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Regressions

Prime Strategy SwingPrime Strategy 🍾 | Swing — NQ & GOLD by NicoCashFx | The Circle Traders Prime Strategy Swing is a Smart Money Concepts (SMC) methodology developed by NicoCashFx in 2025, designed for swing trading on NASDAQ (US100) and GOLD (XAUUSD) using higher timeframe structure. How it works: Structure Detection BOS and CHoCH are identified on a configurable timeframe (default H1) using confirmed pivot highs and lows. Only body closes beyond structure levels register as valid CHoCH — wick-only breaks are displayed as dotted lines and do not change trend direction. Fibonacci Zones Once structure is confirmed, the indicator draws a fixed zone between the last swing high and swing low. The zone is anchored to the exact candle wicks and splits into two halves: the active half (green for buys, red for sells) and a neutral half (transparent gray). Within the active half, two OTE levels are marked: 70% and 85% — the deeper retracement zones where institutional entries tend to occur on the NASDAQ. Zone Logic The zone remains fixed until price invalidates it with a body close beyond the opposite extreme. It only resets on a new CHoCH or invalidation — never on new pivot formation alone. Supporting Tools Nadaraya-Watson envelope filter, Order Blocks, liquidity levels (auto-removed on sweep), session signals with NY timezone filter, configurable max signals per day, and a real-time dashboard showing structure, CHoCH direction, active zone level, and NW filter status. All components toggle independently from settings. Developed by NicoCashFx for The Circle Traders (TCT) community.
Wskaźnik Pine Script®
od NicoCashFx
OTE Structure Flow//@version=6 indicator("OTE • Structure & Flow Map", overlay=true, max_lines_count=500, max_labels_count=500, max_boxes_count=100) //━━━━━━━━━━━━ INPUTS pivotLen = input.int(5, "Swing Pivot Length", minval=1) maxSwings = input.int(40, "Visible Swing Count", minval=10, maxval=100) extendBars = input.int(120, "Flow Extend Bars", minval=20, maxval=300) showSwings = input.bool(true, "Show Market Structure") showOTE = input.bool(true, "Show OTE 0.886") showTarget = input.bool(true, "Show Target 0.618") showCore = input.bool(true, "Show Regression Gravity Line") showLabels = input.bool(true, "Show HH / HL / LH / LL") regLen = input.int(120, "Regression Length", minval=20, maxval=300) regSmooth = input.int(5, "Regression Smooth", minval=1) bullCol = color.lime bearCol = color.red oteCol = color.yellow tpCol = color.aqua grayCol = color.gray //━━━━━━━━━━━━ PIVOTS ph = ta.pivothigh(high, pivotLen, pivotLen) pl = ta.pivotlow(low, pivotLen, pivotLen) phBar = bar_index - pivotLen plBar = bar_index - pivotLen var float pivPrice = array.new_float() var int pivBar = array.new_int() var int pivType = array.new_int() // 1 high, -1 low addPivot(_price, _bar, _type) => sz = array.size(pivType) if sz == 0 array.push(pivPrice, _price) array.push(pivBar, _bar) array.push(pivType, _type) else lastType = array.get(pivType, sz - 1) lastPrice = array.get(pivPrice, sz - 1) if lastType == _type replaceHigh = _type == 1 and _price > lastPrice replaceLow = _type == -1 and _price < lastPrice if replaceHigh or replaceLow array.set(pivPrice, sz - 1, _price) array.set(pivBar, sz - 1, _bar) else array.push(pivPrice, _price) array.push(pivBar, _bar) array.push(pivType, _type) while array.size(pivType) > maxSwings array.shift(pivPrice) array.shift(pivBar) array.shift(pivType) if not na(ph) addPivot(ph, phBar, 1) if not na(pl) addPivot(pl, plBar, -1) //━━━━━━━━━━━━ REGRESSION FLOW CORE src = hl2 reg0 = ta.linreg(src, regLen, 0) reg1 = ta.linreg(src, regLen, 1) reg2 = ta.linreg(src, regLen, 2) slope = reg0 - reg1 curve = (reg0 - reg1) - (reg1 - reg2) gravityRaw = reg0 + slope * 2.0 + curve * regLen * 0.25 gravity = ta.ema(gravityRaw, regSmooth) gravityBull = gravity > gravity gravityBear = gravity < gravity gravityCol = gravityBull ? bullCol : gravityBear ? bearCol : grayCol plot(showCore ? gravity : na, "Market Gravity Regression Line", color=color.new(gravityCol, 0), linewidth=3) //━━━━━━━━━━━━ OBJECTS var line lines = array.new_line() var label labels = array.new_label() var box boxes = array.new_box() clearObjects() => while array.size(lines) > 0 line.delete(array.pop(lines)) while array.size(labels) > 0 label.delete(array.pop(labels)) while array.size(boxes) > 0 box.delete(array.pop(boxes)) addLine(_x1, _y1, _x2, _y2, _col, _style, _width) => ln = line.new(_x1, _y1, _x2, _y2, xloc=xloc.bar_index, extend=extend.none, color=_col, style=_style, width=_width) array.push(lines, ln) addLabel(_x, _y, _txt, _col, _up) => if showLabels lb = label.new(_x, _y, _txt, xloc=xloc.bar_index, style=_up ? label.style_label_up : label.style_label_down, color=_col, textcolor=color.black, size=size.tiny) array.push(labels, lb) addBox(_left, _top, _right, _bottom, _col) => bx = box.new(_left, _top, _right, _bottom, xloc=xloc.bar_index, bgcolor=color.new(_col, 86), border_color=color.new(_col, 25)) array.push(boxes, bx) prevSamePrice(_idx, _type) => float res = na for j = _idx - 1 to 0 if array.get(pivType, j) == _type and na(res) res := array.get(pivPrice, j) res //━━━━━━━━━━━━ DRAW STRUCTURE + OTE FLOW var string mode = "WAIT" if barstate.islast clearObjects() mode := "STRUCTURE FLOW" sz = array.size(pivType) if showSwings and sz >= 2 for i = 1 to sz - 1 pA = array.get(pivPrice, i - 1) pB = array.get(pivPrice, i) bA = array.get(pivBar, i - 1) bB = array.get(pivBar, i) tB = array.get(pivType, i) prev = prevSamePrice(i, tB) isHH = tB == 1 and not na(prev) and pB > prev isLH = tB == 1 and not na(prev) and pB <= prev isHL = tB == -1 and not na(prev) and pB > prev isLL = tB == -1 and not na(prev) and pB <= prev sCol = isHH or isHL ? bullCol : isLH or isLL ? bearCol : grayCol txt = isHH ? "HH" : isLH ? "LH" : isHL ? "HL" : isLL ? "LL" : tB == 1 ? "H" : "L" addLine(bA, pA, bB, pB, sCol, line.style_solid, 2) addLabel(bB, pB, txt, sCol, tB == -1) if sz >= 4 i1 = sz - 4 i2 = sz - 3 i3 = sz - 2 i4 = sz - 1 t1 = array.get(pivType, i1) t2 = array.get(pivType, i2) t3 = array.get(pivType, i3) t4 = array.get(pivType, i4) p1 = array.get(pivPrice, i1) p2 = array.get(pivPrice, i2) p3 = array.get(pivPrice, i3) p4 = array.get(pivPrice, i4) b4 = array.get(pivBar, i4) bullImpulse = t3 == -1 and t4 == 1 and p4 > p2 and p3 > p1 bearImpulse = t3 == 1 and t4 == -1 and p4 < p2 and p3 < p1 if bullImpulse mode := "BULL STRUCTURE FLOW" ote886 = p4 - (p4 - p3) * 0.886 tp618 = p4 - (p4 - p3) * 0.618 boxSize = math.abs(p4 - p3) * 0.025 if showOTE addLine(b4, ote886, bar_index + extendBars, ote886, oteCol, line.style_dashed, 2) addBox(b4, ote886 + boxSize, bar_index + extendBars, ote886 - boxSize, oteCol) addLabel(bar_index + extendBars, ote886, "OTE 0.886", oteCol, true) if showTarget addLine(b4, tp618, bar_index + extendBars, tp618, tpCol, line.style_dashed, 2) addLabel(bar_index + extendBars, tp618, "TARGET 0.618", tpCol, false) if bearImpulse mode := "BEAR STRUCTURE FLOW" ote886 = p4 + (p3 - p4) * 0.886 tp618 = p4 + (p3 - p4) * 0.618 boxSize = math.abs(p3 - p4) * 0.025 if showOTE addLine(b4, ote886, bar_index + extendBars, ote886, oteCol, line.style_dashed, 2) addBox(b4, ote886 + boxSize, bar_index + extendBars, ote886 - boxSize, oteCol) addLabel(bar_index + extendBars, ote886, "OTE 0.886", oteCol, false) if showTarget addLine(b4, tp618, bar_index + extendBars, tp618, tpCol, line.style_dashed, 2) addLabel(bar_index + extendBars, tp618, "TARGET 0.618", tpCol, true) //━━━━━━━━━━━━ PANEL var table pan = table.new(position.top_right, 2, 3, border_width=1) if barstate.islast table.cell(pan, 0, 0, "OTE", text_color=color.white, bgcolor=color.black) table.cell(pan, 1, 0, "STRUCTURE FLOW", text_color=oteCol, bgcolor=color.black) table.cell(pan, 0, 1, "Mode", text_color=color.white) table.cell(pan, 1, 1, mode, text_color=mode == "BULL STRUCTURE FLOW" ? bullCol : mode == "BEAR STRUCTURE FLOW" ? bearCol : grayCol) table.cell(pan, 0, 2, "Swings", text_color=color.white) table.cell(pan, 1, 2, str.tostring(array.size(pivType)), text_color=color.white) OTE Structure Flow, piyasa yapısını, impulsif hareketleri ve optimal geri çekilme bölgelerini görselleştirmek için tasarlanmış yapısal Fibonacci haritalama aracıdır. İndikatör, grafik üzerinde sürekli olarak Higher High (HH), Higher Low (HL), Lower High (LH) ve Lower Low (LL) yapılarını tespit ederek canlı bir piyasa yapısı haritası oluşturur. Manuel Fibonacci çizimlerinden farklı olarak sistem, en son geçerli impulsif hareketi otomatik olarak takip eder ve buna bağlı geri çekilme ile devam hedeflerini hesaplar. Temel Mantık: • Swing tepe ve diplerden piyasa yapısı oluşturulur. • Yapısal ilerleme doğrulandığında impulsif hareket belirlenir. • Son impulsif hareket üzerinden Fibonacci tabanlı OTE (0.886) bölgeleri hesaplanır. • Gerekli yapısal şartlar oluştuğunda Fibonacci 0.618 devam hedefleri otomatik olarak oluşturulur. • Dinamik regresyon tabanlı Gravity Line piyasanın yönsel akışını görselleştirir. Bu indikatör doğrudan AL veya SAT sinyali üretmez. Bunun yerine, fiyat yapısını, geri çekilmeleri ve potansiyel devam hedeflerini analiz etmek için objektif bir yapısal harita sunar. Ana Bileşenler: • HH / HL / LH / LL Yapı Haritası • İmpuls Tespiti • OTE 0.886 Bölgeleri • Fibonacci 0.618 Hedefleri • Dinamik Gravity Flow • Canlı Yapısal Görselleştirme Gerekli yapısal şartlar oluştuğunda sistem otomatik olarak Fibonacci tabanlı OTE bölgelerini ve hedef seviyelerini hesaplayarak grafik üzerinde gösterir.
