visual component — Fair Value line, σ band outlines, gradient fills, Romb diamonds, bar coloring, and dashboard accents — all sharing the same four color axes (`thBull`, `thBear`, `thNeutral`, `thSignal`): | Theme | Bull | Bear | |---|---|---| | Tropic | Cyan steel | Deep orange | | Amber | Warm amber | Indigo blue | | Pastel | Sky blue | Soft lavender | | Cyber | Neon lime | Hot crimson | | Helios | Bright gold | Scarlet | | Electric | Electric aqua | Magenta | | Candy | Neon green | Hot pink | | Bloomberg | Terminal orange | Cyan | | Solar | Solarized olive | Crimson | | Royal | Imperial gold | Deep purple | | Midnight | Deep navy | Dark crimson | | Graphite | Near-black | Silver grey | A separate Display Mode toggle (Dark / Light) controls the dashboard palette independently of the chart theme — so a Bloomberg chart theme with a Light dashboard is a valid configuration, as is Midnight chart + Dark dashboard. 🟦 DASHBOARD A 2-column, 12-row theme-aware status panel that updates only on the last bar (zero historical overhead). Supports Dark and Light display modes, six docking positions, and four text sizes. Renders via `force_overlay = true` on the main price chart. | Row | Label | Content | |---|---|---| | Header | ARTEMIS | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Selected kernel type | | Divider | REGRESSION | — | | Bandwidth | Bandwidth ℓ | Bandwidth value / Phase offset φ | | Filter | Filter | No Filter / Smooth / Zero Lag | | Fair Value | Fair Value | Current Fair Value in chart mintick format | | Divider | BANDS | — | | Spacing | Spacing | Linear 1·/2·/3· or Exp 1·/2·/4· | | Residual σ | Band σ | Rolling residual standard deviation | | Trend | Trend | ▲ BULL / ▼ BEAR / ━ FLAT (bull/bear colored) | | Last Romb | Last Romb | ▲ BUY (N ago) / ▼ SELL (N ago) — bull/bear colored | **Zebra-stripe layout** — alternating `dashBg` / `dashBgAlt` row backgrounds improve scan-ability on narrow cells. Section dividers (REGRESSION, BANDS) use a third background tone (`dashSection`) with the theme's bull accent as the header color — preserving brand identity across both Display Modes. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bullish Trend Flip | Fair Value slope crosses from ≤ 0 into positive territory | | Bearish Trend Flip | Fair Value slope crosses from ≥ 0 into negative territory | | Buy Romb | Confirmed Buy Romb fires — all four signal gates passing | | Sell Romb | Confirmed Sell Romb fires — all four signal gates passing | | Upper Band Touch | Price touches or exceeds the outermost enabled upper band | | Lower Band Touch | Price touches or falls below the outermost enabled lower band | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Artemis Regression Bands: "` for easy parsing in downstream automation. Touch alerts are off by default (can be noisy in trending markets); the four core alerts are on by default. 🟦 RECOMMENDED PRESETS | Style | Bandwidth ℓ | Filter | Phase | Spacing | σ Window | Chart | |---|---|---|---|---|---|---| | Scalper | 10–20 | No Filter | 1 | Linear | 50–80 | 1m–5m | | Day Trader | 20–40 | Smooth | 2 | Linear | 80–120 | 15m–1h | | Swing | 30–60 | Smooth | 2 | Linear or Exp | 100–200 | 4h–1D | | Position | 60–120 | Smooth or Zero Lag | 3 | Exp | 200–300 | 1D–1W | **Kernel type tuning** - **Trending instruments** — Rational Quadratic (α = 1–3) or Gaussian. Smooth multi-scale response. - **Mean-reverting instruments** — Epanechnikov or Tricube. Compact support keeps the band envelope tight. - **Session-cyclic patterns** — Periodic (with p = session length in bars) or Locally Periodic. Resonates with known cycles. **Romb filter tuning** — Keep Kernel Trend Confluence ON for high-conviction setups only. Switch OFF on range-bound instruments to capture both sides of the oscillation. 🟦 COMPATIBILITY - Pine Script v6 - All exchanges, all asset classes (crypto, forex, equities, commodities, indices) - All timeframes (1 minute through Monthly) - Both Dark and Light chart themes — the Display Mode toggle controls dashboard palette independently - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression, residual σ, slope, and trend-state math is delegated to the published library. - **Plot budget** — 6 band plots + 1 Fair Value anchor + 1 Fair Value visible + 6 gradient fills + 4 Romb plotshapes + 1 barcolor = well under Pine's plot limits. - **Table** — Single `var table` rebuilt on `barstate.islast` with `force_overlay = true`; zero historical overhead. - **Signal state** — Two `var int` cooldown anchors (`lastSellBar`, `lastBuyBar`) seeded at −10000 so the very first bar always passes the gap test. A `var int stabCount` warm-up counter blocks signals during early kernel settlement. - **No persistent drawing objects** — no `box.new`, `line.new`, no array rotations; every visual is either a plot or a single-bar plotshape. - **Adaptive trigger resolver** — Romb crossover detection, touch alerts, and diamond positioning all read from the same `triggerUp` / `triggerDn` resolver, so band visibility toggles stay semantically coherent across every layer of the indicator. - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead, no future-bar leakage at any phase value. 