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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.
Pine Script® indicator
by AlgoAlpha
10
PUCK Elite Engine v6 CLEANER CHART STYLEPUCK Elite Engine v6 CLEAN A momentum and market-structure trading system designed for active traders who prioritize speed, volume, trend alignment, and clean price action. Built around the philosophy: Speed First → Spread Second → Volume Third → Structure Fourth → Entry Last ━━━━━━━━━━━━━━━━━━ FEATURES • EMA Stack Engine (5 / 9 / 20 / 50) • EMA Rotation Box (bottom left) • VWAP Trend Filter • Buy / Sell / Neutral Volume Tracking • Tape Speed Detection • BOS (Break of Structure) • CHOCH (Change of Character) • Dynamic Support & Resistance Mapping • ATR Trailing Stop • Regression Trend Channel • OHLC4 Trend Source • Volatility Expansion & Contraction Bands • RSI Status • MACD Status • Buy Watch Signals • Fast Buy Signals • Get Out Signals • Spacious Information Dashboard ━━━━━━━━━━━━━━━━━━ HOW TO USE 1. TAPE SPEED SLOW Low participation. MEDIUM Building interest. FAST Momentum entering. RAGING Potential runner conditions. ━━━━━━━━━━━━━━━━━━ 2. SPREAD CLEAN Healthy structure. THIN / DANGER Possible liquidity traps. CHOP No clear directional edge. ━━━━━━━━━━━━━━━━━━ 3. VOLUME Buy Volume Aggressive buying pressure. Sell Volume Aggressive selling pressure. Neutral Volume Balanced participation. Look for: Buy Volume > Sell Volume combined with: FAST Tape VWAP Above Bull Trend ━━━━━━━━━━━━━━━━━━ 4. EMA STACK Located in bottom-left box. Example: 5↑ 9↑ 20↑ 50↑ Bullish Alignment Example: 50↓ 20↓ 9↓ 5↓ Bearish Alignment The stack automatically reorders itself as EMAs cross. This helps identify trend transitions immediately. ━━━━━━━━━━━━━━━━━━ 5. BOS Break Of Structure BOS ↑ Bullish structure break. BOS ↓ Bearish structure break. Best when accompanied by: Volume Burst VWAP Confirmation Fast Tape ━━━━━━━━━━━━━━━━━━ 6. CHOCH Change Of Character Early warning that trend direction may be shifting. CHOCH often appears before larger BOS signals. ━━━━━━━━━━━━━━━━━━ 7. VWAP ABOVE Institutional bias favors buyers. BELOW Institutional bias favors sellers. ━━━━━━━━━━━━━━━━━━ 8. VOLATILITY TIGHT Compression. NORMAL Balanced. WIDE Expansion. Many strong moves begin after TIGHT conditions expand into WIDE conditions. ━━━━━━━━━━━━━━━━━━ 9. RSI 70+ Extended. 50-70 Bullish. 30-50 Bearish. Below 30 Oversold. ━━━━━━━━━━━━━━━━━━ 10. MACD BULL Momentum improving. BEAR Momentum weakening. ━━━━━━━━━━━━━━━━━━ BEST SETUPS ✓ Fast Tape ✓ Buy Volume Dominating ✓ Above VWAP ✓ EMA Bull Stack ✓ BOS Up ✓ CHOCH Bull ✓ Volatility Expanding ✓ RSI Above 50 ✓ MACD Bull ━━━━━━━━━━━━━━━━━━ AVOID ✗ Thin / Danger ✗ Choppy Volume ✗ Below VWAP ✗ Bear EMA Stack ✗ Weak Tape ✗ Multiple Failed BOS Attempts ━━━━━━━━━━━━━━━━━━ Designed primarily for: • Premarket Runners • Small Caps • Low Floats • Momentum Stocks • Gap and Go Setups • Scalping • Intraday Trading Not financial advice. Always manage risk.
