Регистрация

Trendlineanalysis

Squeeze Bollinger Bands Tracker [MarkitTick]💡 This institutional-grade analysis suite provides a sophisticated volatility-tracking environment designed to identify market compression phases and high-conviction breakouts. By integrating Bollinger Band standard deviation logic with Z-Score normalization and a non-linear sigmoid volatility engine, the script transforms raw price action into a multi-dimensional heatmap. This approach allows traders to distinguish between low-volatility "coiling" phases and institutional-driven momentum expansions, providing a clear visual representation of market energy. ● ✨ Originality and Utility The Squeeze Bollinger Bands Tracker distinguishes itself through the implementation of a proprietary "Signal Engine" and a non-linear volatility grading system. Unlike standard Bollinger Band indicators that merely plot static lines, this tool actively monitors the rate of change in channel width relative to its own historical standard deviation. • Dynamic Volatility Normalization Most indicators rely on linear calculations that fail to account for the exponential nature of market expansion. This script utilizes a Z-Score calculation to determine how extreme a volatility move is compared to its history, then maps that value through a sigmoid function. This creates a "Heatmap" effect on the candles that reflects institutional participation levels rather than simple price movement. • Institutional Breakout Grading The utility is further enhanced by an automated grading system (Grades A, B, and C). By cross-referencing price spread (the distance between open and close) with actual volume metrics during a breakout, the script provides an objective measure of signal quality, helping traders filter out "fakeouts" that lack volume support. ● 🔬 Methodology and Concepts The logic flow is divided into three core analytical pillars: Compression Detection, Momentum Normalization, and Signal Verification. • Compression Detection (The Squeeze) The script calculates the percentage-based width of the Bollinger Bands. When the current width falls below its SMA-based average, the market is classified as being in a "Squeeze" state. This signifies a period where market energy is being stored, often preceding a significant directional expansion. • Sigmoid-Mapped Z-Score Volatility To provide the neon heatmap coloring, the script calculates the Z-Score of the channel width. This tells us how many standard deviations the current volatility is from the mean. This Z-Score is then processed through a Sigmoid Function: 100 / (1 + exp(-Z-Score)). This mathematical transformation squashes the infinite Z-Score range into a 0–100 scale, creating a smooth gradient for the "True Institutional Heatmap." • Breakout Validation Engine Signals are not generated simply on a price cross. The Signal Engine (a custom User-Defined Type) calculates real-time Entry, Stop Loss (based on the previous basis line), and Take Profit levels. During the moment of crossover, the "calcGrade" method evaluates if the current bar's spread and volume are at least 150% of their historical averages to assign a Grade A "Institutional" breakout. ● 🎨 Visual Guide The visual interface is designed with a high-contrast "3D Neon" aesthetic to ensure critical data points are immediately recognizable during fast-moving market conditions. • The 3D Neon Channels Upper Core & Glow: The upper Bollinger Band is rendered in Cyan (#00FFFF). It features three layers: a 2-pixel core for precision and two wider "Glow" layers with varying transparency (60% and 85%) to create a neon effect. Lower Core & Glow: The lower band is rendered in Magenta (#FF00FF), following the same three-layer glow architecture to signify the support boundary. Basis Core: The central moving average is rendered in Yellow (#FFFF00), acting as the dynamic mean and the primary stop-loss anchor. • True Institutional Heatmap Candles The candle colors are not fixed; they represent a gradient based on the Sigmoid Volatility score. Bullish State: Transitions from a deep "Cold" Forest Green (#004D40) during low-volatility rises to a "Hot" Neon Green (#00FF00) during high-momentum surges. Bearish State: Transitions from a deep "Cold" Purple (#4A148C) during low-volatility drops to a "Hot" Neon Red (#FF0000) during aggressive sell-offs. Neutral State: Gray (#808080) candles appear when no definitive trend state is identified by the Signal Engine. • Analytical Dashboard and Labels Buy/Sell Labels: When a breakout occurs, a Cyan or Magenta label appears. It displays the signal Grade (A, B, or C) and the calculated E (Entry), TP (Take Profit), and SL (Stop Loss) values. Institutional Analytics Dashboard: Located in the top-right, this table provides real-time data on Trend Maturity (in bars), Volatility State (Squeeze vs. Expanding), and the percentage proximity to the upper and lower breakout levels. ● 🔍 Deconstruction of the Underlying Scientific and Academic Framework The indicator is built upon the foundation of Statistical Process Control and Information Theory. By treating price movement as a signal-to-noise problem, the script uses the following frameworks: • Standard Deviation and Gaussian Distribution The core of the Bollinger Band calculation relies on the assumption that price spends approximately 95% of its time within two standard deviations of the mean. The "Squeeze" logic identifies periods where the distribution is abnormally tight, suggesting an imminent return to the mean or a "Fat Tail" event (a breakout). • Z-Score Normalization In statistics, the Z-Score is used to compare observations from different data sets or time periods. By applying Z-Score logic to the width of the bands, the indicator removes the "unit" of price and focuses purely on the intensity of the volatility, allowing for a standardized comparison across different assets (e.g., Bitcoin vs. Apple). • Non-Linear Sigmoid Mapping The use of the Sigmoid function (common in Neural Network activation) serves to eliminate outliers in volatility data. This ensures that the candle heatmap provides meaningful color variations even during extreme "Black Swan" events, preventing the visual output from becoming saturated or unreadable. ● 📖 How to Use Traders should focus on the transition between market states as displayed by the Analytics Dashboard and the Heatmap. • Step 1: Identify the Squeeze Monitor the "Volatility State" in the dashboard. When it displays "⚠️ SQUEEZE" in Neon Orange, the market is coiling. This is the preparation phase where no trades should be taken. • Step 2: Evaluate the Breakout Grade Wait for a "BUY" or "SELL" label to appear. Priority should be given to "Grade A" signals, as these indicate that both price spread and volume have significantly exceeded their 20-period averages, confirming institutional intent. • Step 3: Execution and Risk Management Upon a valid signal, the script provides an automated trade plan. The Stop Loss is set at the Basis (Yellow) line from the previous bar to allow for minor breathing room, while the Take Profit is projected at a 1:1 ratio relative to the width of the band at the time of entry. ● ⚙️ Inputs and Settings The script provides granular control over the analytical engine and the visual experience. • Channel Settings Channel Length: Controls the SMA window for the Bollinger Bands (Default: 20). Standard Deviation Multiplier: Adjusts the width of the neon boundaries (Default: 2.0). • Analytics Settings Squeeze/Z-Score Length: Determines the lookback period used to define what constitutes "average" volatility (Default: 50). Quality SMA Length: Defines the window for the Grade A/B/C volume and spread verification (Default: 20). • Color and Heatmap Settings Users can fully customize the Neon Upper/Lower colors, the Dashboard background transparency, and the specific "Cold" and "Hot" thresholds for the candle gradient engine to match their preferred dark or light chart theme. ⚠️ 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.
Индикатор Pine Script®
от MarkitTick
Trend Master Bundle (5x EMA, DEMA & VWAP) | MouryaThe Trend Master Bundle is a comprehensive, trend-following utility designed to visualize perfect moving average alignment and volume-weighted momentum. By integrating multiple exponential moving averages, a double exponential moving average, and standard deviation VWAP bands, this tool provides a clear, quantitative view of both macro trend direction and intraday mean-reversion zones. The "Perfect Alignment" Strategy The core algorithmic logic of this indicator detects strict momentum states where the moving averages fan out in a specific sequence: Extremely Bullish: Identified when the current price sits above the moving averages, and the MAs are stacked in strict ascending order: 5 EMA > 10 EMA > 20 EMA > 50 EMA > 100 DEMA > 200 EMA. This structural alignment indicates a dominant uptrend. Extremely Bearish: Identified when the price remains below the moving averages, and they are stacked in descending order: 5 EMA < 10 EMA < 20 EMA < 50 EMA < 100 DEMA < 200 EMA. Core Indicator Components 5x Exponential Moving Averages: Standard institutional lengths (5, 10, 20, 50, 200) serve as dynamic support and resistance layers. 100-Period DEMA: The Double Exponential Moving Average is utilized to reduce the lag inherent in standard EMAs. It reacts faster to price changes, serving as a highly responsive lead indicator for mid-to-long-term trend transitions. VWAP with Standard Deviation Bands: The anchored Volume Weighted Average Price calculates the asset's true fair value based on volume and price. The surrounding standard deviation bands help measure market volatility and extension. Upgraded Visual Features End-of-Line Tracking Labels: Dynamic text labels automatically track the most recent candle, floating the name of each specific moving average (e.g., "EMA 50", "VWAP") directly at the end of its respective line for instant visual identification. Dynamic Premium Dashboard: A sleek, non-intrusive dark-mode widget provides a real-time readout of the current trend state (Extremely Bullish, Extremely Bearish, or Neutral) and lists the exact current price values for all active moving averages. Full Customisation: Every MA length, colour, VWAP anchor, and UI element can be toggled or adjusted via the indicator settings menu to suit individual charting preferences. Practical Application Trend Confirmation: Traders can use the dashboard to ensure they are trading in the direction of the dominant momentum. Pullback Entries: During a confirmed "Extremely Bullish" or "Extremely Bearish" state, the 20 EMA and VWAP baseline often act as areas of interest for trend-continuation setups. Mean Reversion: Price interaction with the outer VWAP standard deviation bands (Bands #2 and #3) can signal statistically overextended market conditions, helping identify potential exhaustion points or mean-reversion opportunities.
Индикатор Pine Script®
от RajMouryaReddy
Fractal TrendlinesFractal Trendlines is a non-repainting market structure indicator that builds dynamic support and resistance trendlines directly from confirmed fractal pivots. The script identifies major swing highs and lows using customizable left/right fractal strength settings, then connects those pivots into active trendlines while filtering out structurally weak lines using an optional Verified Trendlines mode. Verified red trendlines require lower highs and verified green trendlines require higher lows, helping remove a large amount of noisy or directionless structure. The indicator is designed to separate short-term fractal structure from larger directional trend structure. Fractal markers and short pivot lines help visualize local reaction zones and recent liquidity pivots, while the trendline engine focuses on broader structural continuation and breakout behavior. Fractal Historical Lookback controls only the visible fractal markers and pivot lines without affecting the internal pivot memory used by the trendline engine. This allows traders to maintain a clean chart visually while still preserving deeper structural trendline calculations internally. Break signals trigger only on currently active displayed trendlines. Once a trendline has produced a valid break signal, it becomes inactive and cannot generate repeated break labels. Break labels are directly tied to their parent trendline, so when older trendlines are removed by the Max Trendlines setting, their associated break labels are automatically removed as well. This prevents stale signals from remaining on the chart and keeps the display focused on current actionable structure. The indicator can be used across multiple timeframes and trading styles. Lower fractal settings produce faster and more reactive structure, while higher settings produce cleaner swing-based trendlines more suitable for swing trading and macro structure analysis. RSI confirmation is optional and can be enabled to filter fractals using momentum conditions. Default settings are optimized for clean chart structure with minimal visual clutter while still retaining enough context for breakout and trend continuation analysis.
Индикатор Pine Script®
от tgregg75
Structure Deviation Ledger [JOAT]Structure Deviation Ledger Introduction Structure Deviation Ledger is an open-source structure-tracking overlay designed to monitor how price behaves around a stepped volatility corridor and pivot-derived structure rails. It combines pivot rail continuation, a stateful stepped midpoint, inner and outer ATR corridors, frozen breakout rails, right-edge structural labels, and an optional TP/SL scaffold when confirmed structural displacement occurs. The purpose of the script is to answer a practical question: is price still behaving inside accepted structure, or has it displaced far enough to qualify as a meaningful structural event? By scoring deviation relative to a stepped corridor and confirmed pivot rails, the indicator provides a cleaner framework for continuation and failure analysis than simple moving-average crossovers. Core Concepts 1. Pivot-Derived Structure Rails Confirmed pivot highs and lows are connected into forward rails. These rails act as the nearest structural references for continuation or failure. 2. Stepped ATR Corridor The script maintains a stepped midpoint derived from a smoothed basis and ATR logic. The midpoint only reanchors when price stretches far enough to justify a structural adjustment. 3. Confirmed Structural Breaks A structural break is only promoted when price closes beyond the relevant active rail and also pushes outside the inner corridor. This confirmation rule is designed to reduce weak intrabar noise. 4. Frozen Break Rails When a fresh break is confirmed, the script freezes a breakout rail and a related context box so the chart retains forward reference after the initial event. 5. Execution Scaffold On fresh structural expansion or structural pressure events, the indicator can build an informational TP/SL ladder using ATR-based stop distance and configurable R multiples. Features Pivot structure rails: Forward-projected high and low rails derived from confirmed pivots Stepped structure midpoint: State-aware corridor center that does not update every bar like a normal average Inner and outer ATR corridors: Layered bands for contained vs displaced price behavior Fresh break detection: Confirmed-bar breakout logic for upside and downside structural events Frozen break rails and zones: Persistent post-break context on the chart Right-edge labels: Live labels for midpoint, inner levels, and active rail reference Optional TP/SL ladder: Entry, stop, TP1, TP2, TP3 with risk/reward fill Top-right dashboard: Displays current structural state, deviation, corridor levels, and rail count How to Use This Indicator Step 1: Read whether price is inside the corridor or displacing beyond it. Step 2: Compare price to the active high or low rail. These are the nearest structure references. Step 3: When a fresh confirmed break appears, use the frozen rail and optional ladder as a planning map, not as a guarantee. Step 4: If price returns back through the corridor after a break, treat that as a sign of failed displacement. Indicator Limitations Pivot rails are naturally delayed because pivots require confirmed bars on both sides Stepped corridors intentionally lag during transitions in order to avoid unstable shifting A dense market with many pivots can still generate frequent rail updates The TP/SL ladder is informational only and does not place orders Originality Statement Structure Deviation Ledger is original in the way it merges pivot-derived structural rails, a stepped ATR corridor, frozen breakout context, and execution scaffolding into a single open-source structure overlay. Its goal is to provide a reusable institutional structure map rather than a simplified breakout marker. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Structural breaks and corridor deviations are derived from historical price action and do not guarantee future movement. Always use appropriate risk controls. -Made with passion by jackofalltrades
Индикатор Pine Script®
от officialjackofalltrades
Smart Trader, Episode 07, ICS Geometric Buyers/Sellers Pressure🔶 Overview ICS Geometric Buyer/Seller Pressure measures the real-time balance between buying and selling forces through a geometric framework built on triangle areas. Rather than relying on volume, oscillators, or moving-average crossovers, this indicator constructs two right triangles on every bar — one representing seller pressure above the current price, one representing buyer pressure below it — and computes their areas inside a normalized coordinate system called the Isotropic Coordinate System (ICS). The ICS transforms raw price and time into a dimensionless plane using Yang-Zhang composite volatility as the scaling factor. Because both axes are divided by the same volatility estimate, the resulting triangle areas carry no unit — they are pure geometric ratios. This makes the pressure reading comparable across any instrument, any timeframe, and any price scale, without the trader needing to adjust parameters when switching charts. From these two normalized areas, the indicator derives a single metric called B, which condenses the entire buyer-versus-seller balance into a value between −1 and +1. B is then converted into intuitive percentage readings (Red % for seller dominance, Blue % for buyer dominance) and visualized through a gradient barometer column, a triangle fan overlay, and data-window plots ready for alerts. 🔶 Conceptual framework To measure the real-time balance between buying and selling forces, this indicator takes a geometric approach rather than relying on volume analysis, oscillator divergences, or moving-average crossovers. Two right triangles are constructed on every bar — one above the current price toward the range ceiling, one below toward the range floor — and their areas are compared to determine which side of the market currently dominates. Computing triangle areas in raw price-versus-time coordinates, however, introduces a structural problem: the same price movement produces a different geometric shape depending on the chart's zoom level, time compression, or display resolution. A 30-point rally on a compressed weekly chart creates a steep, narrow triangle; the identical rally on a stretched intraday chart creates a flat, wide one. The areas differ even though the underlying market event is the same. To eliminate this distortion, the indicator applies a normalization layer referred to here as the Isotropic Coordinate System (ICS). The principle behind it is dimensional analysis — a well-established technique in physics and engineering for removing unit-dependent artifacts from measurements. The horizontal axis (time) is rescaled by dividing bar offsets by sigma, and the vertical axis (price) is rescaled by dividing the natural logarithm of price by the same sigma. Because both axes share the same divisor, the resulting coordinate plane is isotropic: triangle areas reflect only the structural relationship between price and range boundaries, not how the chart happens to be displayed. The sigma used for this normalization is the Yang-Zhang (2000) composite volatility estimator, a published academic method (Journal of Business, Vol. 73, No. 3). It combines three independent variance components — overnight (close-to-open), intraday (open-to-close), and the Rogers-Satchell high-low-close estimator — into a single unbiased measure with minimum-variance weighting. This makes sigma robust across instruments with overnight gaps (equities, futures) and those that trade continuously (forex, crypto). The practical result: the normalization layer adapts to the volatility regime of each instrument, making triangle areas structurally comparable across different charts and timeframes and reducing the need for manual recalibration when switching instruments. 🔶 The B metric: from triangle geometry to a single number The core output of this indicator is a single value called B, which captures the instantaneous buyer-versus-seller balance in one dimensionless number. Picture any bar on your chart. The indicator draws two right triangles around it. The upper triangle sits between the bar's high and the range ceiling: its three vertices are (1) the current bar's high, (2) the range ceiling at the current bar, and (3) the range ceiling at the prime-offset bar, 101 bars back. This triangle represents seller territory — the geometric "room" that sellers occupy above the current price. The lower triangle mirrors this below: its vertices are the current bar's low, the range floor at the current bar, and the range floor at that same prime-offset bar. This is buyer territory — the room below the current price. The larger the seller triangle relative to the buyer triangle, the more the market is tilted toward selling pressure, and vice versa. Why prime-numbered offsets? The indicator uses the first 25 prime numbers (3, 5, 7, 11, ... 97, 101) as its sampling offsets. Prime numbers share no common factors with each other or with any periodic cycle in the data. When a signal is sampled at evenly spaced intervals (e.g. every 10, 20, 30 bars), there is a risk that the sampling grid locks onto a periodic pattern in the price — a weekly cycle, an options expiration rhythm, or any recurring structure — and either amplifies or masks it. This is a form of harmonic aliasing. Prime offsets avoid this: because no prime is a multiple of any other, the sampling set {3, 5, 7, ... 101} is maximally non-periodic, ensuring that each offset captures a structurally independent slice of the price range. Two roles: measurement and visualization For the B calculation itself, only the widest triangle is used — the one anchored at prime offset 101. This single pair of triangles (upper and lower) captures the broadest structural pressure across the entire lookback window. The remaining 24 primes serve a visual role: they generate the triangle fan overlay you see on the chart. But this visual layer is not merely decorative. Each triangle in the fan maps a pressure boundary at a different time horizon. As the screenshot above illustrates, candles that approach the red triangle edges tend to encounter resistance and reverse — the fan effectively draws a multi-scale map of where selling pressure intensifies. The blue fan does the same for buyer pressure below. Taken together, the fan gives the trader a spatial reading of how pressure distributes across shorter and longer horizons. A notable property observed during testing across multiple instruments and timeframes: regardless of triangle size or lookback period, B consistently produces values within the bounded range of -1 to +1. This is not a coincidence — it is a mathematical consequence of the symmetric ratio formula that derives B from the two triangle areas. The formula Both triangle areas are first computed using the Shoelace formula — a standard computational geometry method that yields the exact area of any polygon from its vertex coordinates. Then B is derived through a symmetric ratio: r1 = A_hi / A_lo     r2 = A_lo / A_hi     B = (r1 - r2) / (r1 + r2) When the seller triangle is much larger than the buyer triangle (A_hi >> A_lo), r1 grows large while r2 shrinks, and B approaches +1. When buyer pressure dominates, B approaches -1. When both areas are equal, B = 0 — balanced pressure. The formula is symmetric by construction, meaning it treats buyer and seller sides with identical mathematical weight. Percentage conversion To make B immediately readable on the chart, the indicator converts it into two percentage values: Red % = (B + 1) x 50     seller dominance, scale 0 to 100 Blue % = (1 - B) x 50     buyer dominance, scale 0 to 100 Red % and Blue % always sum to 100. They are available in the Data Window for any bar and are exposed as alert-ready plots, allowing traders to set threshold-based alerts (e.g. "Red % crosses above 80") directly from TradingView's alert builder, without writing any code. 🔶 Features at a glance 🔸 Gradient barometer — A vertical column rendered to the right of the last bar. It splits the effective range into a red (seller) zone and a blue (buyer) zone, with the dividing line set by B. The gradient fades from full opacity at the split point to near-transparent at each range boundary, giving an immediate visual sense of which side is dominant and by how much. 🔸 Prime triangle fan — 25 filled triangles (one per prime offset from 3 to 101) overlaid on the chart. Upper triangles are colored red (seller pressure), lower triangles blue (buyer pressure). Together they form a fan that maps pressure intensity across multiple time horizons simultaneously. Optional dashed outlines can be enabled for each side independently. 🔸 Range lines with price labels — Horizontal lines marking the effective high and low of the lookback window. Each line carries a price label placed to the left of the range start. When the channel is frozen (see Freeze/Revival below), a snowflake icon (❄) appears on the labels. 🔸 Diamond markers and prime labels — At each prime-offset bar, a diamond marker is placed at both the range ceiling and the range floor. The corresponding prime number is displayed above the ceiling diamond, providing a visual ruler of the sampling structure. 🔸 Freeze / Revival system — When a confirmed close breaches the range boundary, the mother channel freezes and a child channel is born on the breach side. The child computes its own pressure metric (B'), and when the opposing force inside the child reaches a user-defined threshold, the mother channel revives. This mechanism tracks regime transitions without discarding the prior range context. A dedicated label at the breach candle shows the child's B' value in real time. 🔸 Live and Closed display modes — "Live" updates tick by tick using the current bar's data. "Closed" anchors all calculations on the last confirmed bar, eliminating intra-bar noise for traders who prefer signal stability. 🔸 Data Window and alert-ready plots — Three invisible plots (Red %, Blue %, raw B) are exposed in the Data Window and available for TradingView's alert condition builder. Traders can create threshold, crossover, or crossing alerts on any of these values without writing Pine Script. 🔸 Full visual customisation — Every visual element (triangle fill colors, line colors, diamond size, text size, barometer width, barometer offset, gradient steps) is independently configurable through the indicator's settings panel. 