🟦 DISCLAIMER Artemis Regression Bands is a technical analysis indicator built on the KernelLens Nadaraya–Watson regression library. It is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. The residual-σ envelope describes past dispersion around the kernel estimate — not a forecast of future range — and should always be combined with broader context: higher-timeframe structure, volatility regime, liquidity, news, and risk management. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor. Responsibility for any trading decisions rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of Artemis Regression Bands or the underlying KernelLens library.
Pine Script® göstergesi
a_jabbaroff tarafından
6
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 sensitivity to local regime conditions. The indicator computes a latent equilibrium using a kernel-weighted mean and a dispersion measure using kernel-weighted variance under the same weighting structure. The resulting envelope reflects cycle-consistent deviation with temporal locality, rather than a conventional volatility band. All values are computed exclusively on closed historical bars using a bounded lookback window to ensure non-repainting behavior. This indicator belongs to a broader class of iterative kernel-based envelopes that includes Gaussian, Rational Quadratic, and Periodic variants. All share a common Nadaraya–Watson estimation framework, differentiated by their kernel. TRADING USES The Iterative Locally Periodic Envelope is best interpreted as a cycle-aware structural estimator with adaptive temporal sensitivity, rather than a volatility-based band. The temporal locality component allows the estimator to adapt more readily to emerging regime shifts than the pure periodic variant. Equilibrium Tracking The latent equilibrium represents the phase-conditioned central tendency of price under locally periodic similarity weighting. Oscillations around this level reflect movement within a repeating structural cycle, with more recent phase-aligned observations contributing more strongly than temporally distant ones. Cycle Regime Structure The envelope emphasizes repeating structural behavior through phase recurrence weighting, modulated by temporal decay. Changes in symmetry, amplitude, or persistence of oscillation around the latent equilibrium may indicate transitions between cyclical regimes. Mean Reversion Within Cycles When a stable periodic structure is present, deviations from the latent equilibrium may revert toward phase-consistent levels. Mean-reversion behavior is conditioned on both cycle structure and temporal proximity. Structural Extremes Extreme deviations relative to the envelope correspond to phase-inconsistent states where cyclical structure becomes stretched or destabilized. Because the kernel incorporates temporal decay, these conditions are identified with greater sensitivity to recent price behavior. State Estimation The system defines a latent equilibrium as the inferred central cyclical state under joint phase and temporal weighting, with dispersion derived from kernel-weighted variance under identical constraints. This produces a structurally consistent representation of the market state that is sensitive to both cyclical position and local regime conditions. LOCALLY PERIODIC ENVELOPE CONSTRUCTION The envelope is constructed using kernel-weighted variance under the same locally periodic similarity measure used to estimate the latent equilibrium. The latent equilibrium defines the central state estimate and kernel-weighted variance defines dispersion under identical weighting, producing an endogenously determined envelope. The band width is fixed at ±1 kernel standard deviation with no multiplier, ensuring dispersion remains an intrinsic property of the locally periodic similarity structure rather than an externally imposed scaling parameter. THEORY The locally periodic kernel defines similarity in terms of cyclical phase recurrence modulated by temporal proximity. Observations contribute to the estimator based on alignment within a repeating cycle structure, with influence attenuated by temporal distance from the estimation point. The estimator is formulated as a Nadaraya–Watson kernel regression under a locally periodic kernel, where weights are defined as: k(i) = exp( -2 · sin²(πi / p) / L² ) · exp( -i² / 2L² ) Where: p = period (cycle length) L = lookback window (shared bandwidth parameter; effective smoothing scales with L²) In this MacKay consistent formulation, the lookback window acts as a unified bandwidth parameter governing periodic phase selectivity and the Radial Basis Function (RBF) temporal decay envelope. The two components are coupled through L, producing a kernel that simultaneously emphasizes phase-aligned and temporally proximate observations. As L increases, both