Wskaźnik Pine Script®
od onderarslantas107
Dual Log Regression Channels [BigBeluga]Dual Log Regression Channels is a highly advanced multi-timeframe mathematical modeling terminal engineered for TradingView. It maps, projects, and blends two independent logarithmic regression channels directly onto your asset layout screen to deliver an institutional-grade perspective on trend structure, market cycles, and structural volume distributions. By separating price discovery parameters into a long-term Macro Channel and an execution-focused Short Term Channel, this tool effectively resolves the classic trader conflict of assessing structural trend directions while looking for immediate micro execution setups. Rather than treating market space as flat, standard geometric lines, this engine runs an advanced curve-fitting algorithm over your data to follow the exponential nature of capital expansion and distribution. 🔵 INTUITIVE SYSTEM ARCHITECTURE & ENGINE FEATURES 1. Logarithmic Regression Curve Optimization Non-Linear Structural Tracking: Standard linear regression struggles with volatile crypto or high-growth equity trends over massive lookback structures. This script continuously converts incoming data matrices into mathematical log-space, computes a best-fit ordinary least squares (OLS) linear progression, and converts the output back into exponential value curves. Dual Horizons Convergence Layer: Tracks an extensive trend anchor block (defaulting to 300 bars) simultaneously with a highly responsive, high-velocity swing lookback matrix (defaulting to 50 bars). This exposes localized micro contractions occurring right at major macro boundary extremes. Visual Deviation Spacing Bands: Channels automatically map out distinct volatility boundaries based on real-time Standard Deviation multipliers. This defines predictable mathematical risk corridors where asset expansions typically exhaust and snap back toward the median baseline. 2. Predictive Channel Extension & Real-Time Trend Direction Arrows Dynamic Origin Trend Arrows: The engine processes a dedicated directional diagnostic framework at the precise historical start (origin node) of each lookback channel. It generates sharp, high-visibility glyph trend arrows ( ⇗ for structural uptrends and ⇘ for structural downtrends). These arrows offer an instant, real-time assessment of the mathematically calculated baseline slope, entirely bypassing visual guesswork when channels run relatively flat. Forward-Projected Space Models: When enabled, both the Macro and Short-Term structural bands project forward into the future chart space blank zone (e.g., 50 bars ahead for Macro, 20 bars for Short-Term). This lets you visually identify intercept locations and major trend crossroads long before price action arrives. 3. Adaptive Embedded Channel Volume Profiles (VP) Integrated Block Volume Binning Matrix: Moving beyond basic fixed or visible range volume profiles, this module segments and collects transacted volume profiles exclusively inside the exact coordinate boundaries of each respective channel. Dynamic Coordinate-Aligned Shading Bars: The volume profile rows scale and project outward utilizing advanced polyline geometry arrays, maintaining structural alignment with the slope of the moving channel boundaries. Point of Control (POC) Trailing Baselines: Automatically tracks and renders a crisp, high-visibility solid horizontal baseline ( POC Line ) marking the exact price bin location that attracted the highest volume concentration throughout that lookback phase. 4. Volumetric Delta Tracking Panels Buy vs. Sell Volume Accumulation Blocks: Aggregates total execution volume during the lookback period, classifying volume based on bar polarity. Net Order Flow Delta Percentages: Computes and prints the precise net mathematical buying/selling pressure delta inside the channel. This reveals quiet accumulation behavior or hidden distribution trends directly alongside your spatial boundary drawings. 🔵 SYSTEMATIC EXECUTION STRATEGIES & RISK INTERPRETATION Confluence Zone Intercept Trading: Look for setups where the Short Term Channel’s outer standard deviation boundaries align directly with the Macro Channel's major structural lines. When a high-velocity micro asset trend exhausts itself at a long-term macro floor or ceiling, it marks a highly efficient, asymmetric inflection zone for trend continuation entries or macro reversals. Volume Profile POC Mean Reversion Matrix: The volume profile POC lines show where massive institutional blocks shifted hands inside that channel's lifespan. If the market stretches thin near an upper outer boundary but net volumetric volume indicators begin shifting toward seller control, look for a swift mean-reversion move down toward the high-liquidity POC baseline node. Trend Acceleration vs. Overextended Breakouts: When an asset forces a candle close completely outside the projected log channel boundaries, it flags an exceptional shift in trend velocity. If the Volume Delta percentage prints an explosive spike in that direction, it supports a trend acceleration play. If volume is thin, it warns you of a predatory, overextended fakeout structure that is likely to snap back into the central channel values. 🔵 INTERFACE CONFIGURATION AND PARAMETERS Lookback & Deviation Tuning Blocks: Customize historical calculation boundaries and volatility widths separately for both trend layers to match any asset class or time frame preference. Volume Profile Customization: Control the precise resolution of the volume profile by adjusting row count bins and max bar widths to match your specific layout. Clean Workspace Overrides: Toggle visibility filters to hide median baselines, remove raw background asset lines, or completely customize color theme hex codes to fit cleanly within your setup without causing visual clutter. Transform your charting environment from basic straight lines into an exponential, volume-weighted structural map with the Dual Log Regression Channels terminal.
Wskaźnik Pine Script®
od BigBeluga
2
Range BAR Scalper (100R/1000R)Range Scalper — Adaptive S/R Zones A visualization tool built specifically for range charts (100R/1000R). 🔹 Adaptive S/R zones with automatic pivot detection 🔹 M / R / RSI signals with neon-style markers 🔹 Dashboard showing active levels and last break 🔹 Status panel: trend, bar run, RSI, and last signal (with bars-ago count) The visual style of the signal markers is inspired by Dynamic RSI Regression Bands by Zeiierman, and the adaptive S/R zone concept draws from Adaptive S/R Zones by BigBeluga. Both originals are licensed under CC BY-NC-SA 4.0; this tool follows the same license and is for non-commercial use only. ⚠️ This is a visualization/educational tool, not financial advice. Signals are not a guarantee of results — test it yourself before any real use.Disclaimer This indicator is provided for educational and informational purposes only. It is not financial, investment, or trading advice, and nothing in it constitutes a recommendation to buy, sell, or hold any instrument. Trading and investing carry substantial risk of loss. Past performance — including any signals, levels, or visuals shown by this tool — does not guarantee future results. The signals are not predictions and may produce false or losing setups. You are solely responsible for your own trading decisions. The author accepts no liability for any losses, damages, or other consequences arising from the use of this tool. Always do your own research and consider consulting a licensed financial professional before trading. Licensed under CC BY-NC-SA 4.0 — non-commercial use only. Visual style adapted from work by Zeiierman and BigBeluga.