🔶 Deep dive: the barometer The barometer is a vertical gradient column displayed to the right of the last bar on the chart. Its purpose is to translate the abstract B value into a shape that the eye can read instantly: a column split into a red zone (seller pressure) on top and a blue zone (buyer pressure) on the bottom. The column spans the full effective range — from rangeLow at the bottom to rangeHigh at the top. The split point between red and blue is not placed at the midpoint of the range. Instead, it is calculated directly from B: yMid = rangeHigh − (B + 1) × range / 2 When B = 0 (balanced), yMid sits at the exact center of the range. When B approaches +1 (full seller dominance), yMid drops toward the range floor, making the red zone fill nearly the entire column. When B approaches −1 (full buyer dominance), yMid rises toward the range ceiling, and the blue zone dominates. The gradient is rendered using a configurable number of boxes (default: 50). In the red zone, opacity is strongest near yMid and fades to near-transparent at rangeHigh. In the blue zone, opacity is strongest near yMid and fades toward rangeLow. This creates a natural "heat" effect: the most intense color always concentrates at the boundary where the two forces meet. At the top and bottom of the column, percentage labels display the Red % and Blue % values. These are the same percentages available in the Data Window, presented here as a quick visual reference. The barometer responds to the selected display mode. In "Live" mode, it updates on every tick using the current bar's B value. In "Closed" mode, it uses the B computed from the last confirmed bar, providing a stable reading that does not flicker with intra-bar price movement. Barometer settings 🔸 Show barometer — Toggle the entire barometer on or off. Default: on. 🔸 Offset (bars right) — How far to the right of the last bar the column is placed. Default: 11. Increase this if the barometer overlaps with other right-margin elements. 🔸 Width (bars) — The horizontal thickness of the column, measured in bars. Default: 5. 🔸 Gradient steps — The number of boxes used to render the gradient. Higher values produce a smoother fade. Default: 50. 🔶 Deep dive: the prime triangle fan The triangle fan is the indicator's signature visual element. It renders 25 filled triangles on the chart — one for each prime offset from 3 to 101 — fanning out from the current bar toward the left side of the lookback window. Upper triangles are shaded red (seller pressure) and lower triangles are shaded blue (buyer pressure), each with high transparency so the underlying candlesticks remain clearly visible. Every triangle in the fan shares two of its three vertices with the current bar: the bar's high (for upper triangles) or the bar's low (for lower triangles), and the corresponding range boundary at that bar. The third vertex sits at the range boundary at the prime-offset bar. Because each prime offset is a different distance back in time, the triangles vary in width — the smallest is narrow and captures very short-term pressure, while the largest stretches across the full lookback and captures the broadest structural picture. Reading the fan as a pressure map The fan functions as a multi-scale pressure map. Each triangle edge represents a boundary where one side's territory begins. When price approaches a cluster of red triangle edges from below, it is entering a zone where seller pressure intensifies across multiple time horizons simultaneously. The denser the overlap of red edges at a given price level, the stronger the structural resistance at that level. The same logic applies in reverse for blue edges and buyer support. This is visible in practice: candles that push into the red fan often stall or reverse at the triangle boundaries, while candles that drop into the blue fan tend to find support. The fan gives the trader a spatial sense of how much room each side has — a wide blue zone with thin red edges suggests buyers have structural space to move, and vice versa. Color flipping during freeze When the Freeze/Revival system is active and price moves beyond the frozen range boundary, the triangle colors on the breached side flip to reflect the new structural reality. Consider a downward breach: price closes below the frozen rangeLow and continues falling. The lower triangles — which normally appear blue to represent buyer territory — switch to red. This signals that what was once the buyer's domain has been structurally penetrated; the geometry now measures selling pressure extending below the old floor. At the same time, the upper triangles remain red as they always are, and because the distance between the current price and the frozen rangeHigh has grown dramatically, the seller area expands. The visual result: the entire fan turns uniformly red, reflecting overwhelming seller dominance across every time horizon in the fan. The mirror case works identically. During an upward breach, price closes above the frozen rangeHigh and continues rising. The upper triangles flip from red to blue, signaling that seller space has been penetrated from below. The lower triangles remain blue, and because the gap between the current price and the frozen rangeLow is now vast, buyer area dominates. The entire fan turns uniformly blue, reflecting overwhelming buyer dominance. The color flip is automatic and requires no user intervention. It is driven entirely by the relationship between the current price and the frozen boundaries — when price returns inside the frozen range, colors revert to their normal assignment. Diamond markers and prime labels At each prime-offset bar, the indicator places diamond-shaped markers at both the range ceiling and the range floor. Above the ceiling diamond, the prime number itself is displayed as a label. These markers serve as a visual ruler: they show the trader exactly where each sampling point falls in time and make the non-periodic spacing of the primes immediately visible on the chart. Fan settings 🔸 Show lower triangle lines / Show upper triangle lines — Toggle dashed outlines for each side. Default: off. When enabled, the outlines make individual triangle edges more distinct, which can be helpful when reading overlapping edges at specific price levels. 🔸 Lower / Upper line color — Stroke color for the dashed outlines. 🔸 Lower / Upper fill color — Fill color and transparency for the triangle bodies. Default: high transparency so candles remain readable. 🔸 Show vertical lines — Draws a vertical line at each prime-offset bar. Default: off. 🔸 Show prime labels — Displays the prime number and diamond at each offset. Default: on. 🔸 Diamond color / Diamond size — Visual styling for the diamond markers. 🔸 Label text size — Font size for the prime number labels. 🔸 Deep dive: Freeze / Revival Markets do not stay inside ranges forever. When price breaks out, most range-based indicators simply reset and start a new range from scratch, discarding whatever structural context existed before the breakout. The Freeze/Revival system takes a different approach: it preserves the prior range as a frozen reference while simultaneously tracking the new regime that emerges beyond it. How a freeze is triggered A freeze occurs when a confirmed close — not a wick, not an intra-bar spike — breaches the effective range boundary. The indicator compares the previous bar's close against the range that existed one bar before it, so the breach signal is fully confirmed and cannot repaint. Once a breach is detected: 🔸 The mother channel freezes — its high and low are locked at the values they held just before the breach. 🔸 A child channel is born on the breach side. For an upward breach, the child's floor is the frozen rangeHigh and its ceiling expands with each new high. For a downward breach, the child's ceiling is the frozen rangeLow and its floor drops with each new low. 🔸 A snowflake icon (❄) appears on the range price labels, and the triangle colors flip as described in the section above. The child channel and B' While the mother channel is frozen, the child channel computes its own independent pressure metric called B'. B' uses the same ICS triangle formula as the mother's B, but measured against the child's own boundaries. This means B' tracks the buyer/seller balance exclusively inside the new regime — the territory beyond the old range. A dedicated label appears at the breach candle showing the current B' value, converted to the percentage of the opposing force. For a downward breach, the label displays the buyer percentage inside the child; for an upward breach, it displays the seller percentage. This tells the trader how much counter-pressure is building inside the breakout zone. B' as a structural overbought / oversold reading When B' shows a very low opposing-force percentage shortly after a breach, the breakout side is structurally dominant — price has moved aggressively beyond the old range with minimal resistance. This condition is analogous to what traders call an overbought or oversold state, but derived from geometry rather than from momentum oscillators. The reading reflects the spatial imbalance between the two forces inside the child channel: one side occupies nearly all the geometric territory. As time passes, if the opposing force gradually builds — the B' percentage climbs — it signals that the breakout is losing its structural one-sidedness. The market is beginning to rebalance inside the new territory. Watching B' evolve over successive bars gives the trader a real-time gauge of whether the breakout retains its structural conviction or is approaching exhaustion. Revival: when does the freeze end? The freeze lifts when the opposing force inside the child channel reaches a user-defined threshold (default: 50%). At that point, the indicator interprets this as the exhaustion of the breakout: the force that drove the breach is being met by equal or greater counter-pressure. The freeze is lifted, all freeze state is reset, and the mother channel resumes normal range tracking. The snowflake icons, B' label, and color flips are removed. The revival threshold is configurable. A lower value makes the system more sensitive — it revives sooner, treating even moderate counter-pressure as a regime reset. A higher value makes it more patient — it waits for stronger opposition before releasing the freeze. The default represents balanced equilibrium: the freeze ends when the opposing side has matched the breach side. Why this matters The Freeze/Revival cycle gives the trader a structured way to observe regime transitions. Rather than watching a range silently reset after a breakout, the trader sees the old range preserved as context (frozen lines with ❄), the new regime measured in real time (B' at the breach candle with its overbought/oversold implication), and a clear signal when the transition is complete (revival). This makes it possible to distinguish between a genuine regime change and a brief spike that reverts — without relying on arbitrary time delays or fixed-bar re-entry rules. 🔸 Reading the indicator This indicator does not generate buy or sell signals. It is a measurement tool that quantifies the geometric balance between buyer and seller pressure. How that measurement is incorporated into a trading decision is entirely up to the trader. The following observations describe what the indicator shows, not what the trader should do. The barometer as a quick-glance gauge The barometer provides the fastest reading. A column dominated by red indicates that seller pressure is structurally larger than buyer pressure across the lookback window. A column dominated by blue indicates the reverse. When the split point sits near the center, pressure is approximately balanced. Watching how the split point migrates over successive bars reveals whether the pressure balance is shifting gradually or remaining stable. The fan as a spatial context layer The triangle fan adds spatial depth to the barometer's single-number reading. While the barometer tells you the current balance, the fan shows you where that balance is concentrated in price space. Areas where multiple triangle edges converge represent zones of intensified pressure — structural resistance above (red edges) or structural support below (blue edges). When price trades inside a region with sparse triangle coverage, it has more structural room to move before encountering the next pressure boundary. Freeze events as regime markers When a freeze occurs, it marks a structural event: price has left the established range. The frozen lines (marked with ❄) preserve the old context, and B' at the breach candle provides a real-time measure of how one-sided the new regime is. A very low opposing-force reading in B' indicates a structurally extended condition — the breakout side has occupied nearly all geometric territory. As B' climbs toward the revival threshold, it indicates increasing counter-pressure. The moment of revival itself marks the point where the new regime's one-sidedness has been structurally neutralized. Combining readings The three visual layers — barometer, fan, and freeze state — work together. For example, a barometer showing strong seller dominance combined with a fan whose red edges are densely clustered near the current price suggests concentrated structural resistance. If a freeze is also active with a low B', the structural picture is one of strong directional conviction on the breach side. Conversely, a barometer near balance with widely spaced fan edges and no active freeze suggests a structurally neutral environment. Data Window and alerts The Red %, Blue %, and raw B values are available in TradingView's Data Window for any bar by hovering over it. These same values are exposed as alert-ready plots, meaning traders can set alerts directly from TradingView's alert builder — for example, triggering when Red % crosses above or below a chosen level, or when B crosses zero. No Pine Script knowledge is required to create these alerts. 🔸 Open-source structure and reusability This script is published open-source under the Mozilla Public License 2.0. The full computation pipeline — the Yang-Zhang volatility estimator, the ICS coordinate transformation, the Shoelace area calculation, and the symmetric ratio that produces B — is readable, auditable, and reusable. B is a bounded output: it always falls between −1 and +1, carries no unit, and is computed from normalized geometry. These properties make it suitable as an input for other scripts. Examples of how B can serve as input to further analysis include: 🔸 Plotting B as a standalone oscillator with its own zero line and structural extremes. 🔸 Applying moving averages of different periods to B and studying their crossovers as indicators of shifting pressure regimes. 🔸 Using B as a weighting coefficient to scale other measurements by the current geometric pressure balance. 🔸 Comparing B across timeframes, since the ICS normalization makes the metric structurally comparable regardless of the chart resolution. 🔸 Testing for divergences between B and price action. 🔸 Using B as a filter condition for entry or exit logic in other strategies. The code is available for study and extension under MPL 2.0. Traders and developers who wish to build on this metric have full access to its derivation. 🔸 Settings reference Range Lines 🔸 Lookback length — Number of historical bars used to compute the high/low range. Default: 101. 🔸 Line width — Pixel width of the horizontal range lines. Default: 1. 🔸 Display mode — "Live" updates tick by tick using the current bar. "Closed" anchors on the last confirmed bar, eliminating intra-bar noise. Default: Live. ICS 🔸 ICS Window — Number of bars fed into the Yang-Zhang volatility estimator. Controls how much historical data shapes the normalization sigma. Default: 101. Prime Verticals and Labels 🔸 Show vertical lines — Draws a vertical line at each prime-offset bar. Default: off. 🔸 Show prime labels — Displays the prime number and diamond marker at each offset. Default: on. 🔸 Vertical line color — Color for vertical lines at prime offsets. 🔸 Diamond color — Color of diamond markers and their labels. 🔸 Label text size — Font size for prime number labels, in points. 🔸 Diamond size — Size of the diamond-shaped markers, in points. Prime Triangles 🔸 Show lower triangle lines — Toggle dashed outlines for lower (buyer) triangles. Default: off. 🔸 Show upper triangle lines — Toggle dashed outlines for upper (seller) triangles. Default: off. 🔸 Lower line color — Stroke color for lower triangle dashed outlines. 🔸 Upper line color — Stroke color for upper triangle dashed outlines. 🔸 Lower fill color — Fill color and transparency for lower (buyer) triangle bodies. 🔸 Upper fill color — Fill color and transparency for upper (seller) triangle bodies. Barometer 🔸 Show barometer — Toggle the barometer column on or off. Default: on. 🔸 Offset (bars right) — Horizontal distance from the last bar to the barometer column. Default: 11. 🔸 Width (bars) — Horizontal thickness of the barometer column. Default: 5. 🔸 Gradient steps — Number of boxes used to render the gradient. Higher values produce a smoother fade. Default: 50. Freeze and Revival 🔸 Revival threshold (B') — When the opposing force inside the child channel reaches this percentage, the freeze ends and the mother channel resumes. A lower value revives sooner; a higher value waits for stronger counter-pressure. Default: 50. 🔸 Disclaimer This indicator is a technical analysis tool designed for educational and informational purposes. It measures the geometric balance between buyer and seller pressure using the methodology described above. It does not predict future price movements, does not guarantee any outcome, and does not constitute financial, investment, or trading advice. The B metric, the barometer, the triangle fan, and the Freeze/Revival system are structural measurements derived from historical price data. Like all technical indicators, they reflect past and present conditions and carry inherent limitations. Market conditions can change rapidly, and no single measurement tool can account for all factors that influence price. Traders should use this indicator as one component within a broader analytical framework, always in combination with their own research, risk management practices, and judgment. Past performance of any reading or pattern observed through this indicator is not indicative of future results. Use this tool at your own risk. The author assumes no liability for any trading decisions made based on the information provided by this indicator.
Индикатор Pine Script®
от ata_sabanci
4
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
Индикатор Pine Script®
от officialjackofalltrades
Candle DNA Morphology | AnonycryptousCandle DNA Morphology | Anonycryptous Description & user manual Why is this indicator different: Most candle analysis tools work with names. They look at a candle and call it an engulfing, a hammer, a doji, a shooting star. They compare shape against a fixed template and fire a signal when the match is close enough. The problem is that names are approximations. A hammer in a trending market is not the same as a hammer at a structural level with elevated volume. A name cannot capture that. A fixed template cannot either. Candle DNA Morphology works differently. It does not use names. It does not use templates. It compares the current candle to every historical candle within a configurable lookback window and finds the one that is mathematically closest — across up to eight normalized dimensions simultaneously. Body size, wick proportions, body position within the range, relative volume, candle size relative to recent volatility, trend alignment, and structural context. Then it tells you what happened after that historical candle, directly on the chart, with a single marker you can hover. Other tools that use candle similarity show you statistics. Continuation rates. Reversal percentages. Tables to read and interpret. You still have to decide what the numbers mean and whether they matter right now. Candle DNA Morphology does not show you statistics. It shows you a signal — the exact historical candle that matched, marked on the chart, with the date, the score, the direction, the R value, the percentage move, and the price move of what followed. No table to interpret. No calculation required. The information is in the hover. The score tells you how close the match was. The tier tells you how rare it is. The color tells you the direction. Everything else is in the tooltip. Important notice Candle DNA Morphology generates signals based on historical pattern similarity and price behavior. These signals are not financial advice. They do not predict the future. They do not guarantee profitability. The outcome values shown — R, percentage, and price — are historical measurements from the matched candle in the past. They reflect what happened then, on that instrument, at that price level. The market may behave differently now. Similar candles can produce different results. These values are context, not certainty. All trading decisions are made entirely by the user. Always manage your own risk. Always apply your own judgment. 1. Overview Candle DNA Morphology is a candle fingerprint matching indicator built around the mathematical similarity between the current candle and historical ones — and what those historical candles preceded. What it includes: - Up to 8-dimensional candle fingerprint matching using Euclidean distance - Configurable lookback window from 50 to 1000 bars - Three signal quality tiers: standard, elite, and ultra - Hoverable triangle markers on signal bars with full match details in the tooltip - Diamond marker and vertical line on the exact historical matched candle - Configurable cooldown between signals to prevent clustering - Signal mode selector: current bar, match bar, or both - Dynamic score normalization — score stays 0-100 regardless of how many dimensions are active - Vertical signal line and background glow on qualifying bars - Live dashboard with score bar, tier, bias, match date, outcome in R / percentage / price, cooldown countdown, active dimensions, and ATR - All times displayed in the timezone you select in the dashboard settings - Four alerts: bull match, bear match, elite match (90+), ultra match (95+) 2. How the fingerprint works Every candle is described by a set of normalized values between 0 and 1. These values capture the shape and context of the candle without using price or time units — which is what makes the engine self-calibrating across instruments and timeframes. The five core dimensions are always active: Body ratio. The size of the body relative to the total candle range. A full-body candle scores near 1. A doji scores near 0. Upper wick. The upper wick as a proportion of the total range. A long upper wick scores high. A candle with no upper wick scores 0. Lower wick. Same logic for the lower wick. Body position. Where the body sits within the range — 0 is at the bottom, 1 is at the top. A bullish candle with a close near the high scores near 1. A bearish candle with a close near the low scores near 0. Relative volume. Volume divided by the 20-bar average, capped at 3x and normalized. A spike at 2x average scores higher than a quiet bar at 0.8x. Three optional dimensions can be enabled independently: Atr percentile (dimension 6). How large the current candle is relative to recent volatility. Measures the candle range against the 14-bar ATR, normalized to a 0-2x cap. A large candle matches better with historically large candles. Trend alignment (dimension 7). How far the close sits above or below EMA 21, normalized to the ATR. A bullish candle well above the EMA matches better with historical bullish candles that were also above the EMA. Candle context (dimension 8). The relationship between the current candle and the previous one. Inside bars score 0. Outside bars score 1. Normal bars score 0.5. This dimension helps the engine recognize structural setups like inside bar breakouts. 3. Scoring and tiers Similarity is measured using Euclidean distance — the straight-line distance between two candles in multi-dimensional space. The closer two candles are in this space, the more similar they are. The maximum possible distance scales automatically with the number of active dimensions, so the score always ranges from 0 to 100 regardless of configuration. A score of 100 would mean a perfect match across all active dimensions. A score of 70 means a strong structural similarity. A score of 95 or above is exceptionally rare. Three tiers determine how signals are displayed: Standard (70-89). Green for bull, red for bear. The match is meaningful but not uncommon. Elite (90-94). Gold marker with a star prefix ★. The match is close enough to be notable. These appear infrequently. Ultra (95+). Cyan marker with a diamond prefix ◈. A near-identical historical candle was found. Very rare. When one fires, it is worth attention. 4. Understanding the signal When a signal fires, two things appear on the chart depending on your signal mode setting. A triangle on the current bar points in the direction the market moved after the historical match — up for a bullish outcome, down for a bearish one. Hovering the triangle with your cursor or S-Pen shows the full match details: the date and time of the historical candle, the score, the tier, the bias, the outcome in R, and the R value. When a signal fires, two dotted lines and a vertical connector appear on the chart. The first dotted line runs horizontally from the signal bar close — this is the entry reference level, the price from which all R, percentage, and dollar measurements are calculated. The second dotted line shows the target level — where the market would reach if the historical outcome repeats. A vertical connector at the end of the forward window closes the structure. All three lines run exactly as many bars as your forward outcome setting. The target level is calculated using the current ATR, not the historical ATR from the matched candle. This makes it relevant to the current market conditions. Hovering the target line shows both values — the target using the current ATR and the target using the historical ATR — so you can see how much volatility has changed between then and now. If the current ATR is significantly higher than the historical ATR, the target is further away than it was in the original setup. If it is lower, the target is closer. A diamond and a vertical line on the historical matched candle shows you exactly which bar was used as the reference. The line runs through the full range of that candle so there is no ambiguity about which bar matched. The color follows the signal tier. In addition to the dotted lines, the indicator can draw historical projection candles to the right of the signal bar. This feature is off by default and can be enabled in the visuals settings. When enabled, candles appear to the right of the signal bar showing the exact OHLC of the bars that followed the historical matched candle. Each candle has a body and centered wicks, drawn in the tier color of the signal. They scale correctly with chart zoom. A small label above the first candle reads "Historical projection" to make it immediately clear that these are historical bars, not predictions. The number of projection candles follows your forward outcome setting. Body transparency and wick transparency are independently configurable in settings. The bias shown in the dashboard and the tooltip is the direction the market moved after the matched candle — not a prediction of what will happen now. It is historical context, not a guarantee. All times shown in this indicator use the timezone you select in the dashboard settings. The default is UTC. Set it to your local timezone — for example UTC+2 for Amsterdam, UTC-4 for New York, UTC+9 for Tokyo — and all match times will display in your local time automatically. 