the periodic and RBF components broaden, producing stronger smoothing across phase and time. As L decreases, phase selectivity and temporal locality both increase, making the estimator more sensitive to recent cycle-consistent observations. This induces a similarity structure in which influence concentrates at phase-aligned intervals within a temporally bounded neighborhood. The resulting estimator defines a latent equilibrium governed by phase alignment and temporal proximity that can be interpreted as a locally stationary periodic extension of kernel regression on a circular phase manifold. The key distinction from the pure periodic kernel is that phase-aligned observations at distant lags are progressively suppressed by the RBF decay term, allowing the estimator to adapt to structural drift while preserving cycle-aware weighting. During stable cyclical regimes the two estimators converge; during structural transitions the locally periodic variant adapts faster by downweighting older phase information. CALIBRATION As established in Gaussian Processes for Machine Learning (Rasmussen & Williams, 2006), the period should reflect the recurrence interval of the dominant cycle in the data, while the bandwidth parameter L controls how quickly similarity decays away from perfect phase alignment. For daily charts, common cycle anchors include the trading week (~5 bars), trading month (~21 bars), trading quarter (~63 bars), and trading year (~252 bars). Length (Lookback / Bandwidth) Controls structural depth of the estimator and acts as the unified bandwidth parameter for the periodic and RBF components; as L governs phase selectivity and temporal decay simultaneously, its effect is stronger than in the pure periodic variant. The default of 100 reflects the locally periodic kernel's temporal decay component; at longer lengths the RBF term weakens and behavior converges toward the pure periodic estimator. - 50–100: high responsiveness, strong temporal locality, short-cycle sensitivity - 150–250: balanced regime stability with moderate temporal decay - 300+: broad structural smoothing, weak temporal decay, behavior converges toward pure periodic envelopes Period (Cycle Length) Defines the recurrence interval of the kernel and governs phase alignment and cyclical structure. Shorter periods increase phase resolution and cycle sensitivity, while longer periods emphasize broader structural recurrence. The period should reflect the dominant cycle present in the data, aligned with the anchor scales defined above. Start At Bar Offsets the kernel window backward from the most recent bars and excludes newer observations from the estimator. This ensures all calculations are based strictly on closed historical data and preserves non-repainting behavior. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices. Performance is dependent on the presence of stable cyclical structure; in regimes lacking periodic coherence, the estimator converges toward a local smoother with reduced phase discrimination.
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Iteratively Reweighted Least Squares Range Filter [Jamallo]Intro Iteratively Reweighted Least Squares Range Filter Most filters treat every bar equally. This one doesn't. The IRLS Range Filter uses a robust statistical engine to continuously evaluate how much each bar in the lookback window should be trusted — down-weighting noisy data and rejecting outliers entirely — before computing a consensus price level. The result is a smoother, more honest trend line that is far less susceptible to wicks, gaps, and volatile noise than conventional moving averages. How It Works The indicator runs in three stages: Stage 1 — IRLS Consensus Engine . Every bar in the rolling window is assigned one of three trust states based on how far its price deviates from the current consensus, measured in adaptive sigma units. High Trust bars (within Lo-Trust Band σ) receive full weight. Lo Trust bars receive half weight. Rejected bars receive none. A Schmitt-trigger hysteresis mechanism prevents bars from flickering between states on minor fluctuations. The weighted average of all contributing bars becomes the consensus price. This residual-based weighting scheme is adapted from the IRLS framework described in Li & Deng (2026), arXiv:2603.08158. Stage 2 — Deadband Quantizer . The continuous consensus is passed through a step filter. The output only moves when the consensus has shifted by more than one deadband unit (Step Size × σ). This eliminates micro-jitter and produces a clean staircase line with fewer false trend flips. Stage 2.5 — Sample-and-Hold Bands. The volatility channel is only updated when the filter takes a new step, so band width reflects conditions at the last decisive move rather than real-time noise. An EMA-style smoother (Band Smooth) prevents abrupt band jumps between steps. Reference Li, J., & Deng, F. (2026). Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square. arXiv preprint arXiv:2603.08158v1.