Wskaźnik Pine Script®
od Cyber741
Regression Trend Reversal Signals & Forecasts [AlgoAlpha]🟠 OVERVIEW Regression Trend Reversal Signals & Forecasts combines multiple regression methods into a single trend and reversal framework. It allows traders to choose between Linear Regression, Theil-Sen Regression, LOESS smoothing, Nadaraya-Watson smoothing, Polynomial Regression, and a Kalman Filter to estimate the underlying price path. The selected regression line acts as the center of a dynamic channel. The channel width is based on the standard deviation of the distance between price and the regression line, allowing it to adapt to changing market conditions. The script also identifies potential reversal conditions when price extends beyond the channel and then shows signs of rejection. In addition, it can project the current regression slope forward to provide a simple forecast of the current trend path. 🟠 CONCEPTS Regression Line — A statistical estimate of the underlying price trend. Different methods can be selected, ranging from straight-line regressions to adaptive smoothing techniques. Theil-Sen Regression — A robust regression method that uses median slopes from all point pairs, reducing the influence of outlier price spikes. LOESS Regression — A locally weighted regression that fits nearby observations more heavily than distant observations to create a smooth trend curve. Nadaraya-Watson Smoothing — A kernel-weighted averaging method that estimates trend by assigning larger weights to recent observations. Kalman Filter — A recursive estimation method that continuously updates the trend estimate as new prices arrive. Polynomial Regression — A curved regression model that can capture non-linear trend structures using higher-order polynomial functions. Regression Deviation Bands — Channel boundaries calculated from the standard deviation of price relative to the regression line. Regression Slope — The rate of change of the regression estimate used to determine trend direction and forecast projections. 🟠 FEATURES Regression Channel — Dynamic bands expand and contract based on how far price deviates from the regression line. Trend Flip Signals — Generates directional markers when the regression trend changes from rising to falling or from falling to rising. Reversal Signals — Marks potential bullish and bearish reversals when price extends beyond the channel and begins rejecting those extremes. Forecast Projection — Extends the current regression slope into future bars and optionally displays projected channel boundaries. 🟠 HOW TO USE Select a regression method that matches the market behavior you want to analyze. Linear and Theil-Sen are suited to directional trends, while LOESS, Nadaraya-Watson, and Kalman provide smoother adaptive estimates. Use the regression line as the primary trend reference. Rising regression values indicate strengthening conditions, while falling values indicate weakening conditions. Monitor the channel boundaries for extended price movement away from the regression estimate. Watch for bullish reversal markers below the lower band after downside extensions and bearish reversal markers above the upper band after upside extensions. Use trend flip signals as confirmation that the regression slope has changed direction. Compare price location within the channel to gauge whether price is trading near trend equilibrium or at an extreme deviation. Use the forecast projection as a continuation estimate of the current regression slope rather than a prediction of future market behavior. 🟠 CONCLUSION Regression Reversal Signals combines multiple regression techniques, adaptive deviation channels, reversal detection, and forward projections into a single framework. By allowing traders to switch between several trend estimation methods, it provides different perspectives on trend structure and price deviation. The indicator helps identify trend direction, potential reversals, and areas where price has moved unusually far from its estimated path.
Wskaźnik Pine Script®
od AlgoAlpha
11
Future Time Splitter WanfengFuture Time Splitter (Incremental) — Pine Script v6 This indicator draws vertical time-based split lines on your chart at custom intervals, extending into the future and optionally keeping a configurable number of historical lines. Visualize upcoming time boundaries (e.g., start of each hour, day, or week) directly on price chart. Features: - Custom interval: choose a value and unit (Minutes, Hours, Days, Weeks) - Future projection: lines drawn into the future (e.g., next 10 hours) - Optional historical lines: keep a set number of recent past lines - Full style control: color, width, and line style (Solid, Dashed, Dotted) - Incremental logic: smoothly adds/removes lines, works on any timeframe Inputs: - Split interval value: numeric interval (default 60) - Unit: Minutes, Hours, Days, Weeks (default Minutes) - Future lines count: number of future lines (default 10) - Historical lines count: number of past lines to keep (default 0) - Line color, width, style Important: You must enable "Price scale extends to the right" in chart settings to see future lines. Timezone: lines align with exchange timezone (adjustable at chart bottom-right). Maximum total lines: 500 (future + past combined). Version: Pine Script v6 Type: Overlay on main chart “未来时间分割器（增量版）”——Pine Script v6 该指标会在您的图表上以自定义间隔绘制垂直的时间分割线，这些分割线会延伸到未来；同时，您还可以根据需要保留一定数量的历史数据线。可以直接在价格图表上显示即将到来的时间节点（例如每个小时、每天或每周的开始时间）。 功能特点： - 自定义时间间隔：可以选择时间间隔的值和单位（分钟、小时、天、周）。 - 未来预测功能：可以绘制出未来的价格走势线（例如未来10小时内的价格变化）。 - 可选的历史数据线：可以保留一定数量的近期历史价格数据。 全面的样式设置：可以自由调整线条的颜色、宽度以及线条类型（实线、虚线、点线）。 增量式显示机制：能够平滑地添加或删除价格走势线，适用于任何时间周期的图表显示。 输入参数： - 时间间隔值：以数字形式指定时间间隔（默认为60分钟）。 时间单位：分钟、小时、天、周（默认为分钟）。 未来价格走势线的数量：可指定未来价格走势线的数量（默认为10条）。 历史数据线的数量：可指定需要保留的过去价格数据线的数量（默认为0条）。 线条的颜色、宽度及样式均可进行自定义设置。 重要提示：若要查看未来价格走势线，必须在图表设置中启用“价格刻度向右延伸”的选项。 时区设置：价格走势线会根据交易所的时区进行显示；时区可以在图表右下角进行调整。 总线条数上限为500条（包括未来和过去的走势线）。 版本：Pine Script v6 显示方式：作为主图上的叠加层进行显示。
Wskaźnik Pine Script®
od DONGXV
Adaptive Regression Fibonacci Channel# Adaptive Regression Fibonacci Channel Adaptive Regression Fibonacci Channel is a trend analysis tool that combines Linear Regression with Fibonacci-based channel levels to create a dynamic framework for identifying support, resistance, pullback, and trend continuation zones. Unlike traditional Fibonacci retracements that require manually selected swing highs and lows, this indicator automatically calculates a regression trend line and builds an adaptive channel around recent price action. ## Features • Dynamic Linear Regression Trend Line • Adaptive Upper and Lower Channel Boundaries • Automatic Fibonacci Levels: * 0.236 * 0.382 * 0.500 * 0.618 * 0.786 • Real-Time Volatility Adjustment • Dynamic Support and Resistance Visualization • Compatible with Any Market and Timeframe ## How It Works The indicator calculates a Linear Regression line using the selected lookback period. It then measures the maximum deviation between the regression line and recent price extremes to create an adaptive channel that reflects current market conditions. Fibonacci levels are distributed proportionally between the upper and lower channel boundaries, providing additional reference zones that may act as potential support, resistance, or reaction areas. ## Typical Applications • Trend Analysis Monitor the position of price within the channel structure. • Pullback Identification Use intermediate Fibonacci levels to identify potential retracement areas during established trends. • Support and Resistance Mapping Dynamic Fibonacci bands can highlight areas where price may react. • Market Structure Assessment Evaluate whether price is trading near channel extremes or near its regression mean. ## Notes This indicator is designed as an analytical tool and should be used alongside other forms of technical analysis and risk management techniques. No indicator can predict future market movements with certainty. ## Keywords Linear Regression, Fibonacci Channel, Fibonacci Bands, Trend Analysis, Dynamic Support Resistance, Adaptive Channel, Market Structure, Technical Analysis, Price Action, TradingView Short Description: Dynamic Fibonacci channel built around a Linear Regression trend. Automatically adapts to market structure and volatility to highlight potential support, resistance, pullback, and trend continuation zones.