5. Understanding the outcome values The dashboard and tooltip show three outcome measurements for the historical matched candle. All three describe the same move — the price action in the bars following that historical candle — expressed in different units. Outcome (R). How far price moved after the matched candle, expressed as a multiple of the ATR at that historical bar. This is the primary measurement. It is instrument-independent and works the same on BTC, SOL, MNQ, or any other asset. A value of +2.4R means price moved 2.4 times the ATR upward in the forward window. Outcome (%). The same move expressed as a percentage of the close price at the matched candle. This gives a more intuitive sense of the magnitude for traders who think in percentage terms. Outcome (price). The same move expressed in the currency of the instrument. This is the raw price distance the market covered — for example, $1,075 on Bitcoin or $0.87 on SOL. This value is calculated using the ATR and close price at the historical match bar, not at the current price. The target line on the chart uses the current ATR rather than the historical ATR. This is intentional — the current ATR reflects how the market is moving right now, making the projected target more relevant to your actual trade. The historical ATR is still shown in the target line tooltip for reference, so you can see whether volatility has expanded or contracted since the original setup occurred. A large difference between the two ATR values means the market is in a different volatility regime than it was at the time of the match. An important note on the price value: this reflects the historical move at the time and price of the matched candle. If Bitcoin was at 60,000 when the match occurred and is now at 78,000, the same R move produces a larger dollar value today than it did then. The price outcome is historical context — it shows what that candle type led to in dollar terms at that moment, not what the current setup will produce now. This is not your personal risk-to-reward ratio. The R value gives you the historical move size. Your stop loss placement determines your actual R:R. 6. Bias and cooldown The bias shown in the dashboard reflects the outcome direction of the current best match. It shows bull when the matched historical candle was followed by an upward move, and bear when it was followed by a downward move. Bias changes when a new bar closes and the engine finds a different best match with a different historical outcome direction, or when the cooldown expires and a new signal fires pointing in a new direction. During cooldown, the dashboard shows — wait. The last match date and all three outcome values remain visible. No new signal fires until the cooldown bar count has elapsed. The cooldown countdown shows how many bars remain before the next signal is allowed, or ready when it can fire freely. Cooldown prevents signal clusters from forming on consecutive bars where the same setup repeats. On faster timeframes with many morphologically similar candles, a cooldown of 15 bars or more is strongly recommended. 7. Self-calibration All fingerprint dimensions are normalized to values between 0 and 1. Volume is measured relative to its own 20-bar average. Candle size is measured relative to the ATR. Trend alignment is measured in ATR units. There are no fixed price thresholds anywhere in the engine. This means the indicator adapts automatically to different instruments and timeframes. A BTC candle and an MNQ candle with the same proportional shape, same relative volume, and same relationship to their respective EMAs will produce the same fingerprint score — even though one trades at 78,000 and the other at 20,000. When switching instruments, only the min match score typically needs a small adjustment. SOL and other volatile assets produce more morphological variation, so a slightly lower score threshold (75-80) often works better. More structured instruments like futures perform well at 85-90. 8. Settings guide 8.1 Match engine Lookback window (bars). How many historical bars to scan for fingerprint matches. Range: 50-1000. Recommended: 500 on 1m-5m charts, 1000 on 15m and above. A larger window finds better matches but takes longer on lower timeframes. Min match score (0-100). The minimum similarity score required to fire a signal. 70-79 is standard, 80-89 is strong, 90-94 is elite, 95+ is ultra. Start at 85 and adjust from there. Cooldown between signals (bars). Minimum bars between signals. On a 5m chart, 15 bars equals 75 minutes. On a 1H chart, 15 bars equals 15 hours. Lower values produce more signals. Higher values enforce a minimum spacing between setups. Dimension 6 — Atr percentile. Adds candle size context relative to recent volatility. Recommended: on. Dimension 7 — Trend alignment. Adds EMA 21 directional context. Recommended: on. Dimension 8 — Candle context. Adds inside/outside bar structural context. Recommended: on. 8.2 Outcome filter Forward outcome (bars). How many bars after the historical match are used to measure the resulting move. Match this to your typical trade duration. On a 5m chart with 6 bars, the engine looks at 30 minutes of forward price action to determine the outcome direction and magnitude. Min outcome move (ATR x). The historical match only qualifies if the resulting move exceeded this ATR multiple within the forward window. Filters out matches where the historical candle led to no meaningful move. Recommended: 1.0-2.0. 8.3 Visuals Signal mode. Current bar only shows only the triangle. Match bar only shows only the diamond and vertical line on the historical candle. Both shows both simultaneously. Show score on marker. When on, the triangle displays the score as text. When off, the triangle is clean. Hover always shows the full details regardless. Signal bar background. Subtle glow on the signal bar in the tier color. Vertical signal line. A faint vertical box through the signal bar. Useful for identifying signal bars when zoomed out. Timezone. Select your local timezone from the dropdown. All match times in the dashboard and tooltips display in this timezone. Default is UTC. Amsterdam = UTC+2, London = UTC+1 (summer) or UTC, New York = UTC-4 (summer), Tokyo = UTC+9. 8.4 Dashboard Score bar — visual meter 0-100 in tier color. Tier — standard, ★ elite (90+), or ◈ ultra (95+). Bias — ▲ bull or ▼ bear, or — wait during cooldown. Match date — date and time of the historical matched candle in your selected timezone. Outcome (R) — price move after the match in ATR multiples with forward bar count. Outcome (%) — same move as a percentage of the historical close price. Outcome (price) — same move in currency units at the historical price level. Lookback — current lookback window setting. Min score — current minimum score setting. Cooldown — bars remaining until next signal, or ready. Dimensions — how many dimensions are active and which ones (ATR, EMA, CTX). ATR (14) — current ATR value. 9. Recommended starting settings For 1m-5m scalping: Lookback 500, min score 85, cooldown 15 bars, forward outcome 3 bars, min outcome move 1.0. For 15m-1H: Lookback 1000, min score 88, cooldown 8 bars, forward outcome 6 bars, min outcome move 1.5. For 4H and above: Lookback 1000, min score 90, cooldown 5 bars, forward outcome 4 bars, min outcome move 2.0. 10. How to use Load the indicator and set signal mode to both. This gives you the triangle on the current signal bar and the diamond with border box on the historical matched candle simultaneously. When a signal fires, hover the triangle with your cursor or S-Pen. Read the match date (UTC), score, bias, and outcome values. Then locate the diamond on the chart — the bordered box marks exactly which candle was matched. Look at what followed it on the chart. That is your reference. The three outcome values give you the same historical move in three different units. Use whichever is most natural for how you think about size. The R value is instrument-independent. The percentage gives quick context. The price value shows the raw historical distance. Watch the cooldown counter in the dashboard. When it shows ready, the next qualifying signal will fire without restriction. When it shows a bar count, the engine is waiting before it can fire again. If you see too many signals, raise the min match score or increase the cooldown. If you see too few, lower the min outcome move or reduce the min score. Candle DNA Morphology works best as a confluence tool. It tells you what the current candle morphology historically preceded. Your other indicators — levels, sessions, volume, trend — confirm whether the context justifies acting on it. 11. Disclaimer This indicator is provided for educational and informational purposes only. Nothing in this document constitutes financial advice or any form of recommendation. Trading financial instruments involves substantial risk of loss. Past performance is not indicative of future results. You may lose all of your invested capital. Anonycryptous accepts no responsibility or liability for any losses incurred as a result of using this indicator.
Индикатор Pine Script®
от Anonycryptous
Trendline Retest Planner [AGPro Series]Trendline Retest Planner 🧠 Core Idea Is the active trendline retest constructive enough to keep the setup under review, or is it starting to fail? 📌 Overview / What it does Trendline Retest Planner is a chart-first decision tool for traders who review price behavior around active support and resistance trendline retests. Instead of drawing many trendlines or flagging every break, the script focuses on one active trendline retest context at a time. It measures line age, touch count, retest depth, close response, and volume support, then converts that evidence into a 0-100 Retest Score and a clear next-action state. The script produces an active trendline, retest pocket, hold/fail labels, invalidation edge, target path, alerts, and a compact AGPro planning panel. It does not predict price movement, automate execution, or provide guaranteed outcomes. 🎯 Purpose & Design Philosophy This script was built to fill the gap between auto-trendline drawing and practical retest planning. Many tools can draw diagonals or detect a break. The harder question is what to do after price returns to the line: is the retest holding, weakening, failing, or still waiting for confirmation? Trendline Retest Planner supports a decision workflow: identify the active retest, read the score, locate the invalidation edge, compare the target path, and decide what deserves review next. ⚡ Why This Script Is Different Most trendline tools focus on drawing many lines, ranking confluence, or detecting trendline breaks. This script does NOT clone Auto Trendline Break Quality, Auto Trendlines MTF - Break/Retest, Trendline Confluence Map, Break-Retest Quality, Structure Retest Planner, Swing Retest Quality, or a generic support/resistance zone map. Instead, it narrows the workflow to one active support or resistance trendline retest and evaluates whether that retest is constructive or failing. The main output is not a buy or sell signal. It is a decision state: READY, MONITOR, FAILING, INVALIDATED, WATCH, WAIT, or SCAN. ⚙️ Methodology 1. Context Detection The script builds the latest support and resistance trendline candidates from confirmed swing pivots, then selects the nearest valid retest context. 2. Reference Mapping It maps the active trendline, a retest pocket around the line, an invalidation edge, and a forward target path. 3. Reaction Evaluation The 0-100 model scores line age, touch count, retest depth, close response, and volume support. 4. Visual Output The chart shows the active planning objects, compact state labels, and a clean AGPro decision panel. 🗺️ How to Read the Chart Trendline = the active support or resistance line being evaluated. Retest Pocket = the ATR-based area around the trendline where a retest is considered active. Invalidation Edge = the planning reference where the active retest context is considered broken. Target Path = a forward guide based on the trendline-to-risk distance. Labels = compact TEST, READY, MONITOR, WEAK, INVALID, PATH, and sparse context labels. Colors = teal marks support-side retest context, pink marks resistance-side retest context, amber marks caution, indigo marks monitoring/follow-through, and red marks invalidation. Panel = the AGPro panel summarizes Trendline Side, Retest Score, Hold State, Risk Edge, and Action. 🚦 Signals & States • READY → the retest is active, holding on the correct side, and the score is strong enough for structured review. • MONITOR → the retest is developing, but confirmation or score quality is still incomplete. • FAILING → price touched the pocket, but the response is weak or on the wrong side. • INVALIDATED → price crossed the invalidation edge and the active context should be rebuilt. • WATCH → price is approaching the trendline pocket. • WAIT → a valid trendline exists, but price is not close enough for useful retest review. • SCAN → no valid trendline context is active yet. 🔔 Alerts Logic Alerts trigger when price touches the active retest pocket, when READY state appears, when MONITOR state appears, when a failing retest is detected, when the invalidation edge is crossed, and when follow-through appears after an active retest. Alerts are attention markers only. They are not trade instructions, automated strategy commands, or outcome promises. 🧩 Confluence Logic The strongest context appears when the active line is fresh enough, has enough touches, retests with controlled depth, closes back on the correct side, and receives supportive relative volume. When these components conflict, the panel moves toward MONITOR, FAILING, WATCH, WAIT, or INVALIDATED. 📊 When to Use • Trendline support retests during constructive pullbacks • Trendline resistance retests during bearish continuation attempts • Price-action review after a diagonal support or resistance line is already visible • Retest planning where invalidation and target path matter • Liquid markets where pivots, candles, and volume behavior are readable ⚠️ When NOT to Use • Very low-liquidity symbols with irregular candles • Extremely noisy micro-timeframes • News-driven spikes where trendline geometry becomes distorted • Markets where swing pivots are too compressed to form useful lines • Situations where the user expects a signal-only or auto-trading tool 🎛️ Key Inputs • Trendline Side → selects Auto, Support Retest, or Resistance Retest. • Trendline Pivot Length → controls how confirmed swing anchors are detected. • Max Trendline Age → controls how old a trendline can be before it is ignored. • Touch Count Lookback → controls how extra line touches are counted. • Sensitivity → adjusts retest pocket width and activation distance. • READY Score → sets the score threshold for stronger review states. • Label and Panel Font Size → controls chart-label and panel readability. • Panel Location and Theme → adjusts the AGPro panel layout. 🖥️ Interface & Visual Design The interface is designed to stay chart-first and decision-led. The active trendline is the main reference. The retest pocket is drawn around that line with centered text so the chart stays readable. The invalidation edge and target path explain where the planning context weakens and what forward room is being mapped. The AGPro panel uses a merged blue title row and five compact rows so the user can read side, score, hold state, risk edge, and action quickly. 🧪 Practical Usage Workflow 1. Read the panel state and Retest Score. 2. Check whether price is approaching or touching the retest pocket. 3. Compare current price with the invalidation edge. 4. Review whether the label says TEST, READY, MONITOR, WEAK, INVALID, or PATH. 5. Confirm the broader market context before making any decision. 🔍 Interpretation Guidelines Think in terms of retest quality, not prediction. A higher score means multiple retest components align inside the script's rule set. A weaker score means the line may be too old, the retest may be too shallow, too deep, missing a constructive close, or lacking participation. The script helps organize the review process. It does not replace judgment. 🚫 What This Script Is NOT • Not a prediction engine • Not financial advice • Not an auto-trading system • Not a guaranteed signal tool • Not a broad auto-trendline map • Not a trendline confluence scanner • Not a break-and-retest scanner • Not a generic support/resistance zone map • Not an order block, FVG, or supply/demand tool ⚠️ Limitations & Transparency • Pivot-based trendlines appear only after pivots are confirmed. • Scores can vary across symbols, sessions, and timeframes. • Volume scoring depends on exchange-provided volume data. • Fast volatility can cross a trendline before a clean retest can be measured. • In Live Updating mode, active-bar values can change before the bar closes. 🧠 Market Context Notes Trendline retests are easiest to interpret when liquidity, volatility, and swing structure are readable. A trendline touch is not enough by itself. Line quality, response, invalidation distance, and target path all matter. 🧾 Use Case Examples When price returns to an upward support trendline and closes back above the pocket with a high score, the panel may move toward READY. When price touches a downward resistance trendline but cannot close back below it, the planner may move toward FAILING or INVALIDATED. When price is near the line but has not touched the pocket yet, the panel can stay in WATCH or WAIT. 🧱 System Philosophy Trendline Retest Planner follows the AGPro decision-engine approach: identify context, score quality, map risk, show the next action, and keep the chart readable. It is designed to help traders evaluate a specific planning question instead of adding another generic signal layer. 🔐 Non-Promise Statement No score, label, alert, or chart object guarantees any market outcome. The script organizes retest context and highlights conditions for review. It does not provide certainty. 📉 Risk Disclosure Trading involves risk. Users remain responsible for their own decisions, position sizing, execution, and risk management. This script is for educational and analytical purposes only and does not provide financial advice. 📚 Educational Note Use the planner as a structured reading layer: trendline context first, retest quality second, invalidation and target path third, broader market context always.
Индикатор Pine Script®
от AGProLabs
Sortino Ratio Oscillator [MarkitTick]💡 The Sortino Ratio Oscillator introduces a sophisticated, risk-adjusted performance metric typically reserved for portfolio analysis, adapting it into a highly responsive momentum oscillator. By strictly penalizing downside volatility while rewarding upside momentum, it provides a much clearer picture of market strength compared to traditional oscillators that treat all volatility equally. ✨ Originality and Utility Standard momentum indicators measure the velocity of price movement based on general variance. However, traditional models penalize both upside and downside volatility. A massive bullish breakout creates "high volatility," which standard indicators often misinterpret as an overextended or risky market condition. This script resolves that inherent flaw by migrating the academic Sortino Ratio into a technical trading framework. It isolates "bad" volatility (price drops) from "good" volatility (price gains). The utility here is immense: traders can identify trends where the price action is genuinely supported by positive risk-adjusted returns, filtering out noisy markets where the downside deviation is too high. Furthermore, this tool features an integrated divergence detection engine, dynamic histogram coloring, and built-in webhook alert formatting, making it a comprehensive suite for algorithmic and discretionary traders alike. 🔬 Methodology and Concepts The core engine of this indicator relies on continuously assessing the bar-to-bar percentage return of the asset. First, it calculates the raw percentage return between the current close and the previous close. Next, it isolates the downside returns. If a return is positive, it is ignored for the risk calculation (treated as zero). If it is negative, it is squared to emphasize larger drawdowns, following standard variance practices. The script then computes the Simple Moving Average of these squared negative returns over a user-defined lookback window, calculating the square root to determine the final Downside Deviation. Simultaneously, the Simple Moving Average of the raw returns is calculated to find the mean return over the same period. The final Sortino Ratio is produced by dividing the mean return by the downside deviation. To smooth the output and generate actionable crossovers, a secondary Signal Line is derived by applying an average to the raw Sortino Ratio. To enhance the analytical depth, the script incorporates a robust divergence engine that scans for pivot highs and lows over a customizable lookback window. By comparing price action pivots with the oscillator's momentum peaks and troughs, it systematically maps out both regular and hidden divergences. 🎨 Visual Guide The visual presentation is meticulously structured to provide instant clarity on risk-adjusted momentum states. • The Sortino Histogram The core oscillator is plotted as a multi-colored histogram. It utilizes a four-state coloring system to indicate momentum shifts: Solid Bull Color: The ratio is above zero and rising, indicating accelerating positive risk-adjusted returns. Transparent Bull Color: The ratio is above zero but falling, suggesting positive momentum is decelerating. Solid Bear Color: The ratio is below zero and falling, indicating accelerating downside risk. Transparent Bear Color: The ratio is below zero but rising, showing that downside risk is waning. • Signal Line and Cloud Fill A highlighted Signal Line tracks the moving average of the Sortino Ratio. The space between the Sortino histogram and the Signal Line is filled with a dynamic cloud, helping traders easily spot shifts in immediate trend strength. • Threshold Lines Dashed lines represent the Overbought and Oversold thresholds. A solid gray line marks the Zero Level, acting as the primary baseline for positive versus negative risk-adjusted states. • Divergence Mapping Regular Bullish (RB): Displayed as a solid line connecting price lows to oscillator lows, complete with a label below the candle. Hidden Bullish (HB): Displayed as a dashed line, indicating trend continuation. Regular Bearish (RD): Displayed as a solid line connecting price highs to oscillator highs. Hidden Bearish (HD): Displayed as a dashed line. • Candle Coloring When enabled, the price chart's candles are painted to match the four-state color logic of the Sortino Histogram, linking the oscillator's data directly to the price action on the main chart. 📖 How to Use Traders can interpret the Sortino Ratio Oscillator through several distinct frameworks depending on their trading style. • Zero-Line Crossovers A baseline shift occurs when the histogram crosses the zero line. A cross into positive territory confirms that the average returns now outweigh the downside deviation, signaling a structurally sound bullish environment. Conversely, a drop below zero warns that downside volatility is dominating the asset's behavior. • Signal Line Interactions Watch for the histogram to cross the Signal Line. When the Sortino Ratio spikes above its signal line, momentum is expanding. When it crosses below, it often precedes a consolidation or a reversal, as highlighted by the cloud fill changing colors. • Extremes and Reversals The Overbought and Oversold threshold lines act as exhaustion markers. An asset sustaining a Sortino Ratio above the Overbought level is exhibiting unusually high, unpenalized upside movement. While strong, traders should watch for the histogram to peak and cross back below the Signal Line as an early warning of a pullback. • Trading Divergences Divergences are perhaps the most powerful signals generated by this tool. Look for Regular Bullish Divergences when the price makes a lower low, but the Sortino Ratio makes a higher low. This indicates that despite the price drop, the underlying downside volatility is shrinking relative to the mean return, hinting at a bottom. Hidden Divergences are excellent for trading pullbacks in the direction of the macro trend. ⚙️ Inputs and Settings • Sortino Settings Lookback Length: Defines the period used to calculate the mean return and downside deviation. A shorter length is highly reactive, while a longer length provides macroscopic trend stability. Signal Length: Adjusts the smoothness of the Signal Line. Overbought / Oversold Levels: Customizes the threshold lines for extreme readings. • Candle Coloring A simple toggle to enable or disable the dynamic coloring of the main chart price candles based on the oscillator's state. • Divergence Settings Enable Divergence: Master toggle for the divergence engine. Show Regular / Hidden: Independent toggles to filter specific divergence types. Pivot Lookback Left / Right: Determines the strictness of the pivot point detection. Higher values require more significant peaks and troughs to form a valid pivot, filtering out noise. • Webhook Action Names Customizable string inputs allowing algorithmic traders to map specific script events directly to JSON payloads for automated execution platforms. 🔍 Deconstruction of the Underlying Scientific and Academic Framework The Sortino Ratio, developed by Dr. Frank A. Sortino, is a vital modification of the Sharpe Ratio. In Modern Portfolio Theory, the Sharpe Ratio evaluates the performance of an investment by adjusting for its risk, defined universally as the standard deviation of its returns. However, standard deviation measures total volatility, treating an unexpected positive gain exactly the same as a negative loss. This oscillator resolves that mathematical paradox by isolating downside deviation. The scientific framework dictates that a minimum acceptable return—in this script's case, zero—must be established. Only returns falling strictly below this threshold are aggregated and squared to calculate the downside variance. By exclusively measuring the standard deviation of negative asset returns, the formula effectively removes the penalty for upside volatility. In a purely academic sense, a high Sortino Ratio mathematically proves that the asset is generating its returns without suffering significant, erratic drawdowns. Translated into technical analysis, when the indicator rises, it mathematically proves that the ratio of upward momentum relative to downward variance is expanding. This makes it an incredibly robust statistical measure, completely immune to the standard look-around bias of typical mathematical oscillators that collapse under the weight of sudden, positive price shocks. ⚠️ 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.
Индикатор Pine Script®
от MarkitTick
AI UltraTrend X Pro V1The AI UltraTrend X Pro V1 is a high-performance, signal-based indicator engineered to identify explosive trend reversals while aggressively filtering out sideways market noise. Built specifically to handle high-volatility environments like BankNifty, this tool excels at capturing large point moves by combining institutional flow tracking with advanced price-action breakouts. Key Features Multi-Segment Mastery: While optimized for the fast-paced nature of BankNifty, the logic is universally applicable across Equity, Forex, Commodities, and Crypto. Optimized for 3m+ Timeframes: Designed for the 3-minute duration and above, providing a perfect balance between early entries and noise reduction. Hybrid Execution Logic: Unlike standard indicators that rely solely on crossovers, this script uses a dual-trigger system—line crosses and volatility-backed breakouts—ensuring you never miss a gap-up or a sudden trend explosion. Intraday & Swing Flexibility: Seamlessly transitions between aggressive Intraday scalping and Positional trend following. The indicator maintains its state, allowing users to carry trades or square off at the end of the session based on their own risk profile. Advanced Anti-Chop Shield: Features a built-in Volume & Volatility filter that identifies "dead zones" (gray signal areas) to prevent the "multiple entries/exits" common in flat markets. Dynamic Trailing Stop-Loss: Plots a real-time, ATR-based trailing exit (Yellow Line) that locks in profits while giving the trend enough "breathing room" to survive minor pullbacks. How to Use Enter CE: Triggered when the signal turns Green and price breaks recent resistance. Enter PE: Triggered when the signal turns Red and price breaks recent support. Stay with the Trend: The background remains color-coded (Green/Red) as long as the trend is healthy. Exit: Close your position when the signal label flips. Best Settings Timeframe: 3m, 5m, or 15m. Chart: Works best on standard Candlesticks or Heikin Ashi for smoother trend following.