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Jamallo22 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 computes a latent equilibrium using a kernel-weighted mean and a dispersion measure using kernel-weighted variance under the same weighting structure. The resulting envelope reflects cycle-consistent deviation, rather than a conventional volatility band. All values are computed exclusively on closed historical bars using a bounded lookback window, ensuring non-repainting behavior. This indicator belongs to a broader class of iterative kernel-based envelopes that includes Gaussian and Rational Quadratic variants. All share a common Nadaraya–Watson estimation framework, differentiated by their kernel. TRADING USES The Iterative Periodic Envelope is best interpreted as a cycle-aware structural estimator rather than a volatility-based band. Equilibrium Tracking The latent equilibrium represents the phase-conditioned central tendency of price under periodic similarity weighting. Oscillations around this level reflect movement within a repeating structural cycle rather than directional drift. Cycle Regime Structure The envelope emphasizes repeating structural behavior through phase recurrence weighting. Changes in symmetry, amplitude, or persistence of oscillation around the latent equilibrium may indicate transitions between cyclical regimes. Mean Reversion Within Cycles When a stable periodic structure is present, deviations from the latent equilibrium may revert toward phase-consistent levels. This supports mean-reversion behavior that is conditioned on cycle structure rather than purely statistical dispersion. Structural Extremes Extreme deviations relative to the envelope correspond to phase-inconsistent states where cyclical structure becomes stretched or destabilized. These conditions often precede transitions such as cycle inversion, expansion, or compression. State Estimation The system defines a latent equilibrium as the inferred central cyclical state, with dispersion derived from kernel-weighted variance under identical periodic similarity constraints. This produces a structurally consistent representation of market state. PERIODIC ENVELOPE CONSTRUCTION The envelope is constructed using kernel-weighted variance under the same periodic similarity measure used to estimate the latent equilibrium. The latent equilibrium defines the central state estimate and kernel-weighted variance defines dispersion under identical weighting, producing an endogenously determined envelope. The band width is fixed at ±1 kernel standard deviation with no multiplier, ensuring dispersion remains an intrinsic property of the periodic similarity structure rather than an externally imposed scaling parameter. THEORY The periodic kernel defines similarity in terms of cyclical phase recurrence rather than linear temporal distance. Observations contribute to the estimator based on alignment within a repeating cycle structure. The estimator is formulated as a Nadaraya–Watson kernel regression under a canonical periodic kernel, where weights are defined as: k(i) = exp( -2 · sin²(πi / p) / L² ) Where: p = period (cycle length) L = lookback window (bandwidth parameter; effective smoothing scales with L²) In this MacKay consistent formulation, the lookback window acts as a bandwidth control parameter, governing phase selectivity and structural smoothing. As L increases, the kernel becomes broader, producing stronger smoothing and reduced phase sensitivity. As L decreases, phase selectivity increases and the estimator becomes more locally sensitive to cyclical alignment. This induces a cyclical similarity structure in which influence concentrates at recurring phase intervals. The resulting estimator defines a latent equilibrium governed by phase alignment rather than temporal proximity. This formulation can be interpreted as a periodic extension of kernel regression on a circular phase manifold. CALIBRATION Length (Lookback / Bandwidth) Controls structural depth of the estimator and acts as the primary kernel bandwidth parameter. - 50–100: high responsiveness, short-cycle sensitivity - 150–250: balanced regime stability - 300+: strong structural smoothing, reduced sensitivity to phase noise Period (Cycle Length) Defines the recurrence interval of the kernel and governs phase alignment and cyclical structure. Commonly aligns with dominant market rhythms such as intraday or macro-cycle structure. - Lower values: faster cycle sensitivity - Higher values: slower, broader structural cycles Start At Bar Offsets the kernel window backward from the most recent bars and excludes newer observations from the estimator. This ensures all calculations are based strictly on closed historical data and preserves non-repainting behavior. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices. Performance is dependent on the presence of stable cyclical structure; in regimes lacking periodic coherence, the estimator converges toward a smoother, low-information state.