Pine Script® indicator
by DikFitzwell
Silver X-10 v6 | Signal + S/R + RSI RegressionThe indicator is complete and working correctly at all levels. Here's the final summary of everything built into the Silver X-10 v6:Perfect! Everything is in sync: DXY: 48.1 FALLING ↓ → BUY METALS ↑ — feed казва "DXY falling — buy metals bias active." ✓ Support: 76.1446 ✓ Resistance: 77.0060 ✓ Confidence: 6/8 MED ✓ Profit: +3p in real time ✓Signal Engine — EMA9/21 тренд + bearish/bullish engulf + pin bar + doji + MACD кросс 8 филтъра — Volume, Candle confirm, HTF EMA trend, ATR body, No chop, BB squeeze, RSI direction, DXY correlation Adaptive S/R Zones (BigBeluga) — pivot нива с longevity width, zone boxes, break labels RSI Regression Bands (Zeiierman) — динамичен канал с reset при OB/OS, rejection markers DXY Engine — RISING/FALLING/NEUTRAL, SELL/BUY METALS bias, 4 alerts Таблица (19 реда) — Entry, Profit, TP1✓/TP2✓/TP3✓/SL✕, S/R, Confidence score, DXY status Strip — Watchlist color by signal/trend 17 Alerts — Sell, Buy, TP1/2/3, SL, Reversal zones, DXY OB/OS/Rising/Falling
Pine Script® indicator
by Cyber741
1
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。
Pine Script® indicator
by 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.
Pine Script® indicator
by 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
Pine Script® indicator
by 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.
Pine Script® indicator
by 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！
Pine Script® indicator
by 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.
Pine Script® indicator
by 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.
Pine Script® indicator
by 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.
Pine Script® indicator
by 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.
Pine Script® indicator
by AstralVision
Bitcoin MA Quantile Bands | Astral Vision Bitcoin MA Quantile Bands | Astral Vision 🌠💠 This indicator constructs a dynamic price channel around a configurable moving average by computing the statistical distribution of the log ratio between Bitcoin's price and its MA over a long historical lookback, then mapping specific percentiles of that distribution back into price-space bands. The result is a set of bands that adapt to Bitcoin's actual historical price behavior relative to its trend, rather than using fixed multipliers or standard deviations. Calculation ⚙️ The moving average is computed on daily Bitcoin price data over a configurable length, with an optional 2-day mode that doubles the effective lookback. Five MA types are available: SMA, EMA, WMA, VWMA, and HMA. The Hull Moving Average is computed as WMA(2 × WMA(src, len/2) - WMA(src, len), sqrt(len)), which reduces lag significantly compared to a standard WMA of the same length. For each bar, the log ratio is computed as log(price / MA). This transforms the multiplicative relationship between price and MA into an additive one, making the distribution symmetric and comparable across different price magnitudes. A price trading 50% above its MA produces the same absolute log ratio as a price trading 50% below, which a raw ratio would not. Six percentiles of this log ratio series are then computed over the configurable quantile lookback window using linear interpolation: the 1st, 10th, 35th, 65th, 90th, and 99th percentiles. Each percentile is converted back to a price level by multiplying the MA by the exponential of that percentile value: band price = MA × exp(percentile). This inversion ensures the bands are always centered on the current MA value and scale proportionally with price. The resulting six levels represent: the 1st percentile (historic bottoms, price has been lower than this only 1% of historical bars), the 10th percentile (oversold), the 35th and 65th percentiles (fair value range, price spends roughly 30% of its time between these two), the 90th percentile (overbought), and the 99th percentile (historic peaks). The indicator can be computed on 1-day or 2-day bars, with the 2-day mode doubling both the MA length and the quantile lookback to maintain structural equivalence. Plots 📊 Configurable moving average line Historic Peaks band at the 99th percentile with fill to Overbought Overbought band at the 90th percentile Oversold band at the 10th percentile with fill to Historic Bottoms Historic Bottoms band at the 1st percentile Inputs 🎛️ Timeframe: 1D or 2D bar mode MA Length: lookback period for the moving average MA Type: SMA, EMA, WMA, VWMA, or HMA Quantile Lookback: historical window over which percentiles are computed Colors 🎨 5 Astral Vision presets + custom override. Default: Paradiso. Purpose 🎯 Standard Bollinger Bands use fixed standard deviation multiples, which assume a normal distribution of price deviations around the mean. Bitcoin's price distribution relative to its trend is not normal: it has a heavy right tail during bull markets and periodic extreme downside excursions. Computing bands from actual historical percentiles of the log ratio captures this asymmetry directly, placing the overbought and oversold bands exactly where they have historically been meaningful rather than at a theoretically symmetric distance. The log transformation further ensures that the bands scale correctly with price across Bitcoin's full history, avoiding the distortion that linear band methods produce when comparing early and recent market cycles. 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.