Индикатор Pine Script®
от ankgupta1987
2
Caldera Relative Pressure [JOAT]Caldera Relative Pressure Introduction Caldera Relative Pressure is an open-source effort-versus-result oscillator designed to measure whether price movement is being supported by participation, directional efficiency, and close location within the bar. It is built to distinguish clean directional drive from absorption, exhaustion, and two-way rotation. The problem this script solves is that raw price movement does not explain whether a move is efficient, forced, rejected, or fading. A wide candle on low participation is not the same as a wide candle with expanding participation and strong close location. Caldera converts candle anatomy, relative volume, range behavior, and baseline context into a structured pressure model that is easier to read in real time. Core Concepts 1. Effort-Versus-Result Framework The script blends three weighted components: Effort: candle body and directional spread relative to true range Result: directional efficiency relative to ATR Location: where the bar closes inside its own range, adjusted by wick pressure Those three parts are multiplied by relative volume so that quiet moves and committed moves do not receive the same score. 2. Directional Drive Detection Bull and bear drive states require a sufficiently large composite pressure reading, positive spread between the composite and its signal line, and close location agreement. This keeps small or conflicted moves from being treated as decisive tape control. 3. Absorption Detection Absorption is identified by unusually strong volume combined with limited body progress and asymmetric wick behavior. In practical terms, that means participation increased but result did not expand proportionally. This is often a useful clue that one side is meeting aggressive pressure with passive liquidity. 4. Exhaustion Detection The script also tracks exhaustion. It compares the current pressure state with recent pressure extremes and short-term momentum fade. A move can still be directionally positive or negative while simultaneously losing efficiency. 5. Multi-Layer Pressure Visualization The pane includes a histogram, composite line, signal line, drive quality line, balance line, participation band, efficiency band, location band, rotation ribbon, and reference ladders. These are separate on purpose: The histogram shows raw directional pressure The composite and signal lines show state and rotation Drive quality shows how healthy the move is Participation, efficiency, and location bands show what is contributing to the reading Features Composite pressure engine: Candle anatomy, ATR efficiency, location, and relative participation Bull and bear drive states: Measures directional initiative Bull and bear absorption states: Flags high-effort / low-result behavior Bull and bear exhaustion states: Flags fading pressure after prior extremes Baseline context filter: Can require price to align with a directional baseline Drive quality and balance lines: Separate force from quality Participation, efficiency, and location bands: Show what is driving the current reading Rotation ribbon: Highlights positive and negative carry Dashboard summary: State, bias, strength, regime, context, participation, quality, balance, rotation, and compression Input Parameters Core Engine: Relative Volume Length Range Normalization Length Baseline Context Length Signal Smoothing Pressure Model: Effort Weight Result Weight Location Weight Drive Threshold Expansion Threshold State Logic: Absorption Volume Z Absorption Range Cap Exhaustion Lookback Recent State Window Baseline Context Filter toggle How to Use This Indicator Step 1: Read the State Row The State row tells you whether the market is currently showing directional drive, absorption, exhaustion, or balance. This is the first layer of interpretation. Step 2: Compare Pressure With Quality A strong pressure reading with weak drive quality can be unstable. A smaller pressure reading with improving quality can be more constructive. Use those two together rather than treating histogram height alone as the answer. Step 3: Inspect Participation, Efficiency, and Location These bands explain why the model is leaning in one direction. If participation is strong but efficiency is weak, the move may be absorption. If efficiency and location are strong but participation is weak, the move may be less durable. Step 4: Watch the Rotation Ribbon Rotation tells you whether pressure is continuing, stabilizing, or turning. This can be useful for early changes in internal character even when the headline state has not fully flipped yet. Indicator Limitations Relative volume is broker and instrument dependent, so the same thresholds may not transfer perfectly across markets Absorption and exhaustion are contextual states, not guaranteed turning points High-volatility event bars can temporarily distort effort-versus-result relationships A baseline filter improves context but can delay state recognition during sharp reversals Originality Statement Caldera Relative Pressure is original in the way it turns candle anatomy, participation, efficiency, and location into a layered pressure model with separate drive, absorption, and exhaustion states. The script is not a simple volume oscillator or candle-coloring tool. Its design is centered on explaining how price is moving, not only how far it moved. Disclaimer This script is provided for educational and informational purposes only. It is not financial advice. Pressure readings are based on historical bar data and can misclassify conditions during abnormal liquidity or fast event-driven moves. Always use independent confirmation and prudent risk management.
Индикатор Pine Script®
от officialjackofalltrades
Fair Value GapsFair Value Gaps (FVG) auto-detects bullish (+FVG) and bearish (-FVG) three-candle imbalances on any chart, any timeframe. Tracks each gap's state through its lifecycle and shows a real-time dashboard with active gap counts, nearest gap distance, and exact zone prices. WHAT IS A FAIR VALUE GAP An FVG is a three-candle pattern where the middle candle moves so aggressively that it leaves a gap between the wick of the prior candle and the wick of the following candle. Price often returns to fill these gaps before continuing — making them useful as targets, entry zones, or invalidation levels. A bullish (+FVG) forms when the low of the current candle is above the high of two candles ago, with a bullish middle candle. A bearish (-FVG) is the inverse. FEATURES Auto-detects pip size for JPY pairs, other forex, metals, indices, and crypto — no manual configuration needed Tracks up to 10 active FVGs per side with configurable minimum size filter to ignore noise Auto-removes filled FVGs OR keeps them on chart greyed out (your choice) Optional midline marker (50% mitigation level) Configurable label limit so only your most recent active gaps are annotated — prevents chart clutter Dashboard shows active count, distance to nearest gap, and exact zone prices Cells highlight amber when price is currently inside a gap (mid-mitigation) Movable dashboard position (six options) so it coexists with other indicators Built-in alerts for new bullish and bearish FVG formations HOW TO USE Use FVGs as targets when price has left an unfilled gap behind — markets often return to fill them. Use them as entry zones when price retraces into a gap in the direction of the higher-timeframe trend. Use them as invalidation when price closes through a gap that should have held as support or resistance. Particularly useful for SMC and ICT traders watching for liquidity sweeps followed by displacement into a fair value gap, then a retracement entry. Pairs cleanly with the Key Swing Levels (KSL) indicator from the same author — KSL's dashboard defaults to top-right, FVG's to bottom-left, no overlap. Open-source. Feedback and forks welcome.
Индикатор Pine Script®
от ajaypercy
Segmented Pressure Bands [JOAT]Segmented Pressure Bands Introduction Segmented Pressure Bands (SPB) is an open-source, institutional-grade regression channel system that computes a linear best-fit line and deviation bands from scratch using manual Ordinary Least Squares (OLS) mathematics — no built-in regression functions used. The channel operates in distinct segments: it builds over a dynamic lookback window, freezes all parameters at a minimum length threshold, extrapolates forward using the frozen slope and intercept, and resets automatically when price closes beyond the outer deviation band. Gradient linefill layers between the basis and outer bands communicate channel pressure visually. A volume regime tint adjusts visual weight based on relative volume activity, and ATR-based TP/SL visualization is drawn on each breakout reset. The core problem SPB solves is that standard regression channels repaint continuously as new bars add to the calculation window, making historical channel boundaries unreliable for reference. SPB's freeze-and-extrapolate architecture locks the regression parameters at a fixed point in time, then projects the channel forward. Price that deviates far enough from that projection triggers a segment reset — the channel is redrawn from the breakout point. This creates a clear, non-repainting record of each regression segment and the breakout that ended it. Core Concepts 1. Manual OLS Linear Regression The regression is computed using the standard Ordinary Least Squares normal equations applied to the source series over the active lookback window: float denom = float(length) * sumX2 - sumX * sumX slope := (float(length) * sumXY - sumX * sumY) / denom intercept := (sumY - slope * sumX) / float(length) RMSE (root mean square error) is calculated as the deviation of the source from the fitted line, providing the basis for band width. All accumulator variables (sumX, sumY, sumXY, sumX2) are computed in a per-bar loop, giving full control over the calculation window without relying on built-in functions that may change behavior across versions. 2. Channel Freeze and Extrapolation When the lookback window reaches the minimum length threshold, the slope, intercept, and RMSE are locked into freeze variables. From that point forward, the x-coordinate passed to the regression formula is the number of bars elapsed since the freeze bar, allowing the channel to project forward without recalculating: float xCur = -float(bar_index - freezeBar) basis := frozenIcpt + frozenSlope * xCur This extrapolation means the bands continue to move with the slope direction, but their relative spacing (the RMSE deviation) remains constant from the freeze point. 3. Segment Reset on Breakout When a candle closes beyond the outer upper or lower band, the current segment is terminated. The channel redraws from the current bar using the fresh source data from that point forward. Old linefill objects are explicitly deleted before new ones are created to stay within Pine Script's object limits. 4. Gradient Linefills and Volume Regime Tint N intermediate lines are drawn between the basis and each outer band, filled progressively with increasing transparency from the inner region to the outer edge. This creates a gradient pressure visualization — tighter fills near the basis signal equilibrium, wider fills near the outer band signal stretch. When the volume regime ratio (short-term MA / long-term MA) is elevated above the high threshold, line widths increase and fill opacity deepens to communicate high-activity conditions visually. Features Manual OLS Regression: Slope, intercept, and RMSE computed entirely from first principles — no built-in regression functions Freeze and Extrapolate Architecture: Regression parameters locked at minimum length; channel projected forward along the locked slope Automatic Segment Reset: Outer band close-beyond triggers segment restart — prior segment preserved as a historical record RMSE Deviation Bands: Upper and lower bands placed at configurable RMSE multiples from the basis line Gradient Linefill Layers: N intermediate lines fill the channel space with a visual pressure gradient — configurable step count Volume Regime Tint: Relative volume ratio (short/long MA) adjusts visual weight — elevated volume deepens channel fills and thickens lines ATR TP/SL Visualization: On each breakout reset, ATR-based take profit and stop loss boxes drawn from the breakout close Channel Direction Color: Downward slope (bullish context — price above a declining regression) renders in teal; upward slope (bearish context) renders in rose Non-Repainting Basis: Freeze architecture ensures historical segment boundaries do not move after they are drawn Configurable Source: Basis line source is selectable (close, hl2, hlc3, ohlc4, etc.) Dashboard (Top Right): Current slope, RMSE, volume regime label, band multiplier, and active segment bar count Near-Band Warning Dots: Subtle circle markers appear on the chart when price is within 12% of either channel edge — early warning that price is approaching a band extreme before a breakout occurs Distance-to-Nearest-Band in Dashboard: Current distance from price to the nearest band displayed as a percentage of channel width — provides a precise quantitative read of how stretched or compressed the current position is within the segment Live Regression Slope in Dashboard: Live regression slope value shown in the dashboard — communicates the current directional angle of the frozen channel projection in real time Breakout Win/Loss Tracking: Outcome of every breakout trade tracked against ATR-based TP/SL levels — total breakout trade count and cumulative win rate displayed in the dashboard Expanded Dashboard (7 Rows): Dashboard expanded to 7 rows — now includes distance-to-band percentage, live slope, and breakout win rate alongside existing regime and segment data Input Parameters Regression Settings: Source: Price input for regression calculation (default: close) Lookback Length: Maximum bar window for OLS computation (default: 50) Min Length to Freeze: Bar count at which slope/intercept are locked (default: 20) Band Multiplier: RMSE multiple for outer band placement (default: 2.0) Gradient Settings: Gradient Steps: Number of intermediate fill lines between basis and outer band (default: 5) Volume Regime: Short Vol MA: Short-term volume moving average length (default: 10) Long Vol MA: Long-term volume moving average length (default: 40) High Vol Threshold: Vol ratio above which volume tint activates (default: 1.5) ATR / Risk: ATR Length: Period for ATR calculation (default: 14) ATR SL Multiplier: Stop loss distance on breakout (default: 1.5) Reward:Risk Ratio: Take profit multiple of stop distance (default: 3.0) How to Use This Indicator Step 1: Read the Channel Direction A teal channel indicates a downward-sloping regression — price is above a declining trend line, suggesting bullish pressure within the distribution. A rose channel indicates an upward-sloping regression — price is below a rising channel ceiling, suggesting bearish pressure. The gradient fills communicate how far price has deviated from the basis within that segment. Step 2: Trade Within the Channel Price compressing toward the basis from an outer band (thin fill region narrowing) suggests mean reversion is underway. Price expanding toward the outer band (fills widening) suggests momentum continuation. The outer band itself acts as a stretch boundary — closes beyond it trigger a new segment. Step 3: React to Breakout Resets When a segment resets, the breakout bar is the reference point for directional bias. The ATR TP/SL boxes visualize the immediate risk/reward from that close. The new channel building from the breakout will establish the next directional context. Step 4: Monitor Volume Context Elevated volume regime (shown in dashboard) at a channel boundary gives more conviction to breakout or reversal signals. Low-volume channel touches carry less institutional weight. Indicator Limitations The OLS calculation runs a loop over the lookback window on every bar. On very long lookback lengths with high chart data density, this may increase script execution time — keep lookback below 200 for best performance The freeze architecture means the channel projection can diverge significantly from price if the instrument trends strongly after the freeze point. Segment resets bring the channel back to current price, but wide outer bands may delay that reset on low-volatility instruments Gradient linefills are subject to Pine Script's 50-linefill object limit. SPB manages this with explicit deletion on each segment reset. If the gradient steps setting is set very high (above 10), this limit may be approached in active markets ATR TP/SL boxes on breakout are drawn from the breakout close. They do not adjust for gaps, overnight moves, or instrument-specific spread — manual adjustment of the ATR multiplier may be needed for highly volatile instruments Volume regime calculation uses simple moving averages of volume. On instruments where volume data is synthetic or unavailable, the regime indicator will not reflect true market activity Originality Statement SPB implements a regression channel with a freeze-extrapolate-reset lifecycle that produces stable, non-repainting historical segment boundaries. This design is original for the following reasons: Computing OLS slope, intercept, and RMSE from scratch using raw accumulator mathematics — rather than using ta.linreg() or similar built-ins — gives full control over the calculation window, source, and update behavior, and avoids implicit look-ahead that some built-in functions can introduce The freeze-and-extrapolate architecture is distinct from standard rolling regression, where every new bar shifts the entire historical channel. Once frozen, SPB's channel parameters are immutable — historical band boundaries drawn in past segments are permanent reference levels The gradient linefill layer system communicates statistical deviation pressure visually across the full channel width, rather than drawing only a basis and outer band with no information about the space between them The integration of a volume regime tint directly into the regression channel visualization — adjusting visual weight based on relative volume — provides immediate context for whether current channel position is occurring during active or quiet market conditions Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Regression channels and statistical deviation bands are mathematical constructs applied to historical data — they do not predict future price behavior. Breakout signals at band extremes do not guarantee continuation in any direction. Always apply proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Индикатор Pine Script®
от officialjackofalltrades
PGS - Pareto-Gaussian Skew [Zofesu]PGS - Pareto-Gaussian Skew is a trend gravity indicator built on two mathematical principles: the Gaussian distribution for detecting statistically significant price moves, and the Pareto principle for isolating the minority of moves that drive the majority of directional displacement. The result is a single adaptive line that acts as a gravitational center — pulling toward institutional price displacement while filtering out the noise that makes standard moving averages lag or whipsaw. ───────────────────────────────────── 01 — What is PGS? ───────────────────────────────────── PGS plots a gravity line that tracks where price is being pulled by significant institutional moves. Unlike a standard moving average, it does not react to all price movement equally. It reacts only when price moves beyond one standard deviation from its mean — the threshold where statistically normal noise ends and directional displacement begins. Below that threshold, the line stays anchored to the mean. Above it, the Pareto Skew Factor shifts the gravity line toward the extreme, capturing the 20% of moves that drive 80% of the trend. ───────────────────────────────────── 02 — How it works ───────────────────────────────────── The engine runs in three steps: Step 1 — Mean and standard deviation SMA and standard deviation are calculated over the lookback window (default 100 bars). This defines the Gaussian baseline — the statistical center of recent price behavior. Step 2 — Extreme detection The distance between current close and the mean is measured. If that distance exceeds 1x standard deviation, the move is classified as extreme. Moves within 1 standard deviation are treated as noise and ignored. Step 3 — Pareto displacement Extreme moves are multiplied by the Pareto Skew Factor (default 0.8). This skewed value is then smoothed with an EMA over a quarter of the lookback period and added back to the mean — producing the final gravity line. Formula: gravity = SMA(close, n) + EMA(d × extreme × skew, n/4) where d = close − SMA(close, n), extreme = 1 if |d| > StDev else 0 ───────────────────────────────────── 03 — Visuals ───────────────────────────────────── Blue line — Pareto-Gaussian gravity line Tension Cloud — fill between price and the gravity line. Green fill = price above gravity line (bullish tension). Pink fill = price below gravity line (bearish tension). Note: Price Reference and PGS Reference appear in the indicator's plot list but are invisible on the chart. They are internal anchors required by Pine Script's fill() function and serve no visual or analytical purpose for the user. ───────────────────────────────────── 04 — Settings ───────────────────────────────────── Smith's Memory (Lookback) — default 100 Number of candles used for the Gaussian baseline. Higher = slower, more stable line. Lower = faster, more reactive. Pareto Skew Factor — default 0.8 Weight applied to extreme moves. Higher = gravity line shifts more aggressively toward institutional displacement. Lower = more conservative, stays closer to the mean. ───────────────────────────────────── 05 — How To Use ───────────────────────────────────── Step 1 — Read the Tension Cloud Green fill = price is above the gravity line. Bullish context — look for longs or hold existing positions. Pink fill = price is below the gravity line. Bearish context — look for shorts or avoid longs. Step 2 — Watch for gravity line crossings Price crossing the gravity line from below = potential bullish shift. Price crossing from above = potential bearish shift. Step 3 — Use as dynamic support and resistance In trending markets the gravity line acts as a dynamic S/R level. Price pulling back to the line in a green cloud = potential long entry zone. Step 4 — Combine with higher timeframe context PGS works best as a trend context filter alongside entry tools. It defines the direction — your entry indicator defines the moment. Works on all asset classes: Indices, Forex, Gold, Oil, Crypto. Best timeframes: H1, H4, D1.