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SensitiveSuit tarafından
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 arrows, and a theme-aware dashboard — all driven by a single theme selection from ten optical-brand palettes. 🟦 HOW IT WORKS Meridian Lens PRO calls the KernelLens library's unified dispatcher (`kl.estimate`) three times per bar — once for each kernel line: ``` Fast = kl.estimate(type, src, bw=8, α, period, phase, filter) Medium = kl.estimate(type, src, bw=16, α, period, phase, filter) Slow = kl.estimate(type, src, bw=32, α, period, phase, filter) ``` Each line independently selects its kernel family (Rational Quadratic, Gaussian, Periodic, Locally Periodic, Epanechnikov, Tricube, Triangular, Cosine), its filter mode (No Filter / Smooth / Zero Lag), its bandwidth, shape α, period, phase, and line width. The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, and input validation internally. The Medium line is the primary trend reference — it drives the trailing bands, the main signal arrows, the dashboard trend cell, and the direction variable that colors every visual component. The Fast line provides early-warning reactivity for short-term entry timing. The Slow line anchors the macro trend for crossover logic and confluence scoring. 🟦 KERNEL LIBRARY INTEGRATION Meridian Lens imports the published KernelLens library and uses the following exports: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on user's dropdown selection | | `kl.trendState()` | Returns +1 / −1 / 0 for each kernel's slope — drives dashboard arrows and signal triggers | | `kl.crossSignal()` | Detects Fast × Slow crossovers — drives the Cross row in the dashboard and crossover alerts | The indicator does not reimplement any kernel math — all regression computation is delegated to the library, ensuring that every bug fix or optimization in the library automatically propagates to this indicator. 🟦 THREE KERNEL LINES **Fast Kernel** — The most reactive line. Default bandwidth 8, No Filter. Designed for scalping and short-term entry timing. Flips direction frequently on noisy charts — its signal markers are OFF by default to avoid visual clutter. **Medium Kernel** — The primary trend reference. Default bandwidth 16, Smooth filter. Drives the trailing bands, the main 3-layer glow signal arrows, the dashboard Trend cell, and the direction variable that colors every visual component. This is the indicator's core signal. **Slow Kernel** — The macro trend anchor. Default bandwidth 32, Smooth filter. Provides structural support for crossover logic (Fast × Slow) and triple-line confluence scoring. Its signal markers are ON by default because Slow flips are rare and meaningful. Each kernel group exposes: Show toggle, Kernel Type dropdown (8 families), Bandwidth, Shape α (RQ only), Period (Periodic / Locally Periodic only), Phase (non-repainting offset), Filter (None / Smooth / Zero Lag), and Line Width. 🟦 NON-REPAINTING BEHAVIOR Meridian Lens inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. Each kernel line has its own Phase input (default: 2), which shifts the kernel center into the past by that many bars. - Phase = 0 — live estimate, flickers on the current bar (real-time only; history is immutable) - Phase = 1 — 1-bar lag, non-repainting once the bar is confirmed - Phase = 2 — recommended balance between freshness and stability (default) - Phase = 3+ — extra stability for swing and position trading Historical repainting never occurs at any phase value. The library contains no `request.security` calls, no lookahead, and no array rotation that could leak future data. Every historical bar's plotted value is final once confirmed. 🟦 SIGNAL SYSTEM The indicator produces three tiers of trend-flip signals, each visually distinct: **Medium Signals (Primary)** — 3-layer neon glow arrows rendered when the Medium kernel's direction flips. The outer halo is large and 80% transparent, the middle layer is normal-sized and 50% transparent, and the core arrow is small and fully opaque — creating a luminous halo effect on dark charts. Controlled by the "Glow Effect" toggle. **Slow Signals** — Minimal tiny arrows (40% transparent) that fire when the Slow kernel flips direction. ON by default — these mark rare, meaningful macro trend changes. **Fast Signals** — Minimal tiny arrows (40% transparent) that fire when the Fast kernel flips direction. OFF by default — enable for early-warning entry timing on lower timeframes. 