Wskaźnik Pine Script®
od Onuriano
Half Life Mean ReversionIts a tool for those who work in statistical arbitrage/pair trading in stocks or asset classes. a high correlation between two assets does not mean, they will be "mean reverting" too. half life tells us if they are mean reverting. lower half life is better. Half life of mean reversion = time taken by the "price ratio" of two assets, to revert back, to half way to its mean. script takes 3 lookback periods and calculates the half life of all 3 periods. Then gives it an acceptance score. lower is better. run for different pairs and chose the one with least acceptance scores
Wskaźnik Pine Script®
od LogicMonsters
profitzonetrades S/R + Strength + Sweepsprofitzonetrades S/R + Strength + Sweeps profitzonetrades S/R + Strength + Sweeps is a technical analysis indicator designed to identify dynamic support and resistance zones, liquidity sweep rejections, and market reaction strength using price structure, candle behavior, and optional trend confirmation filters. The script combines several technical analysis concepts into a single workflow focused on liquidity behavior and market structure analysis. Technical Components Used This indicator uses the following concepts and calculations: Pivot Highs & Pivot Lows Support & Resistance analysis Liquidity sweep / fake breakout detection Candle wick rejection analysis Volume confirmation EMA200 trend filtering Dynamic level strength tracking Dynamic Support & Resistance The script automatically detects support and resistance levels using pivot-based swing structure. These levels are: updated dynamically extended with price action ranked by reaction strength automatically removed when no longer significant This helps traders focus on active market reaction zones. Strength-Based Level System The indicator tracks how often price reacts to each level. The script measures: repeated touches reaction persistence level interaction frequency Stronger levels are visually emphasized using: stronger colors thicker lines optional percentage labels This allows traders to quickly identify more significant support and resistance areas. Liquidity Sweep Detection The indicator includes a liquidity sweep detection model designed to identify potential fake breakouts above resistance or below support. The script evaluates: breakouts beyond recent highs or lows wick rejection size candle body structure close position relative to previous liquidity zones Bearish sweeps occur when: price moves above previous highs then rejects back below resistance Bullish sweeps occur when: price moves below previous lows then rejects back above support These conditions may help traders identify liquidity grabs or failed breakout behavior. Wick Rejection Analysis The script compares: candle body size upper wick size lower wick size This helps identify stronger rejection candles and filter weaker breakout attempts. Volume Confirmation Filter An optional volume filter compares current volume against average market volume. This filter may help: validate stronger rejection candles reduce weaker signals improve signal quality during active market participation EMA200 Trend Filter The script optionally uses the Exponential Moving Average 200 (EMA200) as a directional trend filter. EMA200 may be used to: identify broader trend direction filter counter-trend sweep signals align setups with higher timeframe market bias Bullish sweep signals can be filtered above EMA200, while bearish sweep signals can be filtered below EMA200. Alerts Custom alert conditions are included for: bullish liquidity sweeps bearish liquidity sweeps These alerts can be connected to TradingView notifications or external automation systems. Suggested Usage This indicator may be useful for: support and resistance analysis liquidity sweep identification fake breakout detection trend continuation analysis discretionary intraday trading swing trading workflows Many traders may choose to combine the script with: higher timeframe analysis market structure confirmation independent risk management strategies additional technical confluence Notes This script is designed as a technical analysis and chart visualization tool. The indicator does not guarantee future results or profitable performance. Market conditions, volatility, and liquidity may affect signal quality. Users should test settings and parameters according to their own trading approach and market preferences. Signals and levels may update dynamically during live candle formation until candle close.
Wskaźnik Pine Script®
od profitzonetrades
VuManChu B Divergences [V6 Gold Short/Long - Optimized]脚本发布名称：VuManChu B Divergences 📌 脚本简介 本脚本基于经典的 VuManChu Cipher B (VMC) 进行深度重构与数理降噪优化。原版 VMC 指标在短线周期（如 1M、5M）运行时，经常由于微小波动频繁触发虚假的背离信号（Divergences），导致交易者在高位追多或低位割肉。 本优化版（Gold Optimized V6）专门针对短线量能假信号进行了算法重组，非常适合高频交易者和量化策略挂钩测试。 🛠️ 核心优化内容 (Compared to Original VMC) 彻底修复并补全【黄金见顶做空星（★）】： 修正了原版源码中 wtGoldSell 变量缺失的底层逻辑。现在，当市场处于极度超买区、且 RSI 与 WaveTrend 形成标准的结构性熊市背离时，指标顶部会精准触发橙色黄金做空星，捕捉左侧大顶。 新增 MFI（资金流柱状带）方向共振过滤： 引入了主力资金动能过滤。即使 WaveTrend 发生金叉或死叉，如果底部的 MFI 资金流方向不配合（例如金叉时资金仍在净流出），该假信号会被强行拦截。开启此功能可大幅降低震荡市中的无效磨损。 分形检测算法提速（降低信号滞后）： 重写了 f_fractalize 核心函数。将原版两端严重滞后的 5 顶分形，优化为反应更敏锐的动态转折结构，使背离线和信号点的刷新提前了 1 根 K 线，让进场位更有优势。 默认参数全面降噪： 将 WT 通道默认参数由 (9, 12) 升级为更具全局观的 (14, 21)，从数学层面上直接过滤掉微小的白噪音。 💡 交易指南 绿色圆点 (•)：标准的超卖动能金叉（建议顺势使用）。 红色圆点 (•)：标准的超买动能死叉。 底部橙色星星 (★)：极度超卖 + 牛市背离 + RSI 低位共振（高胜率反弹买点）。 顶部橙色星星 (★)：极度超买 + 熊市背离 + RSI 高位共振（高胜率见顶卖点）。 联动接口：脚本内部保留了 mainTrendSource 接口，支持引入主图均线或线性回归中轨进行多周期方向锁定。 本指标目前为内部测试版（Beta Version），欢迎大家将其加载于 BTC/ETH 等高波动品种的短线周期进行回测验证！
Wskaźnik Pine Script®
od luopan88
ATR with Volatility MarkersTradingView Indicator Manual: ATR with Volatility Markers Author: hkpress@CANSLIM Research Overview The ATR with Volatility Markers indicator is designed to measure the "heartbeat" of the market. Rather than predicting price direction, it measures the *energy* and *size* of price movements. By comparing the current short-term volatility (14-period ATR) against a long-term historical baseline (100-period ATR), this script automatically flags periods where the market is "coiling up" or actively "breaking out." The Theory: Volatility, Price, and Volume To use this indicator effectively, you must understand how Average True Range (ATR) interacts with price patterns and trading volume. Markets naturally cycle between periods of high volatility (expansion) and low volatility (contraction). 