Индикатор Pine Script®
от Zofesu
Prism Channel Architecture [JOAT]Prism Channel Architecture Introduction Prism Channel Architecture is a dual-channel overlay indicator that layers two mathematically distinct structural frameworks onto your price chart simultaneously: a best-fit Pivot Channel derived from actual price pivot points, and a Linear Regression Channel built from statistical least-squares fitting. Together they create a structural prism through which trend direction, channel quality, and breakout momentum can be evaluated from multiple angles at once. Most channel tools force you to choose between objectivity and responsiveness. Pivot channels adapt to real market structure but can lag. Regression channels are statistically rigorous but ignore actual swing highs and lows. PCA runs both engines in parallel and highlights the moments when they agree — bull alignment and bear alignment states — as the highest-conviction reads in the system. Core Concepts Pivot Channel Fitting The indicator collects up to a configurable maximum of confirmed pivot highs and pivot lows using TradingView's built-in pivot functions: float pivHigh = ta.pivothigh(high, pivLeft, pivRight) float pivLow = ta.pivotlow( low, pivLeft, pivRight) From those stored pivot arrays, it searches for the best pair of recent pivot highs to fit the upper channel boundary, and the best pair of recent pivot lows to fit the lower channel boundary. The quality score for each candidate pair is computed by checking how many of the recent bars were actually contained below the upper line (or above the lower line) within an ATR tolerance: for k = 0 to checks - 1 float lineY = linePrice(x2, y2, x1, y1, bar_index - k) if high <= lineY + atrVal * 0.3 contained += 1 float q = safeDiv(float(contained), float(checks), 0.0) The pair with the highest containment ratio wins and becomes the drawn channel. This means the upper channel line is always the tightest valid resistance line through recent pivot highs, not an arbitrary parallel projection. Linear Regression Channel The regression channel computes a full manual least-squares fit over the lookback window, producing slope, intercept, and residual standard deviation: float slope = safeDiv(n * sumXY - sumX * sumY, n * sumXSq - sumX * sumX, 0.0) float intc = safeDiv(sumY - slope * sumX, n, close) float stdDev = math.sqrt(safeDiv(ssRes, n, 0.0)) The upper and lower bands are drawn at `stdDev × Deviation Multiplier` distance from the regression midline, giving bands that are statistically calibrated to the actual spread of price around the trend. Color shifts from bull to bear when slope changes sign. Channel Alignment Confluence The system declares a Bull Alignment when both channels simultaneously agree price is in a bullish position — the regression slope is rising AND price is above the regression midline, AND price is in the upper half of the pivot channel (between the midline and the upper band): bool lrBull = close > midNow and slope > 0.0 bool pivBull = close > uMid and close < uNow bool alignBull = lrBull and pivBull This confluence state is highlighted with a subtle background color — a quiet but meaningful signal that two independent structural frameworks are pointing in the same direction. ATR-Based Breakout Detection Breakout signals fire when price moves more than a configurable ATR multiple beyond the prior bar, provided the regression slope confirms direction: bool brkUp = ta.crossover(close, close + crossTol * atrVal) and lrSlope > 0.0 bool brkDn = ta.crossunder(close, close - crossTol * atrVal) and lrSlope < 0.0 Breakout labels (▲ BRK / ▼ BRK) appear above or below the breakout bar and are alert-enabled. Features Pivot Channel — best-fit upper/lower boundaries through recent pivot highs/lows, quality-scored by containment ratio Regression Channel — least-squares midline with statistically calibrated deviation bands, auto-colored by slope direction Channel midline — dashed neutral midline bisecting the pivot channel for zone positioning Bull and Bear Alignment detection — background highlight when both channels agree on direction ATR-normalized breakout labels — ▲ BRK and ▼ BRK when price breaks out with trend confirmation Channel Quality score — displayed in dashboard as percentage of recent bars contained Pivot position classification — Bull Zone (upper half) or Bear Zone (lower half) Up to 40 pivot highs and 40 pivot lows stored and evaluated 10-bar channel projection extended to the right of the last bar Dashboard: LR direction, deviation mult, pivot quality, pivot position, alignment, breakout, ATR, pivot count Alerts for bullish breakout, bearish breakout, bull alignment, and bear alignment Webhook JSON alert format Watermark Input Parameters Pivot Channel Pivot Lookback Left — bars to the left required to confirm a pivot high or low (default 10) Pivot Lookback Right — bars to the right required to confirm a pivot high or low (default 5) Max Pivots Stored — maximum number of pivot highs and lows held in memory (default 30) Quality Check Length — number of recent bars used to score channel containment (default 20) Breakout ATR Mult — ATR multiplier threshold for breakout label generation (default 1.5) Show Pivot Channel — toggle the pivot channel lines on/off Regression Channel Regression Length — bars used in the least-squares fit (default 50) Deviation Mult — standard deviation multiplier for band width (default 2.0) Show Regression Channel — toggle the regression channel lines and fill on/off ATR Settings ATR Length — lookback for ATR calculation used in breakout detection and containment tolerance (default 14) Visuals Bull Color — color for uptrending channels and bullish labels Bear Color — color for downtrending channels and bearish labels Neutral Color — color for channel midlines and neutral dashboard text Show Dashboard — compact structural summary panel Show Watermark Show Breakout Labels — toggle ▲ BRK / ▼ BRK label markers Alerts Webhook JSON Format — switches alert messages to JSON format for automation pipelines How to Use Add PCA to your chart as a main-pane overlay indicator. Let the chart load enough history so both channels initialize. A warmup period of at least 60 bars is enforced before channels begin drawing. Use the Regression Channel to assess macro trend direction. If the midline slope is rising and price is above it, the macro environment is bullish. Use the Pivot Channel to identify the structural support and resistance boundaries formed by actual price pivots. The upper pivot line is the tightest valid resistance. The lower pivot line is the strongest structural support. Watch for Bull Alignment (cyan background) when both systems agree price is in a bullish structural position. This is the highest-conviction environment for long setups. Watch for Bear Alignment (red background) for bearish structural setups. Treat Breakout labels as momentum confirmation signals — they only fire when an ATR-significant price move occurs in the direction of the regression slope. Check the Pivot Quality score in the dashboard. A quality above 65% means the channels are actively containing price well. Below 40% means the channel fit is loose and breakouts are less reliable. Indicator Limitations Pivot channel fitting evaluates only the 8 most recent pivot highs and the 8 most recent pivot lows when searching for the best pair. In very choppy markets with many closely-spaced pivots, the fitted channel may appear narrow or erratic. The regression channel is recalculated on every bar over a fixed lookback window. It will repaint the past visually as new bars are added — the channel reflects the lookback window ending at the current bar, not a fixed historical period. Channel quality scores can be artificially high in low-volatility trending conditions where price barely touches the edges of the channel. Breakout signals require both an ATR threshold move AND a confirming regression slope. In sideways markets the slope condition filters out most breakout candidates, which may lead to missed signals on genuine horizontal range breaks. Originality Statement Prism Channel Architecture is an original Pine Script v6 publication. The dual-engine architecture combining a quality-scored best-fit pivot channel with an independently computed least-squares regression channel, and the definition of alignment confluence as agreement between those two distinct structural systems, is an original design. The pivot quality scoring methodology — measuring the containment ratio of recent bars within the candidate channel bounds with ATR tolerance — is an original technique not derived from any existing published indicator. Disclaimer This indicator is for educational and informational purposes only. Channels, alignment states, and breakout labels are analytical tools and do not constitute financial advice. Channel boundaries can and will be violated without warning. Always apply proper risk management and never trade solely based on indicator signals. -Made with passion by jackofalltrades
Индикатор Pine Script®
от officialjackofalltrades
Auric Regime Classifier [JOAT]Auric Regime Classifier Introduction Auric Regime Classifier (ARC) is an open-source, multi-factor market regime detection engine that classifies every confirmed bar into one of five distinct market states: Strong Bull, Weak Bull, Ranging, Weak Bear, or Strong Bear — with a special Compression override that fires when volatility is contracting. The engine uses five independent data sources — adaptive ATR volatility ratio, Bollinger Band squeeze detection, SMEMA trend slope, ADX directional index, and RSI momentum bias — fused through a weighted scoring system into a single net score that drives the regime classification. The problem ARC solves is that most traders apply a fixed strategy regardless of whether the market is trending strongly, drifting weakly, compressing before a breakout, or chopping without direction. Each of those conditions demands a completely different approach. Applying a trend-following system in a ranging market produces losses. Trading with tight stops in a compression phase produces whipsaws. ARC gives you a clear, real-time label for the current market phase so you can match your approach to the conditions rather than fighting them. Core Concepts 1. SMEMA Adaptive Baseline The indicator uses SMEMA (Simple Moving Average of Exponential Moving Average) as its core trend baseline — a proprietary double-smoothing construct used throughout the JackOfAllTrades indicator suite. SMEMA applies a standard EMA first to capture responsiveness, then a SMA over the same period to suppress noise. The result is a baseline that reacts faster than a raw SMA but is smoother than a raw EMA: smema(float src, int len) => ta.sma(ta.ema(src, len), len) Two SMEMA lines run in parallel: a slow line over the full period (default 20) and a fast line at half the period. Their slope comparison over three bars determines the trend direction score. When the slow SMEMA slopes upward for three consecutive bars, two bull points are awarded. When it slopes downward, two bear points are awarded. 2. Adaptive ATR Volatility Ratio ATR over the input period (default 14) is compared against a long-run SMA of ATR (default 50 bars) to produce a volatility expansion/contraction ratio: float volRatio = safeDiv(atrRaw, atrBase, 1.0) A ratio above 1.1 in the direction of the existing trend awards a bonus point — recognizing that trend moves are more reliable when accompanied by above-average volatility. This prevents the engine from scoring weak, low-volume drifts as strongly as genuine impulsive moves. 3. Bollinger Band Squeeze Detection The indicator measures Bollinger Band bandwidth (upper minus lower) and compares it against its own 100-bar SMA. When bandwidth falls below the configurable threshold fraction (default 0.75) of its smoothed average, the market is classified as Compressing. Compression overrides all other regime classifications — a compressing market has no valid directional edge regardless of what the other signals say: bool isSqz = bbBW < bbBWAvg * sqzPct A Squeeze Release signal fires when compression ends (isSqz transitions from true to false), marking the potential start of an expansion move. 4. ADX Directional Index ADX (Average Directional Index) and the +DI/-DI directional lines are calculated using Pine Script v6's built-in ta.dmi() function. ADX above the threshold (default 25) confirms that the market is in a genuine trending regime rather than a sideways range. The directional bias of +DI vs -DI adds two bull or bear points to the regime score: = ta.dmi(adxLen, adxLen) bool isBullDir = diPlus > diMinus bool isBearDir = diMinus > diPlus 5. Regime Scoring Engine All five components feed a dual-sided scoring system. Bull and bear points are accumulated independently, and the net score (bull minus bear, range -6 to +6) determines the regime code: int bullPts = (trendUp ? 2 : 0) + (isBullDir ? 2 : 0) + (rsiBull ? 1 : 0) + ((volRatio > 1.1 and trendUp) ? 1 : 0) int netScore = bullPts - bearPts int regCode = isSqz ? 0 : netScore >= 4 ? 2 : netScore >= 1 ? 1 : netScore <= -4 ? -2 : netScore <= -1 ? -1 : 0 Strong Bull requires a net score of +4 or higher (all four components aligned). Weak Bull requires +1 to +3. Ranging sits at 0. The mirror applies for bearish regimes. 6. Trend Strength Score (0-100) Beyond the categorical regime label, ARC produces a continuous trend strength score that measures conviction within the current regime. It combines a distance score (how far price is from the SMEMA baseline in ATR units, capped at 50 points) with a momentum score (RSI deviation from 50 in the trend direction, capped at 50 points). A score of 70+ indicates a strong, high-conviction regime. Below 40 indicates a weak or transitional state. Features Five-State Regime Classification: Every bar labeled Strong Bull, Weak Bull, Ranging, Weak Bear, or Strong Bear with a Compression override — no ambiguity SMEMA Ribbon: Fast and slow SMEMA lines with a gradient fill between them, colored by the current regime state for instant visual context Regime Background Tint: Subtle, semi-transparent background coloring that shifts with the regime — green family for bull states, red family for bear, yellow for compression Candle Coloring: Bar colors inherit the regime color at reduced opacity, giving every candle immediate regime context without obscuring price action Squeeze Markers: Circle markers on the SMEMA baseline during compression, with a diamond signal at the moment of squeeze release Trend Strength Score: A 0-100 numeric score with a Strong/Moderate/Weak label updated each bar, shown in the dashboard Regime Change Alerts: Alert fires on every confirmed regime state transition with either plain text or structured JSON for webhook delivery 12-Row Dashboard (Top Right): Displays current regime, trend strength score, ADX value and trending/ranging status, +DI/-DI directional reading, volatility ratio, Bollinger Band state, RSI, timeframe, and version Watermark: JackOfAllTrades signature rendered at chart center-bottom Input Parameters Core Engine: ATR Length: Period for raw ATR calculation (default: 14) ATR Smoothing Period: Baseline ATR lookback for volatility ratio (default: 50) Directional Index: ADX / DI Length: Period for +DI, -DI, and ADX (default: 14) Trend Threshold: ADX level above which the market is considered trending (default: 25) Volatility Band: BB Length: Bollinger Band period (default: 20) BB Multiplier: Standard deviation multiplier (default: 2.0) Squeeze Threshold: Bandwidth fraction of its 100-bar SMA below which compression is declared (default: 0.75) Trend Engine: SMEMA Length: Period for the double-smoothed baseline (default: 20) RSI Length: Momentum confirmation period (default: 14) Visuals / Dashboard / Alerts: Theme: Auto, Dark, or Light — auto-detects chart background Regime Background Tint: Toggle the subtle background color Show SMEMA Baseline: Toggle the ribbon plots Show Squeeze Markers: Toggle the circle and diamond markers Show Dashboard: Toggle the 12-row information panel Show Watermark: Toggle the JackOfAllTrades signature Webhook JSON Format: Switch alert messages between plain text and JSON Color Palette: All six regime colors are individually customizable How to Use This Indicator Step 1: Read the Regime The dashboard regime field and the background tint tell you exactly where the market stands. This single label is the most actionable piece of information — it drives which strategy is appropriate. Step 2: Match Your Approach to the Regime Strong Bull / Strong Bear: All four scoring components are aligned. High-conviction directional trades, trend-following entries on pullbacks to the SMEMA ribbon Weak Bull / Weak Bear: Only one or two components agree. Lighter position sizing, wider stops, prepare for a possible regime shift Ranging: Net score near zero — avoid directional trades, consider mean-reversion or wait for breakout Compression: All directional analysis is suspended. Reduce exposure, prepare for a breakout in either direction, and watch the squeeze release signal for timing Step 3: Use Trend Strength for Conviction Within any directional regime, the strength score tells you how far into that regime the market has moved. A Strong Bull reading with a strength score of 85 is a very different trade environment from one with a strength score of 42. Use the score to scale position size or filter lower-conviction entries. Step 4: Set Alerts on Regime Transitions The regime-change alert fires the moment a new regime is confirmed on bar close. Enable the Strong Bull and Strong Bear alertconditions specifically to catch the high-conviction regime entrances. Indicator Limitations All five components are backward-looking. The regime label describes what has happened over the lookback windows — not what will happen. A Strong Bull classification can reverse on the very next bar The warmup period (equal to the longest lookback, at least 50 bars) means the indicator produces no signals on the first several bars of any chart, including after switching timeframes Compression detection uses a 100-bar SMA of bandwidth, which is a long-run reference. On very short or illiquid charts with few bars, the bandwidth average may not be reliable The five-state classification uses fixed score thresholds (+4 for Strong, +1 for Weak). These thresholds are not auto-calibrated to the instrument. In range-bound markets where ADX rarely exceeds 20, the Strong Bull/Bear states may rarely appear RSI and ADX both work with default periods. No single set of periods is optimal across all assets and timeframes. Users may need to adjust periods when applying to highly volatile assets or longer timeframes Originality Statement ARC is original in its synthesis approach and the use of SMEMA as the core trend baseline. This indicator is published because: The SMEMA construct (SMA of EMA) is a proprietary double-smoothing formula used consistently across the JackOfAllTrades suite — it provides a smoother baseline than raw EMA while retaining more responsiveness than raw SMA, and it is not a standard available in typical indicator libraries The dual-sided bull/bear point system scores each directional component independently before computing a net score. This is distinct from composite oscillators that blend components into a single signed value — the dual-side approach preserves information about how many bear components are active even when the net score is positive The Compression override takes precedence over all directional scores, explicitly suspending regime analysis during volatility contractions. Most regime indicators simply produce lower directional readings in compression without explicitly declaring the compression state The trend strength score combines an ATR-normalized price distance with an RSI momentum deviation to produce a conviction metric that is distinct from the categorical regime label Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Market regime classifications are based entirely on historical data and past behavior. A market classified as Strong Bull can and will reverse at any time. The Compression state does not guarantee a subsequent breakout, and the direction of any eventual breakout cannot be predicted from compression alone. Always apply proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Индикатор Pine Script®
от officialjackofalltrades
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.
Индикатор Pine Script®
от a_jabbaroff
2
Quant Edge Ribbon PRO🟦 Quant Edge Ribbon PRO is a multi-kernel divergence ribbon indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). A primary kernel and eleven longer-bandwidth kernels form a dual-ribbon visualization driven by kernel regression mathematics, an integer trend score in , a theme-aware rendering pipeline, four user-configurable reference levels, and a PRO dashboard. All twelve kernels route through the unified library dispatcher, so the user may select any of the eight kernel families and any of the three filter modes from a single configuration panel. 🟦 HOW IT WORKS Quant Edge Ribbon PRO calls the KernelLens library's unified dispatcher (`kl.estimate`) twelve times per bar — once for the primary kernel and once for each of the eleven outer kernels: ``` primary = kl.estimate(type, src, ℓ, α, period, phase, filter) long00 = kl.estimate(type, src, ℓ + 1·s, α, period, phase, filter) long01 = kl.estimate(type, src, ℓ + 2·s, α, period, phase, filter) ... long10 = kl.estimate(type, src, ℓ + 11·s, α, period, phase, filter) ``` where ℓ is the Primary Bandwidth and s is the Bandwidth Step. All twelve kernels share the same kernel type, filter, shape α, period, and phase — only the bandwidth differs. This guarantees the ribbon behaves as a coherent spectrum of kernel scales rather than a mixture of unrelated signals. The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, division-by-zero guards, and input validation internally. Quant Edge Ribbon PRO does not reimplement any kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Quant Edge Ribbon PRO imports the published KernelLens library and uses the following export: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on the user's Kernel Type dropdown. Called twelve times per bar, once for the primary kernel and once for each of the eleven outer kernels. | Every regression computation — kernel weight evaluation, NA-safe summation, bandwidth-aware loop termination — is delegated to the library. The indicator itself contains zero kernel math; it only orchestrates twelve library calls and aggregates their outputs into the trend score. 🟦 THE TWELVE-KERNEL RIBBON ARCHITECTURE **Primary kernel (the shortest, the anchor)** — A single kernel at bandwidth ℓ that serves two roles: (1) it is the reference baseline for the trend score calculation, and (2) it is plotted as a dedicated highlighted anchor line when the "Highlight Primary Kernel" toggle is ON. **Eleven outer kernels (progressively wider)** — Kernels at bandwidths ℓ+1·s, ℓ+2·s, …, ℓ+11·s, where s is the Bandwidth Step (default: 1). Each outer kernel is plotted as a line on the main chart, all eleven sharing a single score-driven gradient color — so the entire outer ribbon shifts between the theme's bull and bear hues as the trend score moves between −11 and +11. **Eleven inner ribbon plots (lagged primary snapshots)** — The primary kernel plotted at eleven time-lag offsets (0, 1, 2, …, 10 bars). The resulting visual is a gently flowing shadow that makes expansion and contraction of the outer ribbon easier to perceive. Opacity is user-controlled (default: 40 %). 🟦 INTEGER TREND SCORE The trend score is a signed integer in , computed on every bar by eleven pairwise comparisons between the primary kernel (at progressive lag offsets) and the eleven outer kernels: ``` score = 0 for i in 0..10: if primary < long_i: score += 1 else: score -= 1 ``` **Semantic interpretation** — Each comparison pairs an older snapshot of the shortest kernel against a current snapshot of a progressively wider kernel. In an uptrend, past primary values are lower while current wider-kernel values have caught up above them — the inequality resolves positive on most pairs and the score climbs toward +11. The symmetric argument drives the score toward −11 in a downtrend. **Score parity** — Because the score is a sum of eleven ±1 terms (score = 2k − 11, k ∈ ), it is always odd. Reachable values: { −11, −9, −7, −5, −3, −1, 1, 3, 5, 7, 9, 11 }. The score is never exactly zero. 🟦 STRENGTH CATEGORIZATION The absolute score is bucketed into four bands, each with a matched label glyph used throughout the dashboard and the last-bar signal label: | Score Range | Strength | Label | |---|---|---| | \|score\| = 1 | NEUTRAL | ▰▱▱▱ NEUTRAL | | \|score\| ∈ {3, 5} | WEAK | ▰▰▱▱ WEAK BULL / WEAK BEAR | | \|score\| = 7 | STRONG | ▰▰▰▱ STRONG BULL / STRONG BEAR | | \|score\| ∈ {9, 11} | TRIPLE | ▰▰▰▰ TRIPLE BULL / TRIPLE BEAR | The bull / bear suffix is driven by the sign of the score. The progress-bar glyphs (▰▱) give an instant at-a-glance read of confluence intensity without needing to parse the numeric value. 🟦 NON-REPAINTING BEHAVIOR Quant Edge Ribbon PRO inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. A single Phase input (default: 2) shifts every one of the twelve kernel centers 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 value is final once confirmed. 🟦 VISUAL PIPELINE **Outer Ribbon Gradient** — All eleven outer kernels are plotted with a single shared color driven by the trend score via `color.from_gradient(score, -11, 11, thBear, thBull)`. As the score walks across its range, the entire ribbon shifts continuously between the active theme's bearish and bullish hues — producing a smooth visual feedback loop between the math and the palette. **Inner Ribbon Shadow Trail** — Eleven lag-shifted primary snapshots (primary through primary ) drawn in the theme's accent hue with user-controlled opacity. On a trending chart the trail visually expands; on a reversing chart it contracts. Adjust opacity from 0 (invisible) to 100 (fully opaque) — default 40 balances presence and subtlety. **Primary Kernel Anchor Line** — The primary kernel plotted as a dedicated bold line in the theme's accent color at 80 % opacity, distinct from the shadow trail. Provides a clear centerline amid the ribbon flow. Toggleable. **Oscillator Subplot** — The smoothed trend score plotted in a dedicated subplot with a score-gradient vertical fill between the score line and the zero line. Opacity is user-controlled. An optional bold score line (up to 4 px wide) overlays the fill for sharp numeric reading. **Last-Bar Trend Label** — A right-anchored label at the current bar in the oscillator pane. Format: `▲ TRIPLE BULL 11 / 11` (bull) or `▼ WEAK BEAR −3 / 11` (bear). The label is deleted and redrawn on every bar, so only one instance is ever present on the chart. 🟦 OSCILLATOR STYLES The oscillator subplot ships with two visual presets, selectable from the Oscillator Style dropdown: | Style | Plot Style | Fill | Best For | |---|---|---|---| | Classic Gradient | `plot.style_line` (smooth curve) | Continuous vertical gradient from score to zero | Trend flow, slope momentum | | Stepline | `plot.style_stepline` (staircase) | Stepped gradient mirroring the discrete score plateaus | Signal / threshold trading, discrete level crossings | **Classic Gradient** produces a smooth curve traced through the smoothed score values, with the gradient fill flowing continuously between the score line and the zero line. This is the default and suits traders who read trend direction through slope and curvature. **Stepline** renders each bar as a horizontal plateau joined to the next bar by a vertical edge. Because the raw score is always an odd integer in { −11, −9, …, 9, 11 }, the staircase visualization honors the score's true discrete nature — making it easier to identify exact threshold crossings (e.g. the moment the score enters the ±9 extreme zone). The fill inherits the same stepline style, so the entire oscillator pane stays geometrically consistent. Both styles share the same opacity controls, score line toggle, and line width setting — only the geometry of the score line and its fill changes between them. 🟦 THEME SYSTEM Ten cohesive color palettes tuned to the Quant Edge Ribbon PRO optical brand. One selection drives every visual component — outer ribbon gradient, inner ribbon accent, oscillator fill, reference lines, signal label, dashboard accents — all sharing the same bull / bear / accent color axes: | 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 | The oscillator's zero line uses Pine's `chart.fg_color` so it auto-adapts to the actual chart background (white on dark charts, black on light charts) — independent of the Dashboard's Display Mode setting. 🟦 REFERENCE LEVELS Four user-configurable horizontal reference lines mark the score's structural thresholds inside the oscillator subplot: | Level | Style | Meaning | |---|---|---| | +11 / −11 | Dotted | Ceiling / floor — the mathematical maximum (every comparison aligned) | | +9 / −9 | Dashed | Extreme zone — nine or more of the eleven comparisons agree on direction | Each pair (±11 and ±9) has an independent opacity input (0–100 %). A master toggle (Show Reference Levels) collapses all four lines to fully transparent in a single branch — useful for minimalist layouts. An additional `showOsc` gate hides them automatically when the oscillator itself is disabled. 🟦 PRO 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 | Q-EDGE PRO | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Selected kernel type | | Divider | RIBBON | — | | Primary ℓ | Primary ℓ | Primary bandwidth value | | Span | Span | ℓ → ℓ + 11·s | | Filter | Filter | No Filter / Smooth / Zero Lag | | Divider | SCORE | — | | Score | Score | ▲/▼ + integer score + " / 11" (bull/bear colored) | | Bull / Bear | Bull / Bear | Bull comparison count / bear comparison count | | Strength | Strength | ▰-bar + NEUTRAL / WEAK / STRONG / TRIPLE label | | Primary | Primary | Primary kernel value in chart mintick format | **Bull / Bear breakdown** — The eleven pairwise comparisons split into bulls (resolved +1) and bears (resolved −1). Always sums to 11, so this row gives a direct visual of how many kernel scales agree with the net direction. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bullish Flip | Score crosses from ≤ 0 into positive territory | | Bearish Flip | Score crosses from ≥ 0 into negative territory | | Extreme Bullish | Score reaches +9 or higher (first entry into the zone) | | Extreme Bearish | Score reaches −9 or lower (first entry into the zone) | | Full Confluence Up | Score hits +11 — every outer kernel aligned bullishly | | Full Confluence Down | Score hits −11 — every outer kernel aligned bearishly | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Quant Edge Ribbon PRO: "` for easy parsing in downstream automation. 🟦 RECOMMENDED PRESETS | Style | Primary ℓ | Bandwidth Step s | Phase | Filter | Chart | |---|---|---|---|---|---| | Scalper | 8–16 | 1 | 1 | No Filter | 1m–5m | | Day Trader | 16–32 | 1–2 | 2 | Smooth | 15m–1h | | Swing | 25–50 | 1–2 | 2 | Smooth | 4h–1D | | Position | 50–120 | 2–3 | 3 | Smooth | 1D–1W | **Bandwidth Step tuning** — Step = 1 produces a tight ribbon where adjacent outer kernels sit visually close together. Steps of 2–4 spread the eleven outer kernels across a broader spectrum of scales, making expansion / contraction easier to read at a glance. Step 5–6 is reserved for very wide ribbons where each line represents a distinctly different time scale. 🟦 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 — visual elements auto-adapt via `chart.fg_color` - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression math is delegated to the published library - **Plot budget** — 11 outer + 11 inner + 1 primary + 2 oscillator plots + 1 fill + 4 hlines = well under Pine's 64-plot limit - **Table** — Single `var table` created once on `barstate.islast` with `force_overlay = true`; dashboard renders on the main chart pane, zero historical overhead - **No persistent drawing objects** — no `box.new`, `line.new`, no `array.new`; the single trend label is deleted and recreated every bar so only one instance is ever present - **Opacity convention** — every user-facing opacity input follows `0 = invisible, 100 = fully opaque`; conversion to Pine's native transparency is centralized in a single helper function (`f_opac`) - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead, no future-bar leakage at any phase value - **Chart background adaptive** — the oscillator's zero line uses `chart.fg_color`, so it always renders with high contrast regardless of the user's chart color scheme 🟦 DISCLAIMER Quant Edge Ribbon PRO 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 integer trend score is a geometric summary of kernel alignments — not a forecast — 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 Quant Edge Ribbon PRO or the underlying KernelLens library.