Pine Script® indicator
by AstralVision
Shift Share - 5-Model Production MatrixThe 5-Model Stock Breakdown: A Beginner Guide Imagine you are looking at a single stock—let us call it SuperEco Corp—and its price just jumped up by 10 percent over the last few weeks. If you ask a regular person why it went up, they might say, "Because it is a good stock!" But a seasoned investor knows that a stock price does not move in a vacuum. It is being pulled and pushed by different invisible forces. This guide explains how our tracking tool uses a method called Regression to break down that 10 percent jump and reveal exactly who was responsible for the move. You do not need a background in math, economics, or statistics to understand how it works. 1. The Three Invisible Forces Our model assumes that a stock price movement depends on three major factors: The Market (The Rising Tide): This is the entire stock market (like the S&P 500). When the overall economy is booming, optimism runs high, and almost every stock gets lifted. If the market goes up sharply, our stock will likely get dragged up with it, purely by association. The Sector (The Industry Wave): This is the specific neighborhood the stock lives in (such as Technology, Energy, or Healthcare). If electric vehicles are suddenly booming, all EV stocks will rise together, even the poorly managed ones, because money is pouring into that specific industry bucket. The Stock Itself (Its Unique Engine): This is the company own secret sauce. It includes their unique products, their management team, their earnings reports, and their breakthroughs. This is what belongs only to this stock and no one else. What is Regression? Think of a stock total return like a baked cake. You know the cake tastes sweet (the stock went up 10 percent), but you do not know how many cups of sugar, flour, or butter went into it. Regression is a tool that acts like a laboratory test for that cake. It analyzes the final product, compares it to how the broader market and sector were moving at the same time, and extracts the exact recipe. It tells you: "Of this 10 percent move, 5 percent was caused by the market, 3 percent was caused by the sector, and 2 percent was the stock unique engine." 2. Leftover Mystery: The Statistical Error No matter how smart a math model is, it can never perfectly predict human behavior or random events. When we add up what the Market and the Sector should have done to the stock, it rarely perfectly matches the actual final price. The piece that is left over is called the Statistical Error (or Noise Floor). Think of it like static on an old television set, or the background chatter in a crowded restaurant. It represents random daily market zig-zags, algorithmic trading glitches, or minor noise that has no real structural meaning. If our model is working correctly, this error row should be very small, meaning our factors successfully explained the vast majority of the price movement. 3. The 5 Steps of Understanding: Our Dropdown Models To help you see how these forces interact, our indicator lets you switch between 5 different structural models using a simple settings dropdown menu. Think of this like changing lenses on a camera to see the data in different ways: Model 1: No Variables (The Blindfold View) This model assumes that external factors do not exist. It completely ignores the market and the sector. The stock price is treated as moving entirely on its own, plus some random mystery noise. This establishes a baseline to see what the stock looks like when you isolate it from macro events. Model 2: Market Only (The Macro Lens) This model assumes that only the big picture matters. It watches how the stock moves in relation to the main stock market index. It tells you if your stock is just a mirror of the broader economy. If the market explains almost all of the move, your stock is behaving like an index fund. Model 3: Sector Only (The Neighborhood Watch) This model ignores the broader market and looks strictly at the stock specific industry group. This helps you see if the stock is simply riding an industry-wide trend or hype cycle, completely independent of whether the rest of the world markets are up or down. Model 4: Both Market and Sector (The Parallel View) This model looks at both forces simultaneously, but treats them as completely independent. It assumes they run side-by-side like two trains on parallel tracks. This isolates how much of the stock price was swept up by the macro economy versus how much was driven by its specific industry peer group. Model 5: Full Interactive Model (The Chemistry Lab) In the real world, forces do not just run on parallel tracks—they collide and alter each other. This model introduces a special Interaction Term. When a roaring market slams into a hyper-growth sector, they create a compounding synergy that accelerates or dampens stock prices. This is the most realistic model, stripping away all shared group behavior to isolate the stock true standalone engine. 