1. Volatility Contraction (The Setup) Before a major move occurs, markets typically consolidate. Buyers and sellers reach a temporary equilibrium. * Price: Moves sideways in a tight range (forming patterns like bull flags, flat bases, or pennants). * Volume: Dries up significantly, often dropping below its 50-day average. This indicates that large institutions are quietly accumulating (or distributing) shares without aggressively pushing the price. * ATR Change: The daily candles get smaller, causing the 14-period ATR to drop. When it falls below its historical normal baseline, your script flags this as a deep contraction. Think of this as a spring being compressed. 2. Volatility Expansion (The Breakout) A breakout occurs when new catalysts or aggressive institutional orders shatter the equilibrium. * Price: Pierces firmly through a known support or resistance level with a large-range candle. * Volume: Surges well above its average, confirming heavy participation and conviction. * ATR Change: The size of the daily moves rapidly increases. The 14-period ATR hooks sharply upward. When it aggressively crosses above its historical baseline, your script flags this as a high-volatility event. The compressed spring has been released. How to Read the Indicator The indicator uses four primary visual elements to communicate market conditions: 1. Solid Red Line Represents: Current ATR (14-period) How to Interpret It: The current, real-time measurement of price movement size. 2. Solid Gray Line Represents: Normal Baseline (100-period) How to Interpret It: The long-term average volatility. Used as a benchmark to define what is "normal" for this specific asset. 3. Red Dots Represents: Low Volatility Drop How to Interpret It: Fires when current ATR drops below your defined threshold for contraction. Signals consolidation. 4. Green Dots Represents: High Volatility Spike How to Interpret It: Fires when current ATR spikes above your defined threshold for expansion. Signals breakouts. Trading Application: Step-by-Step Observation This indicator is most powerful when combined with your standard price and volume charts. Here is how to use them together: Step 1: Hunt for Red Dots (The Watchlist Phase) * Scan your charts for assets printing Red Dots on the bottom of the indicator pane. * Check the price chart: Is the asset forming a tight, constructive base or pattern? * Check the volume: Is the volume noticeably lower than normal? * *Action:* Put this asset on your immediate watchlist. Do not enter yet; the market is still deciding which way to break. Step 2: Wait for the Trigger (The Breakout Phase) * Monitor the assets on your watchlist for a sudden price move that breaks the tight range (either up through resistance or down through support). * Check the volume: You want to see a massive spike in volume on the breakout candle. * Check the ATR indicator: The red ATR line should be hooking upward sharply. * *Action:* This is your entry signal. The market has chosen a direction with conviction. Step 3: Look for the Green Dot (Confirmation Phase) * Within a few candles of the breakout, a Green Dot should appear at the top of the indicator pane. * *Action:* This confirms that the volatility expansion is severe enough to be a mathematically significant trend change. It validates that your breakout trade has genuine momentum behind it. Adapting to Different Asset Classes (The 5 Volatility Profiles) The percentage change in ATR behaves very differently depending on a stock's inherent volatility (its "Beta" or personality). Because this script looks at *relative* change, you will see different magnitude shifts across different asset classes. To get accurate signals, use the built-in dropdown menu to select the profile that matches the asset you are trading: * Level 1: Extreme (Penny/Meme): Requires massive ATR shifts (2.0x for breakout, 0.4x for contraction). Perfect for filtering out the normal daily chaos of wild crypto altcoins or penny stocks. * Level 2: High (Tech/Crypto): Requires 1.75x breakouts and 0.6x contractions. Ideal for fast movers like TSLA or NVDA. * Level 3: Medium (S&P 500 Avg): The balanced default (1.35x and 0.85x) for everyday mid-to-large cap trading. * Level 4: Low (Blue Chips/ETFs): Tightened thresholds (1.20x and 0.90x) to catch more subtle moves in established, stable companies like KO or AAPL. * Level 5: Very Low (Utilities/Bonds): Extremely sensitive thresholds (1.10x and 0.95x) designed for assets that rarely make large percentage moves. How to Determine a Stock's Volatility Level If you are unsure which of the 5 Levels to apply to a specific stock, use one of these three methods: Method 1: The Visual Calibration Test (Fastest) Let the indicator tell you what settings to use right on your chart: 1. Load your stock and set the indicator to Level 3: Medium. 2. Zoom out and look at the last 1–2 years of price action. * Too Many Dots ("The Christmas Tree"): If dots fire constantly during standard, choppy price action, the stock is too volatile for Level 3. Shift up to Level 2 or Level 1. * Zero Dots ("The Desert"): If the stock has clearly made bases and breakouts but the indicator didn't fire *any* dots, the stock is too stable. Shift down to Level 4 or Level 5. * Just Right: If the dots only trigger at the tightest squeezes and most violent breakouts, you have found the perfect level. Method 2: Check the "Beta" (The Industry Standard) Beta measures how volatile a stock is compared to the S&P 500 (which has a Beta of 1.0). You can find Beta on Yahoo Finance, Finviz, or via a TradingView indicator. Match it here: * Beta > 2.0 $\rightarrow$ Level 1 (Extreme) * Beta 1.3 to 2.0 $\rightarrow$ Level 2 (High) * Beta 0.9 to 1.3 $\rightarrow$ Level 3 (Medium) * Beta 0.5 to 0.9 $\rightarrow$ Level 4 (Low) * Beta < 0.5 $\rightarrow$ Level 5 (Very Low) Method 3: Calculate the "ATR Percentage" (The CANSLIM Way) Measure how much a stock moves relative to its own price. Formula: `(14-Day ATR / Current Stock Price) * 100` * > 5% daily movement $\rightarrow$ Level 1 or 2 * 2% to 4% daily movement $\rightarrow$ Level 3 * < 2% daily movement $\rightarrow$ Level 4 or 5 Indicator Settings Guide You can fully customize the script's behavior via the TradingView inputs menu: * ATR Length (Default: 14): Defines the speed of the red ATR line. A lower number (e.g., 7) makes it highly sensitive to recent days. A higher number (e.g., 20) smooths it out. * Smoothing (Default: RMA): The mathematical formula used for the ATR. RMA is the industry standard created by J. Welles Wilder, but you can switch to SMA, EMA, or WMA. * Normal ATR Baseline Length (Default: 100): The lookback period for the gray baseline. 100 periods provide a highly stable long-term average. * Asset Volatility Profile: Use the dropdown to select Levels 1-5, or select "Custom Profile" to manually set your own Multipliers using the fields below it.