Индикатор Pine Script®
от a_jabbaroff
Trendline Confluence Map [AGPro Series]Trendline Confluence Map Overview Trendline Confluence Map automatically draws ascending and descending trendlines from major and minor swing pivots, then maps the zones where two or more of these trendlines intersect with each other or with key horizontal support and resistance levels. Every confluence zone is scored from 0 to 100 based on touch count, line age, trendline angle and volume, so traders can instantly see which reaction levels are backed by the most structural weight. Drawn as long rectangular zones in the classic S/R style, the map stays anchored to current price and hides old, irrelevant levels automatically. Unique Edge Most trendline tools either draw a single line or flood the chart with dozens of low-quality lines that never interact with each other. This indicator is built around the opposite idea: confluence is the signal, single lines are just noise. Its three design choices make the difference. First, the script maintains a disciplined pool of only the trendlines whose current projection is on-chart, so a zero-visibility clutter problem cannot happen — the panel count always equals what you actually see. Second, horizontal S/R levels participate as first-class confluence sources alongside trendlines, with a guard requiring at least one real trendline per zone so the tool remains true to its name. Third, every zone label shows a source breakdown such as "2 TL" or "1 TL + 1 SR", giving you immediate insight into why a zone was considered high-probability. Methodology Swing pivots are detected at two depths (major and minor) using pivot-high and pivot-low structures, with configurable lookback lengths. Each valid consecutive pivot pair spawns a trendline, and every subsequent bar that wicks into the projected line within an ATR-based tolerance increments its touch count, raising its structural validity. Horizontal S/R levels are extracted from recent major pivots and merged when they fall within 0.4 ATR of each other. On each confirmed bar, the detector builds a combined pool of all on-chart trendline projections plus horizontal levels, then runs a greedy clustering pass using a zone-width-sized tolerance window. Candidate clusters that contain at least one trendline and meet the minimum source count are scored by combining touch strength, line age, slope quality, a rolling volume component and a horizontal S/R bonus. The highest-scoring cluster becomes a confluence zone, drawn as a rectangle whose right edge extends dynamically to the current bar so every active zone stays visually anchored to price. Zones are deduplicated by price and direction, and expire automatically after a configurable age. Signals and Alerts Every new confluence zone is announced with a descriptive alert that carries its mid-price, score and source breakdown. A second alert fires when price first crosses into an active zone from outside, helping traders react to the moment a level is tested. Zone colors encode directional bias: green zones indicate support-favored confluence while pink zones indicate resistance-favored confluence, with the bias derived from the dominant trendline direction in the cluster. Labels on the right edge of each zone show the current score, the source breakdown and the bias tag, so at a glance the trader knows which level is strongest and why. Key Inputs Major Pivot Length and Minor Pivot Length define the swing-detection depth of the two pivot streams. Max Active Trendlines caps the internal pool, while Min Touches to Validate filters out unconfirmed trendlines from the confluence pass. Touch Tolerance in ATR units controls how generously the script counts interactions. Min Sources for Zone sets the confluence requirement, Zone Width in ATR units determines rectangle thickness, and Zone Forward Extension controls how far each zone reaches to the right. Max Active Zones and Max Zone Age in bars keep the map focused on currently relevant structure. Panel Theme, Panel Location and Font Size cover the full visual customization layer. How to Use Start on the timeframe you trade and leave the defaults — they are tuned for 4H crypto and major FX on equivalent timeframes. Scan for zones with a score above 60 in a direction aligned with the higher-timeframe bias. Use the green support-biased zones as pullback entry areas in uptrends and the pink resistance-biased zones as fade or short entry areas in downtrends. Break-and-retest traders can wait for price to pierce a high-score zone and return to its edge; mean-reversion traders can watch for rejection at zones where the source breakdown includes both a trendline and a horizontal level, as those tend to hold more strongly. The panel at the top shows a quick summary of the current map state and the distance to the nearest zone in ATR units, which is useful for position sizing and stop placement. Limitations and Transparency This is not a strategy and not a complete trading system. It does not predict price and does not generate buy or sell instructions. Confluence zones are structural context, not entries, and their score is an internal quality estimate derived from visible price action, not a probability of success. The script needs a reasonable amount of historical bars to build a stable trendline pool. Very low timeframes and very illiquid symbols can produce erratic pivot structures and are not the intended use case. Like all pivot-based tools, the indicator reflects past structure, and levels can weaken or become stale as market regimes change. Zones older than the configured age are removed automatically to reduce this risk. Risk Disclosure Trading involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. This indicator is provided for educational and analytical purposes only and should not be interpreted as financial advice, an investment recommendation or a solicitation to trade. Always combine multiple forms of analysis, manage position size responsibly, and never risk capital you cannot afford to lose.
Индикатор Pine Script®
от AGProLabs
Smart Trader, Episode 06, Isotropic Trend Lines🔷 WHAT IS ST-EP06 — ISOTROPIC TREND LINES? ST-EP06 is a multi-scale structural trend channel indicator built on a σ-normalized coordinate system. It is designed to solve one of the oldest unaddressed problems in technical analysis: trend angles that cannot be compared across instruments, timeframes, or volatility regimes. A trend line drawn on a chart appears to carry a measurable angle — yet that angle is an artifact of the display window, not a property of the market. Resize the chart horizontally and the slope flattens; compress it and the slope steepens. A given price movement on Gold daily and Bitcoin 1-hour may produce visually identical slopes on screen while reflecting entirely different structural conditions. This happens because traditional charts use a coordinate space where the vertical axis (price) and the horizontal axis (time) share no fixed dimensional relationship. The consequence is not merely cosmetic. A trader cannot meaningfully compare the steepness of a trend on one instrument with another — or even across timeframes on the same instrument — because the weight of "one unit of price per bar" varies with the instrument's current volatility. As the author of this indicator, I sought a coordinate system where trend angles would be an intrinsic structural property of the market, independent of charting software or display settings. The goal: a space where a 30° uptrend on EUR/USD weekly carries the same structural meaning as a 30° uptrend on NASDAQ 5-minute — indicating that each market is moving at the same rate relative to its own realized volatility. The solution draws on the principle of dimensional analysis, well established in physics and engineering. Just as the Reynolds number normalizes fluid flow to make behavior comparable across different pipe sizes and fluid viscosities, this indicator normalizes price movement by realized volatility, producing a dimensionless space we call the Isotropic Coordinate System (ICS). In ICS, price is expressed in natural logarithmic form and scaled by a volatility estimate (σ) derived from the Yang-Zhang (2000) method — a drift-invariant estimator that incorporates Open, High, Low, and Close data. The resulting vertical axis is dimensionless: one unit equals one standard deviation of recent realized price behavior. When trend angles are measured in this space, 45° indicates approximately one σ of movement per bar — whether the chart shows a penny stock, a major currency pair, or a commodity index. Traditional chart coordinates assign no fixed relationship between the price axis and the time axis. Resizing the chart window changes the visual slope of the same price movement — a compressed view may show 52° while a stretched view of the same data shows 25°. The angle is a display artifact, not a market property. The Isotropic Coordinate System (ICS) addresses this by normalizing log-price by realized volatility (σ). In this space, the trend angle is designed to remain constant regardless of how the chart is displayed — because it measures price displacement in units of σ per bar, not in pixels per pixel. 🔷 HOW THE MODULES WORK TOGETHER ST-EP06 operates as a deterministic pipeline where each stage consumes the output of the one before it: Realized volatility estimation (σ) → Structural block construction → Monotonic direction detection → ICS angle measurement → Channel boundary fitting → Six-scale parallel analysis → Consensus aggregation → Breakout and retest state tracking → Dashboard narrative generation The Yang-Zhang σ provides the normalization constant for every downstream computation. Price history is then partitioned into structural blocks, each distilled to a single central tendency that resists close-price bias. Consecutive block centers are compared to identify the longest uninterrupted directional segment. The slope of that segment, measured in σ-normalized space, yields the ICS angle. Four price extremes located within the segment define two log-linear channel boundaries. This complete pipeline runs independently at six temporal scales, and their independent outputs are aggregated into a structural consensus. A finite-state machine then tracks the evolving relationship between price and the primary channel — breakout, retest, confirmation, or failure — and translates it into a single-line human-readable narrative. ST-EP06 operates as a deterministic sequential pipeline. Yang-Zhang volatility (σ) provides the normalization constant that flows into every downstream stage. Price history is partitioned into structural blocks, each reduced to a geometric mean. The longest monotonic segment determines direction, and its slope in σ-normalized space yields the ICS angle. Four price extremes define the channel boundaries. This complete pipeline runs independently at six scales — 3, 7, 13, 19, 29, and 47 bars per block — all prime numbers, chosen to minimize harmonic overlap so that multiple scales are unlikely to lock onto the same cyclical artifact. Scale 19 (highlighted) serves as the primary engine: it is the only scale that maps to the user's Trend Block Period input, and the only scale whose output drives the chart-overlay channel lines, the projection, the diamond markers, and the breakout/retest state machine. The other five scales operate at fixed periods and contribute exclusively to the cross-scale consensus count — providing structural context that a single scale cannot offer alone. When 5 or 6 of the 6 scales agree on direction, it suggests a structural trend visible across a broad range of temporal resolutions. 🔷 DATA ANCHORING Every structural computation in ST-EP06 — volatility, block means, direction, channel coordinates, state machine transitions, and dashboard narrative — is governed by a single anchoring reference, selected through the Calculation Bar input. Live Bar mode (default): the anchor is the current forming bar. Values update with each incoming tick. This is standard TradingView behavior and means the indicator may exhibit intra-bar repaint — the live bar's data enters all computations as it evolves. Close Bar mode: the anchor shifts to the last fully confirmed (closed) bar. The forming bar is excluded from every computation. Values lock once a bar closes and do not change retroactively. This mode is intended for structural analysis, back-testing, and any workflow where historical consistency is a priority. One deliberate exception is maintained in both modes: the dashboard header always displays the current live closing price (Live Exception protocol), preserving real-time price awareness regardless of how the indicator's structural engine is anchored. Two modes, same chart moment. In Live Bar the anchor sits on the forming bar, so every value updates tick-by-tick and may repaint within the bar. In Close Bar the anchor shifts to the last closed bar, locking all structural values once the bar closes. The only exception is the dashboard header row, which always displays the live closing price in both modes, so real-time price awareness is never lost. 🔷 YANG-ZHANG VOLATILITY (σ) The foundation of the ICS is a robust volatility estimate. ST-EP06 uses the Yang-Zhang (2000) realized volatility estimator, an academically established method that combines three variance components: Overnight variance — capturing the gap between consecutive sessions, measured from the prior close to the current open. Intraday variance — capturing the movement from open to close within each session. Range-based variance — using the Rogers-Satchell (1991) estimator, which extracts additional information from the high and low prices without assuming zero drift. These three components are blended using an optimal weight that is designed to minimize estimation error. The resulting σ updates every bar, adapts to changing market conditions, and — crucially — is drift-invariant: it is intended to remain unbiased whether the market is trending strongly or mean-reverting. 🔷 BLOCK CONSTRUCTION Rather than analyzing individual bars, ST-EP06 partitions recent price history into consecutive non-overlapping blocks. Each block spans a user-defined number of bars (the Trend Block Period input) and is reduced to a single representative value: the geometric mean of the block's highest high and lowest low, computed in logarithmic space. This log-midpoint serves as the block's central tendency. Unlike a simple average of closing prices, it captures the structural center of the entire price range within the block, avoiding bias toward any single price point. The number of consecutive blocks compared is controlled by the Trend Block Groups input — more groups means deeper lookback and the ability to detect longer structural trends. Price history is partitioned into consecutive non-overlapping blocks. Each block reduces to a single log-midpoint — the geometric mean of its highest high and lowest low. Connecting the midpoints forms the representative chain used for trend detection. 🔷 DIRECTION DETECTION + ICS ANGLE Once blocks are constructed, the engine compares their geometric means in sequence, starting from the most recent. It identifies the longest consecutive segment where each block's central tendency moves in the same direction — either consistently rising or consistently falling. A single reversal terminates the segment. The slope of this segment is then measured in ICS space: the logarithmic price difference between the oldest and newest blocks in the segment, divided by σ, divided by the number of bars between them. The arctangent of this normalized slope produces the ICS angle in degrees. If the absolute angle falls within the Range Threshold (a user-configurable dead zone in degrees), the direction is classified as ranging rather than trending. This threshold acts as a sensitivity filter — wider values require steeper moves before declaring a trend, narrower values respond to subtler directional shifts. An ICS angle of 45° indicates approximately one σ of price movement per bar. An angle near 0° suggests the market may be structurally flat. Because σ adjusts for volatility and the logarithm adjusts for price level, these angles are intended to be directly comparable across any instrument and any timeframe. 🔷 CHANNEL FITTING Within the identified trending segment, the engine locates four price extremes: the highest high, the lowest high, the highest low, and the lowest low — each paired with its bar position. These four points define two linear boundaries in ICS space. During an uptrend, the upper boundary is fitted through the lowest high and highest high (capturing the rising ceiling), while the lower boundary is fitted through the lowest low and highest low (capturing the rising floor). During a downtrend, the fitting order reverses to capture descending structure. During a ranging market, the channel uses horizontal boundaries at the segment's absolute high and low. All boundary computations occur in the σ-normalized logarithmic coordinate system, meaning the channel lines represent geometric (log-linear) paths in price space — curves that naturally follow multiplicative price behavior rather than additive assumptions. Within the trending segment, four extremes — HH, LH, HL, LL — define two log-linear boundaries. In an uptrend, the upper line fits through LH and HH, the lower through LL and HL. The direction reverses the fitting order for downtrends, and a ranging market uses horizontal boundaries. 🔷 6-SCALE PARALLEL ANALYSIS A single temporal scale may capture the trend at one resolution but miss structure at others. ST-EP06 runs the complete pipeline — volatility normalization, block construction, direction detection, ICS angle, and channel fitting — independently at six different scales: 3, 7, 13, 19, 29, and 47 bars per block. These values were chosen as prime numbers to minimize harmonic overlap between scales. Scale 19 serves as the primary engine and maps to the user's Trend Block Period input. The other five scales use fixed periods, providing a structural context that the primary engine alone cannot offer. The dashboard displays each scale's independent trend direction. A consensus count shows how many of the six scales agree: 5/6 or 6/6 agreement suggests a structural trend that is visible across multiple temporal resolutions, while low agreement may indicate transitional or conflicting structure. 🔷 BREAKOUT / RETEST STATE MACHINE ST-EP06 includes a 5-state finite automaton that tracks price's structural relationship to the primary channel boundaries: Inside — price is observed between the channel floor and ceiling. The dashboard shows the position as a percentage: distance from floor and distance to ceiling (summing to 100%). Breakout Up / Breakout Down — price has exited above the ceiling or below the floor. The dashboard shows the breakout price and the percentage of channel width that price has moved beyond the boundary. Retest Up / Retest Down — after a breakout, price has moved at least one σ away from the boundary (establishing distance), then returned to test it. The dashboard shows both the original breakout price and the current retest level. Transitions between states use dynamic σ-based thresholds rather than fixed percentages, meaning the sensitivity automatically adjusts with market volatility. Additional flags track: ✓ Confirmed — a breakout that has been retested and bounced at least one σ away from the boundary. (gap) — price crossed the entire channel width in a single transition. Failed breakout — price re-entered the channel after initially breaking out. Direction reset — the primary trend direction changed, wiping all breakout state. 🔷 VISUAL TOOLS All chart-overlay elements are drawn from the primary engine (scale 19): Channel lines — solid upper and lower boundaries from the segment start to the anchor bar, colored by trend direction (configurable up/down/range colors, width, and line style). Projection lines — dotted forward extension of the channel slopes beyond the anchor bar, providing a visual reference for potential future support and resistance. The projection offset, width, and style are independently configurable. Channel fill — semi-transparent shading between channel boundaries, with independent color selection and adjustable transparency. Applies to both the solid channel and projection segments. Diamond markers (◆) — placed at the channel endpoints on the anchor bar. Hovering reveals a tooltip with the anchored close price, ceiling level, floor level, and the price's position as a percentage of channel width. Direction label — positioned at the midpoint between segment start and projection end. Displays the trend arrow, direction text, and ICS angle (e.g., "▲ UP +7.3°"). Tooltip includes block count. 🔷 DASHBOARD A compact information table appears at the top-right corner of the chart, organized in 5 rows: Header — indicator name, ticker symbol, timeframe, and live price (always live under the Live Exception protocol, even in Close Bar mode). Period — the six scale values (3, 7, 13, user's period, 29, 47) displayed across columns. The primary engine column is highlighted. Trend — per-scale trend direction with directional arrows (▲ UP, ▼ DN, ◈ RNG) and color coding. Agreement — consensus count (e.g., "5/6 UP") with the primary channel ceiling (▲) and floor (▼) price levels. Narrative — a single merged row presenting the breakout/retest state machine output as a human-readable sentence with distance measurements. This row updates dynamically as price interacts with the channel. All dashboard text, tooltips, and narrative phrases are fully localized. 🔷 ALERT CONDITIONS ST-EP06 provides 19 alert conditions organized in 5 categories, all gated by a master Enable Alerts toggle: D · Direction (3 alerts) — fires when the primary engine trend changes to uptrend, downtrend, or range. B · Breakout (4 alerts) — fires on initial breakout above ceiling or below floor, and separately on confirmed breakout (retested and bounced). R · Retest (2 alerts) — fires when price returns to test the boundary after establishing distance. S · Structural (5 alerts) — fires on gap-through events (price crosses entire channel), failed breakouts (price re-enters channel), and direction resets (trend change wipes state). A · Agreement (5 alerts) — fires when cross-scale consensus reaches significant thresholds: full bullish (6/6), strong bullish (5/6), full bearish (6/6), strong bearish (5/6), or range consensus (≥4/6). Important: alerts require Calculation Bar = Live Bar. In Close Bar mode, all alert conditions are automatically suppressed and a visual warning is displayed on the chart — because Close Bar mode intentionally lags by one bar, which is semantically incompatible with live alert delivery. 🔷 LANGUAGE SUPPORT The dashboard, all tooltips, the breakout/retest narrative, and the alert warning label are available in 7 languages: English · Türkçe · العربية · Русский · Italiano · Português (BR) · 中文 Select the preferred language from the Language dropdown in the Display settings group. All structural and numerical outputs remain unchanged — only the display language of text elements is affected. 🔷 HOW TO USE Apply ST-EP06 to any chart — the indicator is designed to work across instruments (equities, forex, crypto, commodities, indices) and timeframes without parameter re-optimization, because the ICS framework normalizes for volatility and price level automatically. Start with the default settings (Period 26, Groups 5, Sigma Length 20) and observe how the channel captures the dominant structural trend. The 6-scale consensus in the dashboard may help assess whether the observed trend is isolated to one temporal resolution or confirmed across multiple scales. The Calculation Bar setting is a structural decision: use Live Bar for real-time monitoring and alert-driven workflows; use Close Bar for analysis and back-testing where historical stability is prioritized. The ICS angle on the direction label provides a quantitative measure of trend intensity. Comparing angles across different instruments or timeframes is one of the intended use cases of the ICS framework — a 15° angle on one chart and a 15° angle on another may suggest similar structural momentum relative to each market's own volatility. The breakout/retest narrative in the dashboard bottom row is designed to provide context-rich status updates without requiring manual chart reading. The σ-based thresholds ensure that breakout sensitivity adapts to current market conditions rather than relying on fixed values. 🔷 SETTINGS Calculation — Calculation Bar (Live/Close Bar anchoring), Trend Block Period (bars per block), Trend Block Groups (consecutive blocks compared), Range Threshold (ICS dead zone in degrees), Yang-Zhang Sigma Length (volatility lookback). Channel Lines — Up Color, Down Color, Range Color, Line Width, Line Style. Projection Lines — Projection Offset (forward bars), Projection Width, Projection Style. Display — Language (7 options), Show Channel (toggle overlay), Show Fill (toggle shading), Show Dashboard (toggle table), Dashboard Font Size. Channel Fill — Fill Up Color, Fill Down Color, Fill Range Color, Fill Transparency. Alerts — Enable Alerts (master toggle, requires Live Bar mode). 🔷 DISCLAIMER ST-EP06 is an educational and analytical tool. It is designed to provide structural context through σ-normalized trend channels and multi-scale analysis. It does not generate buy or sell signals, does not predict future price movement, and is not intended as financial advice. Historical patterns observed through this indicator do not guarantee future outcomes. All trading decisions remain the sole responsibility of the trader.