🟦 VISUAL PIPELINE **Volume-Intensity Adaptive Color** — The Medium line's transparency responds to the current volume reading. High volume = bright line (volume-confirmed trend), low volume = dim line (low-conviction drift). Uses a 33-bar HMA-smoothed normalized volume metric. Disable for a fixed 50% transparency. **Trailing Bands** — Gradient-filled bands on the bullish/bearish side of the Medium line. Band width is driven by the rolling 100-bar average candle body size multiplied by a configurable distance factor (default: 2.0×). Bull bands fill below the Medium line during uptrends, bear bands fill above during downtrends. **Theme System** — Ten cohesive palettes drive every visual component: | Theme | Bull | Bear | |---|---|---| | Prism | Forest green | Crimson red | | Focus | Cyan steel | Deep orange | | Solar | Warm amber | Indigo red | | Frost | Sky blue | Soft lavender | | Laser | Neon lime | Hot crimson | | Aurora | Bright gold | Scarlet | | Plasma | Electric aqua | Magenta | | Bloom | Mint green | Hot pink | | Eclipse | Deep navy | Dark crimson | | Carbon | Near-black | Silver grey | 🟦 PRO DASHBOARD A 2-column, 11-row theme-aware status panel that updates only on the last bar (zero historical overhead). Supports Dark and Light display modes with configurable position and text size. | Row | Label | Content | |---|---|---| | Header | MERIDIAN LENS | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Medium kernel type | | Divider | KERNELS | — | | Fast | Fast | ▲/▼ + price value (bull/bear colored) | | Medium | Medium | ▲/▼ + price value (bull/bear colored) | | Slow | Slow | ▲/▼ + price value (bull/bear colored) | | Divider | SIGNALS | — | | Trend | Trend | ▲ BULL / ▼ BEAR | | Cross | Cross | ↑ UP / ↓ DOWN / — | | Strength | Strength | ▰▰▰ TRIPLE / ▰▰▱ STRONG / ▰▱▱ WEAK / ▱▱▱ NEUTRAL | **Confluence Strength** — Counts how many of the three kernels (Fast, Medium, Slow) have their trend aligned with the Medium's direction. Score 3 = TRIPLE BULL/BEAR, 2 = STRONG, 1 = WEAK, 0 = NEUTRAL. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bull Crossover | Fast line crosses above Slow line | | Bear Crossover | Fast line crosses below Slow line | | Trend Up | Medium kernel trend flips to rising | | Trend Down | Medium kernel trend flips to falling | | Triple Bullish | Fast > Medium > Slow AND Medium rising | | Triple Bearish | Fast < Medium < Slow AND Medium falling | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. 🟦 RECOMMENDED PRESETS | Style | Fast bw | Med bw | Slow bw | Phase | Med Filter | Chart | |---|---|---|---|---|---|---| | Scalper | 4–8 | 8–16 | 16–32 | 1 | No Filter | 1m–5m | | Day Trader | 8–12 | 14–24 | 24–48 | 2 | Smooth | 15m–1h | | Swing | 16–24 | 24–40 | 48–80 | 2 | Smooth | 4h–1D | | Position | 24–48 | 40–80 | 80–200 | 3 | Smooth | 1D–1W | 🟦 COMPATIBILITY - Pine Script v6 - All exchanges, all asset classes (crypto, forex, equities, commodities) - All timeframes (1 minute through Monthly) - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression math is delegated to the library - **Plot budget** — 5 plots + 2 fills + 10 plotshapes = well under Pine's 64-plot limit - **Table** — Single `var table` created once on `barstate.islast`, zero historical overhead - **No persistent drawing objects** — no `box.new`, `label.new`, `line.new` — no garbage collection needed - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead - **Volume-intensity** — uses HMA-smoothed normalized volume (33-bar window) for adaptive transparency 🟦 DISCLAIMER Meridian Lens PRO is a technical analysis overlay indicator built on the KernelLens Nadaraya–Watson regression library. It is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor. Responsibility for any trading decisions rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of Meridian Lens or the underlying KernelLens library.