Wskaźnik Pine Script®
od hkpress
Predictive Breakout Channels | GainzAlgoAbout the Indicator The Predictive Breakout Channels indicator is a predictive machine-learning engine designed to map institutional market structure and calculate the statistical probability of impending breakouts. Instead of relying on traditional lagging indicators, the system dynamically anchors itself to major market pivots using a rolling Linear Regression Channel framework. By evaluating a combination of localized trend correlation, relative strength, institutional volume distribution, and variance metrics, the engine projects real-time target zones while simultaneously calculating a directional probability score directly on the chart the moment a breakout occurs. Dynamically anchors to institutional pivot structures Uses a rolling Linear Regression Channel Evaluates trend correlation, RSI, and variance metrics Projects real-time ATR-based target zones Calculates breakout probability scores directly on-chart Designed to distinguish genuine breakouts from fakeouts The Core Theory of Breakouts Markets spend the majority of their time consolidating rather than trending. During these equilibrium phases, liquidity pools accumulate on both sides of the range while volatility compresses beneath the surface. A breakout represents the structural transition from equilibrium into expansion. When institutional order flow aggressively consumes localized liquidity, price breaches structural boundaries and volatility rapidly expands outward. The challenge for traders has never been identifying that a breakout occurred — the real challenge is determining whether the move has enough structural backing to sustain itself or whether it is simply a liquidity trap designed to reverse shortly afterward. The Predictive Breakout Channels engine was specifically designed to address that exact problem. The Logic Engine — ANOVA & The Power of Variance To help solve the fakeout problem, this engine incorporates ANOVA, short for Analysis of Variance. Originally developed by legendary statistician Ronald Fisher, ANOVA has historically served as one of the foundational statistical tools used throughout medical research, behavioral science, and high-level quantitative analysis. Its purpose is to determine whether differences between groups of data are statistically meaningful or simply random noise. In this indicator, that same statistical framework is adapted directly to price action. The engine continuously evaluates the structural differences between groups of candle data — including highs, lows, and closes — in real time in order to measure the quality and significance of underlying market expansion. The Niche Secret — F-Statistic & Volatility Compression Quantitative modeling revealed a particularly powerful characteristic regarding variance measurements inside the ANOVA engine. When the raw ANOVA F-Statistic becomes drastically elevated, or when the standardized Z-Score breaches extreme thresholds such as 2 standard deviations, it often signals a state of hyper-compressed market consolidation. Think of it like winding a mechanical spring tighter and tighter. As variance compresses to rare statistical extremes, market energy begins building beneath the surface. Eventually that stored pressure releases through aggressive volatility expansion. This variance surge acts as a leading indicator for impending volatility before the actual breakout even occurs. However, variance alone cannot determine directional bias. Because of this, the engine layers in additional confirmation modules such as RSI and Trend Correlation Length to help determine whether institutional momentum is favoring bullish or bearish continuation. Indicator Settings & Customization The system is fully modular, allowing traders to fine-tune the engine based on their preferred asset class, timeframe, or trading style. Anchored LinReg Channel Settings: Customize left and right pivot lookbacks alongside standard deviation multipliers to control how the channel dynamically anchors itself to price structure. ANOVA Confirmation: Fine-tune the lookback period and baseline Z-Score thresholds required for breakout validation. Feature Filters: Adjust RSI and Trend Correlation baseline lengths to make directional probability scoring more aggressive or more selective. High Variance Alert Label: Disabled by default. When enabled, the engine plots visual warning labels whenever variance compression reaches statistically elevated levels. Include HTF Trend Filter: Controls whether breakout signals are filtered using higher timeframe trend conditions. The Strategic Dilemma — Higher Timeframe Trend Filtering The indicator includes a dedicated HTF Trend Filter toggle that leverages higher timeframe EMA spreads to determine whether lower timeframe breakout signals align with broader institutional trend conditions. Choosing whether to enable this filter depends entirely on the type of market environment you prefer trading. 1. HTF Filter ON — Trend Following Regime Filters out a significant amount of lower timeframe noise Produces fewer but statistically stronger breakout signals Aligns entries with broader institutional money flow Increases overall follow-through probability However, because the engine becomes heavily biased toward the macro trend, it may intentionally suppress counter-trend reversals or early-stage trend shifts. 2. HTF Filter OFF — Agile / Mean-Reversion Regime Allows the engine to react dynamically in both directions Captures sharp intraday reversals more aggressively Performs well in swinging or range-bound environments Increases breakout frequency substantially The tradeoff is naturally higher exposure to lower timeframe noise and shorter average continuation during counter-trend conditions. How to Trade with the Indicator When price closes outside the Linear Regression Channel while simultaneously satisfying the statistical validation criteria, the engine prints a breakout entry signal alongside a projected probability score. At the same time, the system projects 4 distinct ATR-based Target Zones labeled T1 through T4. Aggressive Traders: May choose to execute immediately on the breakout close while targeting T2 or T3 with structural stops positioned back within the channel. Conservative Traders: May choose to use the Probability Score as a filter or wait for a localized retest of the broken channel boundary before entering. The High Variance Play When the High Variance Alert label appears, traders should avoid impulsively chasing the immediate candle. Instead, the label should be treated as an early warning that volatility expansion is rapidly approaching. The preferred approach is to wait for the subsequent confirmed breakout signal, then trade the resulting momentum expansion into the projected target zones. High variance does not predict direction It predicts volatility expansion Directional confirmation comes afterward through breakout validation Wrapping It Up The Predictive Breakout Channels indicator bridges quantitative data science with classic market microstructure principles. By treating volatility as a measurable statistical property rather than a visual guessing game, the engine helps traders identify where the market is coiling, estimate the probability of expansion, and navigate breakout environments using structured statistical confirmation instead of emotion. Whether used for momentum continuation, volatility expansion, or intraday breakout trading, the system was designed to provide traders with a clearer framework for distinguishing meaningful expansion from market noise.