Индикатор Pine Script®
от ata_sabanci
24
KernelLens🟦 KernelLens is a professional kernel regression library for Pine Script v6, providing eight mathematically rigorous Nadaraya–Watson estimators, a three-mode filter layer, a unified string dispatcher, and a suite of trading utilities — all built from the ground up on correct non-parametric statistics. Unlike existing Pine smoothing libraries — which inherit a decade-old loop-bound bug that silently reduces every kernel window to a handful of bars, regardless of the bandwidth parameter — KernelLens is built with auditable math, NA-safe iteration, input validation at every entry point, and academic references cited inline next to the formulas they describe. The library integrates eight independent kernel families — Rational Quadratic, Gaussian, Periodic, Locally Periodic, Epanechnikov, Tricube, Triangular, and Cosine — behind a consistent API, with every raw estimator wrapped in a filter layer (None / Smooth / Zero Lag), a unified dispatcher for dropdown-driven kernel selection, and five utility exports covering slope detection, trend state, crossover signaling, residual confidence bands, and Silverman's rule-of-thumb bandwidth recommendation. Every public function validates its inputs, raises descriptive runtime errors on misuse, and returns `na` only when there is genuinely no data — never as a silent fallback. 🟦 MATHEMATICAL FOUNDATION **The Nadaraya–Watson Estimator** Given a source series `y_t` and a symmetric kernel `K` with scale parameter `ℓ` (the "bandwidth"), the Nadaraya–Watson estimator of the regression function `m(x) = E ` evaluated at the current bar is: ``` Σᵢ K(dᵢ / ℓ) · y_{t−i} ŷ(t) = ─────────────────────── Σᵢ K(dᵢ / ℓ) ``` where `dᵢ` is the bar-distance from the kernel center and the sum runs over a finite window determined by the effective support of `K`. The estimator is a locally weighted average: bars close to the kernel center contribute heavily, distant bars contribute proportionally less, and bars outside the support contribute nothing. It is asymptotically unbiased up to `O(ℓ²)` for twice-differentiable `m`, with variance of order `(n·ℓ)⁻¹` — the classical bias–variance trade-off that defines all non-parametric smoothers. **Why Kernel Regression Beats Rolling Means** A simple moving average gives every bar in the window the same weight. Kernel regression gives each bar a weight that decays smoothly with distance, producing: - **Smoother output** — no step artifacts when bars enter / leave the window - **Better bias control** — the peak of the kernel sits exactly on the point being estimated - **Kernel-specific behavior** — compact-support kernels eliminate tail contamination entirely; Rational Quadratic's `α` parameter exposes multi-scale mixing; Periodic kernels resonate with known cycle lengths The math has been the academic standard for non-parametric regression since Nadaraya (1964) and Watson (1964). KernelLens brings it to Pine Script v6 in its correct, bug-free form. 🟦 THE EIGHT KERNELS All eight kernels implement the Nadaraya–Watson weighting scheme. They differ in support (compact versus infinite), smoothness (how many times differentiable), and how weight decays with distance. | # | Kernel | Formula | Support | Smoothness | Character | |---|---|---|---|---|---| | 1 | **Rational Quadratic** | `(1 + d² / (2·α·ℓ²))^(−α)` | ℝ | C∞ | Multi-scale mixer — `α` controls stretch versus wiggle | | 2 | **Gaussian (RBF)** | `exp(−d² / (2·ℓ²))` | ℝ | C∞ | The canonical smoother — smoothest possible with L² optimality | | 3 | **Periodic** | `exp(−2·sin²(π·d/p) / ℓ²)` | ℝ | C∞ | Resonates with repetition distance `p` — ideal for cycles | | 4 | **Locally Periodic** | Periodic · Gaussian | ℝ | C∞ | Seasonal patterns that slowly drift with trend | | 5 | **Epanechnikov** | `(3/4)(1 − u²) · 𝟙{|u|≤1}` | | C⁰ | Asymptotically MSE-optimal (Watson 1964) — no tail contamination | | 6 | **Tricube** | `(70/81)(1 − \|u\|³)³ · 𝟙{|u|≤1}` | | C² | The LOWESS standard — near-Gaussian with compact support | | 7 | **Triangular** | `(1 − \|u\|) · 𝟙{|u|≤1}` | | C⁰ | Simplest non-uniform kernel — fastest to compute | | 8 | **Cosine** | `(π/4)·cos(π·u/2) · 𝟙{|u|≤1}` | | C¹ | Raised-cosine taper — smoother boundary than Epanechnikov | where `u = d/ℓ` and `𝟙` is the indicator function. **Infinite-Support vs Compact-Support — Why Both Matter** | | Infinite Support (RQ, Gauss, Periodic, LocPeriodic) | Compact Support (Epa, Tricube, Triangular, Cosine) | |---|---|---| | **Tail weight** | Never exactly zero | Exactly zero beyond ±ℓ | | **Loop depth** | `3·ℓ` (3-σ cutoff, ≈99.7% mass) | Exactly `ℓ` | | **Bar contamination** | Distant bars still pull the estimate a tiny amount | Distant bars cannot affect the estimate at all | | **Best for** | Smooth trends, Gaussian-process intuition | Robust regression, outlier resistance | KernelLens picks the correct loop depth automatically based on kernel family: `_depthInfinite` for Gaussian-family kernels, `_depthCompact` for bounded kernels, `_depthPeriodic` for Periodic (which must span enough cycles to reach stable weights). **Why Eight, Not Four** Most Pine kernel libraries ship only the four kernels from MacKay's Gaussian process tutorial. KernelLens adds the four compact-support classical kernels because: - **Epanechnikov** minimises asymptotic mean squared error among all non-negative kernels of bounded support (Watson 1964) — it is the MSE-optimal baseline against which all other kernels are measured - **Tricube** is the kernel used by LOWESS (Cleveland 1979), the de-facto standard for robust locally weighted scatterplot smoothing - **Triangular** is the cheapest non-uniform compact kernel — useful when loop-budget matters on intraday charts with huge dataset size - **Cosine** is C¹-continuous at the support boundary, unlike Epanechnikov's C⁰ discontinuity, producing visibly smoother transitions at kernel edges Adding them makes the library an academically complete toolkit, not just a Pine port of one tutorial. 🟦 FILTER LAYER — NONE / SMOOTH / ZERO LAG Every kernel export accepts a `_filter` parameter with three valid values. The filter layer is implemented identically across all eight kernels, so switching kernel families does not change filter behavior. **"No Filter" — Single-Pass Raw Estimate** ``` ŷ = K(y) ``` One Nadaraya–Watson pass over the source. Cheapest mode, most reactive, fully represents the underlying kernel. Use this when you want the kernel's raw behavior with no additional smoothing or lag correction. **"Smooth" — Double-Pass Estimate** ``` ŷ = K(K(y)) ``` The kernel is applied once to the source, then applied again to its own output using the same bandwidth and the same parameters. The result is a more strongly smoothed curve at the cost of one extra loop pass per bar. This is mathematically equivalent to convolving the kernel with itself — the effective kernel is wider and flatter, pulling longer-range context into each estimate without requiring the user to double the bandwidth. **"Zero Lag" — Ehlers De-Lagged Estimate** ``` ŷ = 2·K(y) − K(K(y)) ``` The ZLEMA identity from Ehlers (*Rocket Science for Traders*, 2000): subtract the smoothing lag from the raw estimate, effectively shifting the output back in time to match the source more closely. The intuition: `K(y)` lags `y` by some amount; `K(K(y))` lags `K(y)` by the same amount; so `K(y) − K(K(y))` is an estimate of the lag itself, and adding it back to `K(y)` cancels out. The result tracks the source more tightly than either pass alone, at the cost of slightly noisier turning points. **Lazy Evaluation — No Wasted Cycles** In `"No Filter"` mode, the second pass is skipped entirely — it never runs. The filter branch uses an `if` block (not a ternary), so Pine's short-circuit semantics prevent the unused computation. A single kernel call costs one pass; `"Smooth"` or `"Zero Lag"` costs two. You only pay for what you use. 🟦 KERNEL CENTER OFFSET — THE `_phase` PARAMETER Every KernelLens kernel takes a `_phase` parameter that shifts the kernel center into the past by `_phase` bars. It is the library's non-repainting knob. **_phase = 0 — Live Estimate** The kernel is centered on the current bar. The most recent price has maximum weight, and the estimate is as fresh as possible. Suitable for live signal generation, but the most recent bar can re-evaluate as it develops within its interval — standard Pine real-time behavior. **_phase > 0 — Non-Repainting Historical Estimate** The kernel center is moved `_phase` bars into the past. The estimate becomes the smoothed value *at that historical bar*, not the current bar. Once the bar at `bar_index − _phase` is fully confirmed (`barstate.isconfirmed`), its estimate cannot change again. This is the standard trick for publishing kernel indicators that do not repaint: you get a stable, historically accurate curve at the cost of shifting the entire output `_phase` bars to the right on the chart. A `_phase = 25` call gives a curve that lags live price by 25 bars but is guaranteed stable for every past bar. **Why It Belongs in the Library, Not the Caller** Pushing `_phase` into the kernel's own loop is not the same as evaluating the kernel at a shifted source (`K(src )`). Shifting the source just uses a stale input with a current-bar-centered kernel, which still produces a fresh estimate of a stale series. KernelLens's `_phase` genuinely moves the kernel center, producing a historical-bar estimate that computes over the correct surrounding window. 🟦 NON-REPAINTING BEHAVIOR Repainting is the single most-asked question about any Pine indicator, and the single most common source of silent failure when a retail trader moves from backtest to live. A strategy that looks flawless on historical bars and then bleeds money the moment it is deployed is almost always suffering from some form of repainting. KernelLens is engineered from first principles to eliminate every class of repainting by construction — not by patching symptoms, but by removing the dependencies that cause repainting in the first place. **The Two Forms of Repainting** | Form | Symptom | Typical Cause | |---|---|---| | **Historical repainting** | A bar that was closed days or weeks ago silently changes its plotted value when the chart is refreshed or scrolled | `request.security()` with `lookahead = barmerge.lookahead_on`, un-gated higher-timeframe data, or incorrect array rotation that reads into future bars | | **Real-time repainting** | The plotted value on the live (current developing) bar flickers tick-by-tick as new price ticks arrive, then freezes at a final value when the bar closes | The indicator reads `close ` (or any current-bar value) inside a weighted sum — the current-bar weight changes every tick | KernelLens avoids the first kind **entirely and unconditionally**: the library contains no `request.security` calls, no higher-timeframe lookups, no `lookahead_on` usage, and no array rotation that could leak future bars into the window. Every historical bar plotted by any KernelLens kernel is computed exclusively from bars that existed at the time that bar was closed. The plotted history is immutable. Real-time repainting is controlled explicitly by the `_phase` parameter — it is the user's choice whether to accept tick-by-tick flicker on the live bar in exchange for zero lag (`_phase = 0`) or to eliminate the flicker entirely at the cost of a small fixed lag (`_phase ≥ 1`). **Why Kernel Regression Normally Repaints (And How KernelLens Stops It)** A traditional Nadaraya–Watson call centered on the current bar evaluates: ``` ŷ(t) = Σᵢ K(dᵢ/ℓ) · y_{t−i} for i = 0 … depth ``` On the live bar, the term `y_{t−0} = close ` is the current real-time price — which changes on every tick. Every tick moves the weighted sum, every tick moves the estimate, and the trader watching the chart sees the kernel plot flicker as the bar develops. The historical bars (where `close ` for that past bar is now fixed) are stable, but the live plot is unstable. KernelLens's `_phase` parameter shifts the loop so the kernel runs over `i = _phase … _phase + depth`. With `_phase = 2`: ``` ŷ(t) = Σᵢ K((i−2)/ℓ) · y_{t−i} for i = 2 … 2 + depth ``` The sum no longer touches `close ` or `close ` — every bar it reads is already confirmed and cannot change. The live-bar kernel output is therefore identical from the first tick of the bar to the last tick of the bar, and identical again when the bar finally closes. There is no flicker and nothing to repaint. **The Lag / Stability Trade-Off** | `_phase` | Lag on Live Bar | Live-Bar Flicker | Historical Repainting | Best For | |---|---|---|---|---| | **0** | 0 bars | Yes (real-time only; history is stable) | None | Scalping, academic research, calibration | | **1** | 1 bar | None | None | Fast day-trading; minimum acceptable lag for a live trading desk | | **2** | 2 bars | None | None | Default for most users — the sweet spot between freshness and stability | | **3** | 3 bars | None | None | Swing trading — extra margin against false flickers from erratic ticks | | **5+** | 5+ bars | None | None | Position trading, long-term chart analysis, published signal marks | Even at `_phase = 0`, **historical repainting never occurs** — only the live bar flickers during its own development. Once a bar closes, its plotted value is final; scrolling away and back, refreshing the chart, or re-opening TradingView will never change that historical plot. The flicker is exclusively a live-bar tick-by-tick phenomenon. **KernelLens as a Non-Repainting Primitive** KernelLens exposes real-time flicker as an explicit, user-controlled trade-off rather than a hidden behavior. The caller picks any point on the spectrum from "fully live" (`_phase = 0`, maximum reactivity with tick-by-tick flicker) to "fully confirmed" (`_phase ≥ 1`, one or more bars of lag in exchange for a curve that never redraws) with a single integer parameter. Historical repainting — the dangerous form that silently rewrites past plots — is eliminated unconditionally regardless of `_phase`. **How to Verify Non-Repainting Yourself** Do not trust the word "non-repainting" from any library — always verify. KernelLens can be verified in about thirty seconds: 1. Load a chart with KernelLens on it using `_phase = 2` (or any value > 0). 2. Take a screenshot at any specific historical bar. 3. Scroll far to the left, refresh the chart, or reload the indicator. 4. Return to the same bar. The plotted value at that bar must be pixel-identical to the screenshot — because the computation on that bar used only the bars before it, which have not changed. 5. Repeat with `_phase = 0`. The historical bars must still be pixel-identical — only the live bar's plot can differ between observations, and only because the live bar's `close` is now a different number than it was when you took the screenshot. For a stricter test, use TradingView's **Bar Replay** mode. Enable Bar Replay, step forward one bar at a time, and watch the kernel plot on each newly-closed bar. With `_phase ≥ 1`, the value plotted on each newly-closed bar will exactly match what the indicator shows after you exit replay mode and view the same bar normally. This is the gold-standard test — Bar Replay reproduces live-bar tick arrival in a controlled way. **Common Misconceptions** > *"Any Pine indicator that uses `close` repaints."* False. Using `close` on a confirmed bar does not repaint — the confirmed bar's close is locked. What can repaint is using `close` on the live bar, and only within that live bar's interval. KernelLens with `_phase > 0` never reads the live-bar close at all. > *"`lookahead = barmerge.lookahead_on` is always wrong."* Context-dependent. `lookahead_on` is used correctly in some multi-timeframe indicators to request a higher-TF value that is already settled on the lower TF. KernelLens does not use `request.security` at all, so this question does not apply — but for libraries that do, `lookahead_on` is only problematic when it leaks values from bars that were not yet closed at the lower-TF time of evaluation. > *"Non-repainting means zero lag."* False. Zero lag and non-repainting are orthogonal properties. KernelLens `_phase = 0` is zero lag with real-time flicker; `_phase = 2` is two-bar lag with no flicker. You can have any combination of the two, and the right choice depends on the trading style. > *"The `FILTER_ZEROLAG` mode makes the indicator non-repainting."* False. `FILTER_ZEROLAG` is an Ehlers-style de-lagging filter applied to the kernel output; it reduces the perceived lag of the estimate, but it does not affect whether the live bar flickers. Non-repainting is controlled exclusively by `_phase`. Choose `_phase` for repainting behavior, and `_filter` for smoothness / lag shape — they are independent knobs. **When to Accept Real-Time Flicker (`_phase = 0`)** Despite everything above, there are legitimate reasons to deliberately use `_phase = 0`: - **Academic research and backtesting** — you want the kernel mathematics in its classical form, centered on the point being estimated, with no phase adjustment - **Scalping on very short timeframes** — a 2-bar lag on a 1-minute chart is a 2-minute delay, which can matter when you are exiting within a 4-minute window - **Visual calibration** — when you are choosing a bandwidth by eye, the live-bar flicker actually helps: you see how sensitive the curve is to each incoming tick, which is diagnostic information - **Indicators that read the kernel output only on `barstate.isconfirmed`** — if your signal logic is gated by `if barstate.isconfirmed`, then live-bar flicker is invisible to your signal (it sees only the frozen close-of-bar value), and you can safely use `_phase = 0` with no practical consequence For every other case — and especially for any live alert or automated trading system — use `_phase ≥ 1`. Two bars of lag on a clean, stable curve is almost always worth more than zero lag on a curve that redraws itself several times per bar. 🟦 UNIFIED DISPATCHER — `estimate()` For indicators where the user picks a kernel from a dropdown, writing eight separate ternary branches is tedious and error-prone. KernelLens ships with a unified dispatcher that routes to the correct kernel based on a string argument: ```pine import a_jabbaroff/KernelLens/1 as kl line = kl.estimate( kernelType = kl.KERNEL_GAUSS, src = close, bandwidth = 32, shapeAlpha = 1.0, period = 1, phase = 2, filter = kl.FILTER_SMOOTH) ``` The dispatcher forwards to the matching typed export, so there is no performance penalty versus calling the kernel directly — it is a compile-time routing pass. Unknown kernel names raise a descriptive `runtime.error` naming every valid alternative, so typos fail loudly instead of silently returning `na`. **Public Constants** KernelLens exposes its string constants so callers never type the magic values by hand: | Constant | Value | |---|---| | `FILTER_NONE` | `"No Filter"` | | `FILTER_SMOOTH` | `"Smooth"` | | `FILTER_ZEROLAG` | `"Zero Lag"` | | `KERNEL_RQ` | `"Rational Quadratic"` | | `KERNEL_GAUSS` | `"Gaussian"` | | `KERNEL_PERIODIC` | `"Periodic"` | | `KERNEL_LOCPER` | `"Locally Periodic"` | | `KERNEL_EPA` | `"Epanechnikov"` | | `KERNEL_TRICUBE` | `"Tricube"` | | `KERNEL_TRIANG` | `"Triangular"` | | `KERNEL_COSINE` | `"Cosine"` | Using the constants in your caller code means the Pine compiler — not a runtime string compare — catches typos at edit time. 🟦 UTILITY LAYER — FIVE PROFESSIONAL HELPERS KernelLens ships with five utility exports that complement the core estimators. They are the functions you almost always write immediately after getting a smoothed line, factored out so you don't rewrite them in every indicator. **`slope(estimate, step)` — Discrete First Derivative** Returns `(y_t − y_{t−step}) / step`, the normalized rate of change over `step` bars. Use it to detect whether a kernel output is trending up, flat, or down — the foundation for any trend-following signal built on top of KernelLens. ```pine rising = kl.slope(line, 3) > 0.0 ``` **`trendState(estimate, step)` — Ternary Trend Indicator** Returns `+1` if the estimate is rising, `−1` if falling, `0` if exactly flat over the window. A single-call replacement for hand-rolled `line > line ? 1 : line < line ? -1 : 0` ladders. **`crossSignal(fast, slow)` — Bi-directional Crossover** Returns `+1` on the bar where `fast` crosses above `slow` (bullish), `−1` on a bearish cross, and `0` otherwise. Built on `ta.crossover` / `ta.crossunder`, so the signal is non-repainting once the bar is confirmed. **`confidenceBand(src, estimate, window)` — Residual Standard Deviation** Computes the rolling standard deviation of `(src − estimate)` over a user-defined window. Use the return value as the half-width of a confidence band around the estimate: ```pine est = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH) sigma = kl.confidenceBand(close, est, 50) upper = est + 1.96 * sigma lower = est - 1.96 * sigma ``` This is a computationally cheap proxy for the full kernel-weighted local variance — ideal when you need visual bands without paying for a second weighted pass. **`silvermanBandwidth(src, window)` — Optimal ℓ Suggestion** Returns the Silverman rule-of-thumb bandwidth: ``` h ≈ 1.06 · σ · n^(−1/5) ``` where `σ` is the rolling standard deviation of the source and `n` is the window size. This is the classical starting point for Gaussian-family bandwidths in academic texts (Silverman 1986). Because Pine requires `simple int` for kernel bandwidth, the returned value is intended for diagnostic display — plot it, read it off the chart, then hard-code the rounded integer into the kernel call. 🟦 INPUT VALIDATION — FAIL LOUDLY, FAIL EARLY Every public function in KernelLens validates its inputs through a set of internal `_assert*` helpers. Invalid arguments never produce silent `na` fallbacks or buried zero-divisions — they raise `runtime.error` with a descriptive message identifying the function, the parameter, and the expected range. | Helper | Checks | Raises On | |---|---|---| | `_assertFilter` | Filter string is `FILTER_NONE`, `FILTER_SMOOTH`, or `FILTER_ZEROLAG` | Typos like `"No FIlter"` (capital I) — a bug that exists in at least one published kernel indicator | | `_assertBandwidth` | Bandwidth is a strictly positive integer | Negative or zero bandwidth, which would cause division by zero or infinite loops | | `_assertPeriod` | Period is a strictly positive integer | Zero period, which would cause `sin(π·d/0)` in Periodic kernels | | `_assertAlpha` | Rational Quadratic shape parameter is strictly positive | Zero or negative `α`, which would invert the RQ formula | Error messages are prefixed `KernelLens:` (or `KernelLens.:`) so they are easy to spot in the TradingView runtime log. Every message names the parameter that failed, the value that was passed, and the set of valid alternatives — so a misconfigured chart tells you exactly what to fix. 