4. Reading Your Screen: The Output Matrix Table Explained When you load the script on your chart, it generates a clean summary table in your indicator pane. Here is how to interpret the four columns: Column 1: Source Factor This lists the different forces we are tracking: Market Component, Sector Component, Cross-Factor Interaction, Pure Unique Asset, and Statistical Error Noise. Column 2: Window Return This column shows you the direct, absolute performance footprint measured in percentage points. If you add up every number in this column from top to bottom, it will perfectly equal the final Asset Total Return row at the very bottom. If the final row says +10.00%, and the Market row says +6.00%, the market tide handed your stock 6 of its 10 points of growth. A negative number means that factor acted as a brake, actively dragging your stock backward. Column 3: Share Allocation % While Column 2 tells you the direction and points, this column tells you pure structural influence. It answers the question: "Regardless of whether it was pulling the stock up or pushing it down, what percentage of the total steering wheel did this factor control?" This column always adds up to exactly 100.00%. Column 4: Dynamic Profile State This column displays the real-time operational status of the factors based on your settings. If a factor is turned off in your dropdown choice, this column will clearly say "OFF / Excluded". If a factor is active, it reveals its underlying structural strength using standard financial metrics like Beta (market scaling) or Alpha (sector outperformance). The Ultimate Check: Look at the Header (R2) Right at the very top of the table header, you will see a score labeled R2 (R-Squared). Think of this as the Model Accuracy Grade, scaled from 0.00 (completely guessing in the dark) to 1.00 (absolute flawless perfection). If your R2 is 0.85, it means your chosen model successfully captured and explained 85% of everything that caused the stock to move over that time window. By toggling through models 1 to 5, you can find the framework that creates the highest R2 score, giving you definitive statistical proof of how that specific stock is actually operating.
Pine Script® indicator
by Stephen_F
1
Giovanni's Power Law Model | Astral Vision Giovanni's Power Law Model | Astral Vision 🌠💠 This indicator implements the Bitcoin Power Law model as formalized by physicist Giovanni Santostasi, which models Bitcoin's long-term price trajectory as a power function of the number of days elapsed since the genesis block. Unlike standard log-linear regression applied directly to price, this implementation uses a configurable root transformation of price before fitting, which allows the user to explore how the statistical quality of the power law relationship changes depending on which power of price is used as the dependent variable. Calculation ⚙️ The number of days elapsed since the genesis block (January 3, 2009) is computed for each bar. Both the day count and price are transformed logarithmically: the X variable is log(days), and the Y variable is log(price^(1/k)), where k is a configurable integer exponent. This is equivalent to fitting the relationship log(price) = k × (slope × log(days) + intercept), meaning the model regresses log(price^(1/k)) against log(days) and then raises the result to the power k to recover the price-scale fit. The regression is computed from scratch using ordinary least squares via cumulative sums of X, Y, XY, X², and Y² accumulated across all valid historical bars, then solved analytically at each bar using the standard OLS closed-form solution: slope = (n × ΣXY − ΣX × ΣY) / (n × ΣX² − (ΣX)²) and intercept = (ΣY − slope × ΣX) / n. This produces a true full-history regression that updates on every bar as new data is added, without any approximation. The fitted price at each bar is: fitPrice = exp(intercept + slope × log(days))^k. Upper and lower bands are then derived by multiplying or dividing the fitted price by configurable band multipliers raised to the power k, ensuring the bands maintain consistent proportional spacing in the transformed space rather than in raw price terms. The R² coefficient of determination is computed at the last bar by comparing the explained variance of the regression against the total variance of the Y variable, quantifying how well the power law model fits the full historical data. Plots 📊 Central power law regression line in neutral color Two upper bands with fill between them, colored with the positive theme color Two lower bands with fill between them, colored with the negative