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a_jabbaroff tarafından
Regression-Aligned Candlestick Architect [MarkitTick]💡 The Regression-Aligned Candlestick Architect is an advanced, institutional-grade technical analysis instrument engineered to seamlessly fuse structural market context with precise, deterministic candlestick morphology. Rather than presenting isolated, noisy signals, this indicator operates as a holistic market mapping system. It dynamically identifies changes in market character (CHoCH), anchors statistical regression channels to these pivotal structural nodes, and overlays a rigorously filtered, strength-tiered candlestick pattern recognition engine. This tool empowers analysts to visualize the exact mathematical relationship between micro-level price action anomalies and macro-level statistical deviations. ● ✨ Originality and Utility Standard candlestick recognition tools often suffer from a fatal flaw: signal noise. By identifying every single pattern across the chart regardless of context, they overwhelm the analyst with false positives. This indicator revolutionizes pattern detection by introducing a multi-dimensional filtering matrix. Contextual Awareness: Patterns are cross-referenced against a dynamic Simple Moving Average (SMA) baseline, ensuring that continuation patterns are only validated when aligned with the prevailing macroeconomic trend. Hierarchical Strength Matrix: Patterns are not treated equally. They are mathematically scored and classified into five distinct strength tiers, from baseline indecision to highly reliable structural anomalies. Anchored Statistical Modeling: Instead of static support and resistance lines, this tool maps volatility using an anchored Linear Regression Channel (LRC) that resets automatically upon validated structural breaks (CHoCH), providing an evolving map of fair value and extreme deviation. Institutional Automation Ready: Built-in webhook templates format high-conviction signals directly into actionable JSON payloads, bridging the gap between discretionary charting and algorithmic execution. ● 🔬 Methodology and Concepts The architecture of this script relies on a confluence of three primary mathematical and logical engines. • Quantitative Pattern Recognition The core engine deconstructs each individual candlestick into absolute mathematical variables: body size, total high-low range, upper shadow ratio, and lower shadow ratio. By applying rigid algorithmic tolerance thresholds (e.g., Dojis strictly requiring a body-to-range ratio of less than 5%), the script actively eliminates subjective interpretation. • The Strength Stratification System The indicator systematically grades market geometry into five actionable categories: Strength 1 (Indecision): Identifies market equilibrium phases and compression (e.g., Doji, Spinning Tops). Strength 2 (Weak Signals): Early signs of exhaustion that require further context (e.g., Hanging Man, Inverted Hammer). Strength 3 (Moderate Confirmations): Standard two-candle reversal structures (e.g., Harami, Piercing Line, Dark Cloud Cover). Strength 4 (Strong Confirmations): High-conviction multi-candle configurations (e.g., Engulfing setups, Morning/Evening Stars, Marubozu). Strength 5 (Extreme Conviction): Rare, highly reliable setups signaling massive structural imbalances (e.g., Three White Soldiers, Breakaway Gaps). • Structural CHoCH and LRC Anchoring The script continuously scans for localized Pivot Highs and Pivot Lows using customizable look-left and look-right parameters. When the price closes beyond the most recent opposing pivot node, a Change of Character (CHoCH) is registered. This event immediately triggers the recalculation of the Linear Regression Channel, anchoring the starting point to the critical pivot and projecting statistical deviation bands forward to track the new trend's trajectory. ● 🎨 Visual Guide The interface is meticulously designed with a 3D holographic aesthetic to ensure clarity without cluttering the primary price action. • Candlestick Labels and Holographic Colors Indigo Glass (Strength 1): Muted, translucent tones denoting indecision and market pauses without demanding immediate attention. Cyber Blue & Violet (Strength 2 & 3): Intermediate colors highlighting developing reversals or moderate continuation patterns. Neon Mint (Bullish Strength 4-5): Bright, high-contrast markers indicating strong bullish dominance (e.g., BE+, 3WS), plotted below the bar. Hot Pink (Bearish Strength 4-5): Intense, high-visibility markers warning of severe bearish pressure (e.g., BE-, 3BC), plotted above the bar. Cyber Gold (Special/Exhaustion): Reserved strictly for profound trend exhaustion signatures, such as the Three Line Strike configuration. To learn more about Candlestick patterns, access the following link: Quantitative Analysis of Algorithmic Candlestick Pattern • Linear Regression Channel Bands Active LRC: Displays a Neon Cyan midline representing