Wskaźnik Pine Script®
od GainzAlgo
215
Logarithmic Mean Reversion Bands | Astral Vision Logarithmic Mean Reversion Bands | Astral Vision 🌠💠 This indicator computes Bollinger Bands in log-price space rather than raw price space, producing a statistically symmetric channel around the geometric mean of Bitcoin's price that correctly represents proportional deviations across all price levels and all market cycles. The oscillator in the sub-panel expresses the current price position in units of standard deviations from the log-mean, providing a normalized reading that is directly comparable regardless of where absolute price is trading. Calculation ⚙️ The natural logarithm of Bitcoin's daily close price is taken for each bar. A simple moving average of this log-price series is computed over the configurable lookback window, producing the log-mean. The standard deviation of the log-price series over the same window is then computed. The geometric mean line plotted on the price chart is the exponential of the log-mean: exp(log_mean). This is the geometric mean of price over the lookback window, which differs from the arithmetic mean in a meaningful way for assets with exponential growth trajectories. The geometric mean weights all proportional moves equally, so a period where price doubled from $1,000 to $2,000 contributes the same to the mean as a period where price doubled from $30,000 to $60,000, which is the correct treatment for a multiplicative process like Bitcoin's price. The four band levels are computed as: exp(log_mean ± n × log_std), where n is 2.0 or 2.5. The exponential conversion ensures the bands are multiplicatively symmetric: the upper band is the same percentage above the geometric mean as the lower band is below it. This symmetry does not hold for standard Bollinger Bands computed on raw prices, where the upper band is a larger percentage above the mean than the lower band is below it. The oscillator is the standardized log-price deviation: (log_price - log_mean) / log_std. This is dimensionless and equivalent to a Z-Score of the log-price series, measuring exactly how many standard deviations the current log-price sits above or below its rolling mean. A reading of +2.0 means log-price is two standard deviations above its mean, regardless of the absolute price level. Reversal signals are generated when the oscillator crosses back inside the ±2.0 standard deviation boundary from outside: a downward triangle appears above the bar when the oscillator crosses back below +2.0 after being above it, and an upward triangle appears below the bar when it crosses back above -2.0 after being below it. These signals identify the moment when extreme deviation begins to revert, rather than the moment of maximum extension. Plots 📊 Geometric mean line on the price chart Four band lines at ±2.0σ and ±2.5σ on the price chart Candle coloring on the price chart by band zone: full color outside ±2.5σ, reduced opacity between ±2.0σ and ±2.5σ, neutral between the bands Z-Score oscillator in the sub-panel with matching ±2.0σ and ±2.5σ reference lines Fill highlights when oscillator is beyond the outer ±2.5σ level Static zone fills between the ±2.0σ and ±2.5σ levels Reversal triangles on the price chart when oscillator crosses back inside the ±2.0σ boundary Inputs 🎛️ Length: lookback window for the log-mean and log standard deviation Plot Long/Short Signals: toggle the reversal triangle signals Colors 🎨 5 Astral Vision presets + custom override. Default: Paradiso. Purpose 🎯 Standard Bollinger Bands applied to raw Bitcoin price produce bands that are not proportionally symmetric and whose standard deviation readings are not statistically consistent across different price levels. A +2σ reading at $1,000 and a +2σ reading at $50,000 on a raw-price Bollinger Band do not carry the same statistical weight because the distribution of raw price changes is not stationary across Bitcoin's history. Computing the bands in log space corrects this: the log-price distribution is much closer to stationary, making the standard deviation thresholds directly comparable across all cycles. The reversal signals add an entry timing dimension by marking the transition from extreme to normalizing rather than the peak of the extreme. Disclaimer ⭕️ This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions.
Wskaźnik Pine Script®
od AstralVision
BTC Logarithmic Regression Quantile Bands | Astral Vision Bitcoin Anchored Regression Quantile Bands | Astral Vision 🌠💠 This indicator fits a log-linear regression to Bitcoin's price from a configurable anchor date forward, then constructs a dynamic price channel by mapping the historical quantiles of the regression residuals back into price space. The result is a set of bands that adapt to the actual statistical distribution of how far Bitcoin's price has historically deviated above and below its own long-term trend line, rather than using fixed multipliers or standard deviations. Calculation ⚙️ The natural logarithm of close is taken for each bar from the anchor date onward. A linear regression is fitted to this log-price series over the full accumulated history from the anchor date up to a configurable maximum bar count, producing a trend line that represents Bitcoin's long-term log-linear growth trajectory from the chosen starting point. Working in log space is essential: Bitcoin's price has expanded across six orders of magnitude, and a linear regression on raw prices would be dominated entirely by recent data. In log space, proportional moves are treated equally across all price levels and all time periods. The residual at each bar is the difference between the actual log price and the regression trend value: residual = log(close) - trend. This measures in log-space how far price sits above or below its fitted trend at any given moment. A positive residual means price is above trend; a negative residual means price is below trend. Five quantiles of the residual distribution are then computed over the active history from the anchor date: the 1st and 99th percentiles forming the outer channel, a configurable inner pair (default 5th and 95th) forming the inner channel, and the 50th percentile as the median. Each quantile is converted back to a price level by exponentiating the sum of the trend value and the quantile: band price = exp(trend + quantile). This inversion ensures the bands are always anchored to the current regression trend and scale proportionally with price. The 50th percentile band represents the median regression price, where historically half of all price observations have been above and half below. The inner bands mark the zone within which the configurable percentage of historical price observations have occurred. The outer 1st and 99th percentile bands mark the historical extremes, where price has spent only 1% of its time beyond each boundary. Plots 📊 Outer upper band at the 99th percentile of residuals Inner upper band at the configurable upper quantile Median band at the 50th percentile Inner lower band at the configurable lower quantile Outer lower band at the 1st percentile of residuals Fills between outer and inner bands on both sides Background color when price is outside the inner band pair Bar coloring with six gradient levels reflecting position within the channel Inputs 🎛️ Anchor Date: starting point for the regression, all quantiles are computed from this date forward Regression Length: maximum bar count used in the regression and quantile computation Show Fills: toggle the fill between outer and inner bands Inner Quantile Pair: percentile level for the inner band pair, symmetric around 50 Colors 🎨 5 Astral Vision presets + custom override. Default: Futura. Purpose 🎯 Standard Bollinger Bands use a fixed lookback window and assume a normal distribution of price deviations, which does not hold for Bitcoin's log-price residuals. This indicator uses actual empirical quantiles of the full historical residual distribution from the anchor date, making each band level statistically precise: the 99th percentile band has been exceeded by price exactly 1% of the time since the anchor date, which is a more meaningful and verifiable statement than a fixed 2-standard-deviation level. The anchor date architecture also means the bands incorporate the full available price history rather than only a recent rolling window, making them structurally stable references for long-term cycle analysis. Disclaimer ⭕️ This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions.
Wskaźnik Pine Script®
od AstralVision