theme color Table displaying the current k exponent and the R² goodness-of-fit statistic Inputs 🎛️ K Exponent: the root applied to price before regression, between 1 and 7; higher values compress the price scale more aggressively Upper Band 2 and Upper Band 3: multipliers for the two upper bands Lower Band 2 and Lower Band 3: multipliers for the two lower bands Colors 🎨 5 Astral Vision presets + custom override. Default: Hermes. Purpose 🎯 Standard log-linear regression on Bitcoin price assumes a fixed exponential relationship between time and price. Giovanni Santostasi's formulation derives the power law from first principles of network science and thermodynamics, arguing that Bitcoin's adoption follows the same scaling laws observed in physical and biological systems. The configurable k parameter allows direct exploration of which root transformation produces the tightest regression, with the displayed R² providing an objective statistical measure of fit quality rather than a visual approximation. The OLS computation across the full price history from genesis makes the fitted line anchored to the entire dataset rather than to a recent window, producing a structurally stable long-term reference. 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.
Pine Script® indicator
by AstralVision
Bitcoin Anchored Power Law Bands| Astral Vision Bitcoin Anchored Power Law Bands | Astral Vision 🌠💠 This indicator plots a set of price bands derived from the Bitcoin Power Law framework, which models Bitcoin's long-term price trajectory as a power function of the number of days elapsed since the genesis block. Rather than fitting a regression line to the full price history, this implementation anchors the model to a fixed historical reference point (February 10, 2013) and derives the scaling coefficient analytically from the price level at that date, producing a deterministic model that does not shift as new price data is added. Calculation ⚙️ The number of days elapsed since the genesis block (January 3, 2009) is computed for both the current bar and the anchor date. The fair value line follows the general power law form: price = k × t^5.8, where t is the number of days since genesis and the exponent 5.8 is the empirically observed slope of Bitcoin's long-term log-log price growth. The scaling coefficient k is derived by solving the equation at the anchor point: k = 16.99 / t0^5.8, where t0 is the day count at the anchor date and 16.99 is the calibrated price level at that anchor. This ensures the fair value line passes exactly through the anchor point and projects forward with a fixed slope. The lower support band is set at 70% of the fair value line, marking the zone below which Bitcoin has historically spent very little time and which has corresponded to the deepest capitulation periods. The bubble lower band is set at 2.0 times the fair value line, representing the lower boundary of historically elevated territory. The bubble upper band applies a dynamic multiplier that decays over time: bubble upper multiplier = 30.5 × (t / t0)^(-1.4), meaning the upper band compresses toward the fair value line as Bitcoin matures, consistent with the empirically observed reduction in peak-to-fair-value distance across successive market cycles. A background color highlights periods when price falls below the fair value line, marking structurally undervalued territory relative to the power law model. Plots 📊 Fair value line: the anchored power law baseline Lower support band at 0.7x the fair value line, with fill between the two Bubble lower band at 2.0x the fair value line Bubble upper band with time-decaying dynamic multiplier Background color when price is below the fair value line Inputs 🎛️ No period or length inputs: the model is fully derived from the anchor date and fixed power law exponent Colors 🎨 5 Astral Vision presets + custom override. Default: Paradiso. Purpose 🎯 Standard moving averages and trend channels reset with each new cycle and have no memory of Bitcoin's full price history. The power law model is anchored to time itself, meaning its output is determined solely by how old Bitcoin is rather than by recent price action. The anchoring approach used here further removes the statistical instability of regression-based power law models, where the fitted line shifts as new data points are added. By fixing the calibration to a single historical reference point, the model produces a stable, non-updating fair value line that can be compared against current price with a consistent long-term reference frame. The time-decaying upper band addresses the limitation of fixed-multiplier models by reflecting Bitcoin's historically compressing cycle amplitude. 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.
Pine Script® indicator
by AstralVision