the mean regression. Soft Cyan and Deep Azure dashed and dotted lines represent the first and second standard deviation bands respectively, filled with translucent gradient shading to represent volatility zones. Historical LRC: Previous channels are preserved in deep Magenta and Blue-Violet hues. This allows the analyst to review past structural behavior, momentum shifts, and how price transitioned between volatility states. ● 📖 How to Use This indicator is optimized for confluence trading. Discretionary traders should look for optimal alignment between the LRC boundaries and high-tier candlestick patterns. Define the Structural Boundary: Observe the active Neon Cyan LRC. Determine if the current micro-trend is contained safely within the inner standard deviation bands or if it is stretching into extreme statistical anomaly (touching or piercing Band 2). Wait for Signal Convergence: A standalone pattern is interesting, but confluence is key. A Strength 4 (Neon Mint) Bullish Engulfing pattern occurring exactly at the lower boundary (Band 2) of an ascending LRC presents an exceptionally high-probability mean-reversion or trend-continuation setup. Automate Execution: Utilize the built-in alert system to capture the exact entry price alongside dynamically calculated ATR-based Stop Loss and Take Profit levels when a tier 4 or 5 pattern confirms. Adapt to Trend Shifts: If a CHoCH occurs, the channel will instantly snap to the new trajectory. Immediately shift your directional bias and await new pattern formations that align with the updated regression mean. ● ⚙️ Inputs and Settings The indicator provides granular control over internal parameters, allowing adaptation to varying asset classes and timeframes. • General & Strength Filters Max Patterns to Display: Limits historical label rendering to keep the chart performant and visually clean. Show Only Trend-Appropriate Patterns: A critical toggle that forces the engine to ignore counter-trend signals by filtering outputs through the internal SMA logic. Strength Toggles (S1 - S5): Allows the user to independently enable or disable specific tiers. Professional traders often disable tiers 1-3 to focus exclusively on high-probability tier 4 and 5 formations. • Current Anchored CHoCH LRC Pivot Left/Right Bars: Dictates the sensitivity of the Change of Character detection. Higher numbers require major macro swings to shift the channel, while lower numbers tightly track micro-structure fluctuations. Band Multipliers (1 & 2): Adjusts the mathematical standard deviation widths of the regression channel. Defaulted to standard 1.0 and 2.0 deviations. • Webhook Execution Configuration Action Strings: Define custom text identifiers (e.g., 'long', 'closeshort') that will be injected into the automated JSON payload when high-strength signals or CHoCH events trigger on a confirmed bar close. ● 🔍 Deconstruction of the Underlying Scientific and Academic Framework The analytical depth of this script is heavily grounded in established statistical mathematics and heuristic geometric modeling. • Statistical Modeling via Ordinary Least Squares (OLS) The dynamic Linear Regression Channel is derived using the Ordinary Least Squares method. The script iteratively loops through the dynamically anchored period (from the algorithmic CHoCH trigger to the current bar index) to calculate the line of best fit. It computes the summation of price coordinates, calculating the slope and y-intercept to minimize the sum of the squared residuals. Furthermore, the indicator calculates the population standard deviation of these residuals (errors) to project the outer variance bands. In a normally distributed financial dataset, approximately 68% of price action should remain within Band 1, and 95% within Band 2. When price forcefully breaches Band 2, it statistically indicates an unsustainable momentum extreme, shifting the probability matrix heavily toward imminent mean-reversion. • Quantitative Candlestick Heuristics Traditional Japanese Candlestick theory relies heavily on qualitative visual assessment. This indicator transforms it into a rigorous quantitative science. By expressing wicks, shadows, and true bodies as strict fractional ratios of the total period variance, the algorithm entirely removes psychological bias. For instance, an Engulfing pattern is not merely determined by a visual overlap; the internal logic mathematically validates that the current open and close parameters completely eclipse the previous period's boundaries, while simultaneously verifying that the absolute body size explicitly exceeds the prior via comparative array lookbacks. Furthermore, advanced configurations like the 'Three Line Strike' necessitate the sequential tracking of four independent vector arrays to confirm precise exhaustion geometry and statistical anomaly. ⚠️ Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
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MarkitTick tarafından