🟦 LOOP DEPTH — THE BUG FIX THAT MOTIVATED KERNELLENS The two most popular Pine kernel libraries on TradingView share the same fatal bug: both compute their loop depth as ```pine _size = array.size(array.from(_src)) ``` where `array.from(_src)` creates a **one-element array containing the current value of `_src`**, so `_size` is always `1`. The loop then runs `for i = 0 to 1 + startAtBar`, effectively using only `startAtBar + 2` bars — completely ignoring the user's bandwidth. Every published kernel indicator built on those libraries inherits this silent miscalculation. KernelLens replaces the broken helper with three explicit depth selectors: | Helper | Depth | Used By | |---|---|---| | `_depthInfinite(bw)` | `max(bw · 3, 4)` | Gaussian, Rational Quadratic, Locally Periodic | | `_depthCompact(bw)` | `max(bw, 4)` | Epanechnikov, Tricube, Triangular, Cosine | | `_depthPeriodic(bw, p)` | `max(bw · 3, p · 10, 4)` | Periodic | For Gaussian-family kernels, the `3·ℓ` cutoff captures approximately 99.7% of the kernel mass (the three-sigma rule). For compact-support kernels, the depth equals the bandwidth exactly — the loop terminates at the kernel's natural zero point. For Periodic kernels, the depth is the larger of the scale-based and cycle-based minima, so the loop always spans enough periods to produce a stable weighted average. The loop counter `i` runs over bar offsets starting at `_phase`, every bar lookup is NA-checked before being incorporated into the sum, and the final `num / den` division is guarded against zero denominators. On a fresh chart, the kernel gracefully returns `na` for bars where the window extends past available history, rather than producing poisoned sums from implicit NA arithmetic. 🟦 API REFERENCE **Core Kernel Estimators — Eight Exports** | Export | Signature | |---|---| | `rationalQuadratic` | `(src, bandwidth, shapeAlpha, phase, filter) → float` | | `gaussian` | `(src, bandwidth, phase, filter) → float` | | `periodic` | `(src, bandwidth, period, phase, filter) → float` | | `locallyPeriodic` | `(src, bandwidth, period, phase, filter) → float` | | `epanechnikov` | `(src, bandwidth, phase, filter) → float` | | `tricube` | `(src, bandwidth, phase, filter) → float` | | `triangular` | `(src, bandwidth, phase, filter) → float` | | `cosineKernel` | `(src, bandwidth, phase, filter) → float` | **Unified Dispatcher** | Export | Signature | |---|---| | `estimate` | `(kernelType, src, bandwidth, shapeAlpha, period, phase, filter) → float` | **Utility Layer — Five Exports** | Export | Signature | |---|---| | `slope` | `(estimate, step) → float` | | `trendState` | `(estimate, step) → int` | | `crossSignal` | `(fast, slow) → int` | | `confidenceBand` | `(src, estimate, window) → float` | | `silvermanBandwidth` | `(src, window) → float` | **Parameter Types** | Name | Pine Type | Description | |---|---|---| | `src` | `series float` | Source series (close, hl2, ohlc4, or any other price-derived series) | | `bandwidth` | `simple int` | Kernel scale `ℓ`, must be `> 0` | | `shapeAlpha` | `simple float` | Rational Quadratic shape parameter, must be `> 0` | | `period` | `simple int` | Periodic repetition distance, must be `> 0` | | `phase` | `simple int` | Kernel center offset in bars, must be `≥ 0` | | `filter` | `simple string` | One of `FILTER_NONE`, `FILTER_SMOOTH`, `FILTER_ZEROLAG` | | `kernelType` | `simple string` | One of the eight `KERNEL_*` constants | | `step` | `simple int` | Finite-difference step for `slope` / `trendState`, must be `≥ 1` | | `window` | `simple int` | Rolling window for `confidenceBand` / `silvermanBandwidth`, must be `≥ 2` | 🟦 USAGE EXAMPLES **Minimal — One Gaussian Curve** ```pine //@version=6 indicator("KernelLens — Gaussian Demo", overlay = true) import a_jabbaroff/KernelLens/1 as kl line = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH) plot(line, "Gaussian", color = color.orange, linewidth = 2) ``` **Fast / Slow Crossover System** ```pine //@version=6 indicator("KernelLens — RQ Crossover", overlay = true) import a_jabbaroff/KernelLens/1 as kl fast = kl.rationalQuadratic(close, 8, 1.0, 2, kl.FILTER_NONE) slow = kl.rationalQuadratic(close, 32, 1.0, 2, kl.FILTER_SMOOTH) cross = kl.crossSignal(fast, slow) plot(fast, "Fast", color = color.aqua, linewidth = 2) plot(slow, "Slow", color = color.orange, linewidth = 2) plotshape(cross == 1, "Bull", location = location.belowbar, color = color.lime, style = shape.triangleup, size = size.tiny) plotshape(cross == -1, "Bear", location = location.abovebar, color = color.red, style = shape.triangledown, size = size.tiny) ``` **Confidence Band Envelope** ```pine //@version=6 indicator("KernelLens — Confidence Band", overlay = true) import a_jabbaroff/KernelLens/1 as kl est = kl.tricube(close, 48, 2, kl.FILTER_SMOOTH) sigma = kl.confidenceBand(close, est, 50) k = 1.96 upper = est + k * sigma lower = est - k * sigma plot(est, "Estimate", color = color.orange, linewidth = 2) p1 = plot(upper, "+1.96σ", color = color.new(color.aqua, 70)) p2 = plot(lower, "−1.96σ", color = color.new(color.aqua, 70)) fill(p1, p2, color = color.new(color.aqua, 92)) ``` **Dropdown-Driven Kernel Selection** ```pine //@version=6 indicator("KernelLens — Dropdown", overlay = true) import a_jabbaroff/KernelLens/1 as kl kernelType = input.string(kl.KERNEL_GAUSS, "Kernel", options = ) bandwidth = input.int(32, "Bandwidth", minval = 2) alphaRQ = input.float(1.0,"RQ Alpha", minval = 0.01, step = 0.25) period = input.int(20, "Period", minval = 1) phase = input.int(2, "Phase", minval = 0) filter = input.string(kl.FILTER_SMOOTH, "Filter", options = ) line = kl.estimate(kernelType, close, bandwidth, alphaRQ, period, phase, filter) plot(line, "KernelLens", color = color.orange, linewidth = 2) ``` 🟦 TIMEFRAME PRESETS — BANDWIDTH BY STYLE Kernel bandwidth is the single most important parameter. It controls the trade-off between reactivity (small `ℓ`, tight fit, noisier) and stability (large `ℓ`, smooth curve, slower to react). The presets below are tested starting points — adjust by ±25 % to taste. --- **SCALPER — 1m / 3m / 5m** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 8 | | Phase | 1 | | Filter | `FILTER_NONE` | | Best Kernel | Rational Quadratic or Gaussian | | RQ shapeAlpha | 1.0 | **Why:** Short bandwidth means the kernel reacts within a handful of bars. `FILTER_NONE` removes the double-pass lag, so the estimate tracks price as tightly as possible. Phase 1 keeps the estimate nearly live while still avoiding the current-bar tick noise. --- **DAY TRADER — 15m / 30m / 1H** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 16 | | Phase | 2 | | Filter | `FILTER_SMOOTH` | | Best Kernel | Gaussian or Tricube | | RQ shapeAlpha | 1.0 | **Why:** Balanced reactivity — the 16-bar Gaussian is the default Silverman range for intraday price data, and `FILTER_SMOOTH` removes most of the bar-to-bar chop without significantly increasing lag. Tricube provides near-identical behaviour with strict compact support and is preferred on noisy assets where outlier bars should not influence the curve. --- **SWING TRADER — 4H / 1D** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 32 | | Phase | 3 | | Filter | `FILTER_SMOOTH` | | Best Kernel | Rational Quadratic | | RQ shapeAlpha | 2.0 | **Why:** Swing trades need structural signals, not intraday noise. Rational Quadratic with `α = 2.0` mixes medium and long length scales, producing a curve that ignores transient spikes but catches genuine regime shifts. Phase 3 shifts the estimate three bars back so each swing decision is made against a fully confirmed kernel output. --- **POSITION / LONG-TERM — 1D / 1W / 1M** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 64 | | Phase | 5 | | Filter | `FILTER_SMOOTH` or `FILTER_ZEROLAG` | | Best Kernel | Gaussian or Locally Periodic | | Period (if LP) | 52 (weekly cycle) | **Why:** Position traders care about the macro trajectory. A Gaussian with ℓ = 64 produces a curve that only turns on genuine multi-month inflections. Locally Periodic with `period = 52` is the ideal choice when a clear seasonal cycle is present — it uses both the long-range Gaussian envelope and the 52-bar periodicity to highlight cycle turns that align with trend. --- **RESEARCH — Academic / Backtest** | Parameter | Value | |---|---| | Bandwidth (ℓ) | Compute via `silvermanBandwidth(src, 200)` | | Phase | 0 | | Filter | `FILTER_NONE` | | Best Kernel | Epanechnikov | **Why:** Epanechnikov is the MSE-optimal kernel; `FILTER_NONE` keeps the estimator in its classical single-pass form; `phase = 0` centers the kernel on the bar being evaluated. This is the configuration that matches the statistical literature exactly — use it when publishing research, running Monte-Carlo studies, or calibrating against reference implementations. 🟦 BANDWIDTH SELECTION Bandwidth `ℓ` is the single most consequential choice in kernel regression. Too small and the estimate overfits local noise; too large and it flattens real structure. KernelLens exposes two helpers to support both manual and semi-automated bandwidth selection. **Manual — Start with ℓ ≈ √n** A practical starting point for financial time series: set `ℓ ≈ √window_of_interest`. If you care about 100-bar structure, try `ℓ = 10`. If you care about 400-bar structure, try `ℓ = 20`. Adjust by ±25 % based on how noisy the result looks. **Silverman's Rule of Thumb** The closed-form optimal bandwidth for Gaussian-family kernels under Gaussian source assumptions: ``` h ≈ 1.06 · σ · n^(−1/5) ``` Call `silvermanBandwidth(src, window)` to compute this value live. Because Pine requires `simple int` bandwidth at compile time, the returned value is for diagnostic use — plot it, read the stable value off the chart, then hard-code the rounded integer into your kernel calls. **Leave-One-Out Cross-Validation (Manual)** For academic rigor, compute the leave-one-out mean squared error for a range of bandwidths and pick the minimum. KernelLens does not automate this (it would require `series int` bandwidth, which Pine does not support inside kernel loops), but the formula is straightforward: ``` LOOCV(ℓ) = (1/n) · Σᵢ (yᵢ − ŷᵢ⁻ⁱ(ℓ))² ``` where `ŷᵢ⁻ⁱ` is the kernel estimate at bar `i` computed without including bar `i` in the sum. Evaluate offline, pick the minimum, hard-code the result. 🟦 FILTER SELECTION — WHEN TO USE EACH | Filter | Best For | Avoid When | |---|---|---| | `FILTER_NONE` | Live signal generation, research / calibration, compact-support kernels on noisy data | Choppy markets where you need extra smoothing | | `FILTER_SMOOTH` | Swing and position trades, confidence band midlines, most day-trading setups | Scalping — the double pass adds measurable lag | | `FILTER_ZEROLAG` | Regime detection, crossover systems that need the curve to track price tightly | Low-volume assets — Zero Lag amplifies high-frequency noise | The three filters use the same underlying kernel with the same bandwidth, so switching between them does not require re-tuning. Default to `FILTER_SMOOTH` when in doubt — it is the best-behaved option across the widest range of assets and timeframes. 🟦 COMPATIBILITY KernelLens targets Pine Script v6 and runs on every TradingView chart — no exchange, asset class, or timeframe restriction. - **Crypto** — Spot, futures, perpetual contracts - **Forex** — All majors, minors, and exotics - **Equities** — Stocks, ETFs, indices - **Commodities** — Metals, energy, agriculture - **Timeframes** — 1 minute through Monthly The library is deterministic — given the same source and parameters, every bar of every symbol produces the same estimate. No calibration is needed across assets; the bandwidth parameter alone controls smoothness, and the kernel formulas are scale-free in the source dimension. Silverman's bandwidth helper automatically adapts to each asset's volatility. 🟦 TECHNICAL NOTES - **Pine Script v6** — uses the modern type system, strict type checking, and the `switch` expression in the unified dispatcher - **Non-repainting** — kernel outputs for any confirmed bar depend only on that bar's history; there is no look-ahead, no `request.security` with lookahead, and no dependency on the unconfirmed current bar unless `_phase = 0` is deliberately chosen - **NA-safe iteration** — every bar lookup inside a kernel loop is guarded by `if not na(y)`, so chart history gaps and warm-up bars cannot poison the weighted sum - **Division-by-zero protection** — every kernel's final division checks `den > 0.0` and returns `na` if the denominator collapses (which can only happen on truly empty windows) - **Input validation** — every public function asserts its preconditions up front via `_assertFilter`, `_assertBandwidth`, `_assertPeriod`, `_assertAlpha`, and raises `runtime.error` with a descriptive message on misuse — no silent `na` fallbacks - **Lazy filter evaluation** — the `"No Filter"` path never executes the second kernel pass; the `if`-branch check short-circuits, so single-pass mode is as cheap as a raw kernel call - **Correct loop bounds** — `_depthInfinite`, `_depthCompact`, and `_depthPeriodic` compute the correct window size per kernel family, fixing the silent `_size = 1` bug that plagues every other published Pine kernel library - **No persistent state** — the library is purely functional: no `var`, no arrays, no history buffers that grow over time; every export is a pure expression of `(inputs) → output`, so Pine's `max_*_count` limits cannot be exceeded and the library cannot leak memory - **O(bandwidth) per bar per kernel call** — the loop depth is bounded by the constants in Section 0; there is no hidden quadratic behavior and the cost scales linearly with the user-chosen bandwidth - **Unicode-safe comments** — the source uses academic notation (`σ`, `ℓ`, `α`, `ŷ`, `ℝ`) where it improves readability; all strings are plain ASCII for runtime compatibility 🟦 ACADEMIC REFERENCES Every kernel and every formula in KernelLens is cited inline in the source. The combined bibliography: - **Nadaraya, E. A. (1964).** On estimating regression. *Theory of Probability & Its Applications*, 9(1), 141–142. - **Watson, G. S. (1964).** Smooth regression analysis. *Sankhyā: The Indian Journal of Statistics, Series A*, 26(4), 359–372. - **Cleveland, W. S. (1979).** Robust locally weighted regression and smoothing scatterplots. *Journal of the American Statistical Association*, 74(368), 829–836. *(Tricube kernel, LOWESS.)* - **Silverman, B. W. (1986).** *Density Estimation for Statistics and Data Analysis*. Chapman & Hall, London. *(Bandwidth rule of thumb.)* - **Wand, M. P. & Jones, M. C. (1995).** *Kernel Smoothing*. Chapman & Hall. *(Unified treatment of all eight kernels.)* - **MacKay, D. J. C. (1998).** Introduction to Gaussian Processes. *NIPS Tutorial*. *(Periodic and Rational Quadratic kernels.)* - **Ehlers, J. F. (2000).** *Rocket Science for Traders*. John Wiley & Sons. *(Zero-lag smoothing trick.)* - **Rasmussen, C. E. & Williams, C. K. I. (2006).** *Gaussian Processes for Machine Learning*. MIT Press. *(Locally Periodic and Rational Quadratic kernels.)* 🟦 VERSIONING & LICENSE - **Version** — 1.0.0 - **Pine Script** — v6 - **License** — Mozilla Public License 2.0 - **Status** — Production-ready KernelLens follows semantic versioning. Minor versions add new exports without breaking existing ones; patch versions fix bugs; major versions may change function signatures and will be announced in the changelog. 🟦 DISCLAIMER KernelLens is a mathematical library for non-parametric regression on financial time series using the Nadaraya–Watson method. The library 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 in the kernel itself. Responsibility for any trading decisions made using this library 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 KernelLens or any indicator built on top of it.
Библиотека Pine Script®
от a_jabbaroff
GTS5.alvin - BOS/ChoCH 💥 Smart Market Structure • Break of Structure • Change of Character • Swing Intelligence 🚀 What This Indicator Does The GTS5.alvin - BOS/ChoCH is a precision market structure tracker that automatically identifies: 📈 Higher Highs (HH) & Higher Lows (HL) 📉 Lower Highs (LH) & Lower Lows (LL) ⚡ Breaks of Structure (BOS) 🔄 Change of Character (ChoCH) 🧭 Real-time market bias shifts 📊 Key swing levels with automatic breakout detection This is not a basic structure tool. This is a clean, execution-grade market intelligence system designed to show you what price is actually doing — not what you think it’s doing. 🧬 Why This Indicator Hits Different Most traders lose because they are: Trading random highs/lows Guessing trend direction Entering before structure confirms Reacting emotionally instead of structurally This tool fixes that. It builds a clean narrative of the market: “Where is structure forming?” “Where did it break?” “Was that continuation… or reversal?” Now you always know. ⚔️ Core Features 🧱 True Market Structure Detection MUST use a 11-candle swing model (5 left / pivot / 5 right) to accurately define: Swing highs Swing lows Market structure shifts No noisy micro-swings. Only meaningful structure. 🔥 BOS & ChoCH Engine Automatically distinguishes: 💥 BOS (Break of Structure) → continuation trend behavior 🔁 ChoCH (Change of Character) → potential reversal zones Color-coded and labeled directly on chart for instant clarity. 🧭 Bias Tracking (Hidden Edge) The script continuously tracks directional bias: Bullish (1) Bearish (-1) Neutral (0) This allows perfect classification of: Trend continuation vs reversal breaks LH/HL vs HH/LL behavior shifts 📊 Smart Swing Labels Auto-labels: HH / HL → bullish structure LH / LL → bearish structure Minimal chart clutter. Maximum meaning. 📍 Break Level Mapping Every structural break includes: Horizontal level projection Mid-level BOS/ChoCH label Break candle marker tag You don’t just see the break — you see exactly where it came from. ⚡ Wick or Close Break Logic Choose how structure is validated: Close-based confirmation (cleaner, stricter) Wick-based detection (faster, aggressive) Perfect for scalpers or swing traders. 🔔 Advanced Alert System Built-in alerts for: Bullish Breaks Bearish Breaks HH → BOS continuation events LL → BOS continuation events New Higher Highs New Lower Lows This turns your chart into a semi-automated signal radar. 🧠 How Traders Use It 📈 Trend Following Wait for: BOS in direction of bias Then ride continuation after HH/HL confirmation 🔄 Reversal Detection Look for: ChoCH after strong HH/HL or LL/LH sequence Bias flip + structure break alignment ⚡ Smart Entries Combine: Swing structure Break levels ChoCH confirmation To enter with institutional-style timing instead of retail guessing 🧩 What Makes It Different From Other Structure Tools Most indicators: ❌ Show messy swings ❌ Don’t distinguish BOS vs ChoCH properly ❌ Don’t track bias ❌ Overload charts with noise This one: ✔ Clean structure logic ✔ Context-aware labeling ✔ Bias-driven classification ✔ Minimal but powerful visuals ✔ Designed for real execution decisions 🎯 Ideal For Smart Money Concept traders (SMC) Price action traders ICT-style traders Scalpers & intraday traders Swing traders needing structure confirmation ⚡ Final Thought If you don’t know where structure broke… You’re not trading. You’re guessing. This indicator turns chaos into a readable market story: Structure → Break → Confirmation → Bias → Opportunity No noise. No confusion. Just clarity. ======================= NOTE FROM THE CREATOR: ======================= Combine this indicator with the GTS5.alvin - HMA Tracker 2.0 then use the following strategy to help better your trading edge: Wait for a BOS of a HH/LL Then immediately check to see if the expansion/strength meter measures 50%+ If so, consider entering the trade with the momentum. (If the meter measures less than 50% then consider waiting for another setup.) Take profit: Once expansion/strength meter falls back to 50% then consider exiting trade. Stop Loss: Consider placing behind HH/LL market structure swing. --------------------------------- Disclaimer: This is ONLY for educational purposes only! This indicator will NOT print you money. My success does NOT guarantee your success. I have EXTENSIVE EXPERTISE IN MARKET MANIPULATIONS! Use at your own discretion.
Индикатор Pine Script®
от alvinandfriends
5
Quantitative Linear Regression Channel [Dual Fill + MTF]Abstract / Description: This is a significantly upgraded v6 version of the classic Linear Regression Channel originally created by LonesomeTheBlue. Key Improvements over the original: Updated to Pine Script v6 with modern syntax and better performance. Persistent object pool for lines and labels – eliminates object leakage and improves rendering speed. Optional dual-zone fill (Upper and Lower) with fully customizable colors for clearer visual separation. Broken channel visualization that freezes the previous channel in blue dotted style instead of deleting it immediately. Dynamic MTF Trend Table (5m to 1D) showing slope direction with arrows and color coding. Table position is user-configurable (top_left, top_right, etc.). Support for calculating the channel on a higher timeframe (MTF mode) while displaying it cleanly on the current chart. Cleaner code structure, grouped inputs, improved alerts, and overall more stable behavior across all timeframes. The core regression calculation and broken channel logic remain faithful to the original concept, while adding modern institutional-grade features for better usability and performance. Credits: Base concept and original code by © LonesomeTheBlue (2020). v6 architecture, persistent pool, dual fill, MTF table and optimizations by datapro (2026). Recommended use: Works on any timeframe. Best results when used with the MTF table for confluence.
Индикатор Pine Script®
от datapro
1